Abstract Leafhopper feeding-damage severity is an important factor in the raw-material grading and final quality of Zijin Chan tea, but conventional assessment relies heavily on subjective visual judgment. This study developed a two-stage workflow combining machine-vision screening with offline near-infrared (NIR) spectroscopic reassessment. First, an improved small You Only Look Once version 8 (YOLOv8s) model localized tea-leaf targets and classified slight, moderate, and severe feeding damage. Physical samples corresponding to machine-vision no-result targets were then reassessed using an NIR model integrating standard normal variate preprocessing, Pearson correlation-based spectral-region selection, the successive projections algorithm, and a support vector machine. On 200 test images containing 4,000 annotated targets, the improved YOLOv8s model achieved precision of 88.6%, recall of 87.7%, and mean average precision at an intersection-over-union threshold of 0.5 of 89.3%, representing improvements of 12.2, 9.3, and 8.7 percentage points over the baseline, respectively. Developed from 240 independent physical samples, the NIR model achieved macro-averaged recall of 94.03% on the cross-validated training set and 95.12% on the spectral model-selection set. When the fixed model was applied to physical samples corresponding to 492 machine-vision no-result targets, 473 targets were classified correctly, yielding a reassessment accuracy of 96.14%. Across all targets, standalone machine vision achieved an end-to-end accuracy of 77.7%, whereas the two-stage workflow achieved 89.5%. These results support the feasibility of combining visual and spectral information for laboratory-based grading. Validation using in situ spectra and samples from multiple batches and seasons remains necessary before online deployment.
This study presents a systematic quantitative multi-elemental investigation of four major plant organs (roots, stems, leaves, and flowers) of Nerium oleander using calibration-based Laser-Induced Breakdown Spectroscopy (LIBS), with the results validated by Atomic Absorption Spectroscopy (AAS). Plasma characterization was carried out using Boltzmann plot and Stark broadening analyses, while negligible self-absorption observed through the Hα emission line confirmed optically thin plasma conditions and reliable quantitative measurements. A total of nine elements were detected, including Fe, Zn, Mn, Ca, Mg, K, Na, Cu, and Ni, each exhibiting different concentration levels across the analyzed tissues. Compositional analysis using standard calibration curve-based LIBS demonstrated that elemental concentrations were non-uniform, showing marked variations between the different plant tissues. Among the detected elements, calcium emerged as the most prevalent across all tissues. The highest calcium concentration was observed in leaves (16,385 mg L-1), followed by roots (12,092 mg L-1) and flowers (11,185 mg L-1). Root tissues exhibited elevated concentrations of Fe and Mn, reaching 1918 and 513 mg L-1, respectively. In contrast, flowers showed the highest Mn concentration (1888 mg L-1), while leaves were enriched in Mg (4445 mg L-1) and K (3700 mg L-1). The highest Na concentration was observed in stems (8025 mg L-1). Trace metals, including Cu, Zn, and Ni, were detected at comparatively low concentrations, while Pb remained undetected in all samples. The strong agreement between LIBS and AAS measurements confirms the reliability of the proposed methodology and demonstrates the potential of LIBS as a rapid and non-destructive tool for elemental assessment of medicinal plants.
Volatile organic compounds (VOCs) released by plant leaves play key roles in stress signaling, plant-atmosphere interactions, and plant defense. Among these, wound-induced VOCs (wVOCs) are emitted within seconds of mechanical damage, herbivory, or environmental disturbance. Their emission dynamics depend strongly on the timing, severity, and method of tissue disruption. Yet, accurate quantification remains challenging due to mechanical artifacts, variable exposure conditions, and delays between injury and measurement. This study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange. A surgical-grade cutter was integrated into a portable gas-exchange chamber to enable clean, controlled cuts within a sealed chamber under stable light, humidity, CO2, and temperature conditions. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) continuously measured volatile emissions at the chamber outlet, minimizing delay and signal distortion. This setup resolves emission onset, peak timing, maximum rise rate, and total release with high temporal fidelity. Application of the method to Quercus rubra, Acer platanoides, and Gossypium hirsutum demonstrated its ability to resolve distinct wound-induced emission patterns across contrasting leaf types. By eliminating delays associated with conventional sampling, this method resolves the full kinetic trajectory of wound-induced emissions and overcomes major limitations of previous approaches. It provides a robust framework for studying rapid stress responses in plant physiology, ecological biochemistry, and plant-atmosphere interactions.
Background and aims Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. Methods We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Results Dimorphic species exhibited higher discrimination capacity (average accuracy of 80-83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed considerable overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral covariation yielded R2 = 0.72 (P = 0.003) when sterile and fertile fronds were combined, although PGLS analyses detected no significant phylogenetic signal. The association differed between frond types and is interpreted here as exploratory evidence of morpho-spectral covariation. Conclusions Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Cadmium contamination severely affects rice growth, yield, and quality, making early stress monitoring essential for agricultural management and food safety. However, traditional detection methods are cumbersome and time-consuming, limiting their applicability to early stress diagnosis. This study developed a rapid and accurate approach for discriminating cadmium stress levels in rice. Arginine-modified flower-like silver nanoparticles (Ag NPs-Arg) were synthesized to enhance Raman signals associated with three stress-response indicators: salicylic acid (SA), malondialdehyde (MDA), and peroxidase (POD) activity. Quantitative prediction models for these physiological indicators and a stress-level discrimination model were established. Among the evaluated models, the CNN-Transformer model achieved the best predictive performance, with Rp 2 values of 0.889, 0.832, and 0.802 for SA, MDA, and POD activity, respectively. An objective weighting method was used to integrate the three biochemical reference indicators, providing a multi-indicator physiological basis for comprehensive stress assessment. The resulting stress-level assessment model achieved an accuracy of 95.83%, demonstrating its ability to capture cadmium-induced physiological changes and assess stress levels in rice.
Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe 3+ ) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.
Naincy Sagar · Nassim Belmokhtar · Nathalie Boizot · Ana Alves · David Chassagnaud · R Fichot · José Rodrigues · Luc Pâques
Raman / spectroscopyLeafTissuePhysiological trait estimationLeaf traitsWater status / transpiration
Phenotyping extensive populations remains a major constraint in tree breeding programmes, particularly due to the time-consuming and labour-intensive nature of conventional methods. Near infrared (NIR) spectroscopy, which is a high-throughput phenotyping method, offers an alternative solution, providing a rapid and cost-effective approach for assessing growth- and function-based traits on large numbers of trees. This study aimed to evaluate the potential of NIR spectroscopy-based models for predicting such traits in Larch. Specifically, delta carbon-13 ( δ 13 C), carbon (C), nitrogen (N), specific leaf area (SLA), leaf dry matter content (LDM), and phenolics on needles; the branch hydraulic trait (P 50 ), and lignin and hydroxyphenyl/guaiacyl (H/G) ratio on wood cores from an experimental study on Larix species were predicted using multivariate modelling, specifically, partial least squares regression. Reliable models were obtained for N content (R 2 training = 0.95, r 2 testing = 0.94), lignin (R 2 training = 0.95, r 2 testing = 0.94), and H/G ratio (R 2 training = 0.88, r 2 testing = 0.89), while moderate predictive performance was observed for C content (R 2 training = 0.79, r 2 testing = 0.79) and δ 13 C (R 2 training = 0.76, r 2 testing = 0.69). This methodological approach and its results encourage the transition from traditional laboratory methods to efficient, large-scale-based trait evaluation techniques in forestry.
Urban air pollution may alter plant metabolism long before visible damage becomes apparent. Raman spectroscopy was evaluated as a rapid, non-destructive approach to resolve these biochemical adjustments. Mature Quercus ilex L. trees were sampled along a well-defined pollution gradient in Tuscany (Italy), spanning high, intermediate, and low levels of NO₂ and PM₁₀. Leaf Raman spectra revealed coordinated modulation of primary and secondary metabolism. Pigment-related bands (chlorophylls and carotenoids) increased toward the most polluted site, while inducible flavonoid signals showed site-dependent variation consistent with oxidative pressure in superficial tissues. These patterns were consistent with destructive biochemical analyses and chlorophyll fluorescence measurements, which indicated acclimation rather than photoinhibition damage. A composite Raman index showed a close site-level association with NO₂ exposure, suggesting that nitrogen-related urban pollution was the main exposure component linked to the observed metabolic response. Overall, Raman spectroscopy captures the chronic metabolic imprint of urban air pollution in Q. ilex, resolving coordinated pigment reinforcement and defensive activation without sample destruction. This approach provides a rapid and scalable framework for linking atmospheric chemistry to plant functional status in biomonitoring applications.
Visible-near infrared (Vis-NIR) spectroscopy provides rapid crop disease assessment; however, poor model generalizability remains a major limitation when models developed for a specific period are applied to batches collected at different times, primarily due to variations in physicochemical properties such as chlorophyll content, moisture level, surface texture, and tissue structure, which induce shifts in spectral distributions across batches. This study investigates deep domain adaptation to enhance cross-batch transferability for sugarcane disease classification. Two batches of healthy and symptomatic leaves were collected at different times using the same spectrometer. A customized one-dimensional convolutional neural network (1D-CNN) was trained on Batch 1 and adapted to Batch 2 using labelled samples through two strategies: retraining only the fully connected layers or fine-tuning all network parameters. Both strategies achieved 94% accuracy, with precision 0.92, sensitivity 0.97 and specificity 0.98, outperforming the non-adapted model and standard-free calibration transfer methods, namely Correlation Alignment and Transfer Component Analysis. These findings demonstrate that deep domain adaptation substantially improves the robustness and transferability of Vis-NIR classification models across heterogeneous sampling batches.
Yaning Zhao · Guodong Zhang · Jiaying Rong · Mingyue Gao · Peipei Dang · Tianjun Zhang · Hao Suo · Zhijun Wang · Ziyong Cheng · Panlai Li · Jun Lin
Raman / spectroscopyObject detectionPhysiological trait estimationWater status / transpiration
ABSTRACT Real‐time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional detection methods typically include destructive processes and suffer from response delays. Near‐infrared (NIR) luminescent metal halides offer a novel solution to this challenge, but they still face issues such as ultraviolet excitation and low photoelectric conversion efficiency. Herein, a luminescent material was synthesized based on the blue‐light‐excited lead‐free perovskite Cs 2 HfCl 6 :Te 4+ /Mo 4+ , in which energy transfer (ET) from Te 4+ to Mo 4+ enables highly efficient NIR luminescence in the 800–1200 nm wavelength range. Upon encapsulation with a commercial blue light chip, the fabricated NIR light‐emitting diode device achieved a photoelectric conversion efficiency of up to 16.1%. By utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content. This non‐destructive and precise NIR detection module provides a new solution for the real‐time monitoring of crop physiological status and holds broad application prospects in the fields of precision agriculture and plant science.
Barley ( Hordeum vulgare L.) is a major cereal crop whose soluble dietary fiber (SDF) offers significant health benefits, yet conventional SDF determination methods are time-consuming, labor-intensive, and destructive to samples. This study developed a rapid, non-destructive method for SDF quantification in barley using mid-infrared (MIR) spectroscopy combined with a Boruta-based partial least squares (PLS) hybrid approach. A total of 280 barley grain samples were subjected to Fourier-transform infrared (FTIR) spectral acquisition from 2000 to 650 cm -1 , with reference SDF values determined by the association of official analytical chemists (AOAC) 991.43 enzymatic-gravimetric method. The Boruta algorithm selected 165 informative wavenumbers out of 363, reducing the variable space by 54.5%. The developed Boruta-PLS model achieved excellent predictive performance with a coefficient of determination for the training set ( R 2 C ) of 0.9695 and for the test set ( R 2 P ) of 0.9601 and a root mean squared error for the training set (RMSE C ) of 0.7464%, and for the test set (RMSE P ) of 0.7340%, substantially outperforming the full-spectrum PLS model with an R 2 P of 0.7759 and an RMSE P of 1.7387%, as well as conventional wavelength selection methods including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE). The selected wavenumbers were predominantly located in chemically relevant regions at 1000-1200 cm -1 and 1500-1700 cm -1 , confirming model interpretability. This Boruta-PLS approach provides a rapid, non-destructive, and cost-effective alternative for SDF quantification, with strong potential for high-throughput screening and real-time quality monitoring of barley samples.
The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.
Microplastics (MPs) are persistent and ubiquitous contaminants in aquatic ecosystems, yet their interactions with submerged aquatic plants remain poorly understood. While MP-induced phytotoxicity has been extensively investigated in terrestrial plants, quantitative evidence for MP uptake and internal accumulation in submerged species is still limited. In this study, we investigated the phytotoxicity and accumulation patterns of fluorescent microplastics (FMPs) in two submerged aquatic plants, Bacopa lanigera and Rotala indica, using fluorescence spectroscopy. Plants were exposed to FMPs of two particle sizes (50 nm and 1 µm) across three exposure concentrations (0.001%, 0.01%, and 0.05%). Plant growth, chlorophyll content, fluorescence emission, and FMP accumulation were systematically evaluated. Our results demonstrated clear size- and concentration-dependent responses. Smaller particles (50 nm) showed significantly higher uptake and induced stronger phytotoxic effects than 1 µm particles, with pronounced growth inhibition and chlorophyll reduction observed at the highest concentration (0.05%). Fluorescence-based analysis enabled quantitative estimation of both surface-associated and internalized FMPs within plant tissues. Maximum surface accumulation reached 207 ppm, while internal (cross-sectional) accumulation reached up to 75 ppm, regardless of plant species. Under the respective experimental conditions, B. lanigera exhibited higher estimated FMP accumulation, whereas R. indica showed greater growth inhibition. These findings provide quantitative evidence of microplastic uptake and internal accumulation in submerged aquatic plants and highlight particle size as a critical determinant of phytotoxicity. Moreover, this study establishes a fluorescence-based methodological framework for estimating microplastic concentrations in aquatic plant tissues, contributing to improved ecological risk assessment of microplastics in freshwater ecosystems.
ABSTRACT The present study evaluated the applicability of Portable X‐ray Fluorescence (pXRF) for rapid determination of seed mineral concentrations in cowpea [ Vigna unguiculata (L.) Walp.] by comparing pXRF measurements with those obtained using Atomic Absorption Spectroscopy (AAS). Fifty‐seven cowpea genotypes, including two check varieties, were analysed for iron (Fe), zinc (Zn), manganese (Mn), copper (Cu), potassium (K), and calcium (Ca). Simple linear regression was used to assess the relationship between pXRF‐ and AAS‐derived mineral concentrations using training ( n = 47) and independent validation ( n = 10) datasets. The pXRF measurements showed good agreement with the corresponding AAS values for both macro‐ and micronutrients, with comparatively stronger relationships observed for Fe, Zn, Mn, and Cu. Residual and normal Q–Q plot analyses supported the suitability of the regression models. The findings demonstrate that pXRF enables rapid, simultaneous multielement analysis with minimal sample preparation and provides an efficient approach for high‐throughput mineral phenotyping and biofortification‐oriented cowpea breeding programmes.
White lupin ( Lupinus albus L.) is a cool-season grain legume with seed crude protein of 33-47%, competitive with soybean ( Glycine max L.) meal. It also fixes nitrogen and mobilizes soil phosphorus. Because soybean is a summer crop, white lupin can occupy Southeastern winter fields as a complementary protein source. Breeding for seed protein is limited by the cost and throughput of reference phenotyping. To determine how each is best deployed, we compared the utility of near-infrared spectroscopy (NIRS)-based phenomic selection with genomic selection based on 246,847 SNPs from low-pass, whole genome sequencing in a panel of Auburn University breeding lines and USDA National Plant Germplasm System germplasm. A handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R 2 = 0.81). Under common cross-validation, phenomic predictive ability was 0.93 and genomic was 0.12. The low genomic value was consistent with moderate heritability (H 2 = 0.33) and strong genotype-by-year interaction. Beyond predictive ability, NIRS recovered superior accessions the strictest selection intensity, and 40 to 60 reference assays sufficed to calibrate the model. Handheld NIRS is a low-cost tool for protein calibration and early-generation screening, while genomic prediction remains suited to parental selection, together supporting a complementary strategy for legume breeding Plain Language Summary Soybean meal is the main protein source for livestock and fish farms in the United States. Because soybean is a summer crop, many Southeastern fields sit idle or grow low-value cover crops in winter. White lupin, a cool-season legume whose seeds are as protein-rich as soybean meal, makes a good complementary winter crop: it yields high-protein grain while serving as a cover crop that fixes nitrogen and frees up soil phosphorus for later crops. In our early-stage lupin breeding program, measuring seed protein by standard lab methods is slow and costly. We built a calibration that lets a handheld scanner estimate protein from light, and compared it with predicting protein from the plant’s DNA. The scanner gave accurate, low-cost protein screening from only about 40-60 lab tests, while DNA-based prediction remains suited to guiding parent selection. Used together, these tools offer breeders a practical path to develop high-protein white lupin. Core ideas Handheld NIRS provides screening-grade prediction of white lupin seed crude protein. Spectra carried more usable protein signal than markers by measuring seed chemistry directly. NIRS and genomic prediction serve different stages of a white lupin breeding program. About 40 to 60 reference assays sufficed to calibrate NIRS to near-full accuracy.
Huang Y, Huang X, Huang L, Fu S, Wu Y, Zhou D, Peng J, Liang Y, Kuang M.
ArabidopsisCottonRaman / spectroscopySeed / grain
Key messages High-accuracy NIRS models and GWAS identified a novel QTL qPO-A07-1. GhMYB86 was validated to enhance seed protein content and fiber strength, and a functional KASP marker was developed. Cottonseed is rich in protein and oil; improving its nutritional quality is vital for global food security. In this study, near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 of 0.969 (P -4 ). A novel stable quantitative trait locus (QTL), qPO-A07-1, was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high-protein-content varieties during ovule development. Heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61-3.34%, whereas expression in Saccharomyces cerevisiae increased protein content by 25.81% and reduced triglyceride content by 30.72% in comparison with the control. These results demonstrate that GhMYB86 positively regulates protein content while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher-protein content, lower-oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein content- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides phenotyping tools, reliable genetic resources and a molecular marker for cottonseed nutritional quality breeding, laying a foundation for the improvement in cottonseed protein content and fiber strength.
Nano- and microplastics (NMPs) are now widely detected across agroecosystems and can act as physiological stressors in plants. Exposure occurs through contaminated soil, irrigation water, or airborne deposition, bringing particles into direct contact with roots and above-ground tissues. Reported entry routes include apoplastic transport, cracks formed at lateral root emergence, leaf stomata, and endocytosis once particles have crossed the cell wall. Once internalized, particles may translocate through the xylem and, in some cases, the phloem, accumulating in roots, stems, and leaves depending on particle size, surface charge, and plant structural characteristics. NMPs have been associated with oxidative stress, disrupted photosynthesis, and altered metabolic pathways. Detecting NMPs within heterogeneous, hydrated plant tissues remains challenging, as particles often show low contrast against biological structures and can be mistaken for cellular components. This review examines how microscopy techniques reveal NMPs size, surface attachment, tissue distribution, and cellular-level interactions, while noting that these approaches primarily provide morphological or localization information rather than confirming polymer identity. Complementary spectroscopic and mass-based analytical methods are discussed for their role in chemical confirmation and quantification. This review supports informed selection among imaging, spectroscopic, and quantitative techniques for studying plant-plastic interactions, while highlighting current analytical challenges facing the field.
Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.
Lignins in plant biomass are carbon-negative aromatic biopolymers which hold tremendous potential as multipurpose resources for sustainable bioeconomy, limited only by their chemical heterogeneity. Plant lignified tissues, such as sapwood and seed coats, vary in colors within and between species, indicating that specific lignin topochemistries determine the different colors. Yet, the responsible lignin chromogen(s) are unknown. We developed chemical imaging using UV-Vis microspectroscopy to link lignin color to topochemistry in isolates and plant samples. Using synthetic and technical lignins, we identified the different stable chromogens as homomeric lignin substructures varying in size, unit chemistry and interunit linkages. We controlled the accumulation of specific lignin chromogens using genetic engineering to similarly stain lignified tissues from different plant species. We established plant tissue engineering to cast plant tissues with pre-determined color by adjusting lignin topochemistries. We proved that biotechnological manipulation of the identified lignin chromogens predictably and stably stains lignified plant tissues.
Grain quality is defined as the suitability of grain for a particular use. It is usually designated by chemical composition or physical properties of the grain. The ability to measure grain quality is important for identity preservation of specialty grain market classes, for development of new varieties with improved quality through breeding, and for basic scientific studies on the genetic or biochemical control of grain quality traits. This review introduces official methods for measuring maize compositional traits, including protein, starch, oil, amino acid, phytate, and phosphorus content. Additionally, we discuss two nonofficial methods: measuring phytate and available phosphorus levels, and assessing amino acid balance. Phytate and available phosphorous impact the mineral nutrition of grain, while amino acid balance reflects the value of grain as a protein source and the bioavailability of protein. We also describe the use of near-infrared spectroscopy (NIRS) to assess levels of various compounds in maize. NIRS relies on the fact that compounds with differing molecular properties uniquely interact with the near-infrared region (750-2500 nm) of the electromagnetic radiation spectrum, and thus, generate spectral information that can be used to develop calibration models/equations for predicting the concentration of the compounds in grain samples. We discuss how sensitivity, accuracy, precision, throughput, and cost influence the choice of assay used to assess grain quality. Furthermore, we discuss how appropriate experimental design and data analysis can improve analytical outcomes when assessing grain quality.
A rapid and non-destructive method for predicting dry matter (DM) content in leeks was developed using visible and near-infrared (Vis-NIR) spectroscopy, coupled with what we believe to be a novel wavelength selection algorithm. Reflectance spectra (397.7-1716.7 nm) were acquired from 288 leek samples collected from three production areas in Nantong, China, and DM content was determined by oven-drying. The full-spectrum partial least squares (PLS) model yielded moderate prediction accuracy, with R P2 of 0.7963 and RMSE P of 1.14%. To improve performance, the iterative ranking-based variable elimination PLS (IRIVE-PLS) algorithm was proposed, which integrates multiple importance metrics to iteratively eliminate uninformative wavelengths. The algorithm autonomously identified the red-edge region (680-780 nm) as the most informative spectral feature, enriching its proportion from 9.5% in the full spectrum to 10.6% in the selected set. The IRIVE-PLS model achieved excellent prediction performance, yielding R P2 of 0.9683 and RMSE P of 0.45%, significantly outperforming conventional wavelength selection methods. The proposed approach provides an accurate, interpretable, and non-destructive alternative for leek quality assessment, with strong potential for online sorting applications in the vegetable industry.
Optimizing environmental inputs for indoor crop production by conducting a traditional endpoint growth analysis requires significant time and resources. The most common scientific approach to assessing crop response involves the accumulation of dry mass at the end of a cropping cycle. A growth dynamics analysis also results in the accurate estimation of the crop response to the growth environment through periodic destructive sampling. Measuring crop gas exchange in the same environment in which it is grown offers a powerful alternative to accelerating the environmental optimization process, especially for vegetative crops. This work introduces Minitron III, a third-generation technology advancement capable of continuous gas-exchange monitoring from seed to harvest for small specialty crop stands. For proof of concept, 24 ‘Rouxai’ red oakleaf lettuce plants were grown from seed to harvest over a 25-day cropping cycle. Instantaneous differences in the carbon dioxide (CO 2 ) and water vapor (H 2 O V ) mole fraction between sample/reference lines flowing through/around cuvette/growth space were measured using a differential infrared gas analyzer, allowing determination of net photosynthesis based on a 0.41-m 2 cropping area. Crop stand net photosynthesis was detectable 7 days after sowing seeds, increasing gradually from 0.13 to 0.60 µmol·m −2 ·s −1 over the following week. The crop net photosynthesis rate increased robustly on a daily basis from 15 days after sowing seeds. While the net photosynthesis rate at the beginning of the photoperiod was 0.68 µmol·m −2 ·s −1 on day 15, it increased to 7.7 µmol·m −2 ·s −1 by day 25 after sowing seeds. Crop dark respiration was detectable from 17 days after sowing seeds and ranged from −0.3 to −0.9 µmol·m −2 ·s −1 . Minitron III has potential for rapid optimization of multiple environmental inputs for indoor production of specialty crops based on the near-real-time crop response to environmental inputs.
Accurate assessment of plant nitrate status is critical for growth and productivity, yet early and non-destructive quantification remains challenging. Although Raman spectroscopy has been used to detect nitrate deficiency in plants, quantitative estimation of nitrate concentration from Raman spectra has not been demonstrated. Here, we evaluated whether Raman spectroscopy can be used to quantitatively predict leaf nitrate concentrations during early nitrogen stress. Two-week-old Pak Choi (C3) and Amaranthus (C4) plants were subjected to nitrate deprivation for 1–3 days, and Raman spectra were collected and correlated with nitrate concentrations determined by biochemical assays. A strong linear relationship was observed between nitrate concentration and the intensity ratio of the nitrate-associated Raman peak at 1046 cm - ¹ to the neighboring 1067 cm - ¹ peak. This relationship was consistent among plants of the same species and across different levels of nitrogen deficiency. Linear regression models achieved root-mean-square errors of 101 µg g - ¹ fresh weight (FW) in Pak Choi (~7% of nitrate under sufficient nitrogen) and 32 µg g - ¹ FW in Amaranthus (~19%), closely matching biochemical measurements and revealing species-specific nitrate dynamics. These findings demonstrate that Raman spectroscopy enables rapid, non-destructive, and quantitatively reliable estimation of leaf nitrate levels during early nitrogen stress, providing a promising platform for precision nutrient management and real-time plant phenotyping.
Doubled haploid (DH) technology significantly shortens the breeding cycle for developing homozygous inbred lines in maize ( Zea mays ). Manual sorting of haploids from a larger bulk of hybrid kernels in an induction cross is a major bottleneck in DH development. Automated systems based on near-infrared (NIR) reflectance spectroscopy can be valuable tools for rapid haploid sorting, provided that sorting accuracy is sufficient for incorporation into the DH process. In this study, we evaluated the accuracy of a custom-built single-kernel NIR (skNIR) sorter for classifying haploid kernels from 12 high-oil haploid induction populations generated from two sweet corn and two field corn donors and four high-oil haploid inducers (HOHIs). We evaluated several general classification models that can be applied without population-specific recalibration or prior genotyping, including models that classified haploids based solely on predicted oil content, as well as multivariate methods that used all wavelengths of the NIR spectra. The highest classification accuracy was obtained using a general multivariate support vector machine (SVM) model. When combined with the two best-performing HOHIs, the general SVM model accurately sorted induction populations from two of the three donor backgrounds crossed with these inducers. Two oil-based methods showed less accurate classification than the multivariate SVM model, due to overlapping oil content distributions across the two kernel classes. Overall, this study demonstrates effective skNIR-based sorting of haploid kernels from diverse induction populations using a single general model. The practical deployment of this instrument in maize breeding programs is discussed.
ABSTRACT Phenomic selection (PS) offers a cost‐effective , breeder‐friendly approach for public breeding programmes with limited access to genotyping or restricted financial resources for laboratory infrastructure. Since PS relies on high‐throughput phenotyping data, which is often derived from near‐infrared spectroscopy (NIRS) of harvested seeds, prediction is challenged by the high dimensionality and strong intercorrelation of NIRS data, which means that only a subset of wavelengths is informative. This study evaluates spectral variable selection models for predicting key morpho‐agronomic and quality traits in malt barley ( Hordeum vulgare L.) and assesses their performance under realistic breeding scenarios. Four NIRS‐based regularized regression models (Lasso, Enet, Ridge and a heritability‐filtered Ridge model) were tested to predict 10 morphological, agronomic and quality traits measured in two malt barley trials conducted during the 2022 and 2024 cropping seasons at three locations in Ethiopia using 100 genotypes in each trial. Model performance was evaluated across four practical breeding scenarios: within‐location unseen genotype prediction (WL‐uG), leave‐one‐location‐out prediction (LOLO), target environment unseen genotype prediction (TargetEnv) and across‐location wide adaptability (RuG). Accordingly, Cross‐validation identified stable, informative spectral predictors for each trait, scenario and trial. Among the models tested, Lasso and Enet consistently outperformed Ridge regression, with Enet showing the best predictive performance across scenarios. Prediction ability (r) ranged from 0.15 to 0.85 for quality traits, 0.16 to 0.79 for agronomic traits and 0.07 to 0.89 for morphological traits across scenarios. Thus, these findings underscore the importance of spectral predictor selection in improving predictive ability and demonstrate the transferability of PS in barley breeding.
Background Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.
Traditional methods for detecting the soluble solid content (SSC) of kumquats are often destructive, time-consuming, and inefficient. In this study, a multi-scale convolutional neural network (MS-CNN)-based method is proposed for the rapid and non-destructive prediction of kumquat SSC. By integrating near-infrared spectroscopy (900–1700 nm) with deep learning, 424 spectral samples of kumquats were collected and modeled using the MS-CNN framework. The proposed model adopts a multi-scale feature extraction structure inspired by the Inception architecture, which effectively enhances the representation of spectral features and reduces overfitting. Experimental results showed that the MS-CNN achieved an Rp2 of 0.88, an RMSEP of 0.62 °Brix, and an MAEP of 0.51 °Brix on the internal prediction set. Among the evaluated models, the MS-CNN achieved the highest Rp2, while its RMSEP was comparable to that of PLSR and lower than those of SVR, BP, CNN, and BiLSTM. The proposed approach enables fast, accurate, and non-destructive prediction of kumquat SSC, providing a novel technical solution for fruit quality assessment. This work holds significant theoretical and practical value, and future efforts will focus on expanding the dataset, optimizing the network structure, exploring multi-index joint prediction, and promoting its real-world application.
Seed quality is closely associated with rice yield and grain quality, and seed vigor is a key indicator for seed quality evaluation. High-vigor seeds usually show stronger resistance to environmental and biotic stresses, thereby improving germination and seedling establishment. Thus, rapid and accurate detection of rice seed vigor is essential for breeding, storage management, and crop production. In this study, a non-destructive rice seed vigor detection method based on near-infrared (NIR) spectroscopy, spectral augmentation, and Conv1D-GRU classification was developed. Rice seed samples with different vigor levels were prepared through artificial aging, and seed-level NIR spectra were acquired using a NIR spectrometer. Spectral preprocessing was applied to reduce noise, enhance relevant spectral features, and correct scattering effects. Sparse representation and dictionary learning were used to augment the training spectra and improve sample diversity. In the Conv1D-GRU classifier, the Conv1D layers extracted local spectral features from adjacent wavelength regions, while the GRU layer captured wavelength-order contextual information across the spectral sequence. The key hyperparameters of the classifier were optimized using an integrated population search algorithm. Experimental results showed that the proposed method achieved test accuracies of 0.9844, 0.9740, and 0.9818 for conventional japonica rice, indica-japonica hybrid rice, and japonica glutinous rice, respectively. Compared with PLS-DA, SVM, XGBoost, 1D-CNN, and GRU models, the Conv1D-GRU classifier showed better overall performance under the current experimental conditions. These results indicate that the proposed NIR spectroscopic method provides a promising non-destructive approach for rice seed vigor detection and has potential for seed quality evaluation and agricultural production management.
Coffee is a major export earner for Uganda, raking in over USD 2 billion in 2025. The global price of coffee is tagged to the perceived quality in the cup which in turn is affected by the chemical composition of the green bean. Breeding for market-preferred Arabica coffee varieties is a major objective of coffee breeding programs. Determination of coffee bean chemical constituents is routinely done through expensive, slow and tedious laboratory procedures, making it unsustainable of resource-limited public sector coffee breeding programs. Here, we demonstrate the use of near-infrared spectroscopy (NIRS) and the machine learning algorithms partial least squares (PLS), random forest (RF) and support vector machine (SVM) for the prediction of caffeine, protein and trigonelline in Arabica coffee. NIRS provides a fast, accurate and reliable method of simultaneously predicting multiple sample constituents. Ripe coffee cherries were picked from 172 farmers' fields, air dried in the laboratory at room temperature and processed to green beans. NIRS spectra were taken on the milled green bean at 400-2500 nm, with a 0.5 nanometer (nm) step. Reference data for caffeine, protein and trigonelline were collected on the same sample scanned with NIRS. A set of 12 spectral pretreatments were applied prior to making calibrations with the PLS, RF and SVM algorithms and 70% of the data as a training set and 30% as a test set. Caffeine content of reference samples ranged from 1.94-3.0 g/100 g, protein content ranged from 11.16-15.94% while trigonelline ranged from 0.94-1.23 g/100 g. The best calibrations for all algorithms and analytes were obtained using raw (untreated) spectra, which gave the same results as the Savitzky-Golay (SG) pretreatment. For caffeine, the best model (R 2 p = 0.89, RMSEP = 0.007, RPD = 3.34) was obtained with the SVM algorithm, while for protein, the best model (R 2 p = 0.98, RMSEP = 0.14, RPD = 6.92) was obtained using the PLS algorithm. Finally, for trigonelline, all three models had very high prediction accuracies (R 2 p = 0.98-0.99, RMSEP = 0.007-0.009, RPD = 8.53-10.52). Collectively, these results demonstrate the potential of using NIRS for rapid and simultaneous prediction of coffee green bean constituents to aid selection decisions.
Quantitative imaging of plant tissues by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is hindered by the lack of matrix-matched calibration standards. Established approaches, such as gelatine or homogenized tissue blocks, do not replicate plant matrices accurately. Here, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections, exploiting the low endogenous metal content of the endosperm to generate in situ calibration curves. Calibration performance for Mg, Mn, Cu, Zn and Mo was assessed using LA-ICP-MS imaging and Iolite data processing. The method demonstrated excellent linearity (R2 > 0.98), reproducibility across multiple grains, and sub-ppm limits of detection. Comparative analysis with in-house homogenized blocks and NIST wheat reference material confirmed superior accuracy and reproducibility of the nano-dispenser approach. As a proof-of-concept we have applied the method for the quantitative imaging of metals in a whole barley grain section and the results show excellent agreement with published data obtained by conventional liquid-mode ICP MS. The technique reported here provides a robust, scalable solution for quantitative metallomics studies of plants tissues, enabling improved assessment of nutrient distribution and supporting the development of standardised protocols for LA-ICP-MS Imaging
The endodermis plays a critical role in root function by regulating the movement of water and nutrients. Because endodermal function emerges from coordinated interactions among neighboring cells, the three-dimensional (3D) organization of cellular networks may influence how transport pathways are spatially arranged within root tissues. However, the 3D cellular network topology of the endodermis and its potential functional significance in woody plants remain poorly understood. Here, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta. We found that the balsa endodermis exhibits a distinct network topology characterized by higher local connectivity, lower closeness centrality, and lower edge betweenness centrality than that of Eucalyptus. Confocal Raman spectroscopy revealed broadly similar lignin and suberin signatures in the Casparian strip of the two species. Physiological measurements further showed that balsa roots exhibited significantly higher K + influx than Eucalyptus roots. Together, these observations indicate an association between variation in endodermal network organization and differences in root ion transport characteristics. This study highlights the value of integrating three-dimensional cellular reconstruction with network analysis to investigate structure-function relationships in plant tissues.
Abstract This study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer leveraging engineered Au core/Ag shell nanocubes (Au@AgNCs) to bridge in planta pathogen tracking with airborne defense signal monitoring, enabling unprecedented decoding of plant–plant communication (PPC) kinetics. Within a Pseudomonas aeruginosa (P. aeruginosa)-infected plant biotic stress model, the analyzer achieved: (1) spatiotemporal mapping of virulence kinetics through sensitive detection of P. aeruginosa-specific virulence factor pyocyanin, establishing infection progression timelines and tissue-specific dissemination gradients. (2) Quantification of stress-responsive signaling via dual-functionalized Au@AgNCs, revealing methyl salicylate (MeSA) release kinetics and establishing a direct correlation between pathogen invasion severity and airborne alarm signal—a calibrated defense response heretofore unquantified. (3) Real-time in situ monitoring of MeSA-mediated PPC revealed fundamental plant physiological breakthroughs: First, receiver-specific signaling reprogramming occurs where healthy plants exhibit delayed yet amplified defense hormone kinetics, contrasting sharply with the immediate response of infected emitters. Second, evolutionarily constrained coordination emerges through cross-species signaling divergence, where phylogenetic adaptations in phytohormone perception circuits drive distinct defense strategies−exemplified by Solanaceae amplification versus Poaceae suppression. (4) Validation of systemic acquired resistance (SAR) in PPC-primed plants showing 63.5% reduced infection severity and two days delayed susceptibility. This analyzer integrates molecular-scale pathogen kinetics with ecosystem-level signaling networks, advancing precision agriculture through field-deployable plant immunity diagnostics.
Near-infrared (NIR) spectroscopy combined with machine learning algorithms has been widely adopted for rapid assessment of grain quality attributes. However, conventional calibration models often suffer from overfitting and instability when applied to high-dimensional spectral data with limited sample sizes. In this study, we developed a novel bagging partial least squares (BA-PLS) algorithm for accurate and stable prediction of wheat protein content. A total of 394 wheat samples were collected and their NIR spectra from 950 to 1650 nm were acquired. The BA-PLS algorithm generates multiple bootstrap subsamples, trains PLS models on each subsample, and aggregates their predictions through averaging, effectively reducing prediction variance while preserving the low-bias properties of PLS. The performance of BA-PLS was comprehensively compared with that of standard PLS, support vector regression (SVR), and extreme gradient boosting (XGBoost). The results demonstrated that BA-PLS achieved superior predictive performance with a coefficient of determination ( R P 2 ) of 0.9600 and a root mean square error (RMSE P ) of 0.3058%. Notably, while SVR and XGBoost exhibited severe overfitting with training to test R 2 gaps exceeding 0.4045, BA-PLS exhibited excellent generalization with a minimal R 2 gap of 0.0261. Furthermore, BA-PLS provided reliable prediction uncertainty estimates through the standard deviation of ensemble predictions. The proposed BA-PLS algorithm offers a practical and stable solution for rapid wheat protein quantification, with potential applicability to other cereal quality assessment tasks.
Leaf water status is a key indicator for irrigation scheduling and early stress detection, but many spectroscopic prediction studies have mainly relied on near-infrared features. Here, practical prediction models were developed using mid-infrared (MIR) ATR-FTIR spectra of capsicum (Capsicum annuum L.) leaves collected under glasshouse conditions during a 10-day gradual dehydration period, alongside an irrigated control. Spectra (4000-450 cm⁻1) were measured with minimal sample preparation, and leaf water traits were quantified using fuel moisture content (FMC), equivalent water thickness (EWT), and specific leaf weight (SLW). Water-related MIR bands at 3370 and 1641 cm⁻1 showed the most consistent response to dehydration, and simple band ratios generally provided stronger predictions than single bands. The best ratios were A1641/A2159 for FMC (R2 = 0.81; RMSE = 12.80) and A3370/A2849 for EWT (R2 = 0.72; RMSE = 0.0034) and SLW (R2 = 0.62; RMSE = 6.95 × 10⁻4), while predicted-versus-measured performance yielded R2 values of 0.72 for FMC, 0.68 for EWT, and 0.52 for SLW. These results indicate that MIR ATR-FTIR spectroscopy, when coupled with selected band ratios, can provide a rapid, low-preparation laboratory-based approach for estimating capsicum leaf water traits under controlled dehydration, supporting plant-based water stress assessment under controlled conditions and providing a basis for further irrigation-related sensing studies. However, the models are preliminary and require validation with larger independent datasets and tightly standardised measurement conditions before operational use in irrigation management.
The study of the effect of various stresses like light stress, temperature stress, pollutant stress etc. may be performed using various spectroscopic techniques like absorption spectroscopy, fluorescence spectroscopy, fluorescence kinetics, Fourier Transform Infrared spectroscopy etc. In addition to these techniques the synchronous luminescence technique may be successfully employed to study the effect of stresses on the plant health. In the present attempt we are going to use the synchronous luminescence spectroscopy for the study of plant health and classification. As per our information the type of measurements made by us is the first report of this kind. It is seen that more information can be obtained from the analysis of synchronous luminescence spectra of the plant leaves
Accurate apple origin identification and non-destructive internal quality evaluation are important for fruit traceability, quality grading, and post-harvest management. Unlike previous studies mainly focusing on origin classification, this study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples. Samples were collected from three representative production regions in China: Alar in Xinjiang, Yantai in Shandong, and Luochuan in Shaanxi. Near-infrared diffuse reflectance spectra were acquired from 375 apples, generating 3000 spectral samples for origin classification and 750 SSC-calibrated samples for sugar content prediction. For classification, six deep learning models were evaluated using standardized full-spectrum input without chemometric spectral preprocessing, and the Transformer achieved the best performance, with a test accuracy of 96.22%. For SSC regression, spectra were preprocessed using standard normal variate and Savitzky-Golay filtering. The DNN model achieved the best prediction performance, with MAE = 0.5958 °Brix, RMSE = 0.7333 °Brix, R 2 = 0.8646, and Pearson r = 0.9338. These results indicate that near-infrared spectroscopy combined with deep learning can support both Fuji apple origin authentication and non-destructive local tissue SSC assessment.
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Functional genomics have been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS). The CRISPR/Cas-generated library consisted of 3567 microalgal mutants derived from 2397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unraveled mutations in the violaxanthin de-epoxidase (noVDE) or in the proteasome assembly chaperone 4 (noPAC4) genes. Knocking out all five known noVDEs revealed that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidence suggested two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this forward-genetics approach can greatly accelerate the discovery of genes and pathways.
The spatial organization of essential, nonessential, and toxic metal(loid) elements (MEs) within plant cells underpins physiological function. Yet, comprehensive subcellular imaging of the full ME spectrum remains challenging due to trade-offs among spatial resolution, elemental coverage, and structural correlation. Here, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution. Applying this high-fidelity workflow to Arabidopsis , soybean, and wheat, we constructed single-cell metallome maps revealing an evolutionarily conserved subcellular architecture: chloroplasts enrich essential MEs (e.g., magnesium, iron, copper), whereas vacuoles compartmentalize nonessential [e.g., lanthanum (La)] and toxic MEs [e.g., cadmium (Cd), lead, arsenic]. We demonstrate that while this architecture remains stable under homeostasis, it undergoes dynamic, stimulus-specific, and dose-dependent remodeling under stress. Low-dose La(III) enhances pairwise and higher-order colocalizations of essential MEs within chloroplasts, correlating with improved photosynthetic efficiency and growth. High-dose La(III) induces nonphysiological La-ME associations and, critically, drives aberrant Cd(II) accumulation in chloroplasts-revealing a cross-toxicity mechanism wherein La(III) disrupts native sequestration barriers. In contrast, although high-dose Cd(II) is largely excluded from chloroplasts, it triggers a widespread redistribution of essential MEs, progressively eroding spatial organization. Thus, while both ions inhibit growth, they perturb metallomic networks via distinct mechanisms: La(III)-mediated disruption of sequestration vs. Cd(II)-induced systemic compartmental collapse. Our findings establish that subcellular ME networks are dynamically regulated and orchestrate physiological outcomes.
Introduction Tomato brown rugose fruit virus (ToBRFV) represents a growing threat to global tomato production, causing severe losses in crop yield and fruit quality. Although the standard RT-qPCR assay is highly accurate, its reliance on laboratory processing, specialized equipment, and trained personnel limits its applicability for rapid on-site diagnostics. To address this limitation, this study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves. Methods Following health status confirmation via RT-qPCR, leaf extracts were directly deposited onto silver nanorod arrays for SERS spectral acquisition. Three classification models were evaluated: PCA-LDA, PLS-DA, and SVM. Results The results showed that all models were able to discriminate infected samples from healthy samples in the present dataset. Notably, the SVM model exhibited the best performance, achieving an accuracy of 91.67%, a sensitivity of 100.00%, a specificity of 81.48%, and an area under the ROC curve (AUC) of 0.993. Discussion This result suggests that SERS spectra may contain biochemical information associated with ToBRFV infection and that such information can be used for sample classification using machine learning models. In its present form, this approach is intended as a rapid, low-cost, field-deployable preliminary screening tool - not a replacement for RT-qPCR or other confirmatory molecular assays. The reported accuracy was obtained on mechanically inoculated plants of a single cultivar under controlled greenhouse conditions and should therefore be interpreted as a proof-of-concept upper bound; field-scale validation is the focus of ongoing work.
Ahmed Glili · Sajid AK Bangash · Matthias Koenig · Daniel Smit · J. Draeger · Hee Sung Kang · Berit Ebert · Alois Knoll · Malte C Gather · Stefan A Hey
1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.
Soil salinization poses a severe threat to global food security. However, deciphering the spatiotemporal dynamics of key metabolites and ions in living plants remains a formidable challenge due to the lack of robust in vivo sensing tools. In this study, we developed a nonmetallic MoO x @CuPc core-shell nanoprobe anchored to the plant cell wall, which serves as the cornerstone of an "in vivo-in situ-long term-multitargeted" (VSLM) surface-enhanced Raman spectroscopy (SERS) platform. This design overcomes critical limitations of conventional metallic probes, such as rapid corrosion in saline microenvironments and inability to achieve stable multitarget detection, by synergizing a corrosion-resistant MoO x core with a protective CuPc shell. The optimized interface electronic coupling enables simultaneous tracking of adenosine triphosphate (ATP), salicylic acid (SA), Na + , and K + at nanomolar detection limits, with signal stability maintained over 48 h ( Suaeda salsa ( S. salsa ) under salt stress, revealing a shift from "growth-priority" to "defense-priority" resource allocation alongside coordinated ion partitioning across roots, stems, and leaves. This work presents a novel in situ and multitargeted monitoring methodology, which substantially expands the capability of SERS for complex biological systems and opens a new avenue in analytical chemistry for dynamic, multiparameter life science research.
Abstract The Malaysian jackfruit industry is increasingly threatened by “jackfruit-bronzing,” a disease caused by Pantoea stewartii subsp. stewartii , which manifests as yellowish-orange to reddish discoloration of the pulp while leaving the rind visually unaffected. The cv. ‘Tekam Yellow’ cultivar is particularly vulnerable, resulting in substantial postharvest losses. This study explores the feasibility of employing visible near infrared spectroscopy (Vis-NIRS) as a non-destructive method to predict internal bronzing through the estimation of rind or flesh firmness and rind, flesh or seed moisture content. Spectral reflectance data were acquired non-destructively from the rind surface of jackfruit, and the resulting spectra were used to predict rind firmness, and moisture content of rind, flesh, and seed tissues. Jackfruits at 10, 12, and 14 weeks after anthesis (WAA) were analyzed within the 500–950 nm wavelength range. Partial least squares regression (PLSR) models were developed and optimized using preprocessing techniques such as Savitzky–Golay smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC). The best-performing models yielded high determination coefficients for both calibration (Rc²) and validation (Rv²), reaching up to 0.99, with root mean square error of calibration (RMSEC) and validation (RMSEV) values as low as 0.67 N and 0.74% w.b., respectively. Destructive reference measurements were conducted in parallel and analyzed using ANOVA and Fisher’s protected least significant difference (FPLSD) test at p ≤ 0.05. Results demonstrated that Vis–NIRS applied through the rind surface provided reliable prediction of firmness and moisture-related attributes associated with internal bronzing disorder in jackfruit. The developed approach shows strong potential as a rapid and non-invasive technique for early bronzing detection and postharvest quality assessment in jackfruit.
Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.
Rice bean ( Vigna umbellata L.) is an underutilized legume recognized for its superior nutritional profile, especially high starch, amylose, and protein content. However, mainstream adoption of nutritionally superior rice bean varieties remains limited due to significant bottlenecks in breeding programs, primarily the cumbersome nature of nutritional phenotyping. Traditional analytical methods used to evaluate nutritional traits are labor-intensive, time-consuming, costly, and environmentally unsustainable, thereby hindering breeding efforts aimed at improving nutritional quality. Addressing these constraints, this study developed robust and environmentally sustainable prediction models utilizing Near-Infrared Reflectance (NIR) spectroscopy coupled with Modified Partial Least Squares (MPLS) chemometric approaches. A diverse germplasm collection sourced from India's North Eastern Region was employed to establish high-throughput, rapid, and non-destructive MPLS-based models. These models exhibited excellent prediction accuracy, achieving high coefficient of determination (RSQ) values of 0.97 for starch0.92 for amylose, and 0.98 for protein content, along with strong Residual Prediction Deviation (RPD) values of 5.81, 3.62, and 9.99, respectively. Such rapid and reliable phenotyping methodologies hold promise not only in breeding programs but also in postharvest applications for enabling quick and non-destructive assessment of nutritional quality in harvested beans. Also, these techniques offer significant industrial value by supporting quality control in food processing, formulation of nutrient-rich products, and standardization of raw materials for nutraceutical and functional food industries. Ultimately, these innovative approaches enhance breeding efficiency, promote rice bean’s integration into sustainable agri-food systems, and contribute to global nutritional security.
Akinmade H, Ben-Israel S, Ryan A, Johnson C, Tharayil A, Arias RO, Ulbricht Ferreira RC, Fernandes C, Boz Z, Rios E.
CowpeaRaman / spectroscopySeed / grain
As the demand for plant-based nutrition increases, improving the protein profile of legumes like cowpea has become a breeding priority. Cowpea, a multiuse legume and staple in many low-income regions, provides important dietary protein that can help meet the demand in our growing population. Our research used genome-wide association studies (GWAS) and phenomic tools to investigate the genetic architecture of seed protein content in cowpea and integrated 4 cohorts of undergraduate researchers through a USDA-AFRI REEU program. Using wet chemistry and near-infrared spectroscopy (NIRS), we assessed crude protein (CP) within the University of California Riverside Minicore collection, developed and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86), and performed GWAS with ∼41k single-nucleotide polymorphisms (SNPs). Significant SNPs associated with protein content were identified on chromosomes 1, 3, 7, 10, and 11, and candidate genes were linked to functions including nutrient transport, stress response, and seed storage protein regulation. These results provide a foundation for future marker validation and functional studies, and demonstrate the value of pairing trait discovery with undergraduate training.
Reproduction assets foundThe paper's Data Availability statement deposits the phenotypic data (wet chemistry CP, NIRS-derived CP phenotypes used for calibration and GWAS) in Dryad. No author analysis code or trained NIRS model files are explicitly deposited; other URLs are generic tools or citations.Dataset · publicThe phenotypic data collected and used in this research are available in the Dryad Digital Repository under DOI: https://doi.org/10.5061/dryad.8cz8w9h72 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.8cz8w9h72lines:305-345Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
This study established an integrated analytical method based on near-infrared spectroscopy (NIRS) for the rapid, non-destructive, and quantitative detection of four major nutritional components in faba beans: starch, protein, moisture, and dietary fiber. By systematically comparing individual and combined spectral preprocessing strategies, optimal preprocessing combinations for each component were identified. Seven feature wavelength selection algorithms, including Competitive Adaptive Reweighted Sampling (CARS), were employed to extract key spectral variables. Predictive models were subsequently developed using four modeling approaches: Partial Least Squares (PLS), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results demonstrated that combined preprocessing methods significantly outperformed single techniques. The CARS algorithm exhibited the most robust performance in feature extraction, and the MLP model consistently surpassed traditional machine learning methods in predicting all components. The optimal modeling pipelines for each component were ultimately determined as follows: starch (MLP + CARS + MSC + SG + MSS, R 2 = 0.92), protein (MLP + CARS + SD + SNV + MSC + MSS, R 2 = 0.94), moisture (MLP + SPA + SG + SNV, R 2 = 0.9973), and dietary fiber (MLP + PCA + FD + SNV, R 2 = 0.9999). This study verifies the effectiveness of combining NIRS with deep learning for the simultaneous detection of multiple components in faba beans and provides a reliable methodological framework for the non-destructive quality assessment of agricultural products.
Hydrogen sulfide (H 2 S) is a key gaseous regulator in plant stress responses, but its spatiotemporal dynamics in living plants remain poorly understood due to the lack of noninvasive sensing tools. Here, we report a stomata-infiltratable SERS nanosensor based on Au@Ag@SiO 2 core-shell nanoparticles for real-time monitoring of endogenous H 2 S. The sensor, with an enhancement factor of ∼6.07 × 10 9 and a detection limit of 15 nM, efficiently infiltrates leaves of diverse species (Arabidopsis, spinach, and tomato). Real-time monitoring revealed that H 2 S accumulation kinetics are stress-specific and occur within 20 min of stress onset, preceding visible phenotypic damage. Notably, the nanosensor enabled visualization of stress-induced H 2 S transmission between neighboring plants, suggesting a role for H 2 S as an airborne signal in plant-to-plant communication. Furthermore, a species-dependent kinetic framework describing systemic signal propagation was established. This work demonstrates a versatile SERS-based platform for noninvasive monitoring of gaseous signaling molecules in plants.
Analysis of the element composition of plant materials has many applications, including monitoring nutrient status, detecting biogeochemical indications of mineral deposits and assessing the effectiveness of phytoremediation in contaminated soils. Portable X-ray fluorescence spectroscopy (pXRF) delivers the advantage of real time in situ multi-elemental analysis at low cost, but calibration is affected by factors that include the water content of plant organs. The effect of variation in moisture content on pXRF-determined concentrations of heavy metals Zn, Fe, Cu, Sr and Th, and nutrient elements Si, S, K and Ca, have been evaluated using the foliage of A. imperialis, D. excelsa and A. macrorhiza through a controlled continuous drying experiments. A segmented linear relationship between pXRF measurements and moisture content was observed for most elements with a rapid decrease followed by a moderate decrease as moisture content increased. The position of the inflection point is dependent on leaf thickness and energy of the main X-ray peak measured. Calibration issues related to variation in moisture content comprises a combination of dilution and spectral interference effects. Dilution accounts for most of the underestimation of pXRF-determined concentrations for fresh plant samples compared with laboratory methods on dried samples. As moisture contents increase, the relative influence of spectral interferences decreases. The single-layer thickness of plant sample affects the position of inflection point of linearity and the relative contribution of spectral interference effect. This study provides new insights into the effect of moisture on pXRF-determined elemental concentrations and offers practical suggestions and recommendations for in situ analysis of plant samples using pXRF.
Lin Y, Wu H, Zhao C, Zhang W, Yin Z, Cao Z, Ma Y, Lu Y, Liu L, Hu D.
AppleField / plotRaman / spectroscopyFruitClassificationWater status / transpiration
Visible and near-infrared (Vis/NIR) spectroscopy has been widely applied in fruit quality detection due to its advantages of rapid efficiency, non-invasiveness, and suitability for detecting opaque samples. To address the issue of whether apple watercore occurs during the growth and maturation of apples, a portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore. The device employs the AIOX2000-13 spectrometer as the detection unit, with an STM32F103VET6 ARM-based processor as the main control chip, and integrates a 4G wireless communication module to establish a stable data transmission channel between the processor and the computer. This structure ensures the efficient and stable transmission of apple spectral data and detection results, thereby meeting the need for in-field nondestructive detection of apple watercore on apple trees. The system is based on a self-designed spectral data acquisition mechanism and uses a transmission detection method to collect spectral data from 500 'Fuji' apple samples in two directions. The spectral data were preprocessed using Standard Normal Variate (SNV), and the dataset was divided using the Spectral Projection based on X-Y distances (SPXY) algorithm. Important feature wavelengths related to apple watercore were extracted by combining the Uninformative Variable Elimination method with the Successive Projections Algorithm (UVE-SPA). Subsequently, a detection model, SNV-UVE-SPA-SVM, was constructed using a Support Vector Machine (SVM) optimized by the Honey Badger Algorithm (HBA), achieving a test set accuracy of 96%. After research and analysis, Direction 1 was identified as the optimal acquisition direction, and field verification was conducted on 50 apple samples, with a detection accuracy of 94%. The results show that the detection device has the advantages of portability, high efficiency, and suitability for in-field detection, making it suitable for the rapid in-field detection of apple watercore.
Abstract Key message CRaman imaging combined with a multi-layer perceptron neural network enables non-destructive, label-freeclassifi cation of tobacco BY-2 cells based on carotenoid composition. Abstract Carotenoids are natural tetraterpenoid pigments with important nutritional properties and broad industrial applications. Enhancing their production in plant-based biofactories offers a sustainable alternative to current manufacturing processes. In this work, we developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content. Carotenoid standards analysis, including astaxanthin, canthaxanthin, and β-carotene, was performed by surface-enhanced Raman scattering using hydrophobic gold nanostars due to the low concentration available. This analysis allowed the assignment of characteristic Raman peaks, specifically at 1160 cm −1 and 1520 cm −1 , of key carotenoids and their identification inside of the cells by Raman imaging. The Raman fingerprints were correlated with carotenoid profiles obtained by HPLC, enabling accurate differentiation between wild-type and transgenic cell lines. In the analyzed transgenic lines, carotenoids accumulated in vesicle-like structures near the nucleus and along the cytoplasmic membrane. This method provides a non-destructive, label-free approach with high classification accuracy and sorting potential based on carotenoid composition, and may be a useful tool for plant synthetic biology and metabolic engineering.
Abstract Context Downy mildew, caused by Plasmopara viticola , remains one of the most damaging diseases affecting grapevines, especially in humid viticultural regions such as the “Vinhos Verdes” in northern Portugal. Traditional detection relies on visual inspection and laboratory techniques, which are subjective and reactive, often delaying effective intervention. Aims This study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions. The focus is on providing an early, non-destructive detection method that can be used in precision viticulture, reducing the need for costly, widespread pesticide applications. Methods and Key Results Leaf spectral reflectance data were collected along the 2023 and 2024 growing seasons using a portable spectroradiometer. Measurements were obtained from both untreated and fungicide-treated grapevines, covering different infection stages. Spectral signatures from 600 grapevine leaves were used to train and validate classification models using Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Random Forest (RF) models. Both RF and PLS-LDA models showed an overall accuracy of 95.1% when trained with all spectral features from the dataset. Red edge (700–750 nm) and visible (400–700 nm) wavelengths demonstrated the highest classification contribution. Moreover, the twenty most informative wavelengths for infection discrimination were identified for each model. Conclusion The results confirm the effectiveness of field spectroscopy, making it possible to detect downy mildew symptoms in different stages of infection. This method offers a rapid, cost-effective, and sustainable tool for early disease detection, which can greatly benefit winegrowers by enabling more timely and specific interventions and reducing the reliance on chemical treatments. Implications and Impacts This study demonstrates a novel approach to precision viticulture, offering winegrowers a rapid, cost-effective, and sustainable tool for early disease detection. The methodology not only promotes more disease management strategies but also aligns with environmental and regulatory goals.
Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.
Reflectance spectroscopy offers a powerful approach to integrate high-throughput phenotypic data from herbarium specimens into the digital landscapes of ecology, evolution, and systematics. Because inconsistencies in instrumentation and measurement practices can increase noise and limit dataset compatibility, the International Herbarium Spectral Digitization Working Group (IHerbSpec) has published the Protocol for Spectral Digitization of Herbarium Specimens, currently in version 1.2.1, as an open resource that will continue to evolve through community use and collaboration (https://iherbspec.github.io/protocol). The protocol defines a stepwise measurement workflow, standardized filename conventions, structured metadata tables with controlled vocabularies, and guidance on tissue selection, materials, and instrumentation quality control to ensure that newly generated spectral datasets are robust and comparable. By embedding standardized practices at the point of data collection, it provides a scalable foundation for data synthesis and new quantitative insights into plant diversity across taxonomic, geographic, and temporal scales.
Rachel O. Mariani · Marney E. Isaac · Kimberley A. Cathline · Gavin Robertson · Adam R. Martin
GrapevineRaman / spectroscopyLeafPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
Estimating crop trait data is critical for predicting crop responses to environmental change, enabling more informed diagnoses of crop performance and the development of on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping-specifically reflectance spectroscopy-has emerged as a key element of plant trait research, enabling rapid estimation of plant traits. However, little is known about whether reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits, especially variation that exists among different cultivars or genotypes of the same crop. Using wine grapes (V. vinifera subsp. vinifera) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy to quantify intraspecific variation in 12 leaf traits across 12 different cultivars from seven different varieties. We find significant variability in traits across and within cultivars, especially in gas-exchange and hydraulic traits, with cultivars varying along a resource-conservative-to-resource-acquisitive trait axis. Models based on spectral reflectance data were able to differentiate and predict this fine-scale trait variation among cultivars for seven plant traits, with a predictive power range of R2 = 0.12-0.57. Models predicting leaf chemical (i.e., carbon and nitrogen concentrations), physiological (i.e., maximum rate of light-saturated photosynthesis), and morphological traits (i.e., leaf dry matter content) were more accurate in their predictions, while models predicting leaf water status were less accurate. Our results indicate that reflectance spectroscopy can capture certain dimensions of the fine-scale trait variation that exists within genetically diverse agroecosystems, though spectroscopic estimates of intraspecific variation in leaf water status are less accurate.
Soybean protein content is a key indicator of nutritional value and quality grade, and its determination is important for quality evaluation and cultivar selection. To overcome the time-consuming and costly limitations of conventional chemical assays, this study proposed a multiple linear learner ensemble importance-score wavelength selection (MLLEISWS) method to identify informative wavelengths from soybean near-infrared spectra and establish a partial least squares (PLS) model. MLLEISWS was compared with competitive adaptive reweighted sampling, successive projections algorithm, and uninformative variable elimination. Shapley additive exPlanations (SHAP) were applied to the MLLEISWS algorithm to interpret the selected wavelengths. Results showed that the PLS model developed using MLLEISWS achieved the best performance. With only 29 selected wavelengths, the coefficients of determination for the training and test sets reached 0.941 and 0.933, respectively. Root mean square errors were 0.490% and 0.514%, relative root mean square errors were 1.32% and 1.37%, and residual predictive deviation was 3.863, indicating predictive accuracy and stability. SHAP analysis showed that the selected wavelengths were located in protein-related spectral regions and corresponded to overtone and combination bands information from functional groups. MLLEISWS effectively reduced variable dimensionality while maintaining model performance.
Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP. Plain language summary Cultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimating these values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput method that is increasingly being used for this purpose. It often uses, as predictors, near infrared spectroscopy measurements that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a pre-processing approach that performs as well as the best chemometrics pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core Ideas The SVD-based pre-processing performs as well as the best performing classical chemometrics pre-processing in most cases Using the SVD-based pre-processing reduces computing time of genetic value estimation and requires less parameter optimization than using classical chemometrics pre-processing Spectra are composed of chemical and physical information and classical pre-processing methods remove the physical part of the signal It is likely that chemical information is the most important for phenomic prediction even though physical information remains valuable Performance of the SVD-based pre-processing is likely due to a good estimation of the genetic part of spectra and the conservation of physical information of spectra
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Accurate identification of Allium seed genotypes is essential for cultivar authentication, breeding, and fraud prevention, yet remains challenging due to morphological similarities. This study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes, including shallot, red, white, and yellow onions, bon-sorkh, and two leek varieties. A total of 700 spectra and 70 images were acquired using the Vis-NIR spectrometer and hyperspectral camera, respectively, under controlled conditions and spectral preprocessing was applied to enhance signal quality. For spectrometer data, classification models were developed using soft independent modelling of class analogy (SIMCA), artificial neural networks (ANN), and histogram-based gradient boosting (HisGB). For hyperspectral data, pixel-level spectra were used to train ANN, HisGB, and deep convolutional neural networks (1D and 2D CNNs). Among the spectrometer models, the combination of second derivative preprocessing with HisGB achieved the highest performance (F1-score: 98.52%). For HSI, HisGB yielded the highest pixel-level classification accuracy (F1-score: 97.83%; error: 2.49%), followed by 1D CNN (F1-score: 96.85%). Spatial analysis revealed that HisGB and 1D CNN produced consistent classification maps across genotypes, whereas ANN and 2D CNN exhibited higher misclassification rates, particularly for morphologically similar classes such as shallot and bon-sorkh. At image level, the hyperspectral camera outperformed the Vis-NIR spectrometer, achieving perfect classification across all models. These results demonstrate the potential of hyperspectral imaging, especially when combined with ensemble and deep learning approaches, for high-throughput, non-destructive seed sorting and genotype purity assessment. The study also emphasizes the trade-off between the lower cost but reduced precision of the Vis-NIR spectrometer and the superior accuracy offered by the hyperspectral camera.
Abstract Cottonseed is rich in protein and oil, making the improvement of its nutritional quality essential for global food security. In this study, high-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively. Using these models, 249 upland cotton accessions were phenotyped across five environments and subjected to a genome-wide association study (GWAS) based on a 10K liquid-phase SNP array, resulting in the identification of 24 significant loci. A novel stable QTL, qPO-A07-1 , was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high protein varieties during ovule development. Functional validation demonstrated that heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61% – 3.34%, whereas expression in Saccharomyces cerevisiae reduced triglyceride content by 30.72% relative to the control. These results demonstrate that GhMYB86 positively regulates protein accumulation while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher protein content, lower oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides robust phenotyping tools, reliable genetic resources and molecular markers for cottonseed nutritional quality breeding, laying a foundation for the synergistic improvement of both fiber quality and nutritional quality in cotton.
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the same sample. This study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR). Applying different pre-processing techniques to the spectra collected from intact kernels, the calibration models were developed using partial least squares regression and the reference protein content values obtained from the LECO combustion method. The best model was obtained using multiplicative scatter correction as pre-processing, resulting in a standard error of prediction of 0.83% and a relative predictive determinant of 3.40. These were indicative of the good predictive ability of the model and the instrument to be applied in quality control and sorting applications. These results highlight the potential of SKNIR to capture the inter-kernel variability in sorghum protein content and enhance screening for grain quality in breeding and grain processing.
Reproduction assets foundThe paper's Data Availability Statement deposits the original single-kernel NIR spectra and reference protein data openly in Ag Data Commons, a paper-specific public dataset directly reproducing this study's measurements.Dataset · publicThe original data presented in the study is openly available in Ag Data Commons [https://doi.org/10.15482/USDA.ADC/31316725].Open asset ↗Ag Data Commons · 10.15482/USDA.ADC/31316725html-lines:226-278Plant phenotyping relevance matchCrossref · checked 15 Sept 2026
Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.
Abiotic stresses caused by climate change pose a serious threat to global crop productivity, making the early detection of plant stress responses crucial. Hydrogen sulfide (H 2 S) and hydrogen peroxide (H 2 O 2 ), as key signaling molecules, their dynamic synergistic effects are central to understanding the mechanisms of plant stress adaptation. However, real-time tracking of the dynamic changes of these molecules remains challenging. This study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs), which cleverly combines the high sensitivity of surface-enhanced Raman scattering (SERS) with the gas enrichment capacity of the MOF, incorporates 2D plasmonic membrane assembly technology and 4-mercaptophenylboronic acid (4-MPBA) conjugation strategy, and successfully achieves real-time and synchronous detection of H 2 S and H 2 O 2 in plants. The 24 h dynamic monitoring results showed that under different stress conditions, H 2 S and H 2 O 2 in tomatoes and rice both had specific dynamic change rules, and there was a complex cross-regulation mechanism between them. By combining sensor data with partial least squares discriminant analysis (PLS-DA), the classification accuracy of stress types exceeds 95%. This non-destructive and highly sensitive detection system can provide real-time dynamic data of stress signals, bringing a breakthrough to the in-situ monitoring of plant physiological states.
Tomáš Hanousek · Oleksandr Borysenko · Jan Pisek · Mirjam Uusõue · Lucie Homolová · Joel Kuusk · Petr Lukeš · Nicolas Lauret · Jean Philippe Gastellu-Etchegorry · Růžena Janoutová
Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.
Pteris vittata, an arsenic-hyperaccumulating fern, is widely employed for phytoremediation of arsenic (As). Rapid, accurate assessment of As in P. vittata is crucial for evaluating its accumulation ability. In this study, P. vittata was analyzed using a spectral fusion of laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF). A total of 60 biological samples (roots and fronds) were collected and prepared as 180 compressed tablets for spectroscopic analysis, covering an As concentration range of 88-1956 mg kg -1 . Multivariate analysis methods were employed for full spectra and feature spectra, including partial least squares regression (PLSR), least squares support vector machine (LSSVM), extreme learning machine (ELM), random forest (RF), and adaptive weighting normalization-linear weighted network (AWN-LWNet). The best single-modality model, an XRF-based PLSR model built upon feature spectra selected by the Competitive Adaptive Reweighting Sampling (CARS) algorithm, achieved a prediction performance of R 2 P = 0.969, RMSE P = 54.13 mg kg -1 , and MAE P = 43.00 mg kg -1 . Spectral fusion further enhanced prediction accuracy, with high-level fusion outperforming low- and mid-level methods. The proposed feature-spectra-based decision fusion model achieved the best performance (R 2 P = 0.980, RMSE P = 43.69 mg kg -1 , MAE P = 32.49 mg kg -1 ), corresponding to reductions of 19.3% in RMSE P and 24.4% in MAE P compared to the best single-modality model. These results demonstrate that spectral fusion effectively integrates complementary information, improving the accuracy of As quantification in complex plant matrices. The proposed approach provides a rapid and non-destructive strategy for monitoring arsenic accumulation in phytoremediation plants.
Sugar and organic acid content are crucial factors determining grape quality. Non-destructive testing of these components aids in accurately determining optimal harvest timing and wine-making potential. However, few studies have addressed how varietal differences impact the universality of predictive models. This study combines near-infrared spectroscopy with machine learning, specifically partial least squares regression (PLSR) and convolutional neural networks (CNN). It compares single-variety and mixed-variety modeling strategies for grape sugars (glucose, fructose) and organic acids (malic acid, tartaric acid, shikimic acid). The results indicate that PLSR models constructed based on single varieties demonstrate superior performance in predicting malic acid, glucose, and fructose, with model R 2 P ranging from 0.835 to 0.923, notably outperforming PLSR and CNN models based on mixed varieties. The competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) algorithms successfully compressed the full-spectrum variables to 6-29 key wavelengths. The simplified models maintained high accuracy (R 2 P = 0.777-0.927) while substantially improving model efficiency. Mechanistically, SHapley Additive exPlanations (SHAP) analysis revealed the significance of key variables. The critical variables for glucose and fructose models occur around 1150 nm and 1450 nm, respectively. In contrast, the feature variables for the malic acid model exhibit broader distribution, spanning multiple bands including 1150 nm, 1200 nm, 1600 nm, and 1650 nm. This study provides a solid foundation and mechanistic explanation for non-destructive grape quality assessment, while also offering theoretical support for developing specialized spectral sensors.
MaizeRaman / spectroscopySeed / grainPhysiological trait estimationWater status / transpiration
Using spectroscopic technology for the accurate and non-destructive determination of moisture content (MC) in husk-on fresh corn ( Zea maize L. sinensis Kulesh) is crucial for optimizing harvesting periods, ensuring quality, and maintaining nutritional value. However, corn husks interfere with the propagation of incident photons within corn kernels, leading to acquired spectral signals that contain information unrelated to the kernels themselves, thereby decreasing the accuracy of moisture detection in the kernels. This study developed a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn. The developed system mitigates the interference of husks on the acquired spectral signals by collecting spectral information from multiple detection positions offset at specific distances from the incident light source. Meanwhile, three model building strategies based on deep learning frameworks, including feature-level fusion, data-level fusion, and decision-level fusion, were proposed and compared. Results showed that the decision-level fusion model with standard normal variate (SNV) preprocessing achieved the highest prediction accuracy, with a coefficient of determination (R 2 p ) of 0.897 and a root mean square error of prediction (RMSEP) of 4.13%. Furthermore, multichannel data relatively enhanced model performance, with the four-channel combination achieving the best performance. This study demonstrates the potential of deep learning and multichannel spectral data fusion in improving MC prediction accuracy, offering a practical solution for non-destructive moisture measurement in fresh corn.
Abstract The Malaysian jackfruit ( Artocarpus heterophyllus ) industry is increasingly challenged by a physiological disorder known as jackfruit bronzing , attributed to Pantoea stewartii subsp. stewartii . This disorder manifests as a yellowish-orange to reddish discolouration of the pulp while leaving the rind visually unaffected, leading to substantial postharvest quality and economic losses. The Tekam Yellow cultivar, in particular, has demonstrated high susceptibility to this condition. This study aimed to evaluate the potential of visible near-infrared spectroscopy (Vis–NIRS) as a non-destructive analytical tool for the early detection of internal bronzing through the estimation of rind and flesh colour parameters (L*, a*, b*, C*, ΔE, and h°). Spectral data were collected from the rind surface of intact jackfruit samples at 10, 12, and 14 weeks after anthesis (WAA) across the 500–950 nm wavelength range. Partial Least Squares Regression (PLSR) models were developed to establish the relationship between spectral reflectance and reference colour metrics. Various spectral pre-processing techniques—including Savitzky–Golay smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC)—were applied to enhance signal quality and minimise scattering effects. The optimised models demonstrated high predictive accuracy, with coefficients of determination for calibration ( R c²) and prediction ( R p²) reaching up to 0.98. Correspondingly, root mean square errors of calibration (RMSEC) and prediction (RMSEP) were as low as 0.29. Overall, calibration performance remained consistently strong across traits and maturities ( R c² ≥ 0.62), indicating that the model structures effectively captured the spectral–colour relationships within the calibration dataset. In contrast, the predictive performance of independent validation models varied substantially between normal and bronzing conditions. Under normal conditions, several models achieved excellent predictive accuracy—for instance, rind colour L* at 10 WAA ( R p² = 0.95, RPD = 16.19) and flesh colour L* at 10 WAA ( R p² = 0.98, RPD = 10.88). Conversely, models developed under bronzing conditions frequently exhibited lower predictive coefficients ( R p²) and residual predictive deviation (RPD) values ( p ≤ 0.05 to confirm the significance of observed differences. Collectively, the results demonstrate that Vis–NIRS applied through the rind offers a promising non-invasive approach for the rapid and accurate detection of internal bronzing in Tekam Yellow jackfruit, facilitating improved quality monitoring and early disease detection at both harvest and postharvest stages. This work highlights the potential of Vis–NIRS as a practical, high-throughput phenotyping and quality assurance tool to support sustainable value-chain management in the Malaysian jackfruit industry.
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
This original work explored the potential of near-infrared (NIR), mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI) in visible-near-infrared (Vis-NIR-HSI) and short-wave infrared (SWIR-HSI) range for non-destructive prediction and visualization of protein content in brown rice from 138 rice varieties. Feature selection and predictive modeling were integrated to identify protein-related wavelengths and enable pixel-level protein mapping. Using full-spectrum data, the MIR-based convolutional neural network (CNN) model achieved the highest predictive accuracy (R p 2 = 0.96, RPD = 4.84), confirming the superior chemical sensitivity of MIR spectroscopy. Following feature band selection, the NIR-based uninformative variable elimination-support vector machine (UVE-SVM) model exhibited optimal performance among wavelength-reduced models (R p 2 = 0.90, RPD = 3.16), surpassing Vis-NIR-HSI and SWIR-HSI-based approaches. For multi-spectral feature fusion integrating selected wavelengths from all four spectral modes, the competitive adaptive reweighted sampling-CNN (CARS-CNN) model yielded the best overall performance, demonstrating synergistic advantages of combining complementary spectral information. Furthermore, protein distribution maps from SWIR hyperspectral images displayed superior spatial resolution compared to Vis-NIR-HSI images. Overall, this study establishes a high-performance and transferable protein prediction framework based on multi-spectral fusion and deep learning, and provides a systematic comparison of visible, near-infrared, mid-infrared spectroscopy, and hyperspectral imaging for brown rice quality assessment.
Abstract Pea ( Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near‐infrared spectroscopy data, is a cost‐effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield‐related traits in a panel of elite spring pea lines evaluated across 12 environments. Three cross‐validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic selection is as effective as genomic selection at predicting yield. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as seed yield and seed protein yield. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic‐genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near‐infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain.
Citrus Huanglongbing (HLB) disease is a devastating disease faced by the global citrus industry, for which there is currently no effective cure. This study systematically investigated the anatomical characteristics and infrared spectral properties of different microstructures (phloem, xylem, pith, and cortical tissues) in the midribs of healthy and HLB-infected citrus leaves. Scanning electron microscopy observations revealed obvious phloem breakage and massive starch granule accumulation in various tissues of HLB-infected samples. Using micro-Fourier transform infrared spectroscopy, the spectral acquisition parameters were optimized (slice thickness: 10 μm, spectral resolution: 8 cm -1 , spatial resolution: 10 μm × 10 μm, number of scans: 256), and in-situ spectral information from different tissues were obtained. The results showed significant changes in the intensity and position of absorption peaks in the fingerprint region (1,800 to 675 cm -1 ) of all tissues after HLB infection, particularly enhanced carbohydrate absorption at bands such as 1,099, 1,060, and 1,033 cm -1 , indicating that abnormal carbohydrate accumulation is a typical symptom of HLB. A principal component analysis score plot based on spectral data from the phloem demonstrates a clear spatial separation trend between healthy and HLB-infected samples, providing a theoretical basis and methodological support for the fast, early, nondestructive detection of citrus HLB disease.
Using nanosensors in living plants allows the real-time detection of internal reactive oxygen species (e.g., hydrogen peroxide) signaling in response to environmental stressors. The time-dependent pulse of hydrogen peroxide (H 2 O 2 ) constitutes a signaling waveform ; however, standardized methods of extracting and analyzing such waveforms quantitatively remain elusive. Here, we develop a reference-less framework to extract stress-induced H 2 O 2 waveforms in planta directly from active nanosensors for the first time. We show that waveforms extracted for 3-week-old spinach across different experimental configurations, including 2D nIR imaging and 1D spectroscopy, are identical. Using this standardized approach, we systematically validate an analytical waveform model based on H 2 O 2 reaction-diffusion transport with a large waveform data set and extract the wave velocities and propagation rate constants from different waveforms. A wave-velocity-rate constant map is created for comparative studies. Results suggest that nanosensors can identify distinct waveforms associated with specific plant stressors with the proposed framework, providing opportunities for new diagnostic tools.
Introduction Pearl millet is a high nutritional cereal recognised for its agro-climatic resilience, making it relevant for food security under climate change scenarios. Phenotypic traits are indicative of crop performance, stability and adaptability, yet the potential of metabolomics to predict these traits has not been explored. Objectives This study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models. Methods Grains metabolic profiles were obtained using untargeted UHPLC-LTQ-Orbitrap-HRMS. Phenotypic data were sourced from standardised evaluations conducted by Embrapa across different years and field trials within the Sete Lagoas experimental station (Minas Gerais, Brazil). Generalised linear modelling with penalisation (GLM) and Random Forest was applied to explore the correlation between metabolism and 21 phenotypic traits. Results GLM successfully predicted eight qualitative and seven quantitative traits. Prediction accuracy was higher for qualitative traits, reflecting their comparatively simpler genetic architecture, whereas quantitative traits also achieved satisfactory performance (R² ≥ 0.6). Key predictors included phenolic compounds, amino acids, fatty acids, and carbohydrates. Notably, several associations corresponded to metabolites involved in nitrogen metabolism and vegetative growth, underscoring biologically meaningful links between metabolic profiles and trait variation. Conclusions This exploratory study presents the first metabolome characterisation of a pearl millet germplasm bank, coupled with predictive modelling of phenotypic traits. However, our findings are constrained by the single-environment design and the absence of population-structure assessment. To establish the stability and biological relevance of these results, future work should incorporate multi-environment trials and pathway-level analyses accounting for population structure.
Reproduction assets foundThe paper deposits its metabolomics and phenotypic metadata in a public repository (Recherche Data Gouv, DOI 10.57745/GU6WDG). No author analysis code or trained model deposit is stated; supplementary materials are not linked to a qualifying URL.Dataset · public.623/2023; 26/210.152/2023; 26/201.317/2022), National Council for Scientific and Technological Development (CNPq) (407350/2023-3; 314100/2023-7), Coordination for Improvement of Personnel with Higher Education (CAPES) (financial code 001).
Data availability
The metabolomics and metadata reported in this paper are available via https://doi.org/10.57745/GU6WDG.
Declarations
Competing interests
The authors declare no competing interests.
References
Alonso-Blanco C Méndez-Vigo B
Genetic architecture of naturally occurring quantitative traits in plants: An updated synthesis
Current Opinion in Plant Biology 2014 18 37 43
10.1016/j.pbi.2014.01.002
24565952
Alonso-Blanco, C., & Méndez-VigoOpen asset ↗10.57745 · GU6WDGlines:121-160Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Dimorphic species exhibited higher discrimination capacity (average accuracy of 80–83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed significant overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral integration showed a strong correlation (R² = 0.72; P = 0.003), and both the combined datasets (spectra and outline) and the individual datasets of spectral and shape features revealed a high phylogenetic signal (λ = 1–0.84), indicating partial coevolution between frond shape, chemical composition, and the evolutionary history of the group. Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Reproduction assets foundThe authors state that raw data, processed data, and R analysis code for the frond outline morphometrics are publicly available on GitHub (Microgramma-Outline), and the FT-NIR spectral data repository (Microgramma-FTNIR) is referenced in the methods. Both are paper-specific, public, and actionable.Code · publicSciELO Preprints - Este documento é um preprint e sua situação atual está disponível em: https://doi.org/10.1590/SciELOPreprints.15500
573 The raw data, processed data, and R analysis code are publicly available on GitHub:
574 https://github.com/labevofern/Microgramma-Outline.git.
575
576 REFERENCES
577 Ackerly D.D. (2004) Adaptation, Niche Conservatism, and Convergence: Comparative
578 Studies of Leaf Evolution in the California Chaparral. The American Naturalist, 163, 654–
579 671.
580 Adams D.C., Collyer M.L. (2018) Multivariate Phylogenetic Comparative Methods:
581 Evaluations, Comparisons, and RecoOpen asset ↗labevofern/Microgramma-Outline · Microgramma-Outlinepdf-layout-page:25 lines:1-48Dataset · publicbiting the highest
157 perpendicular distance from the line connecting the first and last bands in the R² × ranking
158 plot (Fig. S2). Following the methods described in Mendonça et al. (2026), spectral data were
159 acquired using a PerkinElmer Frontier™ near-infrared Fourier transform spectrometer (FT-
160 NIR) available at (https://github.com/labevofern/Microgramma-FTNIR).
161 Phylogenetic comparative analyses
162 To provide a phylogenetic framework for comparative morphometric and spectral analyses,
163 we used the pruned version of the Microgramma chloroplast phylogenetic inference from
164 Mendonça et al. (2026). This tree was based on the Bayesian phylogenetic tree published by
165 AOpen asset ↗labevofern/Microgramma-FTNIR · Microgramma-FTNIRpdf-layout-page:8 lines:1-55Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae). Well-preserved foliar compressions of this taxon were collected from the Punta del Barco Formation (Baqueró Group, Aptian, Patagonia, Argentina). TEM sections revealed six types of cell cuticles: two representing the ordinary epidermal cells (OEC) of the upper and lower cuticle, and four related to the stomatal apparatus and associated cells: subsidiary and guard cell cuticles, and inner and outer associated OEC cuticles. Cuticles comprise either a granular A2 layer (cuticle proper) and a spongy-fibrilous B1 layer (cuticular layer), or solely a B1 spongy layer, which is similar to that of Nothopehuen brevis and Brachyphyllum garciarum , two Cretaceous Araucariaceae from Patagonia. The statistical evaluation of quantitative measurements revealed the relationships and hierarchies between cell cuticle types and ultrastructural layers, revealing for the first time the precise identity of Araucariaceae cuticles. TEM-EDS revealed a significant presence of phosphorus (P) and chlorine (Cl), highlighting the potential taxonomic and paleoenvironmental relevance of the P/Cl ratio. Additionally, the six cell cuticle types found in A. grandifolia are shown in a dichotomous key, and a cuticle three-dimensional reconstruction is provided. Finally, the paleoenvironment conditions under which the A. grandifolia plant lived during the Aptian in Patagonia are also inferred.
Capucine Marion · Roman Zweifel · Nina Buchmann · Matthias Brennwald · Rolf Kipfer
Field / plotRaman / spectroscopyTissuePhysiological trait estimationWater status / transpiration
Common hydrogeological methods make use of natural gases as tracers to better understand the spatial and temporal evolution of groundwater flow, to constrain water residence time, and to reconstruct environmental conditions at recharge [1-3]. Noble gases can be used as complement of the stable water isotope tracers for understanding complex hydrological systems [4,5,6].We adapted these methods to in-situ measurements of gases in tree xylem sap to better understand the plant-mediated water and gas flux between the hydrosphere, the biosphere, and the atmosphere. Using a “miniRuedi” portable mass-spectrometer [7] and tailored semi-permeable membrane probes, the partial pressures of He, Ar, Kr, N2, O2, CO2, and CH4 were continuously monitored in-situ in the soil, the tree, and the atmosphere. Diurnal variations of CO2 and O2 were observed that reflected the tree physiological activities [8]. Since transpiration by plants is a major component of the hydrological cycle, such measurement techniques offer new opportunities to better understand plant water and CO₂ dynamics, within the soil-plant-atmosphere continuum.[1] Kipfer et al. (2002), Reviews in Mineralogy and Geochemistry, 47, 615–700; [2] Brennwald et al. (2013), Advances in Isotope Geochemistry – The Noble Gases as Geochemical Tracers, 123-153; [3] Brennwald et al. (2022), Frontiers in Water, 4, 107-115; [4] Althaus et al. (2009), Journal of Hydrology, 370, 64-72. [5] Schilling et al. (2019), Reviews of Geophysics, 57, 146-182. [6] Xu et al. (2017). Hydrogeology Journal, 25(7), 2015–2029; [7] Brennwald et al. (2016), ES&T, 50, 13455-1346; [8] Marion et al. (2024), Tree Physiology, tpae062.
Understanding CO2 plant exchange is essential for quantifying its role in the global carbon cycle, predicting ecosystem responses to environmental change, and evaluating long-term growth under varying environmental conditions across several types of photosynthesis[1,2]. Plants exchange carbon dioxide with the atmosphere through three primary physiological processes: photosynthesis, which assimilates CO₂ during daylight to produce glucose and release O2 as a byproduct; photorespiration, a light-dependent process that recycles harmful byproducts of photosynthesis while releasing excess energy and CO2; mitochondrial respiration, which releases CO₂ and consume O2 to produce energy, occurring both day and night. These CO₂ fluxes are coupled with transpiration that facilitates the loss of water vapor from leaves through stomata[1]. These processes can be accurately quantified using gas-exchange techniques, in which a gas analyzer measures the exchange of CO₂ and H₂O between leaves and the atmosphere. In this study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time. The sensor consists of a quantum cascade laser emitting at 4.234 μm as the light source and a photodetector to measure CO2 absorption along an open optical path of 10 cm. Measurements were performed using an amplitude modulation approach with first-harmonic detection at 10 kHz, employing a phase-sensitive lock-in amplifier. The optical sensor was placed inside a transparent plexiglass enclosure (525x375x300 mm3) containing a plant to monitor CO₂ exchange with the surrounding environment. A temperature and humidity sensor was also installed inside the enclosure, while a non-dispersive infrared CO₂ sensor (SEFRAM 9825) outside the enclosure was used to track ambient CO₂, temperature, and humidity. Continuous measurements were performed over approximately 20 days, covering both daytime and nighttime periods outside the laboratory, in a dedicated open area to minimize disturbances from nearby activity. Measured CO₂ concentrations inside the enclosure reflected both plant exchange and diffusive transport driven by the concentration gradient with the external environment. A differential equation model accounting for these processes was developed and applied to the experimental data to quantitatively determine the plant’s net CO₂ exchange rate.ReferencesNiu, Z., Ye, Z. W. Y., Huang, Q., Peng, C. & Kang, H. Accuracy of photorespiration and mitochondrial respiration in the light fitted by CO2 response model for photosynthesis. Front. Plant Sci. 16, 1455533 (2025).Busch, F. A., Ainsworth, E. A., Amtmann, A., Cavanagh, A. P., Driever, S. M., et al. A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls. Plant Cell Environ. 47, 3344–3364 (2024).
Detecting postharvest tomato ripeness is essential for quality control. To reveal the evolution of complex conductivity σ ∗ and complex permittivityε ∗ during tomato ripening, this study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity. Based on the Maxwell-Wagner equation, σ ∗ and ε ∗ were derived from the measured impedance and conductance data. BIS measurements were conducted on whole tomatoes at four ripening periods and their components (pericarp, chamber, core, cavity). A finite element model was implemented in COMSOL to simulate electrical field distribution and quantify tissue-specific differences. Continuous monitoring of white ripening period tomatoes was used to validate the model, yielding an average accuracy of 85.16%, peaking at 92.86% in red ripening period and dipping to 80.30% in color change period, elucidate the dynamic changes in electrical properties during tomato ripening and provide a basis for nondestructive maturity assessment.
Camilla Dana Jakobsen · Oliver Baines · Signe Normand · Fabian D. Schneider
Field / plotRaman / spectroscopyLeafGrowth / time-series analysisGrowth / development / phenologyPigment / colour / senescenceWater status / transpiration
Mitigation of the ongoing biodiversity crisis requires thorough understanding of species dynamics across scales. However, monitoring plant species and their functional traits is time-consuming and challenging to implement across larger spatial scales and through time. Spectroscopy is emerging as a promising tool for monitoring plant functional and taxonomic diversity within and between ecosystems. This relies on the presence of a functional and taxonomic signal in leaf optical properties, which in turn depends on the spectral similarity of species, intra- and interspecific trait variation, and the timing of the measurements. In order to address these relations in natural and semi-natural ecosystems in Denmark, we are compiling a spectral library of plants. An important methodological aspect of this work is to assess how leaf degradation, and the phenological stage of the plant, affect leaf optical properties. This was assessed by measuring leaf spectra from 350 – 2500 nm of four plant species, representing different plant functional types, from the same site over five months. We measured leaves at the time of sampling, and repeatedly after detachment from the plant to test how sampling strategy, potential leaf degradation after detachment and phenological stage influence leaf optical properties. Furthermore, we collected spectral and functional trait data of dominant plant species in 100 vegetation plots across the Store Åmose nature area in July and August. We present results of the spectral and functional differences among species and taxonomic levels, and the significance of leaf degradation and phenology on measured spectra. Leaf water and chorophyll content are expected to be the major drivers of spectral variation over time. However, subtle spectral signals may reflect other biochemical traits or leaf biophysical changes during the growing season. These dynamics are expected to depend on plant ecology, functional types, and environmental conditions such as wet and dry habitats. Our results will demonstrate the potential (and challenges) of using spectroscopy for taxonomic and functional identification of plants. We will provide insights into the role of leaf sampling strategy and phenology on spectral signals of plant species, which can inform the planning of future remote sensing and field campaigns.
Abstract The Affordable Phenomics special topic in The Plant Phenome Journal showcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies. This collection of 15 articles presented innovative approaches, ranging from low‐cost sensors and open‐source analytical pipelines to artificial intelligence–driven image analysis and spectroscopy, that address the financial and technical barriers limiting widespread adoption of plant phenomics. In this foreword, we highlight the contributions featured in the special topic. The foreword also serves as an overview of the state of the art in affordable phenomics by summarizing the vision and perspectives presented in the invited review “Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding.”
Heterogeneity of cellular and subcellular elemental distribution is poorly captured by conventional techniques due to limited sensitivity, throughput, and resolution. Single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) enables quantitative single-cell element analysis but remains challenging in multicellular plants because of plant cell complexity and lack of protocols. Herein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts. An optimized fixation protocol (1% (v/v) glutaraldehyde for 15 min for protoplasts; 2.5% (v/v) for 30 min for chloroplasts) was applied to preserve structural integrity, with endogenous P and Mg identified as specific indicator elements for protoplasts and chloroplasts, respectively. To reduce interference from broken cells, a broken-signal correction method was employed during data processing. Following isolation, purification, and glutaraldehyde fixation, Pisum sativum leaf protoplasts and chloroplasts were subjected to SC-ICP-MS analysis. Quantitative analysis revealed significant elemental heterogeneity, with P as the most abundant in protoplasts (67.1-135 fg cell -1 ) and Mg as the most abundant in chloroplasts (33.6-41.5 fg cell -1 ). This technique advances single-cell element analysis in plants, enabling new insights into nutrient distribution, metal accumulation, and cellular responses to environmental stress beyond conventional techniques.
Fruits are a critical component of the human diet, as they provide essential dietary nutrients that play an important role in the functioning of the human body and maintaining health. It is well-known that consuming fruits has various benefits, including the prevention of chronic diseases, cancer, and cardiovascular disorders. Thus, wider availability and maintaining the quality of fruits are highly required. Around 25% of global crop losses reported annually are attributed to disease and pest infestations, as per the Food and Agriculture Organization. Fungal pathogens are a major cause of post-harvest diseases, which significantly affect production and lead to economic losses. To address this, disease diagnosis at an early stage is crucial to enable timely monitoring, implementation of prevention techniques, and minimizing storage-related losses. Various methods are available for early pathogen detection; spectroscopic and imaging techniques have been widely applied as they offer cost-effectiveness, potential for real-time analysis, and a non-destructive nature of analysis. When integrated with advanced decision-support tools, these instrumental techniques can enable rapid and accurate detection of fungal diseases in fruits. In recent years, nanotechnology has emerged as a promising approach, with a wide range of nanoparticles being utilized to develop nanobiosensors for various applications. This review also highlights recent advancements in the use of nanomaterials and nanoparticle-based sensing systems for the detection of pathogens, providing an overview of their potential role in improving post-harvest disease diagnostics.
Apple moldy core (AMC) causes substantial postharvest losses, yet early-stage infections remain difficult to detect due to the absence of visible symptoms. This study proposed an integrated, industry-ready approach that combines transmission near-infrared (NIR) spectroscopy with a custom flexible transmission tray and deep-learning classification to enable accurate, high-throughput detection of early AMC. The tray was engineered to stabilize fruit positioning, reduce ambient-light interference, and guide NIR illumination through the fruit core, yielding reproducible transmission spectra. Spectral data were preprocessed with Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and mean centering. The study systematically evaluated wavelength selection strategies (CARS, SCARS and SCARS combined with SPA) and developed two-class (healthy/diseased) and three-class (healthy/mild/severe) classifiers using BP, CNN, LSTM and a hybrid CNN-LSTM architecture. The CNN-LSTM model trained on SCARS-SPA-selected wavelengths achieved the best performance, with classification accuracies of 98.82% (two-class) and 97.65% (three-class). These results demonstrate that the SCARS-SPA + CNN-LSTM pipeline, together with the flexible transmission tray, provides a robust and reproducible framework for early, precise AMC detection. The proposed system is compatible with conveyor-based integration and real-time sorting, offering a practical solution to reduce economic losses and improve quality control in commercial apple supply chains.
Label-free confocal Raman microscopy (CRM) is characterized by its high chemical specificity, making it a promising tool for the in situ quantitative analysis of plant cell walls. However, the simultaneous quantification of components in Gramineous species remains challenging. This is due to the complex "lignin-ferulate-carbohydrate" cross-linked network, as well as the amorphous property of hemicellulose, specifically its weak Raman signal and severe spectral overlap with cellulose. To address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS). We acquired CRM mapping images of rice stems pretreated with acidified sodium chlorite (ASC) for varying durations. The CS values between the preprocessed cell wall spectra and reference spectra (milled wood lignin, microcrystalline cellulose, and xylan) were then calculated and used as quantitative indicators. The results showed that CS values allow for accurate profiling, exhibiting significant positive correlations with the contents of lignin, cellulose, and hemicellulose. These correlations follow piecewise linear relationships with high determination coefficients ( R 2 ) of 0.9728 and 0.9809 for lignin, 0.9592 and 0.9810 for cellulose, and 0.9004 and 0.9901 for hemicellulose. The CS-based method consistently outperforms the conventional characteristic peak intensity approach. In particular, it resolves the difficulty of accurately quantifying hemicellulose, a task where single-band methods typically underperform ( R 2 in situ simultaneous quantification of lignin, cellulose, and hemicellulose contents in rice stem cell walls during ASC pretreatment. Thus, the CRM-CS algorithm enables simultaneous in situ quantification in Gramineous cell walls, offering a valuable approach for crop breeding and the high-value utilization of lignocellulosic biomass.
Abstract Biofortification is a sustainable and cost‐effective strategy that uses plant breeding and agronomic approaches to improve the nutrient content of staple crops consumed by vulnerable populations. The approach requires high‐throughput phenotyping to effectively identify and develop nutrient‐rich genotypes. This study aimed to develop a multielement, nondestructive method to quantify calcium (Ca), manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in whole seed wheat ( Triticum aestivum L.) samples using a benchtop energy dispersive x‐ray fluorescence (EDXRF) spectrometer. Grain samples from 29 and 41 wheat genotypes were used for the EDXRF calibration and validation, respectively. A microwave plasma–atomic emission spectrometer (MP‐AES) provided the analyte reference values for each sample. The EDXRF calibration showed moderate to high correlation with MP‐AES values for Ca, Mn, Cu, and Zn, while Fe exhibited a weak correlation. The limits of quantification (mg kg −1 ) were 103.9 for Ca, 8.5 for Mn, 3.5 for Fe, 4.7 for Zn, and 1.0 for Cu—all below the observed analyte range in wheat grain. The method is suitable for use in early generation selection, as indicated by standard errors of prediction (mg kg −1 ) of 36.4 for Ca, 3.3 for Mn, 2.5 for Fe, 0.3 for Cu, and 1.5 for Zn. This study builds upon previous nondestructive EDXRF methods by introducing additional elements that can be reliably phenotyped in wheat, supporting broader use in biofortification programs.
‘Candidatus Liberibacter asiaticus’ is the major agent associated with citrus “huanglongbing” (HLB) disease, which is the most destructive citrus disease and has caused serious losses to citrus industry worldwide. Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based nontargeted metabolomics and machine learning algorithms were developed for identifying HLB disease in different citrus varieties and growing seasons. In this study, 52 (28 up-regulated and 24 down-regulated) and 33 (26 up-regulated and 7 down-regulated) differential metabolites were screened in Navel orange (Citrus sinensis Osbeck) and Ponkan (Citrus reticulata Blanco cv. Ponkan) leaves, respectively. The variable importance in projection (VIP) algorithm was then used to select the common differential metabolites in HLB diseased samples, and a total of 19 differential metabolite variables were obtained from Navel orange and Ponkan varieties (mainly including primary metabolites such as D-ribose, D-threonate, L-ornithine). Finally, support vector machine (SVM) model based on the metabolites with significant features performed the best for the prediction of citrus HLB disease, with a classification accuracy of 100 %. The results showed that the proposed method was able to provide important and common information about citrus host-'Ca. L. asiaticus' interactions. They also demonstrated that combing untargeted metabolomics with machine learning can be effective tools for distinguishing citrus HLB infection (from asymptomatic to symptomatic) in different growing stages and cultivars.
Soluble solid content (SSC) is a critical indicator of ‘Red Fuji’ apple quality, directly governing fruit grading and maturity assessment processes. Conventional SSC measurement by refractometry is destructive and time-consuming, rendering near-infrared diffuse reflectance spectroscopy (NIR-DRS) a promising nondestructive alternative. In this study, a low-cost and compact embedded spectrometer named as DLP NIR-scan Nano EVM was used to acquire NIR-DRS spectra of ‘Red Fuji’ apples for SSC prediction. To improve prediction accuracy, we combined spectral preprocessing with machine learning methods. The dataset was cleaned using Monte Carlo outlier detection, and samples were divided into calibration and validation sets via Kennard–Stone (KS) and joint X-Y distance (SPXY) algorithms. Among preprocessing methods tested, a 12-point second derivative performed best when paired with KS partitioning. For feature-wavelength selection on the preprocessed KS data, competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, and Random Frog were applied to the second-derivative spectra. Partial least squares regression (PLSR) models were then built using both full-spectrum data and four sets of selected wavelengths. The best preprocessed PLSR model achieved R2c = 0.916, RMSEC = 0.4093%, R2p = 0.8632, and RMSEP = 0.537%. These results demonstrate that NIR-DRS, combined with appropriate preprocessing and modeling strategies, offers a reliable, rapid, and nondestructive method for apple SSC quantification, paving the way for portable, cost-effective instruments for commercial fruit quality monitoring.
Díaz-Álvarez J, Galea-Gragera FA, Chávez de la O F, Salguero-López PA, Llera Cid F.
Raman / spectroscopySeed / grainClassification
Purpose The Lupinus germplasm includes sweet and bitter materials distinguished by compounds responsible for bitterness. Conventional identification is often destructive. This study assesses a non-destructive approach based on visible-near infrared (VIS-NIR) spectroscopy and machine learning to classify whole seeds from seven Lupinus species into sweet or bitter classes. Methods Five machine-learning algorithms were evaluated on two datasets (reflectance and absorbance) acquired with VIS-NIR spectroscopy. Analyses were conducted on raw spectra and on spectra transformed using four spectral-transformation techniques. Because classes were imbalanced, five resampling methods were compared to improve classification performance. Results Performance was assessed using F1-score and ROC-AUC . On reflectance, LGR and SVC reached 92.5 and 92.0%; on absorbance, SVC and RF achieved 93.2 and 92.5%. Hybrid transformations consistently improved discrimination, and resampling reduced overfitting associated with class imbalance. Conclusion The results indicate that combining VIS-NIR spectroscopy with machine learning provides a suitable non-destructive alternative to discriminate sweet and bitter Lupinus materials/ecotypes.
Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices.
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available at the authors' GitHub release URL. The spectral dataset itself is not public and is available only from the corresponding author on request.Code · publicCode availability
The code is available publicly on:
https://github.com/RabihAssaf89/RabihAssaf-codes/releases/tag/v1.0.Open asset ↗RabihAssaf89/RabihAssaf-codes · v1.0html-lines:822-851Plant phenotyping relevance matchCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Sustainable agriculture urgently requires innovative, pesticide‐free strategies to mitigate herbivory and safeguard food security. Ultraviolet‐B (UV‐B) irradiation, with tunable intensity and cost‐effectiveness, has emerged as a promising non‐chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI‐enhanced electronic nose technology for real‐time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV‐B treatments. This approach identified herbivore‐induced volatiles—hexanal, (Z)‐3‐hexenol, octanal, and (Z)‐3‐hexenyl acetate—optimally induced at 1.2 kJ·m −2 UV‐B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L‐phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV‐B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide‐free pest management solutions in precision agriculture.
Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture.
In this work, we present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring. The humidity sensor was fabricated using graphene/PEDOT:PSS hybrid ink deposited via drop-casting method on interdigitated electrodes (IDEs) screen printed on a eco-friendly paper substrate. Contact angle measurement, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) studies were performed to demonstrate hydrophilic nature, surface morphology and elemental analysis, respectively, of the sensing layer. The sensor exhibited excellent sensing performance in the measured relative humidity (%RH) range from 25% RH to 94% RH having a maximum % response of 226.5%. The sensor demonstrated a nearly linear response (adj. R² = 0.99) in the considered range with a slope observed as 3.21%/%RH. Multi-cyclic repeatability and reproducibility analysis further confirmed high reliability and consistent performance of the developed sensor. Furthermore, the capability of the sensor was successfully evaluated through capturing variations in plant physiological health status (under different environmental conditions, such as un-watered, water availability and solar irradiation) via monitoring microclimatic relative humidity (%RH) variations on plant (Epiremnun aureum) leaves. Through establishing the %RH values for healthy crops or plants under normal (well-watered) and stress conditions (un-watered or excessive solar irradiations), sensor seems to demonstrate strong potential for smart agriculture i.e., detecting early plant leaf stress.
Internal potato diseases significantly affect the quality of processed products in production. In this study, a potato quality near-infrared online detection system was designed to improve detection efficiency. The proposed system consists of four parts: a transmission device, a spectral acquisition device, a light source system, and a culling device. To improve the accuracy of the detection model, this study used four preprocessing methods and three feature wavelength extraction algorithms to process the original spectra and develop the soft independent modeling of the class analogy (SIMCA) potato quality discrimination model. The results showed that the SIMCA potato quality identification model using mean-normalization preprocessing and competitive adaptive reweighted sampling (CARS) feature wavelength extraction algorithm was the most effective with sensitivity = 100.00%, specificity = 95.83%, and ACCP = 97.92%. The sensitivity = 100.00%, specificity = 90.48%, and ACCP = 95.24% were tested for the NIR online detection system. The results of this study show that the use of near-infrared reflectance spectroscopy combined with preprocessing algorithms and variable selection algorithms to construct discriminative models can achieve online detection of internal potato diseases.
This study investigates the potential of integration of confocal micro-Raman and UV-Vis spectroscopy with machine learning and deep learning algorithms to assess biochemical responses of mung bean plants exposed to silicon dioxide nanoparticles (SiO 2 NPs) at varying concentrations. The analysis of acquired Raman spectral data reveals a concentration dependent pattern where low concentrations (0.2-0.6 mM) reduce the intensities of key biomolecules such as carotenoids, lignin, pectin, protein, carbohydrate, and cellulose, while higher concentrations (1.2-1.4 mM) trigger enhancement in intensities. The estimation of photosynthetic pigments using UV-Vis spectroscopy complements the Raman spectroscopy results, with chlorophyll and carotenoid levels decreasing at lower concentrations before significantly increasing. Among computational approaches, the application of dimensionality reduction techniques such as LDA- significantly improve the performance of clustering algorithms learnings like AGNES (RI = 1.00), DBSCAN (RI = 0.99), and k-means (RI = 1.00) and deep learning models, achieving high classification accuracy. Supervised algorithms like random forest and support vector machine perform optimally without dimensionality reduction, showing accuracies of 78 % and 79 % respectively. This integrated spectroscopy-computational approach offers a non-invasive, label-free, and robust framework for monitoring plant-nanomaterial interactions.
Abstract Sucrose is the central unit of carbon and energy in plants. As the product of photosynthesis, it is transported from source–to–sink tissues across both short and long distances. Subcellular sucrose concentrations strongly influence rates of transport within cells, tissues, and organs. Moreover, as a central metabolite, its concentration influences the rates of many enzymatic reactions. Measuring sucrose concentration with subcellular resolution remains challenging, especially for the cytosol, which hosts many critical enzymatic reactions and, in many cells, occupies only a thin layer between the vacuole and the plasma membrane. Here, we review the methods that have been utilized to measure subcellular sucrose concentrations in plant cells. The approaches covered include microautoradiography, non-aqueous fractionation, Fourier transform infrared (FTIR) microspectroscopy, Raman microspectroscopy, mass spectrometry imaging, Förster resonance energy transfer (FRET) nanosensors, direct sampling, and theoretical modelling. We provide perspectives on the use cases for these methods and discuss developments towards resolving subcellular sugar concentrations in live tissues.
Volatile organic compounds (VOCs) play a central role in plant communication and ecology, acting as a chemical language that mediates interactions with other organisms and responses to environmental stimuli. Analyzing changes in the plant volatilome enables the effective differentiation between biotic and abiotic stresses. Consequently, monitoring VOC emissions offers valuable insights into plant signaling pathways and health status. These insights position this approach as a promising strategy for improving crop protection. Direct infusion (DI) online analytical techniques, such as proton transfer reaction mass spectrometry (PTR-MS) and adduct ionization mechanism mass spectrometry (AIM-MS), have been developed to detect and characterize VOCs in real time. Here, we evaluated the suitability of PTR-MS and AIM-MS for monitoring VOC emissions in pea plants ( Pisum sativum L.). Comparative analysis revealed that AIM-MS, a recently developed technology, detected a higher number of distinct signals than PTR-MS. Annotation of detected and significant AIM-MS signals indicated a predominance toward those that were putative lipids-derived and amino acids-derived, whereas PTR-MS signals were primarily associated with putative phenolic compounds. These findings suggest that the newly developed AIM reactor offers a broader detection range and may enhance our ability to monitor plant VOC emissions. Consequently, AIM-MS emerges as a promising tool for the real-time assessment of pea plant health and stress responses. Further efforts are needed to improve the portability of DI-MS techniques and to integrate them with GC-MS techniques. Overall, these efforts will allow this technology to be exploited for plant protection in compromised environments.
Low-temperature stress compromises the integrity of plant cell membranes, leading to lipid phase transitions and increased membrane permeability, which subsequently induce physiological damage. However, conventional methods for assessing cold resistance, such as relative electrolyte leakage measurement, growth recovery tests, and LT50 determination, are limited by their highly destructive nature, time-consuming procedures, or insufficient sensitivity. Electrical impedance spectroscopy (EIS), a non-destructive and efficient electrophysiological technique, has emerged as a valuable tool for evaluating cold resistance and screening cold-tolerant plant varieties. By applying multi-frequency alternating current to plant tissues and measuring the resulting impedance responses, EIS enables the extraction of key parameters such as extracellular resistance, intracellular resistance, and cell membrane capacitance. These parameters collectively reflect the structural integrity and physiological condition of cells from multiple perspectives. Notably, under low-temperature stress, plant genotypes with varying degrees of cold resistance exhibit distinct impedance spectral characteristics, allowing EIS to efficiently discriminate cold tolerance among different varieties or treatments. This review summarizes recent advances in EIS-based research on plant cold resistance, covering its underlying electrical principles, equivalent circuit models, and biophysical mechanisms. It also outlines practical applications, including the screening of cold-tolerant woody and herbaceous plants, as well as integration with traditional assessment methods, while highlighting the advantages of EIS in terms of accuracy, universality, and real-time monitoring. Furthermore, the review addresses key challenges such as species specificity, model standardization, and data analysis, and proposes future research directions, including integration with artificial intelligence, development of portable devices, and establishment of standardized stress resistance databases.
Introduction Soybean mosaic virus (SMV) is one of the major pathogens affecting global soybean yield and quality, and its early and accurate detection is essential for disease warning and precision management. This study proposes a non-invasive early detection method by integrating portable Raman spectroscopy with artificial intelligence algorithms. Methods Raman spectra of leaves from both resistant and susceptible soybean cultivars were collected at different infection stages (0, 2, 4, and 6 days post-inoculation), and preprocessed using Savitzky-Golay (S-G) smoothing and adaptive iteratively reweighted penalized least squares (Air-PLS) baseline correction. Four classification models-1D-CNN, SVM, KNN, and BP-ANN-were developed to classify samples from different infection stages. Results Spectral feature analysis revealed significant changes in carotenoid levels caused by viral infection, and distinct spectral responses between resistant and susceptible cultivars during disease progression. Among the four classification models, the 1D-CNN model achieved the highest prediction accuracy of 90%. In addition, principal component analysis (PCA) indicated that the Raman spectroscopy-based method significantly advanced the early detection of SMV (SC3) to 4 days post-inoculation, compared to 7-10 days required by conventional methods. Discussion This evidences the superior capability of Raman spectroscopy for monitoring the dynamics of SMV infection and its potential to considerably reduce the duration of diagnosis. This study confirms the feasibility and efficiency of Raman spectroscopy combined with deep learning for in situ early detection of plant viral diseases and provides a promising reference for non-destructive diagnosis of early-stage foliar infections.
Mora-Poblete F, Valenzuela-Herrera J, Balach M, Martínez-Araya CJ, Maldonado C.
EucalyptusMaizeRaman / spectroscopy
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.
Reproduction assets foundThe paper's authors publicly released the MTMEGPS R package (analysis code/workflow) on GitHub, and the independent multi-environment maize validation dataset (phenotypes and genotypes) is publicly available via the Genomes to Fields initiative DOI. Both are paper-specific, public, and actionable.Dataset · publicnal phenotypic information.
2.2
Independent multi-environment maize validation dataset
The datasets analyzed in this study were obtained from the Genomes to Fields (G2F) initiative ( www.genomes2fields.org ). The dataset comprises 135 unique maize hybrids evaluated across nine experimental sites during the 2018 growing season ( https://doi.org/10.25739/anqq-sg86 ). Phenotypic measurements were collected following standardized protocols provided by the G2F consortium, as detailed in the accompanying documentation available on the project website.
The traits evaluated in this study included plant height (distance from the plant base to the ligule of the flag leaf), ear height (distance fOpen asset ↗10.25739/anqq-sg86lines:51-61Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Sucrose is the primary transport sugar in plants, serving as an essential energy source and signaling molecule. Detection, visualization, and quantification of sucrose in various plant tissues are essential for understanding the metabolic and physiological processes that sustain plant life. Traditional metabolite-mapping techniques have struggled to visualize the quantitative distribution of sucrose at sufficient resolution to distinguish vascular bundles from surrounding tissues. Here, we present a Fourier-transform infrared (FTIR) imaging approach that can visualize sucrose in plant tissues quantitatively at a microscopic resolution (~12 µm). This IR-based, label-free method can be used with both model plants and agriculturally important crops. The assay has a detection range of 20-1000 mM and can map sucrose distribution within complex organs such as stems, leaves, and seeds. Notably, it enables the precise quantification of sucrose levels in vascular tissues. This is a trait of great interest in many current breeding and plant biotechnology approaches aimed at increasing crop yield.
Rice yields are severely affected by blast disease caused by Magnaporthe grisea (MGR), an ascomycete fungus. Plants and pathogens often interact through reprogramming of phytohormone-mediated signalling pathways, which alters the pattern of volatile organic compounds (VOCs) produced. Many of these VOCs can be used to predict specific diseases and are unique to specific pathogen invasions. A high-throughput technique that can detect new pathogen incursions at an early asymptomatic stage can increase our readiness to take mitigation action. In this study, we sought to develop a disease detection method that relies on signature volatile organic compounds (S-VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage. As S-VOCs in rice-MRG interactions have not yet been identified, rice leaves were artificially inoculated and their volatile profiles monitored at three stages: healthy (mock inoculated), MGR challenged (asymptomatic), and MGR challenged (symptomatic). In headspace solid-phase microextraction (HS-SPME), VOCs are collected for analysis by GC-MS. Among the 34 annotated VOCs, two compounds (octadecanal and 1-nonanol) were found only in MGR-inoculated plants at the asymptomatic stage. In addition, compared with healthy control plants, MGR-inoculated plants produced more methyl-salicylate (MeSA) and reactive oxygen species (ROS), indicating that MeSA and ROS play a role in short- and long-range signalling. In the early stages of MGR infection, when symptoms are barely noticeable, octadecanal and 1-nonanol were both able to distinguish between healthy and MGR-infected headspaces. This study further substantiates the potential for non-invasive early disease detection using VOCs.
Published1 Jan 2026Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
As a climate-smart crop, high-quality genetic improvement of corn plays an important strategic role in ensuring global food supply. Protein is a key indicator for evaluating corn quality. Therefore, accurate detection of corn protein content is of great significance for directional regulation of corn quality and smart cultivation decision-making. In view of the problems existing in the current corn protein detection research, such as damaged samples, low precision, and complicated procedures. This paper proposes a corn protein detection model based on near-infrared (NIR) spectroscopy combined with temporal convolutional networks. Firstly, Savitzky-Golay (SG) was applied to preprocess the data to effectively remove the spectral scattering information. Then, a Genetic Algorithm (GA) was used to extract eight effective characteristic wavenumbers from the 1845 preprocessed wavenumbers. Finally, the multivariate time analysis characteristics of the time series model Temporal Convolutional Network (TCN) were used to construct a corn protein detection model with an accuracy of 95.35 %. Compared with Back Propagation neural network (BP), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Transform, and CNN-transform, the accuracy of this model was improved by 25.58 %, 21.45 %, 15.35 %, 41.86 %, and 39.54 %, respectively. This method provides a new idea and approach for the detection of corn protein and other crop proteins.
Phytoalexins are plant secondary antimicrobial compounds that are rapidly and locally accumulated de novo upon pathogen attacks. They are strongly correlated with disease resistance; therefore, the timing and the location of their synthesis and accumulation have been explored transcriptionally and metabolically using various means separately. In this chapter, by focusing on the Arabidopsis camalexin (CA), we describe protocols for multimodal in situ detection of CA and elemental distribution, as well as the transcriptionally active region of its synthesis gene PHYTOALEXIN DEFICIENT 3 (PAD3) within the same leaf sample challenged with a pathogen.
Imaging technologies have become indispensable tools in modern plant phenotyping, transforming visual information into measurable traits essential for analyzing morphology, physiology, biochemistry, and micro- to nanoscale structures. This concise review summarizes recent advances by dividing plant imaging into two major categories: (1) physiological and biochemical, which includes hyperspectral, multispectral, and fluorescence hyperspectral imaging, as well as terahertz imaging, surface-enhanced Raman scattering, and carbon dot-based techniques; and (2) structural and morphological, encompassing RGB, thermal, light detection and ranging (LiDAR), confocal microscopy, and optical coherence tomography. Together, these modalities deliver insights from the canopy to the molecular level, enabling precise monitoring of plant stress, disease, and developmental traits. By integrating these multimodal imaging techniques with artificial intelligence, the review highlights key developments, current challenges, and future perspectives in plant measurement and analysis.
ABSTRACT Agriculture is essential for sustaining life, providing nutrition and contributing trillions of dollars to the global economy. However, increasing global populations and limited natural resources are placing unprecedented pressure on food production systems. These challenges are further exacerbated by plant diseases, environmental pollution and extreme weather events, all of which can significantly reduce crop yields and undermine socioeconomic stability. To ensure food security, there is an urgent need to develop early‐stage plant disease detection systems, optimise resource efficiency and minimise dependence on chemical inputs. Traditional crop inspection methods, which rely heavily on visual assessment and farmer expertise, face significant limitations in accuracy and scalability. In contrast, advanced optical‐based techniques—such as Raman spectroscopy and nanopore sequencing—offer promising alternatives by enabling non‐invasive, highly sensitive and real‐time disease detection. This review explores diagnostic approaches leveraging nanotechnology, as well as emerging advancements in information and communication technology for agriculture. By integrating these cutting‐edge solutions we can revolutionise the global fight against plant pathogens and secure sustainable food production for the future.
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Arsenic contamination in rice poses a potential health risk to populations dependent on their daily consumption. Previous work has shown that Raman spectroscopy is capable of nondestructively diagnosing arsenic uptake in rice; however, its diagnostic specificity in cases of concurrent abiotic and biotic stress remains unclear. As Raman spectroscopy relies on the detection of arsenic-induced stress patterns for diagnosis, the presence of additional stressors could potentially compromise diagnostic reliability. To address this gap, we evaluated the ability of Raman spectroscopy to detect arsenic uptake in the presence of both nitrogen deficiency (abiotic stress) and narrow brown leaf spot (biotic stress) across two Experiments. We found that nitrogen deficiency, while more severe than arsenic stress, did not affect arsenic detection. We also found that the diagnostic accuracy for both abiotic stressors (arsenic and nitrogen deficiency) depended on the plant growth stage, with arsenic detection being most reliable immediately after transplantation and nitrogen deficiency becoming more distinguishable as stress severity increased. Narrow brown leaf spot, though exhibiting minimal symptoms, remained sufficiently detectable. Altogether, these findings demonstrate that Raman spectroscopy remains a reliable method for diagnosing arsenic uptake and assessing overall rice health, even in the presence of additional stressors.
This study aims to develop a rapid and non-destructive method for determining protein content in maize using near-infrared spectroscopy (NIRS). To mitigate the effects of surface irregularities and uneven protein distribution in whole kernels on spectral measurements, maize powder was used as the test material to enhance the uniformity and stability of spectral signals. A total of 90 maize powder samples were collected from major production regions across China, and a custom NIRS acquisition system was constructed. To optimize the spectral data, eight preprocessing methods-including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1D), Savitzky-Golay smoothing (S-G), and their combinations-were systematically evaluated. Subsequently, traditional machine learning models (Partial Least Squares Regression, PLSR; Support Vector Machine, SVM) and deep learning models (ResNet-18, Transformer) were developed to predict protein content, and their performances were compared. Results indicated that the combined preprocessing strategy of First Derivative and Multiplicative Scatter Correction (1D + MSC) was the most effective. Among the models, PLSR demonstrated the best predictive performance, and traditional chemometric methods showed greater practical utility compared to deep learning models. To further enhance model efficiency, four feature wavelength selection methods-Partial Least Squares Regression Coefficients (PLSRC), Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and Uninformative Variable Elimination (UVE)-were applied. It was found that the PLSR model combined with the Successive Projections Algorithm (SPA) yielded the optimal performance, achieving a validation set correlation coefficient ( R p ) of 0.927, a root mean square error of prediction (RMSE P ) of 0.301, and a residual predictive deviation (RPD) of 2.502, along with the fastest computational speed. This study provides a reliable technical solution and theoretical foundation for the rapid and non-destructive detection of protein content in maize, while also validating the advantage of using powdered samples in improving the accuracy of NIRS detection.
Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350-2500 nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700-2200 nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-related biochemical variation. Compared to conventional destructive methods, NIRS provides a non-invasive, rapid, and scalable alternative for nitrogen assessment in field conditions. This is the first study to demonstrate NIRS-based discrimination of nitrogen levels across multiple rice genotypes, offering new avenues for genotype-informed fertilization strategies and improved nitrogen use efficiency (NUE). The results support the objectives of the Green Campus Initiative at the Alliance Bioversity International & CIAT and contribute to broader Sustainable Development Goals (SDGs 2, 12, 13, and 15), promoting data-driven, environmentally responsible nutrient management in rice production.
Łukasz Matuszewski · Jakub Nikonowicz · Jakub Bonczyk · Mateusz Tychowski · Tomasz P. Wyka · Clément Duhart
Raman / spectroscopy
Sensors in plant and crop monitoring play a key role in improving agricultural efficiency by enabling the collection of data on environmental conditions, soil moisture, temperature, sunlight, and nutrient levels. Traditionally, wide-scale wireless sensor networks (WSNs) gather this information in real-time, supporting the optimization of cultivation processes and plant management. Our paper proposes a novel “plant-to-machine” interface, which uses a plant-based biosensor as a primary data source. This model allows for direct monitoring of the plant’s physiological parameters and environmental interactions via Electrical Impedance Spectroscopy (EIS), aiming to reduce the reliance on extensive sensor networks. We present simple data-gathering hardware, a non-invasive single-wire connection, and a machine learning-based framework that supports the automatic analysis and interpretation of collected data. This approach seeks to simplify monitoring infrastructure and decrease the cost of digitizing crop monitoring. Preliminary results demonstrate the feasibility of the proposed model in monitoring plant responses to sunlight exposure.
Ensuring plant health and crop quality is vital for sustainable modern agriculture. Conventional detection methods for stress markers, contaminants, and pathogens are often constrained by labor-intensive procedures, bulky equipment, and reliance on centralized facilities, limiting real-time field monitoring. Surface-enhanced Raman scattering (SERS) has emerged as a promising solution, providing rapid, ultrasensitive, and non-destructive analysis across plant, soil, and water matrices. This review outlines the fundamental SERS mechanisms and strategies that boost sensing performance, and surveys recent advances in monitoring throughout the cultivation cycle, covering plant stress markers, metabolites, contaminants, and plant pathogens under realistic agricultural conditions. Emphasis is placed on substrate architecture (hot-spot control, composites/heterostructures, functionalization, flexible formats), enhancement mechanisms, and analytical performance (typical enhancement factor (EF), limit of detection (LOD), limit of quantitation (LOQ), and relative standard deviation (RSD) ranges). Persistent challenges, including substrate reproducibility, matrix interference, quantitative calibration, and scalable fabrication for field deployment, are evaluated alongside emerging solutions, including matrix-aware calibration (with ratiometric readout), fluorescence-robust preprocessing, and durable, large-area platforms. We close with practical considerations for durability and cost and with future perspectives toward next-generation, field-ready SERS tools for proactive plant-health management and crop-quality assurance.
Phosphorus (P) is a vital macronutrient for plant growth, but its limited availability in soil due to fixation renders up to 80 % of fertilizers ineffective. Visual symptoms for P deficiency appear late or remain inconclusive, complicating timely intervention. The conventional methods are often time-consuming, costly, and labour-intensive. Diffuse reflectance spectroscopy offers a rapid, label-free alternative, though its application is challenged by weak P spectral response. In this study, Arabidopsis thaliana (Col-0) plants were subjected to soilless culture under controlled phosphorus-sufficient (P+) and deficient (P-) conditions. Leaf reflectance spectra were analyzed using Linear Discriminant Analysis (LDA), and the selected wavelengths were used to train three machine learning classifiers such as Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN). Among these, the SVM model demonstrated best performance, achieving a classification accuracy of 97.78 %. Independent validation using biochemical, morphological, and combined datasets, yielded classification accuracies of 100 %, 71.88 %, and 100 %, respectively. This approach offers a rapid, and non-destructive alternative to conventional techniques for sustainable nutrient management in agriculture.
Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.
Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput process. This study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits. The research methodology involved collecting spectral data from 2,236 cassava clones from 19 field trials in Brazil, using two sample types: fresh roots and mashed roots. Six spectral pre-processing methods and three machine learning algorithms-Partial Least Squares (PLS), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB)-were evaluated to optimize predictive models. Model performance was assessed using the coefficient of determination in calibration ([Formula: see text]), the root mean squared error of calibration ([Formula: see text]), and the Kappa index to quantify the consistency of clone selection. Results show that mashed samples consistently yielded superior predictive performance across all models. Specific preprocessing methods, such as Savitzky-Golay filtering combined with Standard Normal Variate (SG + SNV) and first-derivative transformations, significantly enhanced model accuracy. Among the algorithms, PLS demonstrated the best overall performance, with high predictive accuracy ([Formula: see text] >0.96) and low prediction errors ([Formula: see text]<1.3 for DMCo), especially with mashed samples. High Kappa index values, consistently approaching 1.0, confirmed a good alignment between NIRS-based selection and traditional phenotypic methods. This study validates a portable spectrometer as a reliable and efficient tool for high-throughput phenotyping in cassava breeding programs. The findings confirm that portable NIRS devices, when used with optimal sample preparation (mashed roots) and robust modeling (PLS), can effectively yield good predictions for plant selection. This approach can significantly accelerate breeding cycles by enabling rapid, early-stage selection decisions, thereby overcoming a major bottleneck and contributing to a more efficient and sustainable genetic improvement of cassava.
Reproduction assets foundThe paper's spectral and phenotypic data (NIRS spectra from 2,236 cassava clones, DMC/StC trait measurements) are openly deposited on Figshare per the Data Availability statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability: The data that support the findings of this study are openly available in Figshare at https://figshare.com/s/d2e947f467bd8f655ede .Open asset ↗Figsharelines:142-152Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
Under the intensifying impact of global climate change, the frequency of plant disease outbreaks is steadily increasing, posing significant challenges to healthy cultivation and precision management. To enable early diagnosis of plant disease stress, this study used two rose (Rosa chinensis) varieties, ’Red Cap’ and ’Carefree Wonder’ were used to conduct powdery mildew stress experiment. Throughout the stress period, leaf electrical impedance spectroscopy (EIS), physiological parameters, and ultrastructural observations, were collected. A novel lumped equivalent circuit model was proposed, incorporating plant cell electrophysiological properties. The model developed for both rose varieties-featuring Constant Phase Elements (CPE) and Warburg elements (W), successfully characterized the resistive properties of the leaf tissue, including the extracellular resistance (R₁), cell membrane resistance (R₂), intracellular resistance (R₃), and vacuole interior resistance (R₄). Model parameters R₁ and R₃ were significantly correlated with the above physiological indicators, and showed significant differences 3 to 11 days earlier than traditional physiological parameters, demonstrating strong potential for early detection of cellular damage. Overall, this study demonstrates that EIS technology can dynamically reflect electrical property changes in plant tissues under biotic stress, effectively overcoming the lag limitations of conventional physiological measurements, providing a promising tool for early disease diagnosis and resistance screening.
Cotton fiber friction is crucial in regulating fiber flow during spinning and can influence yarn quality. Cotton wax acts as a natural lubricant and may impact fiber-to-fiber friction. Previously, a new method was developed for measuring cotton fiber-to-fiber friction. In the present study, the new method was employed to investigate the effects of noncellulosic materials on fiber-to-fiber friction using white, green, and brown cotton samples. All three cotton samples were subjected to xylene treatments under varying sample forms, durations, and temperatures to remove wax and other noncellulosic components. The new method was then used to measure fiber-to-fiber friction of both control and treated samples. Xylene treatment resulted in increased friction values across all cotton types. Fourier Transform Infrared (FTIR) spectroscopy was used to evaluate changes in the surface chemistry due to xylene treatment. The FTIR analysis showed a reduction in noncellulosic materials, primarily wax, in treated samples. Furthermore, the principal component analysis (PCA) of FTIR data revealed a clear distinction between control and treated samples, supporting the reduction of noncellulosic materials. Overall, wax reduction resulted in higher friction values of xylene-treated samples, and the new method effectively measured this, demonstrating further validation of its effectiveness for cotton fiber-to-fiber friction measurement.
Noah Remy · David Touboul · Edith Nicol · Séverine Humbert · Luminita Duma · Pedro Lameiras · Jean-Hugues Renault · Gabriel Paës
MRI / PETRaman / spectroscopyTissue
Lignocellulosic biomass (LB), which encompasses various plant samples, requires thorough characterization to optimize its use as a carbon resource. Chemical imaging simultaneously provides chemical and spatial information, offering significant benefits for LB analysis. This review presents an overview of the most advanced techniques for achieving this goal. By combining spectrometry and microscopy, microspectroscopy enables chemical imaging using various irradiation sources (IR, Raman, fluorescence, among others), allowing for the quantitative mapping of key LB components such as lignins, cellulose, and hemicelluloses. Mass Spectrometry Imaging (MSI) generates a mass spectrum for each spot of a sample thereby creating a chemical image pixel-by-pixel. MSI techniques like Matrix-Assisted Laser Desorption/Ionization (MALDI), down to 2-5 μm spatial resolution, and Secondary Ion Mass Spectrometry (SIMS), down to 300 nm for molecular analysis, effectively map small molecules in LB. In contrast, Desorption ElectroSpray Ionization (DESI) has been applied to plant extracts but remains largely unexplored for LB applications. Nuclear Magnetic Resonance (NMR) provides insight into various LB properties too. Solid-state NMR (ssNMR) and Dynamic Nuclear Polarization (DNP) help elucidate the structure of LB, sometimes aided by 3D atomistic modeling, whereas micro-Magnetic Resonance Imaging (micro-MRI) and Time-Domain (TD-NMR) probe the impact of water on LB properties.
Plants in natural environments often face unpredictable, co-occurring stresses, such as heatwaves and droughts, a trend that is intensifying with climate change. Reflectance spectroscopy, a valuable tool for monitoring plant health, has been widely used to detect single stress, but its potential for assessing combined stresses remains underexplored. While several reviews have explored plant molecular and physiological responses to combined stress, none has discussed the role of spectroscopy in this context. This review addresses this gap by synthesizing existing findings on plant spectral responses to two common stress combinations: drought + nitrogen deficiency and drought + heat stress. Although a limited number of studies exist, they reveal that plant spectral responses to combined stresses are often unique compared with individual stresses. These results point to three potential pathways by which spectroscopy can enhance plant resilience under combined stress: generating new hypotheses, facilitating the selection of broad-spectrum stress-tolerant genotypes, and improving stress detection for precision management. This review also suggests that spectral responses to combined stresses differ from individual stresses across spectral regions, plant species, scale of spectral sensing, and possibly other factors not yet considered here. To advance reflectance spectroscopy as a tool for studying combined stress, future research should prioritize enhanced experimental designs, standardized data presentation, integrated modeling, and sensor synergies.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
This study introduces a feature selection methodology that uses Near-infrared spectroscopy (NIRS), combining Uniform Variable Elimination (UVE) with the LightGBM algorithm in Gradient Boosting Machines (GBMs) for the swift, non-destructive evaluation of maize starch content. The research initially employed various preprocessing methods on the original spectral data. It assessed their effectiveness by Partial Least Squares Regression (PLSR). The findings demonstrated that first derivative (1D) preprocessing was the most efficacious, yielding an R²C of 0.9994, RMSEC of 0.0187, R²P of 0.9375, RMSEP of 0.2498, and RPD of 3.9985. UVE was subsequently utilized to identify essential wavelengths, while LightGBM further optimized the selection, markedly enhancing modeling efficiency and precision. Multiple feature selection techniques were employed for the comparison of regression models, including Ridge Regression (RR), Gaussian Process Regression (GPR), Multilayer Perceptron Regression (MLPR), and Random Forest (RF). The findings indicated that UVE-LightGBM modeling had a superior coefficient of determination and reduced root mean square error, with an R²P of 0.9972 and an RMSEP of 0.0470. The physical and chemical significance of specific wavelengths was clarified by SHapley Additive exPlanation (SHAP), validating their role in enhancing the model's predicted accuracy and interpretability.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Wheat quality detection plays a crucial role in the processing of grain storage, and moisture distribution is one of the main factors that affect wheat quality. The uniformity of moisture distribution in wheat grains significantly impacts their morphological structures, nutrient distribution, storage period, and stress resistance. This study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS) to scan wheat grains soaked for different times (0, 2, 4, 6, 8, and 10 h) and dried for different times (0, 1, 2, 3, 4, and 5 h). The scanned results are used to observe the water content changes in wheat grains on both temporal and spatial scales. This study calculates the average spectrum of wheat grains to observe the regular changes in the terahertz time-domain spectrum of wheat grains under different soaking and drying degrees. These changes exhibit opposite trends. The frequency domain spectra are obtained through Fast Fourier Transform (FFT), and comparing the imaging effects at different frequency points, it can be observed that there is a good consistency between frequency-domain imaging and time-domain imaging. The experimental results indicate that THz-TDS can be used to effectively observe the moisture distribution in wheat grains during the soaking and drying processes.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
MangoRaman / spectroscopyFruitPhysiological trait estimationWater status / transpiration
Accurate, rapid, and online determination of mango dry matter content (DMC) holds great significance for the mango industry. The integration of near-infrared spectroscopy and deep learning theory offers an opportunity to enhance determination accuracy. In this paper, we propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC. The test results indicate that the model performs optimally when trained on a training set comprising 80 % of the augmented data. The root mean square error (RMSE) of the test set was 0.4073, and the coefficient of determination (R²) was 0.9782. The prediction accuracy of our model surpasses that of models such as Gaussian Process Regression, Support Vector Machines, and Partial Least Squares Regression. This study can assist in fruit quality inspection, processing optimization, variety selection, and breeding, as well as storage and preservation, and has a wide range of application potential and value. It also provides novel insights into data augmentation techniques for near-infrared spectral regression modeling.
Wu H, Lin Y, Zhang W, Cao Z, Zhao C, Yin Z, Lu Y, Liu L, Hu D.
AppleField / plotRaman / spectroscopyFruitClassificationWater status / transpiration
To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications.
Vegetable pea (Pisum sativum L.) is a nutritionally rich food source with a balanced profile of macronutrients and micronutrients, contributing multiple health benefits and plays a crucial role in combating nutritional deficiencies. Its nutritional diversity encompassing high range of protein, starch, soluble sugars, and phenolic content, renders it an ideal candidate for nutritional profiling, which is essential for mining Nutri-dense accessions. Near-infrared reflectance spectroscopy (NIRS) is a valuable alternative to conventional methods for nutritional profiling, offering rapid, accurate, less laborious, cost-effective, and non-destructive analysis with the capability to measure multiple parameters simultaneously for large-scale germplasms. This investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid with spectral pre-processing done by standard normal variate (SNV) and detrending (DT) using 90 vegetable pea (both marketable and mature stages) dried seed flour. The best-performing models were developed for moisture content (0.938, 0.469, 3.989), protein (0.931, 0.709, 3.063), starch (0.814, 1.312, 2.317), amylose (0.847, 0.646, 2.556), TDF (0.932, 0.652, 3.473), phenol (0.925, 0.078, 3.538), TSS (0.918, 0.231, 3.494), and phytic acid (0.898, 0.095, 2.358) corresponding to coefficient of determination (RSQ), corrected standard error of prediction (SEP(C)), and ratio of performance to deviation (RPD), respectively. This study presents the first report on the development of NIRS based prediction models using MPLS method for multi-trait assessment across different developmental stages in diverse vegetable pea germplasm, exhibiting high-throughput capability of the models in an economical and precise way.
Rivera-Romero CA, Pérez-Chimal RJ, Rivera-Romero R, Palacios-Hernández ER, Reyes-Portillo IA, Olivera-Reyna R, Olivera-Reyna R, Vite-Chavez O, Navarrete-Sanchez MA, Muñoz-Minjares JU.
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
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These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Published22 Nov 2025Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 0 · OpenAlex ↗
Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy provides a rapid, reproducible, and non-destructive analytical platform for profiling biochemical variation in biological samples. In this study, we demonstrate its application for diversity assessment in sesame (Sesamum indicum L.) seed oils, highlighting its potential as a methodological tool for high-throughput biochemical phenotyping. Spectral fingerprints were acquired from 64 genotypes and analysed using principal component analysis, hierarchical clustering, and K-means clustering. The first two principal components captured 73% of the total spectral variance, while clustering methods consistently separated genotypes into distinct groups, reflecting underlying biochemical polymorphisms. Key wavenumbers, 3888, 3757, 3564, 3294, 3132, 2902, 2470, 1850, 1685, 1436, 1350, 989, 888, and 788 cm -1 , were identified as major contributors to diversity, serving as spectral markers for oil quality and compositional analysis. The clustering of genotypes was further supported by band ratio analysis highlighting differences in unsaturation and esterification patterns among clusters. Beyond sesame, the workflow established here underscores the analytical capacity of ATR-FTIR, coupled with chemometric approaches, for capturing subtle biochemical variation across complex biological matrices. These results position ATR-FTIR as a broadly applicable method for biochemical screening and diversity studies in plant-derived and other biological systems.
In the context of environmental changes and the increasing demand for sustainable agricultural practices, real-time monitoring of plant health is increasingly important. This work provides an overview of the development and application of nondestructive optical spectroscopy for early detection of stress across a wide range of plant species. The approach combines high-resolution time tracking of leaf transmission with circadian rhythm analysis, allowing the identification of subtle physiological changes that precede visible stress symptoms. This work presents results from several experimental studies, including hydroponically grown herbs, forest species, aquatic plants, ornamentals, and agricultural crops. The methodology enables the early detection of stress caused by nutritional deficiencies, pathogenic infections, and sudden changes in light intensity. Integration of the 640 nm and 665 nm spectrum bands significantly improved system sensitivity, allowing precise characterisation of metabolic responses. These advances are supported by comprehensive metrological validation, which ensures the repeatability and robustness of the data under experimental conditions. The lecture highlights circadian rhythm not only as a fundamental biological process, but also as a new diagnostic marker of the physiological state of the plant. Through a variety of case studies and practical applications, we demonstrate how this optical platform contributes to improving understanding the response of plants to stress and offers new perspectives in plant science, forest monitoring, and precision agriculture.
In the context of environmental changes and the increasing demand for sustainable agricultural practices, real-time monitoring of plant health is increasingly important. This work provides an overview of the development and application of nondestructive optical spectroscopy for early detection of stress across a wide range of plant species. The approach combines high-resolution time tracking of leaf transmission with circadian rhythm analysis, allowing the identification of subtle physiological changes that precede visible stress symptoms. This work presents results from several experimental studies, including hydroponically grown herbs, forest species, aquatic plants, ornamentals, and agricultural crops. The methodology enables the early detection of stress caused by nutritional deficiencies, pathogenic infections, and sudden changes in light intensity. Integration of the 640 nm and 665 nm spectrum bands significantly improved system sensitivity, allowing precise characterisation of metabolic responses. These advances are supported by comprehensive metrological validation, which ensures the repeatability and robustness of the data under experimental conditions. The lecture highlights circadian rhythm not only as a fundamental biological process, but also as a new diagnostic marker of the physiological state of the plant. Through a variety of case studies and practical applications, we demonstrate how this optical platform contributes to improving understanding the response of plants to stress and offers new perspectives in plant science, forest monitoring, and precision agriculture.
Abstract Mass spectrometry imaging (MSI) is a vital tool in botanical research. Image fusion is introduced for resolution enhancement of MSI data from animal samples, but its application to plant MSI data resulted in unsatisfactory visualizations due to the distinct morphological characteristics of plant tissues. Herein, this study presents loss controlled residual network (LCRN), a workflow dedicated to the super‐resolution fusion of plant MSI data. The pipeline used a residual connection‐based neural network implemented with a novel loss metric called edge perceptual loss. Edge perceptual loss is developed for evaluating complex morphological information that can not be properly reflected by common image metrics, and its implementation in loss propagation is vital to the quality of the fusion result. Compared to existing deep learning‐based methods, LCRN is able to generate a high‐quality super‐resolution fusion image of extra high magnification (up to 20‐fold) that combined chemical and morphological information obtained from MSI and microscopy, respectively.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe data that support the findings of this study are available in the supplementary material of this article. Codes are available at https://github.com/codexyster/LCRN‐pr .Open asset ↗codexyster/LCRN‐prlines:245-245Plant phenotyping relevance matchEurope PMC · OpenAlex · checked 6 Sept 2026
Tomato ( S. lycopersicum ) is a precious fruit crop, and flavor quality is one of the most important commodity traits and directly affects the commodity value and economic returns. The composition and content of sugars and acids in tomato fruits, as well as their balance, are closely related to tomato quality, especially soluble sugars and organic acids, and so on. However, the lack of an efficient approach for quality evaluation of tomato significantly hinders progress in flavor quality breeding. Near infrared spectroscopy technology (NIRS) utilizes the absorption characteristics of near-infrared light by molecular vibrations of substances, and establishes a quantitative relationship model between spectra and component content through chemometric methods. Therefore, this study aimed to establish an NIRS assay for high-throughput analysis of tomato fruit quality, including fructose, sucrose, glucose, malic acid, and citric acid content. A total of 190 representative samples were utilized, and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Partial least squares regression (PLSR) model were developed with an excellent coefficient of determination for the coefficient of determination of calibration (R C 2 ) and coefficient of determination of validation (R v 2 ) of this model, with 0.962 and 0.942, respectively. what's more, the root mean square error of calibration (RMSEc) and root mean square error of prediction (RMSEP) were 0.36 mg/g and 0.44 mg/g,respectively.This model can effectively compress useless variables and interference information in near-infrared spectra. Overall, these NIRS models provide a feasible approach for high-throughput analysis of fruit quality and permit large-scale screening of elite germplasm in future tomato breeding.
Abstract Background and aims Determining elemental concentrations in plant tissues is crucial for any study on plant nutrition, physiology, contamination and food safety. However, existing methodologies based on acid digestion of samples coupled to inductively coupled plasma-atomic emission spectrometry (ICP-AES) or plasma-mass spectrometry (ICP-MS) are time-consuming and expensive. Methods This study introduces an innovative approach for the rapid and reliable analysis of light, transition, and heavy elements in plant samples using a novel monochromatic X-ray fluorescence (MXRF) spectrometer. Results The MXRF method was tested for the detection of 12 different elements, including light elements (K, Ca), transition metals (Mn, Fe, Co, Ni, Cu, Zn), metalloids (As, Se), and post-transition “heavy” elements (Tl, Pb), covering concentrations from 1 to 10,000 mg·kg −1 . The limits of detection and quantification ranged from 1.41 to 4.71 mg·kg −1 . The recovery rates varied from 84.74% to 89.34%, with intraday relative standard deviations (RSD) ≤ 2.31% and inter-day RSD ≤ 4.17%. A method-comparison study using 144 plant samples analysed by both MXRF and ICP-AES showed strong correlations ( R 2 > 0.87) for K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Pb, and TI. Conclusions This study demonstrates the reliability of the MXRF technique for the quantification of K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Pb, and Tl in plant samples. Given that MXRF can also be applied to the analysis of elemental concentrations in soil and water samples, future research will focus on refining and establishing methodologies for these sample types.
Mycorrhizal associations between fungi and plants are a fundamental aspect of terrestrial ecosystems. Mycorrhizas occur in c. 85% of extant plants, yet their geological record remains sparse. Rare fossil evidence from early terrestrial environments offers crucial insights into these ancient symbioses, but visualizing fossil fungi at the microscale within plant tissues is challenging. Here, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant from the Windyfield Chert, a stratigraphically distinct fossiliferous unit from Rhynie (Scotland). We also applied Raman spectroscopy to investigate the carbon framework of both fungal and plant tissues. This integrative approach revealed fungal structures in unprecedented detail. The fungus, Rugososporomyces lavoisierae gen. nov., sp. nov., exhibits features resembling extant Glomeromycotina arbuscular mycorrhizal fungi. This is the first record of mycorrhizas from the Windyfield Chert. FLIM further distinguished features at the subcellular level, while Raman spectroscopy showed that fungal arbuscules and vesicles of the plant water-conducting cells underwent geological alterations, resulting in a similar chemical composition. These findings expand our understanding of ancient and extremely rare plant-fungal symbioses and highlight the potential of confocal-FLIM for advancing palaeobotanical research.
Reproduction assets foundThe authors deposited all confocal imaging data used in this fossil mycorrhiza study (CLSM/FLIM datasets of Rugososporomyces lavoisierae in Aglaophyton majus) in a public Zenodo repository under a CC BY 4.0 license. This is a paper-specific, publicly accessible dataset of the phenotyping/imaging measurements.Dataset · publicAll confocal data collected and used in this study are deposited in the Zenodo repository under a Creative Commons Attribution 4.0 international license https://doi.org/10.5281/zenodo.15194427 (Strullu‐Derrien et al ., 2025 ).Open asset ↗Zenodo · 10.5281/zenodo.15194427lines:252-551Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Abstract Background and Aims: Portable X-ray fluorescence spectrometry (pXRF) has emerged as a robust analytical approach for elemental determination in plant tissues, enabling rapid, non-destructive, and reagent-free measurements. This study developed and validated an empirical calibration of pXRF for quantifying silicon (Si) in plants, using autoclave-induced digestion (AID) as the reference method. Methods A total of 374 samples from seven plant species (rice, maize, soybean, cowpea, sorghum, lettuce, and beet) were analyzed. Silicon concentrations obtained via AID ranged from 1.07 to 19.23 g kg − ¹ (mean = 4.48 g kg − ¹; coefficient of variation = 67%), reflecting substantial interspecific variability. Each sample was also analyzed by pXRF under optimized instrumental conditions, and a calibration model was constructed using 75% of the dataset to predict Si concentrations relative to AID values. Results The pXRF calibration exhibited a strong linear relationship with AID results (R² = 0.94; R = 0.97; p
Non-destructive testing (NDT) methods are playing a crucial role in modern agriculture by providing efficient, rapid, and non-invasive means of evaluating agricultural materials. This shift from traditional, often destructive, testing methods is driven by the need for better quality control, improved food safety, and the demands of intelligent and precise agriculture Polarization spectroscopy analysis (PSA) has emerged as an advanced, non-destructive testing method of growing importance in agricultural engineering. By integrating polarization characteristics with spectral data, PSA enables the detailed analysis of various agricultural products and processes.This review provides a systematic overview of the principles and key parameters of polarimetry. Furthermore, it highlights a wide range of PSA applications in agricultural materials, such as crop health assessment, pest detection, chlorophyll estimation, and the evaluation of water, nitrogen, phosphorus, and potassium content. In addition, it sheds light on further applications, including non-destructive testing of seed health and agricultural product quality, soil moisture and pollution monitoring, underwater and nighttime environmental imaging, and integration with hyperspectral and multispectral technologies.Polarization spectroscopy is an analytical technology capable of revealing physical structural information unresolved by traditional spectroscopy, especially in complex environments where it demonstrates greater resistance to interference. With its ability to monitor plant nutrition, predict seed germination, assess fruit and vegetable quality, and detect early pests and diseases, this technology holds great promise for precision agriculture. Future efforts should optimize data fusion, build efficient models, miniaturize intelligent equipment, and enhance the real-time performance and adaptability of non-destructive testing to support smart agriculture..
Abstract Rice cultivation is a significant source of agricultural methane (CH₄), yet routine quantification still relies heavily on gas chromatography (GC), which limits throughput and field deployment. Here, we evaluated a portable tunable-diode-laser absorption spectroscopy (TDLAS) detector (PMD) as an alternative to GC for measuring CH₄ released from pot-grown rice plants under field conditions. Weekly closed-chamber samples from five cultivars were analyzed in parallel by GC (FID/MS) and the PMD. Standard gas tests showed an excellent linear relationship for the PMD (R 2 = 0.9995), indicating a near-ideal response. Across field samples, GC and PMD were strongly associated (R 2 = 0.9943). Bland–Altman analysis revealed a mean bias (GC − PMD) of 8.55 with 95% limits of agreement − 9.16 to 26.26, and Lin’s concordance correlation coefficient was 0.991, evidencing near-perfect agreement despite a slight systematic offset. A simple calibration with a linear regression eliminated the bias and narrowed the limits of agreement, while preserving the high correlation. Residual analyses suggested a modest influence of CO₂ (but not N₂O) on between-method differences. Taken together, the PMD provides rapid, robust, and labor-efficient CH₄ measurements that closely match GC when a fixed calibration is applied, enabling high-throughput phenotyping of rice genotypes and management practices in both laboratory and field settings. This calibrated, portable approach lowers barriers to large-scale screening for low-emission rice, supporting climate-smart crop improvement.
Abstract In agroecosystems, the variable expression of crop functional traits is expected to play a role in key processes, including plant nutrient cycling and water acquisition, that confer ecosystem resistance and/ or resilience to environmental change. The ability to estimate crop trait data is therefore critical to predict crop responses to environmental change, enabling more informed diagnosis of crop performance and on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping— specifically reflectance spectroscopy— has emerged as a key element of plant trait research, capable of estimating plant traits more rapidly. However, little is known about whether or not reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits. Using wine grapes ( V. vinifera subsp. vinifera ) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy and the subsequent partial least squares regression modelling approach to quantify intraspecific variation in 12 functional traits across 12 different cultivars. Results showed significant differences in traits, especially in the photosynthetic and hydraulic traits, among closely related cultivars, falling along a resource-conservative to resource-acquisitive axis of variation. We also found that reflectance differentiated this fine-scale trait variation, specifically in leaf chemical and morphological traits, contributing to higher accuracy, and indicating that this HTP approach is viable for detailed trait estimation in diverse agroecosystems.
As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay
Dry matter content (DMC) and starch content (StC) are key quality traits in cassava breeding, yet traditional phenotyping methods are time-consuming and limit scalability. This study aimed to develop and compare predictive models for DMC and StC using near-infrared (NIR) spectroscopy, evaluating two devices-a benchtop spectrometer (Büchi NIRFlex N-500; 1000-2500 nm) and a portable device (QualitySpec Trek; 350-2500 nm)-and assessing the influence of sample type (fresh vs. processed). A total of 3,391 cassava clones from the Embrapa breeding program were analyzed from 2018 to 2023. Reference values were obtained via gravimetric analysis (DMCg), oven drying (DMCo), and manual StC extraction. Spectral data were used to train and validate models using Partial Least Squares (PLS), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB). PLS consistently delivered the highest predictive accuracy across traits and devices. KNN slightly outperformed PLS for DMCg using the benchtop device, while XGB was comparable to PLS in select scenarios (e.g., StC with the benchtop: 0.88 vs. 0.89; DMCo with the portable: 0.92 vs. 0.95). Processed samples yielded higher model accuracy than fresh ones. The portable NIR device showed better performance with processed samples and even surpassed the benchtop for DMCg and StC in external validation (0.74 and 0.76 vs. 0.71 and 0.72, respectively). Overall, processed sample preparation significantly improved model performance, and the portable spectrometer proved to be a practical, accurate, and scalable alternative for high-throughput phenotyping in cassava breeding.
Domesticated pennycress (Thlaspi arvense L.), a newly winter annual oilseed crop grown in the Upper Midwestern USA, has garnered significant interest because of its high seed oil content. Compared to native field pennycress, domesticated varieties possess improved agronomic and compositional properties that allow grain and meal to be used as an animal feed ingredient. Near infrared spectroscopy (NIRS) is a well-established, non-destructive method for rapidly analyzing seed composition, however NIRS has been challenging to utilize in native field pennycress because of the limited natural variation in the seed composition does not allow the development of robust NIRS calibration equations. Domesticated pennycress exhibits notable differences in certain seed composition components and using this variation we observed moderate to strong correlations between the wet lab analyses and NIRS predictions for fourteen traits evaluated. For moisture, crude fat, protein, and sinigrin content, coefficient of determination (r²) between wet lab values and the NIRS predictions were 0.98, 0.96, 0.97 and 0.94 respectively. For fatty acid content, moderate r² for oleic acid (0.87), linoleic acid (0.69), linolenic acid (0.88) and erucic acid (0.87) were observed. Additionally, a comparison of grain samples harvested in 2024 was made for results from the NIRS equations and standard laboratory methods executed by external analytical laboratories. These results collectively demonstrate that NIRS is an effective tool for compositional analysis of seed components of domesticated pennycress and can serve as a substitute for more time-consuming analytical methods.
This study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK. Reliable wet chemistry procedures for quantifying TS and AA in single sorghum kernel (SSK) were established, which achieved high accuracy with test errors below 1.0 %. The partial least squares (PLS) model with 2 latent variables (LVs) for AA prediction had coefficients of determination of 0.91 (R²cal) and 0.85 (R²cv), and root mean square errors (RMSE) of 1.90 % and 2.47 % for calibration (RMSEC) and cross-validation (RMSECV), respectively. It showed an R²pred of 0.83 and RMSE of 2.58 % for prediction (RMSEP) when validated with the independent validation set. The optimal SSK-TS NIR PLS calibration model was built from 187 calibration sorghum kernels with 10 LVs, which had a R²cal of 0.79, RMSEC of 2.76 % and RMSECV of 4.93 % and showed a R²pred of 0.72 and RMSEP of 3.19 % when applied to an independent validation set of 93 samples. Overall, this study successfully developed wet chemistry methods for measuring AA and TS contents in SSK and established NIR models for nondestructive prediction and sorting of sorghum kernels by their TS or AA content, serving as useful tools for sorghum breeding and application research.
The evaluation of rice varieties is a complex, time-consuming process requiring advanced equipment. This study aimed to discriminate 22 commercial rice varieties from six types by analyzing biochemical, physicochemical, and cooking properties. Near-infrared (NIR) spectroscopy, combined with machine learning, linked molecular properties with quality traits, offering a high-throughput solution. Partial Least Squares (PLS) models accurately predicted parameters such as whiteness (R² = 0.94), width (R² = 0.94), resilience (R² = 0.96), and springiness (R² = 0.98), highlighting key wavelength regions. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a 17 % error rate in external predictions. Spectral markers at A6032/4457 cm⁻¹, A7004/5241 cm⁻¹, and A7004/4749 cm⁻¹ reflected biomolecular differences among varieties. This innovative approach enables precise quantification, classification, and differentiation of rice types, enhancing quality control, improving consumer satisfaction, and optimizing breeding selection processes efficiently.
Published1 Nov 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
It is essential to develop low-cost, rapid and portable systems for detecting the quality of rapeseed planting, harvesting, and storage. A multichannel spectral detection system for the quantitative assessment of rapeseed oil, protein, glucosinolate, and moisture content was developed in this study. The core hardware of the system comprises a custom-designed spectral acquisition module and a Raspberry Pi. The spectral module consists of a spectrum sensor and a characteristic wavelength LED, featuring 10 channels with a wavelength range of 850–1550 nm included. The results from the test set indicate that the most accurate oil predictions can be achieved using the SPXY+SNV+CARS+PLS method. For protein predictions, the optimal results were obtained using the Random+MSC +UVE+PLS approach. The best predictions for glucosinolates and moisture content were achieved with the Random+SNV+CARS+PLS method. To verify the performance of this systems, independent data were used for external validation. The RMSE, R², MAE results for oil, protein, glucosinolates, and moisture were 2.04 %, 0.69, 1.58 %, 1.52 %, 0.67, 1.25 %, 18.86μmol·g⁻¹, 0.52, 15.03μmol·g⁻¹, 0.36 %, 0.74, 0.33 %, respectively. In general, the developed detection system has potential for rapid detection of rapeseed in the field or market.
The application of Fourier transform infrared (FTIR) spectroscopy for non-structural carbohydrates (NSC) prediction as a tool for pre-breeding screening has immense potential but remains to be unexplored, because of technical challenges associated with these measurements. This study investigated the potential of employing FTIR spectroscopy as a high-throughput tool for forecasting NSC content, including total soluble sugar (TSS) and starch content, of 30 rice accessions from the Rice Diversity Panel 1 (RDP1) germplasm and RiceTec hybrids grown in 2019 (320 genotypes) and 2020 cropping (312 genotypes). Partial Least Squares (PLS) regression analysis was used to construct predictive models to estimate NSC content in flag leaves and stem of rice exposed to elevated and ambient nighttime air temperature during the flowering stage of rice. The TSS model exhibited a coefficient of determination (R 2 ) value of 0.63 and root mean square error of prediction (RMSEP) values of 3.62 mg g - 1 . Notably, the NSC model demonstrated a superior metric performance, with R 2 = 0.66 and RMSEP of 5.58 mg g - 1 . The predictive model created in this research effectively measured the NSC composition present in the flag leaves of rice. Expanding the sample size and incorporating additional principal components may enhance the model's predictive accuracy. The FTIR technique can produce fast accurate results and resolve the high analytical costs. Overall, the use of FTIR in conjunction with PLS regression analysis provides a potential tool to advance our understanding of various rice genotypes, particularly concerning their ability to withstand abiotic stress such as HNT.
Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture.
Climate change has led growers with uncertainty on crop growth, development, quality and yield. Thus there is a critical need to set up proper tools to help growers follow up their crop during the growth period and ensure better production at the end. In this context we used predictive machine learning models for predictions of Okra development in a controlled lighting environment based on UV-VIS-NIR spectroscopy. Fluorescence and reflectance spectroscopy data was collected from several leaves of Okra grown under different artificial lighting condition, then vegetation spectral indices were computed and used as features for the prediction of four growth and development parameters namely Plant Height (PH), Leaf Number (LN), stem diameter (SD) and Leaf Area Index (LAI). The different trained machine learning models explicitly Linear regression, K-nearest Neighbor, Support Vector Machine, Single Tree, Random Forest, Gradient Boosting, extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) give good performance in the prediction of PH (R2 ranged from 0.93 to 0.97), LN (R2 ranged from 0.88 to 0.94), SD (R2 ranged from 0.95 to 0.98) with the tree-based algorithm outperformed the others. However, these trained models give poor performance on the prediction of LAI (R2 ranged from 0.25 to 0.37). Furthermore, the most responsive features and vegetation spectral indices were also identified using Shapley Additive Explanations. This work aims to help growers to follow up their crops development and moreover intend to be used as a decision tool in an overall horticultural management process to engineer their crop development.
Barzaghi S, Ferrari B, Biazzi E, Tava A, Annicchiarico P.
Raman / spectroscopySeed / grainClassification
White lupin improvement is challenged by the need to select for low seed content of total quinolizidine alkaloids (QAs) when crossing low-alkaloid (sweet-seed) with bitter-seed (landrace) material. This study, which focused on 45 international landraces and 142 broadly sweet-seed breeding lines, aimed at (a) assessing the ability of Near-Infrared Spectroscopy (NIRS) to distinguish broadly sweet-seed from bitter-seed material and, possibly, lines with particularly low QA content within broadly sweet-seed material; and (b) comparing landrace and breeding material in terms of the composition and amount of QA compounds. QA content was analyzed using a gas chromatography-mass spectrometry method. NIRS analyses were performed either on whole-seed samples or ground samples. The range of variation for total QA was 95-990 mg/kg among breeding lines and 14,041-37,321 among landraces. NIRS was able to discriminate broadly sweet-seed from bitter-seed material when using flour samples, non-destructive 10-seed samples, and even individual whole seeds (with <1% misclassification). It was unable to identify material with particularly low QA content. Landrace and breeding line germplasm differed in the proportions of individual QAs. Patterns of geographical variation for total QA content of landraces were identified. Our results can contribute to define an efficient NIRS-based pipeline to select for low total QA content.
Borrelli C, Delannoy L, Chepca H, Calcaterra M, Chedid E, Arnold G, Dumas V, Baltenweck R, Maia-Grondard A, Hugueney P, Merdinoglu D, Duchêne É, Avia K.
Accelerating grapevine breeding for disease resistance and climate adaptation remains constrained by long generation cycles. We benchmarked genomic (SNP), phenomic (NIRS), and metabolomic (untargeted LC-MS) prediction for 24 agronomic traits in a biparental population phenotyped over three years. Seven statistical frameworks and four tissue x timepoint combinations (wood; vineyard leaves at budbreak and flowering; greenhouse leaves at flowering) were evaluated, together with feature-wise BLUPs across samples. Cross-year and cross-population analyses with two additional populations assessed temporal robustness and transferability. Genomic prediction was most accurate (up to r = 0.83), metabolomic prediction was intermediate (up to r = 0.59), and phenomic prediction was lowest (up to r = 0.39) despite its lower acquisition cost. Metabolite features were more heritable than NIR wavelengths, for which most unexplained variation remained residual under the fitted model. Multi-omics integration produced limited overall gains. These results support genomic selection as the primary approach, with metabolomic or phenomic screening considered only for traits and sampling designs that show reproducible predictive signal.
Field / plotRaman / spectroscopyLeafPhysiological trait estimationStress response / toleranceWater status / transpiration
Monitoring plant water status is vital for optimizing irrigation in precision agriculture. This study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues. This approach is well-suited for the realization of large networks of distributed sensors wirelessly connected to a central hub. An outdoor experiment was conducted over two phases of 20 day-experiment involving six Hydrangea macrophylla plants subjected to two irrigation treatments: a control group (well-irrigated) and a test group (poorly irrigated) designed to induce water stress. The standard relative water content (RWC) method validated the treatment effects on the plants, and both EIS and IR sensors effectively distinguished between the two groups. Impedance-derived parameters, particularly the normalized intracellular resistance (R0) and the cell membrane capacitance (C0), exhibited statistically significant differences between the treatments. In addition, the IR measurements showed moderate correlations with RWC, with determination coefficients of R2 = 0.56 and R2 = 0.51 for first and second phases of the experiment, respectively. Despite some limitations concerning the electrode–leaf conformity and external sunlight interference, the results point to the advantages of these methods for real-time plant monitoring and decision-making in smart irrigation systems.
Common beanLaboratory / benchtopRaman / spectroscopySeed / grainPhysiological trait estimation
Common bean ( Phaseolus vulgaris L.) is the world's most important legume crop and a vital staple food for millions of people in Latin America and Africa. Given the increasing trend in bean consumption and its importance for nutrition and food security in these regions, there is an urgent need to enhance common bean seeds' nutritional value through breeding. This requires rapidly assessing large and diverse germplasm collections to uncover key nutritional traits in the available genetic diversity. To address this challenge, Near-Infrared Spectroscopy (NIRS) offers a large-scale, cost-effective and non-destructive approach for accurately predicting nutrient content in intact common bean seeds. This study describes the development of predictive models based on NIRS to predict nitrogen (N), iron (Fe) and zinc (Zn) content, using whole common bean seeds from a germplasm core collection held at the International Center for Tropical Agriculture. Spectra were captured for 1754 accessions (wild and domesticated), and reference values for N, Fe, and Zn content were measured with conventional destructive methods in a panel of 401 accessions. Prediction models of N content achieved a concordance correlation coefficient (CCC) of 0.84, while for Fe and Zn, CCC was 0.4. NIRS quantification detected higher N content in wild accessions than in domesticated accessions. These results demonstrate that NIRS can effectively estimate the N content of common bean seeds in a non-destructive manner, while providing valuable nutritional information to enhance access to large genebank collections for bean breeding.
Abstract Insertion of nanoparticles (NPs) in plants induce various biophysical changes as well as modulate ion channels and transporters, resulting in improved water and nutrient uptake. High concentration of NPs has toxic effect like excessive production of reactive oxygen species, hormonal imbalances and impaired cellular processes. These biophysical changes also change the complex impedance of plant leaves. Here, we use impedance spectroscopy to probe, for the first time, the electrochemical response of the succulent Crassula ovata leaves following exposure to water-soluble carbon nanodots (CNDs) and zinc oxide (ZnO) nanoparticles. Nanoparticles were introduced through static root immersion in aqueous suspensions at varying concentrations (1, 5, and 10 mg L-1). Quantitative analysis revealed strikingly different dielectric signatures. CND treatment caused grain boundary (gb) resistance to rise from ~256 Ω in the control sample to ~27.6 kΩ at 10 mg L-1 accompanied by a consistent suppression of permittivity, reflecting progressive obstruction of ionic pathways and space-charge accumulation, on NP insertion. ZnO NPs, in contrast, showed a saturation effect: gb resistance peaked at ~14.6 kΩ at 5 mg L-1 but declined to ~7.3 kΩ at 10 mg L-1, where conductivity and dielectric relaxation partially recovered through Zn2+-mediated defect pathways. Equivalent-circuit modelling and Jonscher analysis corroborated these concentration-dependent shifts, revealing nanomaterial-specific modulation of ionic mobility and capacitive behaviour. Together, these findings establish a mechanistic contrast between carbon-based and metal-oxide nanomaterials in plant systems, underscoring nanoparticle chemistry as a key determinant of electrochemical response. This comparative framework advances plant nanobionics by linking material composition to bioelectrical function, with implications for bioelectronics, sensing, and sustainable energy interfaces.
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Leaf total phosphorus content (LTP) is a key indicator for assessing fruit nutrition status. As a rapid non-destructive inspection method, Near-infrared spectroscopy technology is susceptible to the influence of changes in plant growth periods and spectral noise on its prediction accuracy. At present, how to synergistically utilize growth period information and Spectral pre - processing methods to optimize the LTP Prediction model remains to be further studied. The study systematically collected Leaf sample and their near-infrared Spectral data during three key growth periods of Korla fragrant pear (fruit-setting period, fruit swelling period, and Maturity period). In the Spectral pre-processing stage, multiple scattering correction, Savitzky-Golay Smooth, First Derivative (FD), Second Derivative (SD) and their combined algorithms were comprehensively applied. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was used for characteristic wavelength selection, and based on this, Growth period specificity BP neural network model and cross-growth period general prediction models were constructed respectively to evaluate the performance of different Modeling strategies. Results The study showed that LTP content exhibited a significant differential distribution across different growing stage. In the characteristic wavelength bands, after processing with Combined pre-processing method (e.g., MSC+ FD), the correlation coefficient between the spectrum and LTP content significantly increased to approximately 0.90. The predictive performance of the Growth-period-specific model was comprehensively superior to that of the general model, with the Validation set coefficient of determination remaining above 0.83. Compared with the general model, the Coefficient of determination (R 2 ) increased by 0.05-0.16, and the root mean square error decreased by 0.0029-0.0079. This study successfully constructed a technical system of "Growth period-Preprocessing-Model". The results indicated that the Modeling strategy considering the characteristics of crop growing stage could significantly improve the predictive ability of near-infrared spectroscopy models. This study provides a reliable technical framework for Precision nutrient management in orchard, and the established methodology can also serve as a reference for nutrient Surveillance of other fruit tree plants.
Abstract Background and Aims Rare Earth Elements (REE) are essential for the development of clean technologies. Hyperaccumulator plants are metal-loving organisms that can be used to remove metals from contaminated soils. This study aimed to discover new REE hyperaccumulators in the Australasian region among the Blechnaceae and Gleicheniaceae families using specimens stored at the Queensland Herbarium. Methods A handheld X-ray fluorescence (XRF) instrument was harnessed to scan herbarium specimens, and this data was analysed with Dynamic Analysis in GeoPIXE. Selected specimens were further analysed to validate the XRF results: elemental analysis was conducted with inductively coupled plasma optical emission spectroscopy (ICP-OES), an elemental distribution map through micro-X-ray fluorescence (µXRF) and scanning electron microscopy (SEM) to rule out airborne contamination of plant samples. Results From the 3256 specimens analysed with the portable XRF, 73 specimens met the criteria to be considered REE hyperaccumulators (yttrium >50 µg g-1 on XRF analysis). Among this group, 11 new hyperaccumulator taxa were discovered, and the elemental analysis reported a total REE concentration around 1000 µg g-1, i.e. Diploblechnum neglectum (978 µg g-1), Sticherus flabellatus (1130 µg g-1), Sticheropsis milnei (1290 µg g-1). We validated the strong REE hyperaccumulating capacity of the previously reported ferns Blechnopsis orientalis (3850 µg g-1 total REEs) and Dicranopteris linearis (1280 µg g-1 total REEs). Conclusions The use of non-destructive portable XRF to scan herbaria collections is a tool to discover hyperaccumulator plants and this information could also be used as a bioprospecting tool to find REE deposits for potential REE phytomining.
Boron is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. This study evaluated the potential of leaf spectroscopy combined with machine learning to identify boron-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under boron treatments ranging from 0.5 to 8 ppm, measuring leaf boron accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance. The results revealed substantial genotypic variation in boron exclusion, with some genotypes maintaining low leaf boron concentration despite high substrate concentrations. Classification models (partial least squares discriminant analysis and random forest classification) outperformed regression models (partial least squares regression and random forest regression) in distinguishing boron-excluding genotypes, achieving 68 % to 79 % accuracy within just eight days after stress initiation. Reflectance-based vegetation indices such as the Normalized Difference Vegetation Index, Photochemical Reflectance Index, Structure Insensitive Pigment Index, and Chlorophyll Index indicated that boron stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf boron concentration proved more challenging, simulations showed that even modest prediction accuracies (~60 %) can substantially boost genetic gains if larger populations are screened and selection intensities are increased. These findings underscore the value of leaf spectroscopy for high-throughput phenotyping, allowing breeders to rapidly identify and advance boron-tolerant rootstocks.
Wheat diseases pose a significant threat to global food security by severely reducing crop yields. Rapid, accurate, and reliable identification of wheat infection status, disease types, and disease severity levels is essential for effective disease management. Volatile organic compounds (VOCs), which act as early indicators of plant stress, play a critical role in the early detection and diagnosis of wheat diseases. However, the low concentrations, transient nature, and complex composition of VOCs, combined with the dense canopy structure of wheat plants, present considerable challenges for VOC-based disease identification. To overcome these limitations, this study employed proton-transfer-reaction mass spectrometry (PTR-MS) for the rapid detection of wheat diseases, focusing on mitigating fragment ion interference and mass-to-charge ratio overlap during VOCs spectral characterization. A novel feature recombination method was proposed to improve disease identification accuracy. This approach combines prior knowledge-guided feature recombination with convolutional neural networks for feature extraction, enhancing spectral interpretability and reducing feature redundancy, and enabling rapid VOC-based detection of wheat powdery mildew and stripe rust. Experimental validation demonstrates that the proposed method achieves an accuracy of 90.67% in classifying wheat diseases across different severity levels. Importantly, although this method was designed for wheat disease detection, its framework is adaptable and may be extended to other plant health monitoring applications using PTR-MS.
Published1 Oct 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
To mitigate the impact of storage period variations on fruit sugar content prediction models and further enhance the universality of sorting models, this study investigated the influence of different storage periods on tomato brix prediction using near-infrared (NIR) spectroscopy and established a partial least squares (PLS) brix content prediction model. Experiments revealed that when the storage periods of the calibration set and the prediction set differed, the predictive performance of the PLS model significantly declined. To address this issue, the study found that optimizing spectral data with standard normal variate (SNV) transformation and adopting a mixed-modeling strategy incorporating multiple storage periods substantially improved the accuracy of the universal model: the correlation coefficient of the prediction set (Rp) increased from 0.803 to 0.934, the root mean square error of prediction (RMSEP) decreased from 0.476 to 0.375, and the residual predictive deviation (RPD) rose from 2.11 to 3.26. Finally, the competitive adaptive reweighted sampling (CARS) algorithm was employed to screen key wavelengths, effectively reducing data dimensionality while minimizing interference from storage period differences. Compared with the successive projections algorithm (SPA), the CARS method demonstrated superior performance, ultimately establishing a highly robust universal prediction model for tomato brix.
The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating disease. Traditional diagnostic methods, which often rely on manual expertise or destructive sampling, are limited by low efficiency and high subjectivity. In recent years, Raman spectroscopy has emerged as a promising solution due to its rapid, non-destructive, and highly sensitive characteristics for plant disease detection. In this study, we analyzed cotton stems using Raman spectroscopy, applying Savitzky-Golay (SG) smoothing combined with multiple preprocessing methods including Scaling and Shifting (SS), Standard Normal Variate (SNV), inverse first-order differential (1/SG)', and multiplicative scatter correction (MSC). For baseline correction, we employed polynomial fitting (PolyFit) and adaptive iterative weighted penalized least squares (airPLS). Feature selection was performed using principal component analysis (PCA), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS).Three optimized models were developed: support vector machine (SVM) with weighted mean of vectors (INFO) algorithm, random forest (RF) enhanced by particle swarm optimization (PSO), and long short-term memory (LSTM) network optimized via chameleon swarm algorithm (CSA).The results show that the INFO-SVM model with SG-airPLS-(1/SG)' -CARS preprocessing demonstrated superior performance, achieving 97.5% accuracy (0.974 F1-score) on training data and 90.0% accuracy (0.867 F1-score) on validation data, outperforming both PSO-RF and CSA-LSTM models. These results confirm that Raman spectroscopy integrated with optimized machine learning enables accurate VW classification in cotton stems. This method enables early disease detection during infection, facilitating timely fungicide application and reducing yield losses.
Nitrogen, as a vital element for plant growth and development, significantly influences crop yields. Nitrogen deficiency severely impairs crop growth, while excess nitrogen harms the environment. To address this, there is an urgent need for rapid and on-site methods to assess the physiological status of crops under nitrogen stress. In this study, we utilized Raman spectroscopy, a non-destructive and rapid analytical technique, to evaluate the physiological status of wheat plants subjected to various nitrogen treatments. These treatments included optimal, low, excessive and zero nitrogen application. By leveraging Raman spectroscopy’s ability to identify characteristic peaks of metabolites in plant leaves and quantify them based on peak intensity, we analyzed the levels of carotenoids, chlorophylls, cellulose, lignin, and aliphatic components. Our results revealed significant differences in metabolite peak intensity under different nitrogen treatments. Optimal nitrogen application promoted the accumulation of metabolites, while nitrogen deficiency led to a marked decrease in photosynthetic pigments and structural components. Excessive nitrogen caused a reduction in lignin and cellulose. To diagnose nitrogen stress, we developed classification models that accurately distinguished between healthy and nitrogen-stressed plants, achieving a training set accuracy of 99 %, a 5-fold cross-validation accuracy of 92 %, and a prediction set accuracy of 93 %. Furthermore, we differentiated wheat plants with varying degrees of nitrogen deficiency, achieving a maximum accuracy of 78 %. When considering both nitrogen deficiency and excess, the maximum accuracy reached 58 %. This study provides a fast, accurate, and non-destructive analytical method for analyzing and diagnosing nitrogen stress in field wheat based on Raman spectroscopy. Future research aims to extend this approach to the diagnosis of nitrogen stress in other crops and to explore its applications in nitrogen fertilization management.
PotatoRaman / spectroscopyPhysiological trait estimationWater status / transpiration
Near infrared spectroscopy (NIRS) has been widely used as a nondestructive testing technique and plays a crucial role in the quality inspection of agricultural products. However, the variability between different batches of samples hinders the application of commercial NIRS processes. Therefore, model transfer is usually performed on new samples to enhance the generalizability of the device. In this study, a new algorithm was developed based on the slope and bias correction algorithm (SBC) for model transfer between two different batches of samples, using potatoes of different origins as experimental samples for prediction models of potato quality, and based on this algorithm, model transfer between two different batches of samples was successfully implemented in a self-made portable non-destructive potato detection device. The results showed that the device developed based on the new algorithm gives good results for subsamples prediction. In the dry matter model, the correlation coefficient (R), root mean square error (RMSE) and relative standard deviation (RSD) of the new algorithm optimized compared with the traditional SBC algorithm were improved from 0.7843, 1.2080% and 6.59% to 0.8251, 1.1307 and 6.17%, respectively; in the starch model, the new algorithm optimized R, RMSE and RPD improved from 0.7971, 1.0023% and 7.43% to 0.8176, 0.9570% and 7.31%, respectively, compared with the traditional SBC algorithm. The transfer of NIR correction models for the dry matter and starch content of potatoes was basically achieved, which provided technical and theoretical support to enhance the model universality of convenient nondestructive detection devices.
Functional diversity can be assessed remotely from optical sensors using vegetation index-based plant traits. Without effective corrections, employed reflectance values are affected by absorption and scattering processes in the atmosphere and on the ground, which modify radiance and irradiance values used for the reflectance retrieval. Additionally, the anisotropic nature of vegetation canopies induces observation and illumination angle-dependent reflectance variations. Often, however, the reflectance retrieval is not accurate enough to compensate for these effects in the atmosphere and on the surface, resulting in uncertain reflectance values. Furthermore, the effects in retrieved reflectance values propagate into derived products, like the vegetation indices used for calculating functional diversity, where they manifest as apparent differences between temporally close observations of the same area. A key to compensating for these effects lies in the capacity and consideration of several processing steps, such as atmospheric, topographic, and anisotropy correction. To date, it is unknown how these effects and their correction influence the estimation of functional richness. Here, we estimate functional richness based on three differently retrieved reflectance datasets in the overlapping area of three consecutively acquired flight lines with short temporal differences but with three distinct acquisition geometries. We analyze how atmospheric, topographic, and anisotropy effects influence functional richness estimates and how functional richness varies due to different observation and illumination angles. We show that reflectance data before correction for atmospheric, topographic, and anisotropy effects yield up to 15% larger median functional richness estimates compared to data after respective corrections. We discuss under which circumstances comprehensive data processing can reduce between-observation differences. Furthermore, we show that resulting functional richness estimates correlate with the number of shaded pixels (r 2 ≈ 0.7). Consequently, observations in the solar principal plane with more or fewer shadows can lead to larger or smaller functional richness estimates and to differences compared to observations perpendicular to the solar principal plane. We conclude with recommendations concerning best-suited data processing and acquisition geometry for reliable and repeatable assessments of functional richness from optical remote sensing data and discuss applications to aerial and space-based observations of functional diversity.
In fresh maize breeding, developing robust and accurate near-infrared (NIR) calibration models traditionally requires significant time, cost, and labor. To address these challenges, a novel machine learning approach is proposed using a Prediction-Correction Neural Network (PCNN) that enables effective modeling from small sample sets augmented with synthetic data based on NIR spectroscopy. For key quality traits such as amylopectin, protein, crude fiber, and total sugar, the PCNN achieved residual predictive deviation (RPD) values between 2.821 and 4.862, and coefficients of determination ( RV2$R_V^2$ ) ranging from 0.869 to 0.951, using an average of only 32 calibration samples. For sugars including fructose, glucose, and sucrose, the model yielded RPD >2 and RV2≥0.747$R_V^2 \ge 0.747$ with just 62 samples. The PCNN method has also been successfully applied to NIR model development for small sample sets in intact kernel of fresh maize and other crops, including forage maize, rice, wheat, and barley. Compared to Partial Least Squares (PLS) and traditional Artificial Neural Networks (ANN), PCNN delivered RPD improvements of 38.99%-63.20% over PLS and 7.07%-25.82% over ANN. These results highlight the PCNN's high efficiency and accuracy, offering a scalable and cost-effective solution for rapid quality evaluation in fresh maize and other cereals.
Vannesa Montoya‐Sánchez · Anna K. Schweiger · Michael Schlund · Gustavo Brant Paterno · Stefan Erasmi · Holger Kreft · Dirk Hölscher · Fabian Brambach · Bambang Irawan · Leti Sundawati · Delphine Clara Zemp
Oil palmAerial / UAVField / plotLiDAR / point cloudMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / field
Abstract Assessing plant diversity using remote sensing, including airborne imaging spectroscopy, shows promise for large‐scale biodiversity monitoring in landscape restoration and conservation. Enriching plantations with native trees is a key restoration strategy to enhance biodiversity and ecosystem functions in agricultural lands. In this study, we tested how well imaging spectroscopy characterizes plant diversity in 37 experimental plots of varying sizes and planted diversity levels in a biodiversity‐enriched oil palm plantation in Sumatra, Indonesia. Six years after establishing the plots, we acquired airborne imaging spectroscopy data comprising 160 spectral bands (400–1000 nm, at ~3.7 nm bandwidth) at 0.3 m spatial resolution. We calculated spectral diversity as the variance among image pixels and partitioned spectral diversity into alpha and beta diversity components. After controlling for differences in sampling area through rarefaction, we found no significant relationship between spectral and plant alpha diversity. Further, the relationships between the local contribution of spectral beta diversity and plant beta diversity revealed no significant trends. Spectral variability within plots was substantially higher than among plots (spectral alpha diversity ~82%–87%, spectral beta diversity ~11%–18%). These discrepancies are likely due to the structural dominance of oil palm crowns, which absorbed most of the light, while most of the plant diversity occurring below the oil palm canopy was not detectable by airborne spectroscopy. Our study highlights that remote sensing of plant diversity in ecosystems with strong vertical stratification and high understory diversity, such as agroforests, would benefit from combining data from passive with data from active sensors, such as LiDAR, to capture structural diversity.
The application of in-field and aerial spectroscopy to assess functional and phylogenetic variation in plants has led to novel ecological insights and supports global assessments of plant biodiversity. Understanding how plant genetic variation influences reflectance spectra will help harness this potential for biodiversity monitoring and improve understanding of why plants differ in functional responses to environmental change. Here, we use a well-resolved genetic mapping population derived from Multi-parent Advanced Generation Inter-cross (MAGIC) lines of Nicotiana attenuata to associate genetic differences with differences in leaf spectra between plants in a field experiment in their natural environment. We analyzed the leaf reflectance spectra using a hand-held spectroradiometer (350-2500 nm) on 616 fully genotyped plants of N. attenuata grown in a randomized block design. We tested three approaches to conducting Genome-Wide Association Studies on spectral variants. We introduce a new Hierarchical Spectral Clustering with Parallel Analysis (HSC-PA) method. This method efficiently captured the variation in our high-dimensional dataset and allowed us to discover a novel association, between a locus on chromosome 1 and the 734-1143 nm spectral range, spanning the red-edge and near-infrared regions that are sensitive to leaf structure and photosynthetic activity. This locus contains a candidate gene annotated as carbonic anhydrase, an enzyme involved in CO2 hydration and regulation of photosynthetic efficiency, suggesting a physiological link between variation in leaf optical properties and carbon assimilation. In contrast, an approach treating single wavelengths as phenotypes identified the same associations as HSC-PA, but without the statistical power to pinpoint significant associations. An index-based approach, which reduces complex spectra to a few dimensionless variables, detected two significant associations for ARDSI_Cw (a water-content-related index) with loci on chromosome 1 near genes annotated as a Zeta toxin domain-containing protein, and an Exocyst subunit Exo70 family protein. While these findings are biologically plausible, they represent a very narrow subset of the spectral variation captured by HSC-PA. The HSC-PA approach supports a comprehensive understanding of the genetic determinants of leaf spectral variation which is data-driven but human-interpretable, and lays a robust foundation for future research in linking plant genetics with biodiversity monitoring, large-scale ecological assessment and remote-sensing applications.
Terahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively, for example over the course of the day or at the onset of drought stress. By studying a wildtype and a genetically modified variant of Arabidopsis thaliana, we observed significant differences in their dehydration dynamics. For a better understanding of the underlying processes, we modelled this behaviour with a simple rate equation model, compared the results with the experimental data and correlated our model with the biological regulatory mechanisms. In particular, under drought stress, we found an almost three times ([Formula: see text]) higher maximal stomatal opening in the mutant than in the wildtype. Over the course of the day, the degree of stomatal opening shows an exponential decrease with a half-life [Formula: see text] of [Formula: see text]2.6 h in the wildtype and [Formula: see text]0.8 h in the mutant.
Abstract Fractional vegetation cover (FVC) is a critical canopy structural variable essential for understanding vegetation dynamics. Estimating FVC in arid environments often remains difficult due to sparse vegetation, weak spectral signals, and high spectral confusion with the background. This study investigates the potential of spaceborne imaging spectroscopy to overcome these limitations by estimating the FVC of Tillandsia landbeckii , a fog-dependent bromeliad endemic to the Atacama Desert. Owing to its low reflectance and lack of chlorophyll absorption features, Tillandsia remains largely undetectable using conventional multispectral sensors. We used hyperspectral data from the EnMAP satellite and applied six semi-supervised sparse spectral unmixing algorithms at both local and regional scales. Unlike traditional supervised approaches, our method does not rely on labeled training data. Instead, it uses a limited set of field- and image-based endmember spectra to perform subpixel unmixing. Validation was conducted using a high-resolution reference dataset combining a UAV orthomosaic (1.7 cm) and Pléiades Neo imagery (30 cm). While the best local model reached a mean absolute error (MAE) of 3.1%, restricting the regional regression to confirmed Tillandsia pixels further reduced the MAE to 1.8%. This study presents the first operational demonstration of EnMAP for subpixel mapping of Tillandsia landbeckii cover, highlighting the value of semi-supervised unmixing techniques for vegetation analysis in hyper-arid environments with limited reference data and strong spectral background similarity. The approach establishes a transferable framework for estimating low-signal vegetation in hyper-arid regions, advancing the state of the art in fractional cover mapping.
In recent years, phenotyping approaches in plant breeding have expanded in both methodology and data collection capacity. One such tool, Near-Infrared Spectroscopy (NIRS) generates a wealth of reflectance values for biological samples. To test the potential of NIRS-based predictions, a hundred grain sorghum hybrids generated from a 10 × 10 factorial mating design were evaluated across eight environments. Hybrids were phenotyped for grain yield, days to anthesis, plant height, kernel hardness index, kernel diameter, and kernel weight. Hybrid grain samples were scanned with NIRS to generate phenomic data while parental lines were genotyped using genotyping by sequencing. Three different predictive models: genomic prediction (GP), phenomic prediction (PP), and GP + PP were fitted. Three different cross-validation schemes of untested hybrids in characterized environments (CV1), tested hybrids in uncharacterized environments (CV2), and untested hybrids in uncharacterized environments (CV3) were completed. GP + PP significantly improved over GP for days to anthesis, kernel hardness index, kernel diameter, and kernel weight for CV1. Prediction accuracy of GP + PP was also significantly improved for the kernel hardness index and kernel weight for CV2 and CV3. Depending on logistics, phenomic prediction has the potential to complement or supplement genomic data for predictive strategies in sorghum.
Sustainable agriculture urgently requires innovative, pesticide-free strategies to mitigate herbivory and safeguard food security. Ultraviolet-B (UV-B) irradiation, with tunable intensity and cost-effectiveness, has emerged as a promising non-chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI-enhanced electronic nose technology for real-time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV-B treatments. This approach identified herbivore-induced volatiles—hexanal, (Z)-3-hexenol, octanal, and (Z)-3-hexenyl acetate—optimally induced at 1.2 kJ·m -2 UV-B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L-phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV-B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide-free pest management solutions in precision agriculture.
The vertical distribution of leaves plays a crucial role in the growth process of maize. Understanding the vertical spectral characteristics of maize leaves is crucial for monitoring their growth. However, accurate estimation of the vertical distribution of leaf area remains a significant challenge in practical investigations. To address this, we used a 3D RTM to simulate the layered canopy spectra of maize, revealing the impact of canopy structure on remote sensing penetration depth across different growth stages and planting densities. The results of this study revealed differences in detection depth across growth stages. During the early growth stage, the depth was concentrated in the bottom 1 to 3 leaves of the canopy, reaching 1 to 4 leaves at the ear stage and 1 to 7 leaves during the grain-filling stage. The planting density had a notable effect on the detection depth at the bottom of the canopy. Moreover, compared with the other spectral bands, the near-infrared spectral range exhibited greater sensitivity to density variations. In terms of LAI inversion, a FuseBell-Hybrid model was constructed. We analyzed VIs across different planting density and canopy structural scenarios and found that compared with lower layers, increased density reduced the relative change rate in the upper leaf layers. The sensitivity patterns differed between plant architectures: VIred exhibited density-dependent sensitivity, with distinct responses between plant types, and MTVI2 demonstrated optimal performance for mid-canopy monitoring. This study highlights the influence of the heterogeneous structural characteristics of maize canopies on remote sensing detection depth during different phenological stages, providing theoretical support for enhancing multilayer crop monitoring in precision agriculture.
Reproduction assets foundThe paper states its analysis code was uploaded to a public GitHub repository, which qualifies as an authors' public code asset for the LAI phenotyping analysis. No separate phenotype dataset or model checkpoint deposit is explicitly stated.Code · publicData availability
The code have been uploaded to Github: https://github.com/aaawitch/code .Open asset ↗https://github.com/aaawitch/codelines:290-311Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
The escalating global demand for vegetable oils underscores the need to enhance the quality and yield of oilseed crops with Brassica species, due to their rich oil content and nutritional benefits. Traditional methods for assessing seed quality traits are often slow and destructive, limiting their scalability in breeding programs. This study presents Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid, non-destructive alternative to evaluate these critical traits across 80 diverse Brassica genotypes, including three species, namely, Brassica juncea, Brassica napus , and Brassica rapa . By integrating FT-NIR with principal component analysis and partial least squares regression, we developed robust calibration models, achieving high predictive accuracy (R 2 > 0.85 for key fatty acids; R 2 = 0.92 for oil content) and low error rates (MAE Brassica cultivars with optimized nutritional profiles high in beneficial polyunsaturated fatty acids and low in anti-nutritional factors.
Fusiform rust, caused by the pathogen Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme, is the most important disease of loblolly pine (Pinus taeda L.) in the U.S., causing millions of dollars in damage each year. Using resistant genotypes has proven a successful strategy to limit the disease, but resistance selection still relies on visual inspection for symptoms, which can lead to misclassification due to human error and the presence of ‘escaped susceptibles’ (i.e., susceptible individuals with no visible symptoms due to either an extended asymptomatic phase of the disease or the lack of adequate disease pressure to become infected). Here, we propose the use of near-infrared (NIR) spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated. We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S. using a portable, handheld NIR spectrometer. We also used a benchtop Fourier-transformed mid-infrared (FT-IR) spectrometer to analyze phloem phenolic extracts of the same samples, as this phenotyping approach has proved successful in other pathosystems. Our results show a moderate association between the phloem spectra and resistance, and models built with NIR spectra were able to classify extremes (i.e., very resistant or very susceptible) with up to 69 % testing accuracy. This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.
L.) in the U.S., causing millions of dollars in damage each year. Using resistant genotypes has proven a successful strategy to limit the disease, but resistance selection still relies on visual inspection for symptoms, which can lead to misclassification due to human error and the presence of 'escaped susceptibles' (i.e., susceptible individuals with no visible symptoms due to either an extended asymptomatic phase of the disease or the lack of adequate disease pressure to become infected). Here, we propose the use of near-infrared (NIR) spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated. We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S. using a portable, handheld NIR spectrometer. We also used a benchtop Fourier-transformed mid-infrared (FT-IR) spectrometer to analyze phloem phenolic extracts of the same samples, as this phenotyping approach has proved successful in other pathosystems. Our results show a moderate association between the phloem spectra and resistance, and models built with NIR spectra were able to classify extremes (i.e., very resistant or very susceptible) with up to 69 % testing accuracy. This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.
Rapid and accurate quantification of mineral elements in plants facilitates the optimization of cultivation strategies and provides theoretical support for heavy metal pollution control. Compared to traditional chemical detection methods, laser-induced breakdown spectroscopy (LIBS) offers rapid, simultaneous multi-element analysis. However, the quantitative accuracy of LIBS is often hindered by challenges such as sample heterogeneity and the inherent matrix effects arising from the physical and chemical properties of samples. These limitations highlight the need for innovative approaches to improve the reliability and precision of LIBS-based elemental quantification. In this study, we proposed a low-cost image-spectroscopy dual-modal rapid detection system combined with a dual-modal hierarchical fusion network (DMH-FNet). Compared with a standalone LIBS system, the quantification performance improved for the seven elements, namely P, Ca, Mg, Zn, Mn, K, and Si. During the validation phase, feature map visualization was used to interpret the feature extraction process of DMH-FNet. The results indicate that the model shifted its focus from low-level features of ablation crater details to high-level global features of the sample. Subsequently, SHapley additive exPlanations (SHAP) was used to explain the decision-making process of the optimal quantitative model and visualize key image features. The results demonstrate that DMH-FNet efficiently extracts features highly correlated with ablation crater information using its neural network capabilities and enhances the quantification of mineral elements through complementary fusion with LIBS spectral features. This study is the first to leverage the superior feature extraction capability of neural networks to capture valuable information from ablation images, thereby improving the quantification performance for multiple mineral elements. In conclusion, the proposed detection system, with its low-cost equipment, real-time data acquisition, and DMH-FNet, enables simultaneous, rapid, and accurate prediction of multiple mineral element contents.
Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization-Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry.
Leafy vegetables present challenges for Raman-based carotenoid analysis due to strong fluorescence from chlorophyll and the coexistence of complex biomolecules. This study introduces a non-destructive approach combining Raman spectroscopy with Linear Discriminant Analysis (LDA) to classify carotenoid content levels. Arabidopsis thaliana mutants with controlled carotenoid levels were used to build and validate the model, which was then applied to cultivated Spinacia oleracea. Various spectral preprocessing methods and Raman shift subsets were tested to optimize model performance. The LDA model successfully distinguished lutein and β-carotene concentration levels, achieving up to 95.45 % accuracy in Arabidopsis and 90.91 % in spinach. This classification-based strategy offers practical advantages over continuous quantification, particularly in food quality monitoring and nutritional labeling. The findings demonstrate the potential of LDA-assisted Raman spectroscopy as a selective and reliable tool for carotenoid analysis in chlorophyll-rich vegetables, with strong applicability for non-destructive quality control across the food production and distribution chain.
This study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK. Reliable wet chemistry procedures for quantifying TS and AA in single sorghum kernel (SSK) were established, which achieved high accuracy with test errors below 1.0 %. The partial least squares (PLS) model with 2 latent variables (LVs) for AA prediction had coefficients of determination of 0.91 (R 2 cal ) and 0.85 (R 2 cv ), and root mean square errors (RMSE) of 1.90 % and 2.47 % for calibration (RMSEC) and cross-validation (RMSECV), respectively. It showed an R 2 pred of 0.83 and RMSE of 2.58 % for prediction (RMSEP) when validated with the independent validation set. The optimal SSK-TS NIR PLS calibration model was built from 187 calibration sorghum kernels with 10 LVs, which had a R 2 cal of 0.79, RMSEC of 2.76 % and RMSECV of 4.93 % and showed a R 2 pred of 0.72 and RMSEP of 3.19 % when applied to an independent validation set of 93 samples. Overall, this study successfully developed wet chemistry methods for measuring AA and TS contents in SSK and established NIR models for nondestructive prediction and sorting of sorghum kernels by their TS or AA content, serving as useful tools for sorghum breeding and application research.
Here, we used Raman spectroscopy to characterize the effects of chitin treatment and fungal inoculations on Arabidopsis thaliana and Brassica vegetables. Chitin, a recognized fungal pathogen-associated molecular pattern (PAMP), elicited a dose dependent positive Elicitor Response Index (ERI) in wild-type Arabidopsis. Mutant plants lacking chitin receptors ( cerk1 and lyk4/5 ) displayed minimal ERI, whereas fls2 mutant deficient in the bacterial-specific flg22 receptor was hyper-responsive. These results confirm critical role of chitin receptors in activating downstream pathways and highlighting distinct responses in two separate pattern-triggered immunity (PTI) systems. Inoculations of Colletotrichum higginsianum and Alternaria brassicicola induced significant changes in Infection Response Index (IRI) values, with the former giving positive IRI at 12-48 hours post-inoculation whereas the latter exhibited a transient negative IRI before transitioning to positive values. Notably, Raman shifts could predict fungal infection before the appearance of visible symptoms, establishing Raman shifts as a potential early diagnostic marker. Comparative analyses of infected Brassica vegetables revealed varied sensitivity to fungal pathogens and a correlation between symptom severity and IRI values. Furthermore, randomized controlled trials validated the reliability of Raman technology for early, pre-symptomatic detection of fungal infections, achieving an accuracy rate of 76.2% in Arabidopsis and 72.5% in Pak-Choy ( Brassica rapa chinensis ). Principal component analysis differentiated Raman spectral features associated with fungal and bacterial infections, emphasizing their unique profiles and reinforcing the utility of Raman spectroscopy for early detection of pathogen-related plant stress. Our work supports the application of non-invasive diagnostic techniques in agricultural practices, enabling timely intervention against crop diseases.
Abstract Pea ( Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near-infrared spectroscopy data, is a cost-effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield-related traits in a panel of elite spring pea lines evaluated across twelve environments. Three cross-validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic prediction is as effective as genomic selection at predicting yield, and is more accurate for seed protein content. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as grain yield and seed protein. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic-genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near-infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain. Key message The integration of spectral data into prediction models enhances the predictive ability for complex traits in pea.
The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73-[Formula: see text] overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70-[Formula: see text] with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to [Formula: see text] with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.
Reproduction assets foundThe paper's Data Availability statement points to a public 4TU.ResearchData deposit containing the seed tuber and plant canopy (drone-derived vigor) datasets plus the Python code needed to reproduce the regression results — a paper-specific, publicly actionable asset.Dataset · publicBoth the seed tuber and plant canopy datasets are available at http://doi.org/10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55e . The data also includes the Python code necessary to reproduce the results of regression.Open asset ↗10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55elines:267-336Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Detecting heavy metals in plants is highly important for diagnosing plant health and understanding the stress mechanisms induced by heavy metals. However, the minimally invasive detection of heavy metals in plants remains a challenge. A novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants. The PBSED included a stainless-steel capillary and heavy metal ion enrichment filter paper (HMIE-FP). The stainless-steel capillary was inserted into the plant stem, where plant sap was transported onto the paper substrate through capillary action. The heavy metal ions (HMIs) in the plants were enriched on the HMIE-FP, and LIBS was used to detect Cd(Ⅱ) and Pb(Ⅱ) on the HMIE-FP to determine the Cd(Ⅱ) and Pb(Ⅱ) concentration within the plant. COMSOL simulations were employed to analyse the flow dynamics of plant sap within the PBSED. To increase the heavy metal enrichment amount, the HMIE-FP was modified with AuAg bimetallic nanoparticles (AuAgBNPs). The PBSED–LIBS method was applied to detect Cd(Ⅱ) and Pb(Ⅱ) in cucumber plants, and the results were strongly correlated with the inductively coupled plasma mass spectrometry (ICP–MS) results (R² = 0.99 for Cd(Ⅱ) and 0.96 for Pb(Ⅱ)). The proposed PBSED–LIBS method demonstrated high sensitivity and minimal invasiveness; thus, it is suitable for rapid, in vivo detection of HMIs in plants. These findings provide valuable insights for the development of efficient, nondestructive tools for environmental applications.
Plants biosynthesize a wide range of antioxidants capable of attenuating ROS-induced oxidative damage. There exist several in vitro methods to analyze antioxidants and total antioxidant capacity from different tissues and of various plant species. We have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods for determination of the level of the key antioxidants phenolics, anthocyanins and flavonoids in combination with the determination of total antioxidant capacity using ferric reducing antioxidant power (FRAP) and trolox equivalent antioxidant capacity (TEAC). The method was optimized and verified with samples from different strawberry species and cultivars with known differences in the parameters measured. This method proved to be suitable for analyses of eight model and crop plants, and distinct antioxidant signatures were determined for the different tissues and organs analyzed, including leaf, root, fruit, spike, and tuber samples. The method was robust and was shown in two case studies to be a resource-efficient and fast experimental platform also to assess biotic and abiotic stress responses, notably including fungal infection and the impact of a progressive drought regime. Since method was adapted for a semi-high throughput 96-well assay format it is well-suited for integration of cell physiological phenotyping into a holistic phenomics approach for germplasm assessment and plant breeding screening. This analytical platform uses microplate spectrophotometer which proved to be suitable to determine the antioxidant contents and total antioxidant capacity signatures of various plant species and tissues with similar findings as reported in literature.
In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits.
Safflower is a drought-resilient crop with seed-oil rich in unsaturated linoleic and oleic acids . However, the low oil content has deterred its adoption as a major oilseed . We profiled a global collection of 1497 safflower accessions from twelve regional gene pools for their seed-oil content and fatty-acid composition. Oil content was estimated using the non-destructive Near-Infrared Reflectance Spectroscopy method based on a newly calibrated equation. Our findings highlight significant variability in the global reference collection for oil content, which ranged from 11.24 % to 59.16 %. The robustness of our calibrated equation was demonstrated by the strong correlation between datasets from two years (0.68 %). Furthermore, germplasm evaluation for fatty acid composition using gas chromatography revealed substantial diversity in linoleic (8.22–87.27 %) and oleic acid (6.98–85.84 %) content of safflower seeds . Collectively, accessions from USA and Indian subcontinent gene pools exhibited greatest variability in seed oil traits. In total, we identified eight accessions with very high oil content (>50 %), four accessions with high oil content (>40 %) coupled with desirable oleic acid (>75 %). The present work will aid safflower breeding programs.
Published1 Aug 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Sharma M, Variath MT, Kauśika P, Ambreen H, Kumar S, Patial V, Aggarwal M, Kumar A, Jagannath A, Goel S.
Raman / spectroscopySeed / grain
Safflower is a drought-resilient crop with seed-oil rich in unsaturated linoleic and oleic acids. However, the low oil content has deterred its adoption as a major oilseed. We profiled a global collection of 1497 safflower accessions from twelve regional gene pools for their seed-oil content and fatty-acid composition. Oil content was estimated using the non-destructive Near-Infrared Reflectance Spectroscopy method based on a newly calibrated equation. Our findings highlight significant variability in the global reference collection for oil content, which ranged from 11.24 % to 59.16 %. The robustness of our calibrated equation was demonstrated by the strong correlation between datasets from two years (0.68 %). Furthermore, germplasm evaluation for fatty acid composition using gas chromatography revealed substantial diversity in linoleic (8.22–87.27 %) and oleic acid (6.98–85.84 %) content of safflower seeds. Collectively, accessions from USA and Indian subcontinent gene pools exhibited greatest variability in seed oil traits. In total, we identified eight accessions with very high oil content (>50 %), four accessions with high oil content (>40 %) coupled with desirable oleic acid (>75 %). The present work will aid safflower breeding programs.
Abstract Functional genomics has been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). The CRISPR/Cas-generated library consists of 3,567 microalgal mutants derived from 2,397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unravels mutations in the violaxanthin de-epoxidase ( noVDE ) or in the proteasome assembly chaperone 4 ( noPAC4 ) genes. Knocking out all five known noVDE s reveal that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidences suggest two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this new forward-genetics approach can greatly accelerate the discovery of new genes and pathways.
Introduction Volatile organic compounds (VOCs) are products of plant secondary metabolism with the potential for signalling early stress response. This pilot study investigated the potential of VOCs as markers for drought stress in potato. We hypothesised that differences in VOC emissions between cultivars may reflect genotypes with greater adaptive efficiency to drought stress. Methods Using thermal desorption collection and gas chromatography-mass spectrometry (GC-MS) techniques, we profiled the VOCs emitted by two potato cultivars, Maris Piper and Désirée, under well-watered and drought conditions, across a four-week period ( n = 3 per cultivar, treatment, and time-point). Results We identified 23 compounds, and tentatively identified another 49 compounds, including sesquiterpenes, alkanes, monoterpenes, and methylbenzenes. Statistical analysis revealed that seven compounds showed significant differences between cultivars and drought/well-watered treatments. Two farnesene isomers, a xylene isomer, 2,6-dimethyldecane, decahydronaphthalene, and 2-methyldecalin were identified as tentative markers of drought stress. Discussion Our findings suggest that VOCs could be used for detection of drought stress in potato plants, contributing to improved irrigation management and the breeding of more drought-tolerant varieties. Further research is needed to validate these findings and explore the underlying mechanisms.
Introduction Microplastics (MPs), as emerging environmental contaminants, pose a significant threat to global food security. In order to rapidly screen and diagnosis rice seedling under MPs stress at an early stage, it is essential to develop efficient and non-destructive detection methods. Methods In this study, rice seedlings exposed to different concentrations (0, 10, and 100 mg/L) of polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs stress were constructed. Two complementary spectroscopic techniques, visible/near-infrared hyperspectral imaging (VNIR-HSI) and synchrotron radiation-based Fourier Transform Infrared spectroscopy (SR-FTIR), were employed to capture the biochemical changes of leaf organic molecules. Results The spectral information of rice seedlings under MPs stress was obtained by using VNIR-HSI, and the low-dimensional clustering distribution analysis of the original spectra was conducted. An improved SE-LSTM full-spectral detection model was proposed, and the detection accuracy rate was greater than 93.88%. Characteristic wavelengths were extracted to build a simplified detection model, and the SHapley Additive exPlanations (SHAP) framework was applied to interpret the model by identifying the bands associated with chlorophyll, carotenoids, water content, and cellulose. Meanwhile, SR-FTIR spectroscopy was used to investigate compositional changes in both leaf lamina and veins, and two-dimensional correlation spectroscopy (2DCOS) was employed to reveal the sequential interactions among molecular components. Discussion In conclusion, the combination of spectral technology and deep learning to capture the physiological and biochemical reactions of leaves could provide a rapid and interpretable method for detecting rice seedlings under MPs stress. This method could provide a solution for the early detection of external stress on other crops.
Jacquin L, Guerra W, Lewandowski M, Patocchi A, Rymenants M, Durel C, Laurens F, Aranzana MJ, Lozano L, Muranty H.
AppleMaizeRiceRaman / spectroscopy
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Raman spectroscopy enables non-destructive detection of nitrates and other nitrogen-related biochemical markers, including chlorophyll and polyphenols, with unparalleled specificity and sensitivity. Integrating Raman spectroscopy with proximal optical sensors, such as Dualex (Dx) and Multiplex (Mx), offers a transformative approach to precision nitrogen management in broccoli seedlings, complementing their ability to rapidly estimate nitrogen balance indices and key vegetation compounds. The integration demonstrated strong correlations between Raman spectral bands, optical indices, and biochemical parameters across varying nitrogen levels, enhancing the precision of nitrogen status assessment, resulting in a robust, scalable, and information-rich system. By combining molecular-level detail with practical field applications, this hybrid strategy represents a significant advancement in sustainable agriculture. Future research will explore the applicability of this integrated methodology to other plant species.
Abstract Correlative imaging is a powerful tool for revealing information on cell-type structures and their biochemistry, with the potential to inform healthier food choices and improved dietary recommendations. Determination of plant structures and their structural biochemistry advances our understanding of specific structures designed to store different biomolecules within cells and tissues. Compared to the classical biochemical separation techniques, the key advantage of sequential correlative imaging techniques is in relating spatial plant (micro)structures to their biochemistry in a nondestructive manner. Sequential imaging reported here comprises six methodologies on a single sample, a cross-section of a Tartary buckwheat (Fagopyrum tataricum) grain, namely, bright-field and autofluorescence microscopy, fluorescence microspectroscopy, MeV-secondary ion mass spectrometry, micro-particle-induced X-ray emission, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry. Results confirm that the stepwise addition of the desired information across several classes of biomolecules and several spatial scales informs the quality and safety of plant-based produce across scales. Therefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity. The advantages and disadvantages of the selected methodologies were critically evaluated.
Reproduction assets foundThe paper explicitly points to a public Zenodo deposit containing the correlative imaging data (SEM, micro-PIXE, MeV-SIMS maps) used in its analyses, with instructions for reproducing image fusion in Wolfram Mathematica or ImageJ.Dataset · publicsed to reveal the allocation of K
to cotyledons (Supplementary Fused Image 1). Similarly, on
the same SEM image, MeV-SIMS distribution maps under the
selected peak were overlaid (Supplementary Fused Image 2).
Custom combinations can be done in the Wolfram
Mathematica program or in ImageJ (Merge Channels) using
data available at https://doi.org/10.5281/zenodo.14628251, fol
lowing the instructions in the Materials and Methods.
Conclusions
The low emission properties of fluorescence biomolecules,
when excited with 405 nm light, inherently limit the informa
tion acquired using fluorescence imaging. At this excitation
wavelength, catechin may be the primary fluorophore in
Tartary buckwheat cotOpen asset ↗zenodo · 10.5281/zenodo.14628251pdf-raw-page:13 lines:1-89Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
The study of sodium content in plants is crucial for the improvement of saline-alkali soil. Existing metal element detection methods pose challenges because they are complicated and time-consuming. In this study, we propose a quantitative detection model, FusionNet, that integrates Laser-Induced Breakdown Spectroscopy (LIBS) and Near-Infrared Hyperspectral Imaging (NIR-HSI) to realize the detection of Na element content in sorghum roots. To address the small-sample dataset, A Generative Adversarial Network (GAN) was employed to increase the diversity of the samples. The results indicated that data augmentation effectively enhanced the diversity of the original dataset and improved model performance. The modeling results from the FusionNet network achieved R 2 cv of 0.9915 and RMSECV of 0.7418, while R 2 p and RMSEP were 0.9808 and 0.6693. Compared to training with LIBS data alone, FusionNet achieved improvements of 4.94 % in R 2 cv and 5.61 % in R 2 p. This study provides a new method for detecting metal elements in plants.
Summary Reflectance spectroscopy is a rapid method for estimating traits and discriminating species. Spectral libraries from herbarium specimens represent an untapped resource for generating broad phenomic datasets across space, time, and taxa. We conducted a proof‐of‐concept study using trait data and spectra from herbarium specimens up to 179 yr old, alongside data from recently dried and pressed leaves. We validated model accuracy and transferability for trait prediction and taxonomic discrimination. Trait models from herbarium spectra predicted leaf mass per area (LMA) with R 2 = 0.94 and %RMSE = 4.86%. Models for LMA prediction were transferable between herbarium and pressed spectra, achieving R 2 = 0.88, %RMSE = 8.76% for herbarium to pressed spectra, and R 2 = 0.76, %RMSE = 10.5% for the reverse transfer. Discriminant models classified leaf spectra from 25 species with 74% accuracy, and classification probabilities were significantly associated with several herbarium specimen quality metrics. The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate that trait modeling can benefit from the complementary use of pressed‐leaf and herbarium‐leaf spectral datasets. These promising advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations.
Nitrogen (N) is essential for plant growth, yet excessive fertilizer use contributes to environmental degradation. Actinorhizal trees like Alnus glutinosa form symbiotic relationships with nitrogen-fixing bacteria of the genus Frankia, reducing reliance on synthetic fertilizers. However, distinguishing between soil-derived and symbiotically fixed nitrogen remains a challenge. This study investigates the potential of NIR spectroscopy as a nondestructive tool for differentiating N sources in A. glutinosa . Seedlings were grown in sterilized soil under controlled conditions with and without Frankia inoculation, and across a gradient of NH 4 NO 3 fertilization (0-20 mM). We measured leaf chlorophyll, nitrogen content, biomass, and NIR reflectance (330-1100 nm) of the third fully expanded leaf. principal component analysis (PCA) and partial least squares (PLS) regression revealed that spectral signatures significantly differed between inoculated and uninoculated plants, particularly in the visible range around 555 nm. Despite similar leaf chlorophyll levels, Frankia -inoculated plants and those fertilized with 20 mM NH 4 NO 3 exhibited spectral differences that could otherwise not be detected by SPAD measurements. PLS regression explained up to 54.8% of spectral variance based on nitrogen source, even in the absence of unique spectral peaks. These findings highlight the potential of NIR spectroscopy for rapid, in vivo and in vitro assessment of symbiotic N-fixation in trees, offering a novel and more precise approach than SPAD measurements.
Reproduction assets foundThe paper's Data Availability Statement deposits the study's data (NIR spectra and plant phenotyping measurements) on Zenodo with an explicit public DOI, which is an allowed URL. No author analysis code repository is stated; the other allowed URLs are generic R package documentation.Dataset · publicData Availability Statement
The data is deposited in Zenodo under (DOI): https://doi.org/10.5281/zenodo.15533926 .Open asset ↗Zenodo · 10.5281/zenodo.15533926lines:126-163Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Published4 Jul 2025Bulletin of environmental contamination and toxicologyCited by 1 · OpenAlex ↗
The increase in environmental contaminants such as heavy metals in aquatic ecosystems has been on the increase due to an increase in anthropogenic activities. In this study, the effect of varying concentrations (0.8 µg/L, 5.7 µg/L, 9.5 µg/L, 13.1 µg/L, 24.6 µg/L, 37.9 µg/L) of cadmium on Lemna minor was evaluated using an automated image-based analysis PlantCV v.3.8.0 through processing pipeline developed in the Python programing language (v.3.7.3). Chlorophyll was determined using a UV-VIS Spectrophotometer at wavelengths of 664, 647 and 664 nm. There was significant variation (p < 0.05) in the concentration course inhibitory effect of Cd, with the highest inhibition for frond area, convex hull, perimeter and solidity at Cd concentration of 37.9 µg/L. This study demonstrates the applicability of the developed PlantCV measurement pipeline as an efficient and reproducible approach for assessing plant responses, thereby supporting enhanced monitoring and management of aquatic ecosystem health.
Early-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (i) reduction of leaf chlorophyll and water contents; (ii) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (iii) decrease of spectral reflectance at 530–590, 740–1200 nm; (iv) decrease the slope and height of the red edge; (v) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (vi) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (vii) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants.
High temperature stress (HT) plays an important role in soybean selection and breeding, it can cause changes in soybean physiological, biochemical and morphological traits, and directly affect the growth and yield of soybean plants. Among these changes, soybean leaves are particularly sensitive to HT during growth and development. It is important to establish a non-destructive method to distinguish the phenotypic differences between soybean plants under HT and control (CK). In this study, data from two years of soybean field trials were used. In the first year, phenotypic information was collected by near-infrared spectroscopy (NIR), microscopic images, and further difference analysis and classification modelling experiments were conducted. In the second year, multispectral image data were collected and analyzed by Soybean high temperature mask autoencoder (SHT_MAE). The SHT_MAE model with a 75% masking ratio achieved an accuracy of 89.16% and an F1-score of 89.18%. Compared with one-dimensional near-infrared and two-dimensional microscopic image fusion models, the classification accuracy of HT and CK is improved by 2.68%. The accuracy of SHT_MAE multispectral model was improved by 16.84% and 6.88%, respectively, compared with models using only NIR or microscopic images. Both spectral and imaging methods effectively distinguish the phenotypic differences between HT and CK soybean leaves, with the multispectral approach based on the SHT_MAE model demonstrating a clear advantage. This study realized the effective distinction of soybean leaves under HT and CK. It provides theoretical support for HT intelligent breeding (using artificial intelligence and data analysis to optimize breeding decisions) and high temperature grade prediction.
The fast evaluation of seed performance is crucial for the agricultural industry. In this work, we apply NMR to identify specific metabolites that are related to the germination capacity of seeds. As our results show, NMR is a fast method with great potential to discover new accumulated metabolites during seed ageing and to predict the germination of a seed batch. In an initial study, we compared the metabolomic profile of Arabidopsis fresh and naturally aged seeds applying Partial Least Square Discriminant Analysis (OPLS-DA) and identified several sugars, amino acids, lactate, and methyl-nicotinate (MeNA), among others, as differentially accumulated metabolites in aged versus fresh seeds. Furthermore, we used our NMR metabolomics data to predict seed viability. A multivariate Partial Least Squares regression (PLS) analysis showed a direct correlation between the metabolomic profile and the seed germination rate, which allows for the prediction of seed germination. We then applied the same approach to natural and artificially aged wheat seeds, where we identified samples with high (91%) and low (0%) germination with 0.92 accuracy for artificially aged seeds and 0.80 accuracy for naturally aged seeds. In addition, we found a decrease in glucose and an increase in the dimethylamine content in wheat aged seeds, like in Arabidopsis. MeNA, a metabolite accumulated in aged Arabidopsis seeds but not statistically relevant in wheat, inhibited germination in both species via an ABA-independent mechanism involving the repression of the transcription of PARP3 and ERF72 genes in both species.
The waxy gene of maize is a high value breeding target, but it is time consuming to separate waxy and wild-type kernels. A common method involves staining the endosperm with iodine. Near-infrared reflectance (NIR) spectroscopy has been used in several species including maize with success. A custom-built single kernel NIR spectroscopy instrument was used to scan 2880 individual kernels from 60 samples with a diversity of pedigrees, with both waxy, wild type, and heterozygous kernels represented. Chemical analysis was performed to classify the kernels with the waxy or wild type phenotypes. Linear discriminant analysis (LDA) was conducted to develop a prediction equation for single kernel NIR spectroscopy. The discriminant results showed that there was an 88% accuracy in predicting waxy kernels as waxy, and a 96% accuracy in predicting wild type kernels as wild type. A receiver operating characteristic (ROC) curve was determined to allow threshold adjustment to meet desired true positive or false negative rates. Thus, the prediction equation can be used in breeding programs to select for waxy kernels in an efficient and effective manner using a single kernel NIR instrument. This approach will benefit breeders of waxy corn by providing a rapid, automated non-destructive method for identification of waxy kernels in segregating breeding populations.
Predicting leaf mineral composition is critical for monitoring plant health and optimizing agricultural practices. This study combines Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) and machine learning (ML) to specific macronutrients, namely nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), in fig leaves (Ficus carica L.). A dataset of 90 leaves was analyzed, with FTIR spectra (450-4000 cm⁻ 1 ) preprocessed via baseline correction and second-derivative transformations. Three ML models were evaluated using fivefold cross-validation including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB), with performance assessed via root mean square error (RMSE), coefficient of determination (R 2 ), and ratio of performance to deviation (RPD). GB outperformed other models, achieving validation RMSE/RPD values of 0.133/1.60 (nitrogen, N), 0.0107/1.79 (phosphorus, P), 0.1328/1.65 (potassium, K), 0.0636/1.96 (magnesium, Mg), and 0.2657/1.60 (calcium, Ca). Predictions for Mg (validation R 2 = 0.7351) and P (validation R 2 = 0.6873) exhibited the highest accuracy, potentially attributed to their stronger or more distinct spectral features (e.g., Mg-O stretching around 1050- 1150 cm⁻ 1 ; P-O vibrations around 1240 cm⁻ 1 ). Cross-validation revealed robust generalization for GB; while mean training RMSE was very low ( 1.5). Despite evidence of overfitting (training R 2 ≈ 0.999 vs. validation R 2 = 0.61-0.74), GB's performance evaluated using both RMSE and RPD confirmed its superiority over RF and SVR, which showed higher errors (e.g., SVR for Ca: RMSE = 0.4574, RPD = 1.07). This study demonstrates that FTIR-ATR coupled with ML is a rapid, non-destructive alternative to conventional destructive chemical analysis and that GB's reliability, as indicated by RPD values > 1.5, offers actionable insights for precision nutrient management in sustainable agriculture.
Shalini Krishnamoorthi · Sally Shuxian Koh · Mervin Chun‐Yi Ang · Mark Ju Teng Teo · Randall Ang Jie · U. S. Dinish · Michael S. Strano · Daisuke Urano
Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.
Contamination of farmland with heavy metals (HMs), particularly arsenic, cadmium, and lead, poses significant risks to human health and food security, especially through HM bioaccumulation in rice ( Oryza Sativa ). Current methods of detection for HMs, such as ICP-MS, provide accurate measurements but are destructive and labor-intensive, limiting their feasibility for widespread agricultural use. In this study, we investigated the potential of Raman spectroscopy (RS) as a nondestructive, cost-effective alternative for the detection of HM stress and thereby uptake in rice. Using a dose-response experimental design, we examined the sensitivity of RS for detecting varying levels of arsenic, cadmium, and lead-induced stress. Our analyses revealed several dose-dependent changes in Raman peaks associated with carotenoid and phenylpropanoid abundance. We found these changes were specific to each HM, reflecting the activation of distinct stress-response mechanisms. We also performed ICP-MS of harvested rice tissue, allowing us to build Raman-based calibration curves for predicting the HM concentration within rice. Lastly, we built a machine-learning algorithm that could interpret the Raman spectra to diagnose the specific type of HM toxicity with an average of 84.5% accuracy after only 1 week of HM stress. These findings highlight the promise of RS as a valuable tool for real-time, nondestructive monitoring of HM contamination in rice crops. Notably, the dose-response experimental design demonstrated RS's ability to detect HM stress levels that aligned with typical environmental contamination.
The germination rate of maize seeds is a critical indicator for ensuring high-quality sowing and suitability for food processing. To address the limitations of traditional germination tests, a rapid and non-destructive evaluation method based on near-infrared (NIR) spectroscopy combined with Gaussian Process Regression (GPR) was developed. Various spectral data preprocessing techniques were applied, and a hybrid kernel function integrating Gaussian and Linear kernels was constructed. Particle Swarm Optimization (PSO) was used to optimize the kernel parameters. The PSO-GPR model achieved excellent performance, with determination coefficients (R 2 ) of 1.000 and 0.9899 for the training and validation sets, respectively. The root mean square errors (RMSE) were 0.0059 and 0.0033, and the residual predictive deviation (RPD) reached 9.3, outperforming PLSR and SVM models. This study provides a novel strategy for the non-destructive evaluation of crop seed quality and contributes to developing smart agricultural practices.
Zhao Z, Li L, Li W, Tian Y, Zhang Y, Zhang Y, Ibba MI, He Z, Hao Y, Tian W.
WheatRaman / spectroscopySeed / grain
Bread wheat (Triticum aestivum L.) plays a vital role in global food security and processing. Understanding the rheological properties of dough is crucial in the food industry and wheat breeding programs to select high-quality varieties. Traditional tests such as Farinograph and Extensograph are essential, but labor-intensive and impractical for high-throughput screening. Near-infrared spectroscopy is a rapid and cost-effective alternative to grain quality assessment. This study aimed to develop calibration models for key rheological properties of dough in wheat using a dataset of 1082 representative samples. Various spectral pre-processing, variable selection, and regression algorithms have been employed for model calibration. The partial least squares regression model for Farinograph water absorption demonstrated strong predictive capabilities (R 2 c = 0.92, R 2 v = 0.90, and RPD = 3.20), while qualitative analysis was feasible for other characteristics with high accuracy from 80.23 % to 94.27 %. The developed NIR models provide an efficient method for evaluating wheat quality in food processing and wheat breeding.
The increasing demand for food production requires innovative approaches to protect crops from pathogens that significantly reduce yield and quality. Phytoplasmas, persistent bacterial pathogens transmitted by phloem-feeding insects, cause severe damage to economically important crops, including tomato plants. Early detection of these pathogens can be crucial considering that traditional molecular diagnostic methods, such as polymerase chain reaction (PCR), often fail during early infection stages due to low pathogen concentrations. In this study, we explore the use of Raman spectroscopy as a rapid, non-invasive tool for monitoring alterations in plant metabolites caused by Candidatus Phytoplasma solani infection in tomato plants. Grafting experiments were performed, and Raman spectra were collected at different time intervals post-infection. Changes in the spectral intensities of chlorophyll, carotenoids, and polyphenols were identified as early as two weeks post-infection, prior to the pathogen's detectability by molecular methods. These findings highlight the potential of Raman spectroscopy to fill the diagnostic gap in the early stages of phytoplasma infections, offering a window for timely intervention and a further tool in precision agriculture.
Non-destructive tree phenotyping for resistance screening and early, presymptomatic disease detection figures prominently among the most important practical limitations inherent in forest health management. The need for point-of-care tools is particularly acute for managing diseases caused by non-native pathogens, often resulting in difficult-to-control biological invasions. One such case is represented by ash dieback in Europe, caused by Hymenoscyphus fraxineus, which has led Sweden to red-list its main host, European ash ( Fraxinus excelsior ). We evaluated the use of near-infrared (NIR) spectroscopy and machine learning for detection of presymptomatic infections by H. fraxineus and identification of disease-resistance European ash accessions. Here, we show that presymptomatic infected trees can be distinguished from pathogen-free trees with a testing error rate of 0.161 in a controlled inoculation experiment. We also show that the same approach can be used to identify disease-resistant European ash accessions based on data from two independent, multiyear clonal trials, with a testing error rate of 0.155. These results confirm that NIR spectroscopy combined with machine learning is sensitive enough for early disease detection and resistance screening in this system. This is consistent with prior findings in other tree pathosystems and suggests that this approach could be developed into an operational tool to facilitate the management of biological invasions of forest environments by non-native pathogens, including habitat restoration with resistant germplasm.
Reproduction assets foundThe paper's NIR spectral/phenotype datasets (presymptomatic infection detection and resistance phenotyping of European ash) are deposited publicly on Dryad under DOI 10.5061/dryad.s1rn8pkkn, per the data availability statement. No author analysis code repository is stated; cited R packages (caret, FDA, R) are generic,非Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://datadryad.org/stash , 10.5061/dryad.s1rn8pkkn .Open asset ↗datadryad.org · 10.5061/dryad.s1rn8pkknlines:402-432Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Published13 Jun 2025Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 16 · OpenAlex ↗
TeaRaman / spectroscopyLeafPhysiological trait estimationWater status / transpiration
Moisture significantly affects tea plants' growth and quality. Traditional methods of leaf moisture detection are usually destructive to samples, slow and labour-intensive. In this study, visible-near infrared (VIS-NIR) spectroscopy was used to detect the moisture content of tea leaves quickly and accurately in the spectral range of 500-870 nm. The experimental materials are "Longjing 43″, which are divided into two batches. The first batch consists of 135 tea samples collected in April 2022, and the second batch includes 349 tea samples collected in April 2024.The FD + SNV + CARS + ε-SVR model had the best prediction effect on the moisture content of tea leaf in 2024, with the prediction effects of R c , R p , RMSEC, RMSEP and RPD being 0.9676, 0.903, 0.0221, 0.04 and 2.3367, respectively. However, the prediction result R P of the constructed model applied to the 2022 data was only 0.138. In order to improve the generalisation of the model, this study proposes stacking ensemble learning and instance-based transfer learning. In particular, the transfer learning model only needed 55 transfer samples, and the R P was the highest at 0.851. Compared with the stacking ensemble, which required 60 samples, the R P was the highest at 0.85, which realised the use of fewer samples to achieve a better prediction effect. These studies not only confirmed the potential of VIS-NIR spectroscopy to assess the moisture content of tea leaves but also investigated the transfer optimisation of the model, which was helpful to improve the generalisation ability of the model.
Forestry industry requires high-quantity and quality seeds for afforestation and assisted migration programs. Finding reliable non-destructive methods to characterize seeds would significantly enhance efforts to identify climate-adapted populations. This study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively. We focus on Abies alba Mill., a key European forest tree with genetic variation along climatic gradients and seeds with shallow physiological dormancy. Seeds from six populations were analyzed using NIR spectroscopy, and germination was tested at 15°C, 20°C, and 25°C after stratification treatments at 4°C (0 or 3 weeks). Population classification accuracy using Partial Least Squares Discriminant Analysis was 69%, with significant NIR peaks at 1712, 1929, and 2111 nm, linked to moisture content and storage compounds. NIR spectra explained 51% and 65% of the variation in germination probability and timing using Partial Least Squares Regression, with significant peaks at 1712, 1929, 2111, 1632, and 2073 nm. General Linear Mixed-Effects Models showed that a NIR predictor contributed to 39% of the germination probability variance explained by fixed-effects, and the stratification treatment was the most important driver explaining germination time. Our results proved the utility of NIR-based tools to effectively classify bulked seeds and predict germination, opening new perspectives to nursery and forestry sectors and populations’ adaptation and adjustments to warming climate. This study will facilitate further investigations on the physiological processes that occur during dormancy, a critical process for forest regeneration given the expected impact of shorter and warmer winters on seed behavior.
Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R2) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R2 value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees.
The application of high-throughput phenotyping techniques at various stages of the agricultural process can foster crop improvements when combined with genetic strategies. In this study, a portable infrared (IR) spectrometer was used for in-field determination of multiple important quality traits in processing tomato varieties. Tomato fruits, harvested from 2021 to 2023 seasons, were cut in half; one half was used to determine brix, pH and predicted paste Bostwick (PPB) of raw juice, while the other half was used to prepare hot-break juice (cooked juice) for determining brix, juice Bostwick (JB), kinematic viscosity of the supernatant from centrifuged tomato paste (KVost) and PPB of cooked samples. Duplicate spectra of each tomato juice sample were acquired in the field using a portable IR system operating in attenuated total reflectance (ATR) mode. Regression models were developed using Partial least squares regression (PLSR) and orthogonal PLSR (OPLSR). OPLSR models exhibited better calibration performance, although they showed comparable prediction performance to PLSR models when validated with an independent data set. The correlation coefficient for prediction in the PLSR models exceeded 0.77, with a low standard error of prediction (RMSEP = 0.04 to 0.70). Furthermore, the generated regression algorithms were integrated with the portable IR system for predicting the desired variables at-site. Analyzing the samples soon after harvesting from the fields and generating the calibration models at-site made the predictive models reliable and robust. The implementation of portable and high-throughput monitoring of phenotypic traits at-site allows for the simultaneous determination of multiple quality traits, facilitating cost-effective and rapid decision-making with minimal use of consumables compared to traditional analytical techniques that require laboratory facilities, skilled labor, and are expensive and time-consuming. • Portable FTIR rapidly predicts the quality traits in processing tomato varieties. • A diverse sample set (n > 1800) captured expected variations in composition. • Reference and spectral data were collected at-site. • OPLSR showed comparable prediction performance to PLSR models. • Portable MIR was deployed in-field for real-time monitoring of tomato quality.
Sweetpotato is a major root crop with high yield and nutritional benefits. However, existing methods for evaluating sugars level are inefficient, limiting the breeding and processing of high-quality varieties. This study utilized near-infrared spectroscopy (NIRS) coupled with machine learning algorithms to develop a high-throughput assay for fructose, glucose, sucrose, and maltose in sweetpotatoes across their raw, steamed, and baked states. Leveraging representative samples, characteristic spectral variables, and advanced learning algorithms, twelve optimal models were established for the four sugar indicators under three processing states. These models exhibited outstanding performance in calibration ( R 2 C : 0.941–0.984), cross-validation ( R 2 CV : 0.926–0.976), external validation ( R 2 V : 0.898–0.971), and the ratio of prediction to deviation (RPD: 5.83–10.3), confirming their robust predictive capacity. The findings suggest that these machine learning-enhanced NIRS models enable rapid, high-throughput analysis of sweetpotato sugars, significantly benefiting both breeding programs and food processing applications. • Machine learning enhances NIRS for high-throughput sweetpotato sugar phenotyping. • Robust models quantify sugars across raw, steamed, and baked sweetpotato states. • Optimized models ensure high accuracy and reliability for sugar content prediction. • Efficient NIRS reduces costs and time for postharvest quality assessment. • Insights aid sweetpotato breeding and consumer-oriented product development.
Current cultivation and research on Watercore apples lack precise evaluation methods and non-destructive detection techniques for Watercore content. In response, this study exploits the intrinsic distribution characteristics of Watercore and utilizes a RIFE interpolation-based feature slice stacking method to reconstruct a 3D model of individual Watercore—a task unattainable using conventional approaches. Employing the complete 3D Watercore model as a reference, the study further integrates near-infrared spectroscopy with the GAF-ConvNeXt algorithm to achieve five-class non-destructive detection of Watercore. Experimental results demonstrate that the MIoU between the RIFE-interpolated features and the original Watercore features attains a value of 0.826, thereby indicating high reliability. The reconstructed 3D models typically exhibit a central void, multiple uniformly distributed independent pillar-like structures along the periphery, and a greater volume in the upper half relative to the lower half. Furthermore, the five-class detection accuracy achieved using the GAF-ConvNeXt algorithm attains 98.10 %, thereby offering a more precise and scientifically robust method for the non-destructive evaluation of Watercore content in apples.
• Hyperspectral imaging can detect Botrytis cinerea 1 day after inoculation. • Hyperspectral imaging detects it 1 day earlier than colour imaging. • Chemometric approaches allowed visualisation of disease progression. • Disease severity can be explained with R 2 = 0.84 using near-infrared spectroscopy. Botrytis cinerea is a fungal pathogen that can affect a wide range of plants, including roses. Resistance against Botrytis is quantitative, making breeding for resistance challenging. To enable proper genetic marker development, high-throughput and objective data on Botrytis sensitivity is essential. Rose petal discs of different cultivars were manually infected with Botrytis and were monitored with hyperspectral imaging using a fully automated spectral imaging setup. Predictive modelling analysis involved both detection of Botrytis and explaining the severity of infection by linking the spectral data to visual scoring by human eye. Furthermore, band selection analysis was performed to detect key spectral bands relevant for Botrytis detection and to facilitate development of lower cost multi spectral systems for detection of Botrytis infected areas in roses. The presented approach can help plant breeders to explore and adapt to new plant phenotyping technologies such as hyperspectral imaging for breeding against biotic and abiotic stresses.
Estimating leaf nutrient concentration in field crops is essential to increase crop yield by optimum fertiliser application. Notably, these practices become more critical for short-cycle crops like potatoes (Solanum tuberosum L.), where conventionally, laborious in-field plant sampling and laboratory analysis take a long time. Multiple samples are frequently required to reach the field’s representation and reliability. The alternative technique of optical spectroscopy, which reports the canopy reflectance to the specific band of the electromagnetic spectrum, can be used to estimate the plant nutrient concentration. Previous studies have made such efforts using the electromagnetic spectrum’s visible to near-infrared (VNIR, 400–1100 nm) and short-wave infrared (SWIR, 1100–2400 nm) ranges. In this study, we are testing the ability of the spectroscopy with a full-range spectroradiometer (400–2400 nm) along with a comparison of VNIR and SWIR to estimate the total Kjeldahl nitrogen (TKN), phosphorus (P), potassium (K), and sulphur (S) nutrient concentration in freshly picked petiole/leaf samples of potato plants. Results show that the full-range spectrum predicted TKN with an accuracy of R² = 0.91 external validation (0.74 internal validation), followed by K, R² = 0.87 (0.69), P, R² = 0.86 (0.82), and S with R² = 0.75 (0.68). It was also reported that the maximum difference in the estimation accuracy among VNIR and SWIR was reported for K, where VNIR had R² = 0.48 (0.54) and SWIR had R² = 0.86 (0.80). This study lays a foundation for further development of models that can estimate the canopy nutrient concentration in the field with spectral reflectance and scale up these models with hyperspectral imaging.
Published1 Jun 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Due to polygenic nature of grain protein and essential amino acids, a high throughput methodology is required for identification of desired segregants and improvement of rice simultaneously for protein quality and quantity in a cost-effective way. Data from chemical analysis of samples of 150 rice genotypes with a substantially wide range were used here to develop prediction models for high-throughput estimation of grain protein content (GPC) and essential amino acids (EAA) content using near-infrared spectroscopy (NIRS). Various mathematical pretreatments were employed under modified partial least squares(mPLS) models to ascertain the optimal mathematical equation for prediction based on the lowest standard error of cross-validation, the highest 1-VER (1minus variance ratio), the highest coefficient of determination (RSQ) and the lowest standard error of calibration (SEC). The optimal pretreatment for GPC and EAA were 1,6,6,1 and 2,8,8,1, respectively. These models were validated through paired t-tests. Association (R²) between the predicted and reference values varied from 0.909 to 0.967, revealing prediction models' higher accuracy and effectiveness. This study was further extended by applying those prediction models for estimating GPC and EAA in a mapping population to identify genomic regions for qualitative and quantitative improvement of grain protein.
The contents and ratios of 7S and 11S globulins are crucial for the nutritional value and functional properties of soybean proteins. Typically sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is used to detect 7S and 11S globulin in soybeans however this method involves slow analysis procedures and high costs. Near-infrared (NIR) spectroscopy technology has emerged for detecting soybean protein content, enabling rapid non-destructive testing with advantages of convenient measurement, minimal sample processing requirements, and simultaneous determination of multiple components. To resolve the issue of shared quantitative prediction models between NIR spectroscopy-based 7S and 11S protein content predictions for various soybean seed and soybean powders, a transfer method of standard-free model based on transfer learning (TL) was proposed. Firstly, the NIR data of different forms of soybean samples were collected, and the near-infrared prediction models of 7S and 11S protein content were established. Secondly, the direct standardization (DS) and piecewise direct standardization (PDS) algorithms were improved to propose a DS-PDS-based model transfer method, with the influence of the sequence of preprocessing and model transfer algorithm on overall model transfer scheme was explored. Then, IRM is used to force the model to learn invariant features with causal relationship with labels by constraining the optimal classifier consistency of the model in different environments. Finally, aiming at standard sample sets corresponding to master-slave spectra required by traditional model transfer methods, the model transfer effect was investigated using a model transfer method based on standard-free migration learning. Results showed that the model transfer method based on without standard transfer learning was more suitable for 7S and 11S globulin content modeling between soybean seeds and soybean powders. It is intended to provide efficient and accurate 7S and 11S protein content detection methods for soybean processing enterprises and support quality control of soybean protein products and production of functional products.
Published1 Jun 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Near-infrared (NIR) spectroscopy has been widely used as the non-destructive technique for fruit SSC measurement. This study explored the combination of NIR spectroscopy and machine learning regression to predict SSC in 288 citrus fruits (cv. Ponkan mandarin) considering the influence of sampling positions. This research analyzed spectral variations in different sampling positions, as well as the average spectra. Machine learning algorithms, including support vector regression (SVR) and partial least squares regression (PLSR), were used to establish the prediction models for SSC using the single-position spectra, spectra of all sampling positions and the average spectra. Feature wavelengths were identified by the combination of correlation analysis and regression coefficient of PLSR models from the single-position spectra and the average spectra. Using the full spectra or feature wavelengths, the models based on the average spectra significantly outperformed those based on the sample-position spectra, indicating that the average spectra may be more suitable for SSC prediction of Ponkan mandarin. This study indicated the variations among different sampling positions and samples were one of the key factors affecting the precise and robust models for SSC prediction, and future attempts should be conducted to cover the sample variations improve the model robustness and generalization ability.
Published1 Jun 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Pearl millet, known for its nutritional excellence and climatic resilience, is becoming important in addressing food and nutritional security Current work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains. The model is quick, economic and non-destructive alternative to traditional methods, useful in advancing the single plant progenies for improving nutrient content in segregating generations. Spectra were acquired from 403 varied genotypes, and mathematical optimizations using derivatives were performed to enhance the models. The optimal configurations were "2,36,6,2" (order of derivatives, gap, first smoothing and second smoothing, respectively) for amylose, "2,32,6,2" for starch, "2,32,8,2" for oil and protein, and "3,36,6,2" for phytic acid. The models were refined using modified partial least squares (MPLS) regression on spectra processed to eliminate variations with standard normal variate (SNV) and detrending (DT) techniques. The adjusted MPLS models exhibited impressive coefficients of determination of 0.985, 0.984, 0.986, 0.969 and 0.993 for amylose, protein, oil, starch and phytic acid, respectively. The SEP(C) values for amylose (0.347), starch (0.732), protein (0.313), phytic acid (0.014), and oil (0.162) suggest variable levels of predictive precision. Validation with independent samples showed superior predictive performance with coefficients of determination values ranging from 0.878 for phytic acid to 0.976 for protein, minimal bias, high ratios of prediction to deviation (2.93–5.81), and no significant differences between the predicted and reference values (p > 0.05). These advanced Near-Infrared Spectroscopy models allow quick and cost-effective nutritional assessment of pearl millet germplasm and breeding lines, supporting biofortification initiatives and enhancing nutritional security.
Colonization of the Moon and other planets is an aspiration of NASA and may yield important benefits for our civilization. The feasibility of such endeavors depends on both innovative engineering concepts and the successful adaptation of life forms that exist on Earth to inhospitable environments. In this study, we investigate the potential of Raman spectroscopy (RS) in a non-invasive and non-destructive assessment of changes in the biochemistry of plants exposed to zero gravity on the International Space Station and during growth on lunar regolith simulants on Earth. We report that RS can sense changes in plant carotenoids, pectin, cellulose, and phenolics, which in turn, could be used to gauge the degree of plant stress in new environments. Our findings also demonstrate that RS can monitor the efficiency of soil supplements that can be used to mitigate nutrient-free regolith media. We conclude that RS can serve as a highly efficient approach for monitoring plant health in exotic environments.
Accurate prediction of apple soluble solids content (SSC) is essential for fruit quality evaluation. Aiming at the problem of spectral aberration caused by the variation of fruit diameter in the existing visible/near infrared spectroscopy (VIS/NIR) detection, a novel spectral correction strategy was proposed in this study. By systematically analysing the correlation law between light intensity attenuation and fruit size in different wavelength bands (600-1000 nm), it was found that the traditional single-parameter correction models (exponential function method, hyperbolic sine function method) had limitations of applicability in a wide spectral range. Based on this, this paper innovatively proposed the band-specific segmented extinction correction method, established the mapping relationship between spectral intervals and size compensation parameters, and realised the multi-band synergistic correction. Experiments showed that the extinction coefficient method based on the exponential function and the diameter transformation method based on the hyperbolic sine function exhibited significant results in spectral correction for specific bands, resulting in a 6 %-10 % improvement in modelling accuracy at global size. However, when we introduced the band-specific segmented extinction correction, the spectra were effectively corrected over the entire band range, the light intensity differences between samples of different sizes were significantly reduced, and the modelling accuracy at global size jumped by 15 %. Specifically, the partial least squares regression (PLSR) model had a coefficient of determination (R 2 ) of 0.90 and a root mean square error (RMSE) of 0.55, and the convolutional neural network (CNN) model had the R 2 of 0.95 and the RMSE of 0.44, after corrected for the band-specific segmented extinction. Finally, this paper set up additional validation experiments to test the calibration effect of the three methods, and the results showed that band-specific segmented extinction correction method improved the modelling effect of the model most significantly. Therefore, the band-specific segmented extinction correction method proposed could effectively reduce the effect of apple diameter on the transmission spectrum and further improve the apple SSC's prediction accuracy.
The plant’s phenotype changes under biotic and abiotic stress, reflecting its adaptations in gene expression and metabolism. For crop management, rapid detection of plant stress responses is crucial. To facilitate rapid detection of stress responses in crops, we explored the potential of UCPH’s PhenoLab for assessing barley disease resistance under both biotic and abiotic stress. We used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts. At specific wavelengths, PhenoLab spectral signatures clearly distinguished cultivars with different levels of susceptibility to the obligate biotroph pathogen Blumeria graminis (powdery mildew). Microscope phenotyping at similar reflectance and fluorescence settings parallelled the PhenoLab-derived spectral signatures. However, a specific systemic resistance response emerged three days after inoculation, detectable only by microscopy when targeting infected and non-infected leaf areas. We hypothesized that combined stresses would work additively and used phenotyping to study the response of the resistant and susceptible barley cultivar to a combination of drought with powdery mildew infection. Surprisingly, drought made the resistant cultivar less resistant and the susceptible one less susceptible according to changes in reflectance and fluorescence at defined wavelengths. The spectroscopic absorbance assay confirmed this result biochemically. This proof-of-concept study showcases the potential of holistic functional phenomics, using non-invasive imaging to identify predictive spectral signatures for barley pathogen resistance.
The development of novel SERS-active MAPbCl 3 nanofibers substrate offers a rapid, sensitive, and label-free method for critical stress phytohormone abscisic acid (ABA) detection as compared to conventional methods. Aptamers can act as a specific molecular recognition element that bring ABA molecules closer to the SERS-active MAPbCl 3 nanofiber surface, leading to a stronger localized electromagnetic field enhancement. The stable cross dimerized self-complementary (CDSC) aptamer configuration has the lowest Gibbs free energy (ΔG) of -9.75 kcal/mol according to thermodynamics. Bioinformatic analysis of aptamer using OligoAnalyzer® tool offered the thermodynamic properties and functional stability of the aptamer sequence designed to target the LOC109791758 gene encoding the Glycine-Rich Cell Wall Protein (GRCWP) in Cajanus cajan. This facilitated to develop the novel SERS-active single crystal MAPbCl 3 nanofiber-based Self-CoAptaNano (SCAN) substrate by improving the target molecule binding and sensitivity even at low concentrations. Material's structure and properties were characterized by using SEM, UV-Visible, and SERS in this study. The currently developed MAPbCl 3 nanofiber-based SCAN substrate for ABA detection resulted in better LOD of 1.17 × 10 -12 M for SERS and 2.14 × 10 -9 M for FLI as compared to previously developed substrates. Moreover, the EF was recorded as 1.08 × 10 7 M with the recovery rate close to 100 % and RSD of 3.24 % under SERS and 4.13 % under fluorescence exposure in complex matrices for ABA in real plant samples. The adaptability of MAPbCl 3 nanofiber-based SCAN as a substrate for aptamer-specific analysis via SERS can underscore their immense potential for broader applications in analytical chemistry and biotechnology.
Cannabis sativa L. is cultivated for therapeutic and recreational use. Delta-9 tetrahydrocannabinol (THC) and cannabidiol (CBD) are primarily responsible for its psychoactive and medicinal effects. As the global cannabis industry continues to expand, constant review and optimization of horticultural practices are needed to ensure a reliable harvest and improved crop quality. There is currently uncertainty about the optimal harvest time of C. sativa , i.e., when cannabinoid concentrations are at their highest during inflorescence maturation. At present, growers observe the color transition of stigmas from white to amber as an indicator of harvest time. This research investigates the relationship between stigma color and cannabinoid concentration using liquid chromatography-mass spectrometry (LCMS) and digital image analysis. Additionally, early screening prediction models have also been developed for six cannabinoids using near-infrared (NIR) spectroscopy and LCMS to assist in early cannabinoid determination. Among the genotypes grown, 22 of 25 showed cannabinoid concentration peaks between the third (mostly amber) and fourth (fully amber) stages; however, some genotypes peaked within the first (no amber) and second (some amber) stages. We have determined that the current 'rule of thumb' of harvesting when a cannabis plant is mostly amber is still a useful approximation in most cases; however, studies on individual genotypes should be performed to determine their individual optimal harvest time based on the desired cannabinoid profile or total cannabinoid concentration.
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data—including electrical impedance spectroscopy, temperature, and humidity—were used to capture plant physiological responses under environmental stress conditions. The key task addressed was the prediction of stress-related parameters using machine learning. A novel boosting-based ensemble method, AdapTree, combining AdaBoost and decision trees, was proposed to improve predictive accuracy and model interpretability. Experimental evaluation across multiple regression metrics demonstrated that AdapTree outperformed baseline models, achieving an R2 score of 0.993 for impedance magnitude prediction and 0.999 for both relative humidity (RH) and temperature, along with low root mean squared error (134.565 for impedance, 0.006966 for RH, and 0.0050099 for temperature) and mean absolute error values (22.789 for impedance; 1.51 × 10−5 for RH and 2.51 × 10−5 for temperature). These findings validate the reliability and effectiveness of the proposed AI-driven framework in accurately interpreting sensor data for plant stress detection. The approach offers a scalable, data-driven solution to enhance precision agriculture and agricultural sustainability. Furthermore, this method can be extended to monitor additional stress markers or applied across diverse plant species and field conditions, supporting future developments in intelligent crop monitoring systems.
Wang J, Liu X, Qi X, Wu X, Long Y, Feng Y, Dong Q, Yan J, Huang L, Luo Y, Cao M, Xu K, Zhao C, Wang Y, Hu T, Wu J, Liu L, Su Y.
Field / plotRaman / spectroscopyLeafMorphology / geometry measurementLeaf traits
Leaf spectroscopy, combined with partial least squares regression (PLSR), is recognized as an efficient and precise tool for measuring plant leaf traits. However, the feasibility of developing a generalizable model remains unclear, primarily due to limited understanding of PLSR model transferability. Here, we collected six key leaf traits along with paired leaf reflectance spectra from 1967 samples of 349 tree species in eight forest sites across China. Using this dataset, we explored the transferability of PLSR models, factors affecting model transferability, and the feasibility of developing generalizable PLSR models for leaf trait prediction. Overall, PLSR models trained at a specific study site demonstrate limited transferability to other study sites. Dissimilarities in plant evolutionary history and environmental conditions between study sites are the primary factors influencing the transferability of PLSR models. Incorporating training data from diverse evolutionary histories and environmental conditions can improve the transferability of PLSR models, achieving accuracy equivalent to that of site-specific models. Our findings provide guidelines for the use of spectroscopy in leaf trait prediction and underscore the urgent need for collaborative efforts to build an open database of leaf traits and reflectance spectra, thereby promoting the development of universal PLSR models for plant leaf trait prediction.
Horse gram (Macrotyloma uniflorum (Lam.) Verd.) is an underutilised legume from the Indian subcontinent. Being a nutritious legume, it plays an important role in human nutrition in developing countries like India. Conventional assessment of nutritional traits, are labour and time intensive for screening of huge germplasm, hence alternative and rapid technique for conventional method for the determination of nutritional components of horse gram flour is needed. NIRS can be used for this purpose as it gives rapid and precise results for most of the plant products. In this study, a highly diverse collection of 139 horse gram accessions was utilized to generate reference data. Prediction models were developed for protein, starch, TSS, phenols, and phytic acid using MPLS regression method with spectral preprocessing using SNV-DT to remove scatter effects and baseline noise. Models were optimized for derivatives, gap selection, and smoothening and evaluated using different statistics including RSQ, bias and RPD. The RSQ and RPD for the best fit models obtained were protein (0.701; 1.85), starch (0.987; 4.03), TSS (0.800; 4.06), phenols (0.778; 2.15) and phytic acid (0.730; 1.88) indicating developed models are good for screening large number of germplasm collections and market samples. Statistical analyses, including paired t-tests, correlation, and reliability assessments, validated the strength of these models. This study represents the first report introducing a rapid, multi-trait evaluation approach for horse gram germplasm, highlighting its high predictive accuracy for pre-breeding applications. High throughput germplasm screening can be done through these developed models to identify trait-specific germplasm, which can be recommended to develop healthy products and thus can also be recommended for production in the farmer field simultaneously.
Assessing the nutritional quality traits of pastures is crucial for germplasm and breeding evaluations, enabling the selection of high-quality forages to enhance livestock productivity. However, traditional laboratory analytical methods are logistically demanding and costly, particularly in large-scale trials, underscoring the need for rapid, precise, and high-throughput evaluation methods. Near-Infrared Spectroscopy (NIRS) optimizes the estimation of forage nutritional quality parameters by developing chemometric models that predict these parameters with high accuracy and precision, based on the association between NIRS data and wet chemistry analyses. This dataset, collected over ten years by the Tropical Forages Program at the International Center for Tropical Agriculture (CIAT) in Colombia, comprises 1112 samples. It includes 995 measurements of Neutral Detergent Fiber (NDF), 996 of Acid Detergent Fiber (ADF), 995 of In Vitro Dry Matter (IVDMD), and 469 of Crude Protein (CP), all obtained through wet chemistry methodologies. Additionally, the 1112 samples contain absorbance data spanning 400 to 2498 nanometers (nm) in 2 nm intervals, generating 1050 spectral data points per sample. Finally, this dataset is a valuable resource for predicting forage nutritional quality beyond conventional parameters, incorporating plant reflectance attributes to enhance selection strategies for optimized forage selection.
Reproduction assets foundThe paper is a data descriptor for a paper-specific public dataset: 1112 Urochloa humidicola samples with wet-chemistry traits (NDF, ADF, IVDMD, CP) and 1050-point NIR absorbance spectra (400–2498 nm), deposited in the Harvard Dataverse (DOI 10.7910/DVN/XPNIQY). The deposit is explicitly public and actionable; however,Dataset · publicData accessibility
Repository name: Harvard database
Data identification number: 10.7910/DVN/XPNIQYHarvard database · 10.7910/DVN/XPNIQYlines:1-49Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R 2 of 0.992, RMSEC of 0.478 mg g -1 DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R 2 of 0.976, RMSEC of 0.098 mg g -1 DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R 2 of 0.790, RMSEC of 1.777 units g -1 DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
Abstract This study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes. The standard analytical approach for TDF analysis is based on the Association of Official Analytical Collaboration method 985.29, which requires extensive sample preparation with extended analysis times of up to 30 h. The FT‐MIR approach was developed to enhance rapid and non‐destructive analysis and minimize the traditional workload associated with phenotyping TDF in pulse crops by accomplishing the same task in a shorter time and at minimal cost. Partial least squares regression (PLSR) was applied with chemometric modeling in MIR regions (650–1480 and 2771–3700 cm −1 ), encompassing spectral bands associated with undigested polysaccharides and partially or undigested protein and fatty acid methyl ester fractions that fingerprint TDF. K‐fold cross‐validation was used for PLSR modeling to enhance computational speeds with large‐scale data processing. These PLSR models for chickpea, dry pea, and lentil have coefficients of determination ( R 2 ) as 0.91, 0.96, and 0.94 with root mean square errors of prediction in the range of 0.05–0.5 g/100 g. This technique supports rapid phenotyping of TDF from raw flour in <1 min. The FT‐MIR technique can relieve the phenotyping bottleneck in pulse breeding and pulse‐based food and feed industries, targeting the measurements of TDF and ensuring a rapid and high‐throughput pipeline for plant breeding and cultivar development.
Broccoli's pigments enhance its nutritional value by affecting color and antioxidant properties. Traditional methods like high-performance liquid chromatography (HPLC) and spectrophotometry are accurate but destructive, labor-intensive, and unsuitable for high-throughput screening. This study constructed non-destructive models based on near-infrared spectroscopy (NIRS) technology to predict pigment compounds in broccoli. The optimal models for total chlorophyll (Chl), Chl a, and Chl b were established with the use of SNV / 2nd derivative / PLS, which yielded an R² of 0.992, RMSEC of 0.478 mg g⁻¹ DW, and RPD of 6.476. For carotenoids (CAR), the SNV / 1st derivative / PLS model provided the best results, with an R² of 0.976, RMSEC of 0.098 mg g⁻¹ DW, and RPD of 4.455. However, the ACN model based on SNV / 1st derivative / PLS exhibited relative lower accuracy, with an R² of 0.790, RMSEC of 1.777 units g⁻¹ DW, RPD of 1.267, suggesting the necessity for preliminary analysis. This study fills a critical gap in NIRS applications for plant pigment analysis, presenting a rapid, non-destructive, and high-throughput approach for quality assessment and breeding selection.
Arabinoxylan (AX) is the primary dietary fiber found in wheat. Analysing AX through biochemical methods is costly and time-consuming. Developing a secondary screening method using near-infrared spectroscopy (NIRS) coupled with chemometric analysis was successful. A NIRS-based prediction model for total arabinoxylan (TOT-AX) and water-extractable arabinoxylan (WE-AX) content was developed using a panel of wheat germplasm lines, including exotic collections, indigenous cultivars and landraces, employing modified partial least square (mPLS) regression. Among various mathematical treatments, the third derivative produced better results, and the model showed RSQᵢₙₜₑᵣₙₐₗ values of 0.841 and 0.828, as well as RSQₑₓₜₑᵣₙₐₗ values of 0.694 and 0.605 for the whole wheat flour TOT-AX and WE-AX, respectively present in whole wheat flour. Upon validation on independent sets, an RSQ value of 0.883 for TOT-AX and 0.761 for WE-AX was obtained, proving the reliability of the model. This prediction model provides a rapid and cost-effective method to screen large germplasm, allowing for the identification of high arabinoxylan wheat genotypes.
In this study, novel methods using portable NIR and Raman spectroscopy instruments associated with multivariate classification were developed to classify cotton fibers according to their length. The Upper Half Mean (UHM) length is considered a quality parameter by the cotton fiber market and is traditionally determined using a high-volume system (HVI), which entails high installation costs and labor-intensive analyses. As UHM correlates with cellulose polymerization, its determination can be achieved through vibrational spectroscopy techniques such as near-infrared (NIR) and Raman. These technologies offer advantages such as low cost, ease of handling, and rapid data acquisition, making them suitable for field use. This study aimed to develop a method and demonstrate the feasibility of using portable NIR and Raman spectrometers coupled with pattern recognition (PR) methods for routine analysis of cotton fibers, serving as a proof of concept for practical application in the industry. A total of 142 samples of cotton fibers from cotton improvement experiments conducted by the Brazilian Agricultural Research Corporation (EMBRAPA) were employed. Two classification approaches based on cotton lint length and the related economic value were employed. The first aimed to differentiate between short (SM) and long (LF) fibers, while the second aimed to further classify long fibers into internal classes (L, VL, and EL). Overall, methods using portable Raman spectrometer exhibited 100% accuracy performance regardless of the PR technique used. Meanwhile, methods based on NIR spectrometers achieved accuracies of 100% depending on the PR method and variable selection employed. The use of GLSW resulted in a reduction of a latent variable. In conclusion, the use of portable NIR and Raman spectrometers combined with PR methods emerges as an innovative and viable technology for the classification of cotton fibers based on their length.
Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) are small fungus-farming beetles that damage stressed nursery trees directly through tunneling and structural weakening, and indirectly by introducing pathogenic fungi. Stressed trees emit ethanol, which is the primary host-locating cue for ambrosia beetles. This study evaluated the efficacy of low-cost ethanol detectors as a solution for the early detection of flood-stressed trees susceptible to ambrosia beetle infestation. Experiments were conducted using 48 native dogwoods (Cornus florida L.) subjected to flooded or non-flooded conditions. The attacks of ambrosia beetles were significantly higher in flooded trees, indicating a clear preference and validating the use of flood stress as a reliable method for susceptibility assessment. Ethanol emitted from these trees was measured using low-cost alcohol saliva test strips and Dräger Pac 8000 personal gas detectors alongside a solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) for ethanol confirmation. In addition to stem tissue analysis for ethanol detection via SPME-GC-MS, we found that twig and root tissue samples can also be assessed effectively using low-cost detectors such as alcohol strips and Dräger devices. GC-MS, a reliable method for volatile compound identification and quantification, confirmed ethanol as the dominant volatile in flooded trees, with both low-cost detectors correlating positively with SPME-GC-MS results. These detectors could offer a rapid, cost-effective method for identifying trees at risk of ambrosia beetle attack. However, their accuracy can be limited by false positives, as some plant genera emit aromatic volatiles such as eugenol, which may interfere with ethanol detection. More work is needed to optimize these tools for use by nursery growers, consultants, and researchers as an early-warning system and aid in ambrosia beetle management decision-making.
Here we investigated whether FT-Raman spectroscopy could be used to detect biochemical changes in small-leaved basil (Ocimum basilicum L. var. minimum Alef.) callus culture (CC). To address the effect of culture conditions and elicitor treatments, CC established on 1 mg L -1 2,4-D + 0.5 mg L -1 BAP, or 2.5 mg L -1 NAA + 0.5 mg L -1 KIN was exposed to various spectral light treatments during four weeks and compared to those grown in dark. The composition of CC was analysed both using an FT-Raman spectrometer equipped with laser 1064 nm, and spectrophotometrically. The spectral composition of light had a higher influence on the chemical composition of CC grown on NAA + KIN than on 2,4-D + BAP medium. Spectrophotometrically, no differences in the content of protein or sugar were determined in relation to the plant growth regulators applied. However, significant differences in frequencies and intensities of vibrational bands associated with proteins (S-S disulfide stretching, tyrosine, cystine, and methionine at lower spectral ranges, and amide III stretching in the higher spectral range), and carbohydrates (C-O-C skeletal mode at lower spectral ranges, and C-O-H vibrations at higher spectral ranges) within the Raman spectra were estimated and discussed. The 1525 cm -1 and 1606 cm -1 peaks with high intensities of vibration bands were identified and assigned to carotenoids and phenolics. In all treatments applied the major Raman peaks were detected at 1606, 1629, and 1633 cm -1 . PCA analysis showed that CC under blue-red light and blue-red light + UVa (2,4-D + BAP) had higher content of carotenoids and ester groups, while chlorophyll a and phenolics were found in CC grown on NAA + KIN under blue-red light + UVa and blue-red light + far-red. Compared to traditional methods of analysis, which are preceded by the sample destruction before extraction and analysis, it can be concluded that the FT-Raman spectroscopy may serve as a valuable tool for the non-destructive and non-invasive identification of major biochemical changes in basil CC without any sample preparation.
Volatile organic compounds (VOCs) produced by plants during plant-pathogen interactions can be highly informative for early disease detection. The real-time capability of field asymmetric ion mobility spectrometry (FAIMS) offers a valuable opportunity to monitor plant VOCs nondestructively and dynamically. This study evaluated the FAIMS system reliability in measuring VOC profiles for an early diagnosis of Aphanomyces root rot (ARR) in pea ( Pisum sativum L.). This evaluation utilized pea lines with a major quantitative trait locus (QTL Ae-Ps7.6 ) and lines without QTL, identified to provide partial resistance against ARR. For the first time, a VOC biomarker associated with ARR was detected as early as 2 days after inoculation (DAI). Furthermore, at 7 DAI, one of the biomarkers showed significant differences between lines with and without QTL Ae-Ps7.6 in the noninoculated samples. These findings demonstrate the potential applicability of the FAIMS system as a valuable tool for detecting volatile biomarkers for early plant disease detection.
Societal Impact Statement Characterizing variability in crop traits is key for understanding agroecosystem responses to environmental change. However, trait data are often time‐consuming to collect and therefore still limit our understanding and predictions of agriculture responses to environmental change. We tested the ability of reflectance spectroscopy—a high‐throughput technique—to rapidly amass trait data for multiple wine grape cultivars. Reflectance spectroscopy predicts important wine grape leaf traits including photosynthesis and biochemistry with a good degree accuracy, but in a fraction of the time compared to traditional techniques. Reflectance spectroscopy can therefore rapidly characterize wine grape phenotypes and, in doing so, inform predictions of how vines, clones and cultivars will respond to environmental change. Summary Reflectance spectroscopy has emerged as a powerful tool for non‐destructive and high‐throughput phenotyping in plants. While the ability of reflectance spectroscopy to predict traits across diverse plant species and ecosystems has received considerable attention, whether or not this technique is able to quantify within species trait variation—especially physiological traits—has been less extensively explored. Quantifying intraspecific variation in traits through reflectance spectroscopy is especially appealing in agroecology, where it may present an approach for better understanding crop performance, fitness and trait‐based responses to environmental conditions. We tested if reflectance spectroscopy coupled with partial least square regression (PLSR) predicts photosynthetic carbon assimilation ( A 420 ), RuBisCO carboxylation ( V cmax ) and electron transport ( J max ) rates, as well as leaf mass per area (LMA) and leaf nitrogen (N) concentrations, across six wine grape ( Vitis vinifera ) cultivars (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot noir, Viognier, Sauvignon blanc). PLSR models showed good capability in predicting intraspecific trait variation in wine grapes, explaining up to 55%, 58%, 62% and 62% of the variation in observed J max , V cmax , leaf N and LMA values, respectively. However, predictions of A 420 were less strong, with reflectance spectra explaining only up to 29% of the variation in this trait. Our results indicate that trait variation within species and crops is less well‐predicted by reflectance spectroscopy, than trait variation that exists among species. However, our results indicate that reflectance spectroscopy still presents a viable technique for quantifying trait variation in wine grapes specifically, and agroecosystems more broadly.
Excessive use of copper (Cu) chemicals has led to soil contamination. This study utilized surface-enhanced Raman spectroscopy (SERS) to investigate the effects of 10 commonly encountered concentrations of Cu stress in orchards on apple rootstocks. Spectral preprocessing methods were employed to eliminate baseline drift and fluorescence background interference from the Raman spectra, while data augmentation techniques were incorporated to develop a one-dimensional stacked autoencoder convolutional neural network (1D-SAE-CNN) for classifying Cu stress levels, resulting in evaluation indices greater than 0.9. Scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) quantified Cu distribution in root, stem, and leaf tissues, while micro-Raman imaging visualized lignin, cellulose, and pigments under Cu stress. The results indicate that SERS combined with a deep learning model enables rapid and accurate differentiation of Cu stress levels in apple rootstocks in orchards, while SEM-EDS and micro-Raman imaging techniques reveal the migration effect of Cu 2+ within apple rootstock tissues and the ″low concentration promotion, high concentration inhibition″ effect of Cu on apple rootstock growth. Therefore, this approach showcases rapid and accurate detection of heavy metal Cu stress in apple rootstock tissues and has great potential for analyzing various types of heavy metal pollution in agricultural orchard ecosystems.
Plants biosynthesize a wide range of antioxidants capable of attenuating ROS-induced oxidative damage. There exist several in vitro methods to analyze antioxidants and total antioxidant capacity from different tissues and of various plant species. We have established a single, fast and cost-efficient extraction protocol combined with a semihigh throughput 96-well plate assay methods for determination of the level of the key antioxidants phenolics, anthocyanins and flavonoids in combination with the determination of total antioxidant capacity using ferric reducing antioxidant power (FRAP) and trolox equivalent antioxidant capacity (TEAC). The method was optimized and verified with samples from different strawberry species and cultivars with known differences in the parameters measured. This method proved to be suitable for analyses of eight model and crop plants, and distinct antioxidant signatures were determined for the different tissues and organs analyzed, including leaf, root, fruit, spike, and tuber samples. The method was robust and was shown in two case studies to be a resource-efficient and fast experimental platform also to assess biotic and abiotic stress responses, notably including fungal infection and the impact of a progressive drought regime. Since method was adapted for a semi-high throughput 96-well assay format it is well-suited for integration of cell physiological phenotyping into a holistic phenomics approach for germplasm assessment and plant breeding screening. This analytical platform uses microplate spectrophotometer which proved to be suitable to determine the antioxidant contents and total antioxidant capacity signatures of various plant species and tissues with similar findings as reported in literature.
Detecting heavy metals in plants is highly important for diagnosing plant health and understanding the stress mechanisms induced by heavy metals. However, the minimally invasive detection of heavy metals in plants remains a challenge. A novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants. The PBSED included a stainless-steel capillary and heavy metal ion enrichment filter paper (HMIE-FP). The stainless-steel capillary was inserted into the plant stem, where plant sap was transported onto the paper substrate through capillary action. The heavy metal ions (HMIs) in the plants were enriched on the HMIE-FP, and LIBS was used to detect Cd(Ⅱ) and Pb(Ⅱ) on the HMIE-FP to determine the Cd(Ⅱ) and Pb(Ⅱ) concentration within the plant. COMSOL simulations were employed to analyse the flow dynamics of plant sap within the PBSED. To increase the heavy metal enrichment amount, the HMIE-FP was modified with AuAg bimetallic nanoparticles (AuAgBNPs). The PBSED-LIBS method was applied to detect Cd(Ⅱ) and Pb(Ⅱ) in cucumber plants, and the results were strongly correlated with the inductively coupled plasma mass spectrometry (ICP-MS) results (R² = 0.99 for Cd(Ⅱ) and 0.96 for Pb(Ⅱ)). The proposed PBSED-LIBS method demonstrated high sensitivity and minimal invasiveness; thus, it is suitable for rapid, in vivo detection of HMIs in plants. These findings provide valuable insights for the development of efficient, nondestructive tools for environmental applications.
Mandarin ( Citrus reticulata L.) is consumed worldwide. Improper storage temperatures cause flavor loss and shorten shelf lives, reducing marketability. Mandarins' quality is difficult to assess visually, as they show no apparent changes during storage. Therefore, a simple, non-destructive method is needed to assess their freshness as affected by temperature. This work utilized non-invasive bioimpedance spectroscopy (BIS) on mandarins stored at different temperatures. Eight machine learning (ML) models were trained with bioimpedance data to classify storage temperature. Also, we confirmed whether integrating diameter and time-series changes into the bioimpedance could improve the ML models' accuracies by minimizing sample variations. Additionally, we evaluated the effectiveness of equivalent circuit (EC) parameters derived from bioimpedance data for ML training. Although slightly less accurate than using raw bioimpedance data, EC parameters can efficiently reduce data dimensionality. Among all models, the SVM model trained with changes in bioimpedance integrated with diameter data achieved the highest accuracy of 0.92. It was a significant improvement compared to the accuracy of 0.76 achieved when using only the raw bioimpedance data. Thus, this study suggests a novel method of integrating diameter and bioimpedance changes to assess the storage temperature of mandarins. This approach can also be applied to other fruits when utilizing BIS.
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Plant phenotyping relevance matchEurope PMC · checked 14 Sept 2026
Published16 Apr 2025Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 6 · OpenAlex ↗
The industrial implementation of non-destructive techniques for the classification of watermelons, according to their quality standards and stage of maturity, is highly sought by the handling and processing industry. This study aimed to evaluate the feasibility of near-infrared spectroscopy (NIRS) for the individual internal quality assessment of intact watermelons, simulating industrial sorting lines. Two online near infrared (NIR) sensors, a diode array (DA) and a Fourier-transform (FT) spectrometer, each characterised by distinct optical configurations and technical specifications, were utilised. These sensors operated in reflectance mode, analysing the fruits in both static mode (conveyor belt stopped) and dynamic mode on a moving conveyor belt at two different speeds. Regression and classification models were developed for the prediction of soluble solid content (SSC) and the classification of the maturity stage, respectively, by applying various signal pre-treatment methods to the NIR spectra. The best results for SSC prediction were achieved using the DA instrument in dynamic mode, with no significant differences (P > 0.05) between the two conveyor speeds tested. Specifically, a residual predictive deviation for cross-validation (RPD cv ) of 1.41 was achieved with the DA sensor in dynamic mode and a conveyor speed of 10.5 cm s -1 . Furthermore, for the same instrument, mode, and speed, the proportion of fruits accurately classified as 'mature' and 'immature' in the training set was 76 % and 82 %, respectively, with corresponding values of 90 % and 70 % for the validation set. The findings are promising for the horticultural industry, demonstrating the potential for incorporating NIRS technology into industrial sorting lines for the internal quality assessment of individual watermelons.
Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy.
Rapid detection of crop grain components is crucial for effective production and energy conversion. We used the sample set division method to divide multiple sample sets and optimize NIRS models for rapid prediction of protein and fat content. 1243 and 415 crop grain samples were screened and divided into 5 and 4 sets, respectively. The aim was to establish NIRS models for protein and fat content prediction. The best modeling methods for protein were N (Norris Derivative)+D (detrending)-C (CARS)-P (PLS) and N+M (MC-UVE)-C-P, while those for fat were N+M-C-P and N+S (Savitzky-Golay)-C-P. The SS (Soybean Set), KS (Sorghum Set), and FS (Full Samples Set) data sets provided accurate protein content analysis, while the FS and SS data sets were suitable for both protein content prediction and evaluation. For fat, the FS, SS, and CS (Cereal Set) models met content analysis requirements, with the FS model suitable for external validation. It compared and analyzed the fitness, robustness, and accuracy of different NIRS set models, employing various division methods in this study, which provided a new idea of green method theoretical and technical support for major component rapid detection of biomass raw materials.
Cotton stickiness, mostly resulting from honeydew depositions of whiteflies and aphids, presents a worldwide problem for cotton growers and processors consistently. To meet the challenge of measuring the cotton stickiness, a few direct and indirect techniques exist. Previous study showed that Fourier transform near-infrared (FT-NIR) spectroscopy can be used to detect Minicard stickiness in raw cotton from partial least-squares (PLS) analysis. In the present investigation, visible-NIR (vis-NIR) as an analytical technique was explored for potential classification of four-class Minicard cotton stickiness, in combination mainly with the data-driven version of soft independent modeling of class analogy (DD-SIMCA) as one-class classification. Both PLS prediction-based classification and DD-SIMCA models in different spectral regions were developed to optimize the identification efficiency. Compared to an optimal PLS prediction-based classification model indicating a four-class correct classification of 77.8% in the calibration set and 69.2% in the validation set from the 750-1850 nm NIR region, an optimal DD-SIMCA model from the same spectral region could reach an improved discrimination of >95.0%, with a 98.1% correct identification in the calibration set and a 96.2% success in the validation set. This observation emphasized that vis-NIR spectroscopy with an DD-SIMCA approach could be a rapid and nondestructive tool for screening the Minicard stickiness in cottons.
Published1 Apr 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Early sex identification of the dioecious and medicinal spice plant, Litsea cubeba (Lour.) Pers (LC), is necessary for expanding the production and application of LC. We describe a Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) method combined with machine learning techniques. Based on the RS and SERS features, we combined principal component analysis and linear discriminant analysis (PCA-LDA) to dimension reduction for the data. And we combined seven machine learning algorithms, including logistic regression (LR), Naive Bayes (NB), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machines (SVM), and extreme gradient boosting (XGBoost) algorithms to construct a gender prediction model for LC. The results demonstrated that the surface reinforcement treatment with drops of silver sol combined with LR, SVM, and XGBoost models achieved an accuracy of 84.62 % in distinguishing male and female leaves. Furthermore, compared to the discrimination effect of scanning the leaf surface only, the gender recognition accuracy of the surface reinforcement treatment increased by 17.31 %, 19.24 % and 19.24 %, respectively. Portable Raman spectroscopy combined with machine learning algorithms can be promoted for use as a tool for early sex identification in most plants, which can be applied to large-scale plant cultivation and breeding programmes.
Published1 Apr 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Fusarium head blight (FHB) poses a significant threat to global wheat health and seriously affects the quality of the wheat and its products. Therefore, detection of early FHB infection in wheat is crucial for preventing its rapid spread and ensuring food safety. This study proposed an innovative fusion method for detecting the severity of FHB invasion in wheat based on near-infrared spectroscopy and microscopic visual images. This method concatenated 512 features from near-infrared spectra and microscopic visual images of wheat and used neural architecture search (NAS) to build a model for fused features to achieve accurate classification of the degree of infection caused by pathogens in wheat, with accuracy of 90.60 % and F1-score of 90.95 %. This represented significant improvements of 20.80 % and 21.79 % over single spectral data modelling and 11.41 % and 12.67 % over single image data modelling, respectively. The study results showed that this method enables more accurate and non-destructive detection of FHB in wheat, providing a solution for the early identification of potential fungal diseases, which is valuable for improving the quality and yield of wheat.
A real-time, calibration-free, and high-sensitively CO2 sensor based on cavity ringdown spectroscopy (CRDS) has been developed, incorporating a Fabry-Pérot (F-P) cavity with a finesse of 6500 and an STM32-controlled ringdown acquisition scheme. The sensor enables CO2 concentration measurements at the ppb level. Allan variance analysis indicates a minimum detectable sensitivity of 1.452 × 10-9 cm-1 at an integration time of 452 s, corresponding to the minimum detectable gas concentration of 400 ppb, which demonstrates the sensor's long-term stability. In addition, using this sensor, the effects of different soaking durations on rice seed respiration were investigated. The results indicate that the sensor can effectively monitor and differentiate the respiration intensity and rate of seeds under varying soaking times, allowing real-time observation of seed respiration status.
Wheat is an important food crop, valued for its stable yield and substantial nutritional value. However, its growth and development during the tillering stage are significantly inhibited by heat stress (HS) and ultraviolet-B stress (UV-BS). Therefore, early identification of these stresses is essential to ensure the healthy growth of wheat. Hyperspectral technology has been widely used to identify abiotic stress in plants, yet the spectral similarity among different abiotic stresses could confound accurate identification. To address this issue, endogenous methane (CH₄), a critical response indicator of wheat under HS and UV-BS, was also employed as an important marker to differentiate between these stresses. In this paper, the early identification model of HS and UV-BS in wheat was effectively established through the combination of hyperspectral technology and the tunable diode laser absorption spectroscopy (TDLAS)-based gas detection method. The model was established using trend-based feature extraction methods and an extreme learning machine (ELM) algorithm optimized with improved particle swarm optimization (iPSO). The time-segmented HS and UV-BS identification models established solely based on spectral data exhibit an accuracy range of 80.36 % to 91.07 %. In contrast, models established by integrating spectral data with CH₄ concentration data have significantly improved their accuracy, achieving an accuracy range of 95.54 % to 98.88 %. Therefore, a multi-temporal integrated model for early identification of HS and UV-BS in wheat was established based on the integrated data, achieving an accuracy of 93.4 %. The results demonstrate that the combination of hyperspectral technology and gas detection method is effective for the early identification of HS and UV-BS in wheat.
Resin biosynthesis in conifer is a complex process, controlled by multiple quantitative trait loci (QTLs). Quantifying resin components is traditionally expensive and labor-intensive. In this study, we employed near infrared (NIR) spectroscopy to quantify resin components in Slash pine using 240 genotypes. A partial least squares regression model was applied to identify the characteristic bands responsed to variations in Alpha and Beta pinene levels. Genome-wide association study (GWAS) identified 35 significant SNPs involved in terpenoid precursor biosynthesis, transport, modification, and abiotic stress resistance. eQTL mapping co-localized four candidate genes: PeCHITINASE (c166891.graph_c0), PeGLYCOSYLTRANSFERASE (c160167.graph_c0), PeASIL2 (c324347.graph_c0), and PeERF168 (c311225.graph_c0). Mutations in two SNPs increased the expression of PeASIL2 and PeERF168, leading to higher levels of Alpha and Beta pinene. Further heterologous transformation experiments confirmed that the PeERF168 gene regulates the concentration of both monoterpenes and sesquiterpenes. These findings provide valuable insights into the molecular mechanisms of resin biosynthesis, facilitating cost-effective gene discovery through high-throughput resin component detection and genomics integration, with substantial potential to enhance molecular breeding and improve resin yield and quality.
Little cherry disease (LCD) and X-disease have critically impacted the Pacific Northwest sweet cherry ( Prunus avium ) industry. Current detection methods rely on laborious visual scouting or molecular analyses. This study evaluates the suitability of field asymmetric ion mobility spectrometry (FAIMS) for rapid detection of LCD and X-disease infection in three sweet cherry cultivars ('Benton', 'Cristalina', and 'Tieton') at the post-harvest stage. Stem cuttings with leaves were collected from commercial orchards and greenhouse trees. FAIMS operated at 1.5 L/min and 50 kPa, was used for headspace analysis. Molecular analyses confirmed symptomatic and asymptomatic samples. FAIMS data were processed for ion current sum (I sum ), maximum ion current (I max ), and area under the curve (I AUC ). Symptomatic samples showed higher ion currents in specific FAIMS regions ( p < 0.05), with clear differences between symptomatic and asymptomatic samples across compensation voltage and dispersion field ranges. Cultivar-specific variation was also observed in the data. FAIMS spectra for LCD/X-disease symptomatic samples differed from those for asymptomatic samples in other Prunus species, such as peach and nectarines. These findings support FAIMS as a potential diagnostic tool for LCD/X disease. Further studies with controlled variables and key growth stages are recommended to realize early-stage detection.
The accurate evaluation of crop quality is vital for sustainable agriculture and optimized production. Raman spectroscopy, renowned for its insensitivity to water interference and its ability to deliver molecular-specific information, presents significant potential as a remote sensing technology. This study explores the feasibility of adapting advanced Raman spectroscopy as a remote crop quality sensor for the precise assessment of carotenoids. Carotenoids were chosen due to their dual role as key stress indicators in crops and their well-established antioxidant benefits for human health.To explore carotenoid variability, Arabidopsis thaliana and Spinacia oleracea were analyzed. Raman spectroscopy measurements were performed on two leaves per plant using a 785 nm laser. For the carotenoid quantification, Linear Discriminant Analysis (LDA) was adapted. The spectra were processed through smoothing, background removal, and normalization, followed by modification with an amplifying factor. This study evaluated the impact of these processing methods, particularly the application of the amplifying factor, on the accuracy of the model. High-Performance Liquid Chromatography (HPLC) was employed as the reference method for validation. Three-quarters of the samples were used to construct the model, while the remaining one-quarter was reserved for validation. As a result, the model utilizing spectra modified with the amplifying factor in most cases achieved higher validation accuracy compared to models based on unmodified spectra.This study introduces a novel Raman spectroscopy-based remote sensing approach for crop quality assessment, establishing an enhanced model for interpreting spectral data. By enabling precise detection of stress-induced changes in plant chemical composition, including carotenoids, this technique paves the way for scalable, real-time monitoring through Raman-equipped machinery or drones, advancing sustainable agriculture practices.
Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500–640 nm and 740–1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R2) of 0.70 and a low mean bias error (MBE) of 0.04 mg g−1. The Cubist model performed even better, demonstrating an R2 of 0.77 and an exceptionally low MBE of 0.01 mg g−1. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.
Online detection of internal quality of strawberries presents challenges particularly concerning fruit damage, detection accuracy, and processing efficiency. This study explores the feasibility of using Vis/NIRS for online detection of SSC in strawberries during hanging transportation. After analyzing SSC distribution in strawberries, an optical sensing system was developed, and optimal configurations were identified using PLSR models. When employing a horizontal optical beam through the strawberry center, the PLSR model combined with SNV preprocessing and CARS feature selection achieved the best conventional chemometric results (RPD of 4.793). Additionally, three 1D-CNN approaches were investigated, with the 1D-CNN-LSTM method exhibiting superior performance (R p 2 of 0.963, RMSEP of 0.209°Brix, RPD of 5.332). These findings demonstrate the excellent capability of our developed system, enhanced by deep learning methods, for online detection of SSC in strawberries. This work may open new avenues for the online assessment of internal quality in small and delicate fruits.
As the material basis of phytoremediation for heavy metal-contaminated soils, hyperaccumulator plants are capable of metal hyperaccumulation. Rapid and accurate detection and visualization of heavy metals in hyperaccumulators are essential for investigating metal translocation and monitoring phytoremediation. However, existing analytical and mapping techniques are time-consuming and inefficient in quantification, hindering remediation tracking and timely decision-making, while their high cost and limited accessibility reduce economic feasibility for agricultural and environmental applications. Laser-induced breakdown spectroscopy (LIBS) is a promising alternative for elemental mapping due to its complementary analytical performance and cost-effective instrumentation. Here, LIBS assisted with artificial intelligence was used to establish a quantitative elemental mapping method for Cd and Zn in Sedum alfredii, an important Cd/Zn co-hyperaccumulator. A dataset of 288 samples, comprising shoots and roots of hydroponically and soil-cultivated S. alfredii, was used to develop and test quantitative models. Traditional machine learning encountered overfitting and poor performance in low-concentration samples. To overcome the challenge, a convolutional neural network (CNN) with feature fusion was applied to enhance predictability, reduce matrix effect interference, and enable integrated detection across shoots and roots (Cd: coefficient of determination (R²ₜₑₛₜ) = 0.9887, residual prediction deviation (RPD) = 9.46; Zn: R²ₜₑₛₜ = 0.9887, RPD = 15.21). SHapley Additive exPlanations (SHAP) offered model interpretability by quantifying feature importance, promoting future development of portable LIBS devices. Based on optimal models, quantitative mapping across the leaf-stem-root system was generated, revealing Cd and Zn uptake through root tips and lateral roots, with translocation from roots to shoots and older to younger shoots. The LIBS-based quantitative elemental mapping provides an effective tool for monitoring heavy metals in hyperaccumulators, guiding phytoremediation, and securing agricultural production.
Clavibacter michiganensis subsp. michiganensis (Cmm) causes bacterial canker in tomatoes, causing severe yield loss. It would be of practical research interest within smart agriculture to develop an effective and quick method to distinguish pre-symptomatic infected tomato plants from healthy ones to take protective measures in time. In this study, artificially inoculated tomato plants with Cmm were grown in a temperature-controlled chamber. Using the Relief method, 25 wavelengths in the visible spectrum (cyan and red regions) showed the highest statistical differences, between healthy and asymptomatic infected tomato plants, two days before the first appearance of the foliar symptoms, in each plant. In addition, inoculated tomato plantlets showed differences in contrast to healthy ones, in the near-infrared spectrum, thirteen days after the inoculation with Cmm. The spectral data were used for the creation of early detection models of healthy and inoculated pre-symptomatic plants, in a specific number of days before the appearance of the first symptoms, in each plant, and in a specific number of days-post inoculation, using two ML algorithms (SVMs and kNN). The algorithms proved effective and robust in the discrimination of the two classes of the two instances mentioned. Furthermore, three patterns of data-preprocessing followed before the training of the algorithms, i.e. the case of multidimensionality, the application of PCA, and the use of Relief method. Finally, six models were created for datasets that contain spectral data of asymptomatic inoculated with Cmm and healthy tomato transplants, all of which showed very high overall accuracy, ranging from 92 to 100%.
Published1 Mar 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
The study explores the use of NIR spectroscopy with chemometric techniques as a non-destructive method to determine carotenoids in fresh ‘IAC Rurik,’ a new yellow potato rich in these compounds. Tubers were harvested in 2022 and in 2023. Reflectance FT-NIR spectra were acquired on the periderm of 200 tubers, in two positions. Spectra were pre-processed and regression models were developed using partial least square (PLS), support vector (SVR), ridge regression, k-nearest neighbors (KNN), interval-partial least squares (iPLS), and kernel-partial least squares (kPLS) regressions. Multivariate classification was carried out by applying principal component analysis with linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). Carotenoid content prediction was better obtained using mean-centered spectra and ridge regression (RMSEP = 0.0028 g kg⁻¹, R²P = 0.90, RPD = 2.57, RER = 4.21 %). The classification of three groups (low = 0.031–0.045 g kg⁻¹; average = 0.045–0.065 g kg⁻¹; and high = 0.065–0.078 g kg⁻¹) was possible by applying PLS-DA with a correct classification of 93 %, 70 %, and 86 %, respectively for low, average, and high carotenoid content. Thus, NIR spectroscopy can be used as a non-destructive method to predict carotenoids and classify ‘IAC Rurik’ tubers based on their carotenoid content.
Rationale Understanding plant defense mechanisms against pathogens is essential for enhancing agricultural productivity and crop protection. This study focuses on the quantification of camalexin and scopoletin, two critical phytoalexins in Arabidopsis thaliana, using mass spectrometry techniques. Precise measurement of these compounds provides insights into plant resistance and supports agricultural research. Methods Camalexin and scopoletin were quantified using matrix-assisted laser desorption ionization high-resolution mass spectrometry (MALDI-HRMS). The matrix and solvent conditions were optimized to maximize sensitivity and accuracy. MS/MS experiments confirmed compound identification with high mass accuracy (mass error Results The method exhibited high linearity for scopoletin (R 2 = 0.9992) and camalexin (R 2 = 0.9987) across concentration ranges of 0.16-5 and 0.31-5 μM, respectively. Limits of detection (LOD) were 0.16 μM for camalexin and 0.04 μM for scopoletin, with limits of quantification (LOQ) at 0.2 μM and 0.08 μM, respectively. Samples analysis demonstrated reliable quantification in WT and mutant lines, with significant reductions in camalexin and scopoletin levels observed in the atwrky33-2 and atmyb15-1 mutants, respectively. Additionally, the method detected sub-physiological concentrations, confirming its sensitivity and robustness for low-level detection. Conclusions This study presents a validated, precise, and accurate MALDI-HRMS method for the quantification of camalexin and scopoletin in Arabidopsis thaliana. The approach not only enhances understanding of plant defense mechanisms but also offers potential applications for biotechnological and agricultural research, especially for investigating genetic variations and stress-induced phytoalexin production.
Kuhn P, Umazekabiri R, Römermann C, Bruelheide H, Wesche K.
Field / plotRaman / spectroscopyLeafPhysiological trait estimationGrowth / time-series analysis
Arable fields and mesic meadows have been affected by intensifying agricultural management and nutrient input during the 20th century, but direct evidence for the long‐term impact of intensification on plant nutrient contents remains scarce. Non‐destructive novel spectroscopic methods can produce such data from herbarium specimens, making it possible to investigate how contents of leaf nutrient traits, especially nitrogen and phosphorus, changed over the last century, and what role habitat type and management practices play. We carried out a resurvey study of functional traits in arable field and mesic meadow communities. We used specimens from two German herbaria with a high coverage of their local floras: the herbaria Senckenberg Görlitz and Senckenberg Haussknecht in Jena. Following specimen information, the same plant species were resampled in the field in 2022 at the same locations. We employed near‐infrared spectroscopy to predict leaf nitrogen, phosphorus and carbon content of herbarium and field specimens. Nutrient content changes over time were compared with public records of regional P and N fertilization. Overall, 1270 specimens of 76 species from both herbarium and field were studied, the oldest from the 19th century. Leaf nitrogen and the leaf nitrogen:phosphorus ratio increased significantly through time, while leaf phosphorus and carbon content decreased significantly over time. Arable field species showed a stronger response in leaf phosphorus content and leaf nitrogen:phosphorus ratio than mesic meadow species. The total amount of nitrogen or phosphorus fertilizer applied per year on a regional scale was found to be significantly correlated with the respective leaf nutrient content levels. Synthesis: Our study shows a long‐term increase of leaf nitrogen in the studied habitats, paralleling increased chemical fertilizer applications in Germany. Our data indicate a shift from predominantly N‐limited towards more P‐limited growth conditions. The stronger response of species from arable fields compared to species from mesic meadows could indicate a faster adjustment to environmental pressures. This study thus also serves to showcase the potential of the combination of herbarium collections and NIR spectroscopy.
Key message Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.
Reproduction assets foundThe paper explicitly states that all code and data used in the analyses are publicly available in the authors' GitHub repository (Resende-Lab/Graciano_skNIR_Phenomic_Seleciton), and additionally points to a second public repository (Resende-Lab/PLS_skNIR_Audrey) containing the kernel composition trait dataset derived/详Code · publicAll the codes and the data used in the analyses are available at https://github.com/Resende-Lab/Graciano_skNIR_Phenomic_Seleciton .Open asset ↗Resende-Lab/Graciano_skNIR_Phenomic_Selecitonlines:120-132Dataset · publicFor further information, the dataset is available at: https://github.com/Resende-Lab/PLS_skNIR_Audrey .Open asset ↗Resende-Lab/PLS_skNIR_Audreylines:78-85Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Perennial ryegrass is an important temperate grass used for forage and turf worldwide. It forms symbiotic relationships with endophytic fungi (endophytes), conferring pasture persistence and resistance to herbivory. Endophyte performance can be influenced by the host genotype, as well as environmental factors such as seed storage conditions. It is therefore critical to confirm seed quality and purity before a seed is sown. DNA-based methods are often used for quality control purposes. Recently, near-infrared spectroscopy (NIRS) coupled with hyperspectral imaging was used to discriminate perennial ryegrass cultivars and endophyte presence in individual seeds. Here, a NIRS-based analysis of bulk seeds was used to develop models for discriminating perennial ryegrass cultivars (Alto, Maxsyn, Trojan and Bronsyn), each hosting a suite of eight to eleven different endophyte strains. Sub-sampling, six per bag of seed, was employed to minimize misclassification error. Using a nested PLS-DA approach, cultivars were classified with an overall accuracy of 94.1-98.6% of sub-samples, whilst endophyte presence or absence was discriminated with overall accuracies between 77.8% and 96.3% of sub-samples. Hierarchical classification models were developed to discriminate bulked seed samples quickly and easily with minimal misclassifications of cultivars (<8.9% of sub-samples) or endophyte status within each cultivar (<11.3% of sub-samples). In all cases, greater than four of the six sub-samples were correctly classified, indicating that innate variation within a bag of seeds can be overcome using this strategy. These models could benefit turf- and pasture-based industries by providing a tool that is easy, cost effective, and can quickly discriminate seed bulks based on cultivar and endophyte content.
ATR-Fourier transform infrared spectroscopy was used to determine the carbon and nitrogen content in the leaves of herbaceous forest plant species and functional traits associated with the leaf economic spectrum (one of the two-dimensional global spectrum of plant form and function), and monitoring plant physiological status under elevated temperature conditions. The content of carbon and nitrogen determined by traditional methods validated the accuracy of ATR-FTIR method. It was also shown that in the case of forest herbs, the ATR-FTIR method is an efficient tool for determining functional traits (such as specific leaf area (SLA) and leaf dry matter content (LDMC)) related to the leaf economics spectrum, and to diagnose the photophysiological state of plants after changes of temperature (changes of day/night temperature from 21/13 °C to 25/17 °C). Measuring the areas of three absorption bands of the ATR-FTIR spectra related to amides I and II (between 1700 cm -1 and 1500 cm -1 ), carbohydrates (cellulose and hemicellulose; between 1200 cm -1 - 850 cm -1 ) and amide III (between 1290 cm -1 and 1190 cm- 1 ) allowed for determination of all analysed chemical and functional properties of leaves. Based on selected absorption bands accurately estimated C and N content, with coefficients of correlation (r) of 0.88 for C and 0.84 for N. SLA and LDMC were also predicted, with r values of 0.88 and -0.91, respectively. Moreover, ATR-FTIR proved to be a rapid, non-destructive tool for monitoring the plant physiological status, as demonstrated by the significant correlation (r = 0.99) between the chlorophyll fluorescence performance index (PI) and ATR-FTIR data. ATR-FTIR has been demonstrated as an efficient tool for simultaneous quantification of leaf carbon and nitrogen content, economic functional traits, and physiological status of forest herb plants.
Reflectance spectroscopy is a non-destructive, rapid, and robust method for estimating functional traits and distinguishing species. Spectral reflectance libraries generated from herbarium specimens are an untapped and promising resource for generating broad phenomic datasets across space, time, and species. We conducted a proof-of-concept study using functional trait data and spectra from recently dried, pressed leaves, alongside data from herbarium specimens up to 179 years old. We assessed the utility and transferability of these datasets for functional trait prediction and taxonomic discrimination. Herbarium spectra discriminated species with 74% accuracy and predicted leaf mass per area (LMA) with R2=0.92 and %RMSE=5.8%. Models for LMA prediction were transferable between herbarium and pressed spectra, achieving R2=0.88, %RMSE=8.76% for herbarium to pressed spectra, and R2=0.76, %RMSE=10.5% for the reverse transfer. The results demonstrate the feasibility of using herbarium spectral data for functional trait prediction and taxonomic discrimination. This success provides methodological guidance for advancing the global Metaherbarium and integrating spectral reflectance into next-generation digitization efforts for plant biodiversity collections.
Jacquin L, Guerra W, Lewandowski M, Patocchi A, Rymenants M, Durel C, Laurens F, Aranzana MJ, Muranty H.
AppleMaizeRiceRaman / spectroscopy
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Nitrogen and phosphorus are essential nutrients for the growth and development of tea plants.However, the nitrogen content (NC) and phosphorus content (PC) in different parts of fresh tea has not been paid attention. In this study, the NC and PC responses different nitrogen stress were analyzed, and a quantitative regression model for predicting NC and PC was established by using Vis/NIR spectroscopy and a variety of intelligent algorithms. Among them, NC and PC of different parts had significant difference. The selection of preprocessing algorithms has a significant impact on the predictive performance of the model. The VMDSG-D1-VCPA-IRIV-SVR prediction model for NC and the VMDSG-CARS-Stacking prediction model for PC have better prediction effects, and the correlation coefficients of the test set are more than 0.85, and the RPD is greater than 1.8. In conclusion, this study is helpful to guide the precise fertilization and in-situ detection of fresh tea leaves.
Current methods for measuring wheat quality and dough rheology in the later stages of wheat breeding programs, including extensographs and farinographs, are costly and time-consuming. There is a significant interest in the Australian wheat industry for developing non-destructive, field-based, rapid dough-making quality assessment methods for Australian wheat varieties throughout earlier and later stages of the wheat breeding process. Fourier transform infrared (FTIR) spectroscopy is a valuable tool for analysis and quality control in the food industry as it is a simple and rapid technique requiring no sample pre-treatment before analysis. We aimed to investigate the application of FTIR spectroscopy coupled with partial least squares (PLSR) regression data analysis to rapidly assess wheat flour's dough-making quality. Results indicated that using FTIR data, PLSR could be applied to accurately predict multiple dough-making qualities, including protein content, extensibility, water absorption, dough development time (DDT), dough stability, and maximum resistance to tension (R max ). FTIR spectroscopy could not only be used to accurately predict the dough making quality of wheat lines from an in-sample test dataset, but this method also outperformed genetic predictive analysis, an established quality-prediction method in wheat breeding, in predicting dough making quality using out-of-sample data.
ABSTRACT Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R² > 0.58), while the poro-fluorometer was efficient for predicting net CO₂ assimilation (R² > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R² > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.
Bienvenu C, Garin V, Salas N, Théra K, Tekete ML, Sarathjith MC, Diallo C, Berger A, Calatayud C, De Bellis F, Rami J, Vaksmann M, Segura V, Pot D, de Verdal H.
Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs’ efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key message Phenomic prediction is promising for sorghum breeding. Geneticists’ methods may not be suited to optimally extract spectral information.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicWe determined a unique genetic consensus map by projecting the physical distance of the 51,545
markers on a high-quality genetic consensus map (Guindo et al., 2019) using the R package ziplinR
(https://github.com/jframi/ziplinR).Open asset ↗jframi/ziplinRpdf-page:6 lines:1-43Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Zhu J, Zhu X, Yan B, Ren F, Chen B, Han Z, Yao X, He S, Liu H.
PeaRaman / spectroscopySeed / grainClassification
The swift detection of allergenic protein and other nutritional indicators in pea protein is crucial for food and breeding efforts, facilitating the targeted selection of specific pea varieties and the advancement and processing of healthful foods. Using near-infrared (NIR) spectroscopy, spectral data for different pea varieties in the range of 908-1676 nm were collected, which were subsequently integrated with chemical values obtained by conventional methods. Multivariate statistical analysis was employed to optimize, develop, and validate the model for the spectral data. The correlation coefficients of the calibration set based on partial least squares regression (PLSR) models ranged from 0.74 to 0.99, while those of the validation set ranged from 0.20 to 0.99. This study offers a precise and straightforward approach for evaluating the levels of several nutritional indicators, including allergenic proteins in peas, and for classifying different types.
The cuticle is a polymeric membrane covering all plant aerial organs of primary origin. It regulates water loss and defends against environmental stressors and pathogens. Despite its significance, understanding of the micro-mechanical properties of the cuticle (cuticular membrane; CM) remains limited. In this study, non-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit. The BLS signal arises from the photon interaction with thermally induced pressure waves and allows for imaging with mechanical contrast. The derived loss tangent showed significant differences with wax extraction from the CM and further with carbohydrate extraction from the DCM, consistent with tensile test results. Spatial heterogeneity between anticlinal and periclinal regions was observed by BLS microscopy of CM and DCM, but not in CU. The key conclusions are: (1) BLS is sensitive to micro-mechanical variations, particularly the strain-stiffening effect of the cutin framework, offering insights into the CM's micro-mechanical behavior and underlying chemical structures; (2) CM and DCM exhibit spatial micro-mechanical heterogeneity between periclinal and anticlinal regions.
Reproduction assets foundThe paper deposits its underlying Brillouin light scattering measurement data (primary and supplementary figures) in a public LUIS repository (DOI 10.25835/xvsi5g6m). The Brillouin analysis python script is only available on request, so it is not a public code asset.Dataset · publicThe underlying data for all the primary and Supplementary Figs. has been deposited in a publicly accessible repository [ https://doi.org/10.25835/xvsi5g6m ] 67 . Raw data may be obtained from the authors upon reasonable request.Open asset ↗10.25835/xvsi5g6mlines:177-254Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R 2 (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.
Accurate and repeated whole-body fat (WBF) measurements across production phases are important for optimizing feed utilization, reducing production waste, safeguarding fish health, ensuring product quality and improving overall salmon production. However, the chemical extraction (reference) methods for WBF recording, although precise and accurate, are costly, destructive and have limited applicability for repeated measurements on live fish. This study validates two digital phenotyping technologies: Dielectric spectroscopy (DS) and Near-infrared spectroscopy (NIR) against the reference Soxhlet chemical extraction method. DS is a fast and non-destructive commercial fat meter, whereas the specially designed NIR interactance allows deep penetration (up to 10 mm) through the fish skin into the body. Approximately 2800 fish belonging to 35 full sibs fish families were recorded for WBF at mean body weight (BW) of ∼110, 300 and 750 g in parr, pre-smolt and post-smolt phases, respectively. The WBF percentage changed across the studied production phases: with parr having a mean of 11.3 % and coefficient of variation (CV = 11 %), pre-smolt decreasing slightly in both mean 10.9 – 11.1 % and CV (5.5 – 8.8 %) and post-smolt with an increase to 14.9 % and CV (8.5 %). Both methods performed well relative to the reference, with NIR (R² = 0.77 – 0.91) slightly outperforming the DS (R² = 0.61 – 0.71). Significantly high genetic estimates for NIR (h² = 0.57 ± 0.04 – 0.62 ± 0.06) and DS (h² = 0.38 ± 0.07 – 0.56 ± 0.10) signify the potential use of these digital technologies for improving WBF in future selective breeding. The low genetic correlations (rg = 0.22 ± 0.13 – 0.33 ± 0.14) across fresh and seawater phases raise questions about the generalizability of metabolic, lipid and feed efficiency research conducted in parr and pre-smolt phases in freshwater, to the post-smolt stage in seawater. The study highlighted the unexplored detail of the genetic regulation of WBF as fish undergoes production phases. Overall, these results will add to our understanding of genetic architecture for WBF and pave the way for digital phenotyping technologies to record complex but economically important traits and refine breeding strategies.
This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.
Moisés R. Vallejo Pérez · Juan J. Cetina Denis · Mariana A. Chan Ley · Jesús A. Sosa Herrera · Juan C. Delgado Ortiz · Ángel G. Rodríguez Vázquez · Hugo R. Navarro Contreras
Jia Dong · Seth W Croslow · Stephan T Lane · Daniel C Castro · Jantana Blanford · Shuaizhen Zhou · Kiyoul Park · Steven Burgess · Mike Root · Edgar B Cahoon · John Shanklin · Jonathan V Sweedler · Huimin Zhao · Matthew E Hudson
Abstract Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.
Leng T, Wang Y, Wang Z, Hu X, Yuan T, Yu Q, Xie J, Chen Y.
Raman / spectroscopySeed / grainClassification
Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared [MIR] spectroscopy) and analytical models (Discriminant Analysis [DA], Partial Least Squares [PLS], and Artificial Neural Networks [ANN]), seeking to classify Camellia seed varieties and estimate oil and principal FAs composition. The PCA analysis effectively discriminated among various Camellia seed varieties, likely due to variations in their oil and principal FAs compositions. Significantly, the NIR-based DA model significantly outperformed MIR, achieving 100 % accuracy in distinguishing Camellia seed varieties. In terms of predicting the oil and principal FAs compositions in Camellia seeds, NIR-based predictions models outperformed those derived from MIR, with PLS models surpassing ANN models. This study validated the potential of NIR technology combined with chemometrics for rapid, high-throughput, non-destructive identification of Camellia seeds.
The seed coat plays a pivotal role in seed development and germination, acting as a protective barrier and mediating interac-tions with the external environment. Traditional histochemical techniques and analytical methods have provided valuable insights into seed coat composition and function. However, these methods often suffer from limitations such as indirect chemical signatures and lack of spatial resolution. Here, we introduce stimulated Raman scattering (SRS) microscopy as a novel analytical tool for non-destructive, label-free, high-resolution mapping of biopolymers, water and applied active ingre-dients (AIs) in intact seed coats. We demonstrate the capability of SRS microscopy to perform depth-resolved, chemically selective imaging of major seed coat biopolymers (pectin, tannin, and suberin). By comparing wild type arabidopsis thali-ana seeds with genetically modified mutants deficient in suberin and tannin, we illustrate the potential for semi-quantitative analysis of biopolymer content. Furthermore, we show that SRS microscopy can track the permeability of seed coats to wa-ter using deuterated water (D 2 O) uptake studies. Real-time imaging reveals differences in water permeation between wild type and suberin deficient seeds, highlighting the importance of seed coat composition in regulating water uptake during germina-tion. Additionally, we extend the application of SRS microscopy to large seeds, such as brassica oleracea, utilizing epi -detected imaging for surface studies. Finally, using a deuterated insecticide (clothianidin-d3), we demonstrate the capability of SRS microscopy to visualize the incorporation of AIs into seed coats. Our study presents SRS microscopy as a powerful tool for characterizing seed coat composition and understanding the diffusion of low molecular weight compounds into seeds. This technique offers new opportunities for designing seeds with tailored properties for improved germination and resilience to environmental stressors.
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicR codes, including functions and examples, are available at Zenodo: https://doi.org/10.5281/zenodo.13983114Open asset ↗Zenodo · 10.5281/zenodo.13983114lines:153-226Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Jung M, Hodel M, Knauf A, Kupper D, Neuditschko M, Bühlmann-Schütz S, Studer B, Patocchi A, Broggini GA.
AppleRaman / spectroscopy
Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Results Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical. Conclusions Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.
Reproduction assets foundThe paper's own phenotypic, genomic, and near-infrared spectroscopy (NIRS) data acquired in this study are publicly deposited in the ETH Research Collection, directly reproducing the paper's phenotyping measurements and phenomic/genomic prediction analysis inputs. The NCBI SRA deposit contains only raw RADseq reads (m-Dataset · publicThe phenotypic, genomic, and near-infrared spectroscopy data acquired in this study are available in the ETH Research Collection at https://doi.org/10.3929/ethz-b-000699803 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000699803lines:180-211Plant phenotyping relevance matchEurope PMC · checked 6 Sept 2026
Javaid T, Venkataraghavan A, Bhattarai M, Debnath D, Zhao W, Wang T, Faik A.
Laboratory / benchtopRaman / spectroscopyTissue
Background Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The 13 C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with 13 C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of 13 C-labeling of plant materials using 13 CO 2, and most of them require large amounts of 13 CO 2 in larger growth chambers. Results In this study, a simplified protocol for 13 C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of 13 C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both 13 C-labeled glucose and 13 CO 2 using a vacuum-desiccator. The protocol does not require large amounts of 13 CO 2 . Conclusion This study shows that our 13 C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.
Mundi HK, Sharma S, Kaur H, Devi J, Atri C, Gupta M.
Field / plotRaman / spectroscopySeed / grainPhysiological trait estimation
Tocopherol content and composition ( α- , γ- ) in Brassica juncea seeds are normally determined using wet chemistry methods, which are time-consuming, labor-intensive, and hazardous to human health. We attempted the development and validation of the first near-infrared reflectance spectroscopy (NIRS) model as a quick alternative. A total of 356 B. juncea seed samples were collected from a germplasm diversity set of 178 B. juncea genotypes. These were evaluated over the course of two crop seasons (2019-20 and 2020-21) and scanned by NIRS. Modified Partial least square (MPLS) method was used to regress their reference values against spectral transformations. The development of a reliable NIRS calibration equation was made possible by the availability of a wide range of variation for α -tocopherol (11.18-84.6 mg/kg) and γ -tocopherol (57.27-255.5 mg/kg) in the seeds of diversity panel. A model with the highest coefficient of determination (RSQ) was identified for strong association between NIRS-predicted values and ultra-performance liquid chromatography (UPLC)-based reference values. The newly developed model exhibited RSQ of 0.786, 0.896, 0.906 for α- , γ- , and total tocopherols, respectively. This model was further validated using external set of samples and the results confirmed the robustness of the equation with high RSQ values. Supplementary information The online version contains supplementary material available at 10.1007/s13197-025-06204-3.
Seedlessness in table grapes is a desirable trait for consumers. Plant growth regulators (PGRs) have been extensively utilized to induce seedlessness. However, the efficacy of these PGRs is not uniformly successful. In addition, the seedlessness is difficult to detect by cutting and counting technique. The shortwave-near infrared spectroscopy (SW-NIRS), coupled with suitable chemometric analysis, is a non-destructive method for sorting and prediction of seedlessness grapes. The NIRS is higher efficiency than original technique in term of accuracy, measuring time and waste reduction.•The SW-NIR spectra of 240 grape berries were recorded. Each reflectance spectrum was acquired in the wavenumber of 3996-12,489 cm -1 . After that all grape berries were cut and count for seedlessness sorting.All spectral together with seedlessness sorting were be analysis by chemometrics.•The NIR spectral data were analyzed using principal component analysis (PCA). In addition, supervised self-organizing map (SSOM) and quadratic discriminant analysis (QDA) were applied to classify the seedlessness.•The PCA results represented a negative tendency to classify the seedlessness. Clear classification tendency can be obtained from SOMs. Good predictive results from SSOM were obtained, as it gave a percentage correctly classified of 97.14 and 94.64% for training and test sample sets, respectively.
Key message Phenomic selection based on parental spectra can be used to predict GCA and SCA in a sparse factorial design. Prediction approaches such as genomic selection can be game changers in hybrid breeding. They allow predicting the genetic values of hybrids without the need for their physical production. This leads to significant reductions in breeding cycle length, and so to the increase in genetic progress. However, these methods are often underutilized in breeding programs due to the substantial cost involved in genotyping thousands of candidate parental lines annually. To address this limitation, we propose a cost-effective alternative based on phenomic selection, where genotyping of parental lines is replaced by NIR spectroscopy. Standard prediction models are then applied for genomic and phenomic selection, using similarity matrices derived from either genotyping data (genomic selection) or NIR spectral data (phenomic selection). Our hypothesis is that the chemical composition of parental tissues captured by NIRS reflects the genetic similarity between parental lines. We evaluated both strategies using a sparse factorial design, whose hybrids have been phenotyped in a multi-environment trial network, and with NIR spectra acquired on the parental lines on two independent environments. Both genomic and phenomic prediction approaches demonstrated moderate-to-high predictive abilities across various cross-validation scenarios. Our results also showcase the capability of phenomic selection to predict Mendelian sampling. This study serves as a proof of concept that low-cost high-throughput phenomics of parental lines can effectively be used to predict maize hybrids in independent trials. This paves the way for widespread adoption of prediction approaches at the very first stages of hybrid breeding, benefiting both major and orphan species.
The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered. Spectral data emphasized genetic similarities and differences among the cultivars, capturing changes in the pigment content and macro components of the apricot samples. In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. In this study, prediction models were developed based on a multilayer perceptron artificial neural network (ANN-MLP) combined with the Levenberg-Marquardt learning algorithm. Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R 2 = 0.855) and DM (R 2 = 0.857), while the performance for TA was unsatisfactory (R 2 = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R 2 = 0.904; DM: R 2 = 0.918, TA: R 2 = 0.811), as confirmed by external validation. Moreover, the ANN allowed us to identify the most predictive input spectral regions for each model. The results showed the potential of Vis/NIR spectroscopy as an alternative to traditional destructive methods to monitor the qualitative traits of apricot fruits, reducing the time and costs of analyses.
Plant diseases constantly threaten crops and food systems, while global connectivity further increases the risks of spreading existing and exotic pathogens. Here, we first explore how an integrative approach involving plant pathway knowledgegraphs, differential gene expression data, and biochemical data informing Raman spectroscopy could be used to detect plant pathways responding to pathogen attacks. The Plant Reactome (https://plantreactome.gramene.org) demonstrates the potential to synthesize knowledgegraphs depicting plant-pathogen interactions, leveraging availability of publicly available OMIC data sets related to major diseases of rice and maize. Plant pathway signatures may then guide the development of drone and satellite remote-sensing methods for early monitoring of disease outbreaks across farms and landscapes. A review of current proximal- and remote-sensing technology demonstrates the potential for actionable early pathogen detection. We furthermore identify knowledge gaps that need to be addressed for developing these tools as components of effective strategies for safeguarding global food security against current and emerging pathogens.
The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed. The accuracy of SSC and TA prediction models with multi-region combination (raw) spectra was better compared to navel-region and equator-region, suggesting that multi-region combination spectra collection method was more suitable. Subsequently, the spectral pre-processing, including Savitzky-Golay smoothing (SGS), maximum normalization (MN), multiplicative scatter correction (MSC), linear baseline correction (LBC) and first derivative (1stD), were performed. The performance of SSC and TA prediction models with different pre-processing spectra was further compared. The PLS with SGS spectra (SGS-PLS) and MFNN with raw spectra (Raw-MFNN) exhibited superior validation effects for SSC and TA prediction, respectively. In a subsequent prediction in new samples, SGS-PLS achieved an R P 2 of 0.875, an RMSEP of 0.572% and a MAEP of 0.469% for SSC prediction, and Raw-MFNN achieved an R P 2 of 0.800, an RMSEP of 0.0322% and a MAEP of 0.0249% for TA prediction, indicating excellent generalization ability. These results indicate the great potential of benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality at mass-scale level.
The growing interest in the rapid measurement of seed ingredients using single‐kernel NIR (SKNIR) spectroscopy as a nondestructive measurement technique allows fast analysis of sample seed variance that can have effects on breeding and end‐use processing. Flax (Linum usitatissimum), an oilseed crop grown in the Northwest United States and worldwide, is highly beneficial for human health, food, and fiber. Its health benefits include its high protein and omega‐3 fatty acids content. Therefore, seed composition profiles are an important aspect of breeding. The goals of this research were the development of single seed NIR calibration models for protein, oil, and weight of intact flax seeds. In this study, SKNIR spectroscopy was used on a diverse set of flax accessions comprising of 306 samples to create prediction models on a custom built SKNIR instrument. Spectra data and reference protein, oil, and weight were used to build partial least squares (PLS) models. Calibration models provided reasonable prediction of these traits and could be used for screening purposes. PLS statistics were oil (R² = 0.82, SEP = 1.72), weight (R² = 0.74, SEP = 0.71), and protein (R² = 0.62, SEP = 0.96) for validation data sets comprising of one‐third of the total samples. In conclusion, prediction models showed that SKNIR spectroscopy could be a very beneficial nondestructive technique to determine oil and weight as well as rapid screening of protein in single flax seeds while not requiring extensive preparation as compared to traditional techniques.
Wang J, Liu X, Qi X, Wu X, Long Y, Feng Y, Dong Q, Yan J, Huang L, Luo Y, Cao M, Xu K, Zhao C, Wang Y, Hu T, Wu J, Liu L, Su Y.
Field / plotRaman / spectroscopyLeafMorphology / geometry measurementLeaf traits
Leaf spectroscopy, combined with partial least squares regression (PLSR), is recognized as an efficient and precise tool for measuring plant leaf traits. However, the feasibility of developing a generalizable model remains unclear, primarily due to limited understanding of PLSR model transferability. Here, we collected six key leaf traits along with paired leaf reflectance spectra from 1,967 samples of 349 tree species in eight forest sites across China. Using this dataset, we explored the transferability of PLSR models, factors affecting model transferability, and the feasibility of developing generalizable PLSR models for leaf trait prediction. Overall, PLSR models trained at a specific study site demonstrate limited transferability to other study sites. Dissimilarities in plant evolutionary history and environmental conditions between study sites are the primary factors influencing the transferability of PLSR models. Incorporating training data from diverse evolutionary histories and environmental conditions can improve the transferability of PLSR models, achieving accuracy equivalent to that of site-specific models. Our findings provide guidelines for the use of spectroscopy in leaf trait prediction and underscore the urgent need for collaborative efforts to build an open database of leaf traits and reflectance spectra, thereby promoting the development of universal PLSR models for plant leaf trait prediction.
Published1 Jan 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQₑₓₜₑᵣₙₐₗ, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQₑₓₜₑᵣₙₐₗ value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample t-test p-value of 0.757 (p > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.