Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract The application of small unmanned aircraft systems (sUAS)‐based high‐throughput phenotyping in plant breeding has advanced significantly over the past decade. Hyperspectral images and machine learning approaches offer potential to enhance drought resistance screening in turfgrass. However, large‐scale field applications remain limited, and the transition from controlled environments to real‐world phenotyping is not well understood. This study aimed to develop an sUAS‐based hyperspectral image workflow to monitor changes in turfgrass canopy reflectance during drought, validate previously reported indices from controlled environment studies in a large‐scale field study, and estimate visual turfgrass quality (TQ) from hyperspectral images. Images were collected from a zoysiagrass ( Zoysia spp.) mapping population at three dates under varying soil moisture conditions. Vegetation indices (VIs) related to light use efficiency, leaf pigments, senescence, water status, and green vegetation were computed and compared. Top‐performing genotypes under drought exhibited greater absorption in blue and red wavelengths and higher near‐infrared reflectance than poor‐performing ones. The photochemical reflectance index and plant senescence reflectance index were highly correlated with TQ ( r = 0.84 and −0.76), showed higher coefficient of variation (range 18%–37%), and had higher broad‐sense heritability (0.73–0.74) than normalized difference vegetation index (0.69), warranting their use in large‐scale field study. Machine learning models estimated TQ with a mean absolute error of 0.46. These findings highlight the importance of integrating VIs related to light use efficiency, leaf pigments, senescence, and water status to gain deeper insights into turfgrass drought response and support breeding for stress tolerance.
Reproduction assets foundThe paper's data availability statement points to a Zenodo-hosted dataset of spectral reflectance measurements from the zoysiagrass mapping population under drought, which directly reproduces this paper's phenotyping measurements. No author analysis code or trained models were identified.Dataset · publicDATA AVA I L A B I L I T Y S TAT E M E N T
The data referenced in this paper are available in a repository
hosted by Zenodo (Zhang, 2025).Open asset ↗Zenodopdf-raw-page:17 lines:1-85Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.
Reproduction assets foundThe paper deposits its phenotype measurement datasets (raw data, ANOVA statistics, time series) both in MDPI Supplementary Materials and in a public AgDataCommons dataset. Plant images are only available upon request, and no author analysis code repository with a public URL is stated.Dataset · publicDatasets are supplied in the Supplementary Materials and at https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_Visualizing_Plant_Responses_Novel_Insights_Possible_through_Affordable_Imaging_Techniques_in_the_Greenhouse/26527447 , accessed on 13 August 2024; images are available upon request.Open asset ↗agdatacommons.nal.usda.gov · 26527447lines:97-173Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24206676/s1 . Supplementary S1: F-values. Supplementary S2: p -values for experimental effects ANOVA ( Table 4 ), nine additional individual time series charts. Supplementary S3: of BA, %C, %G, DGCI, HSVi, NDRE, NIR, RED, and RE metrics. Supplementary S4: Additional discussion text. Supplementary S5: Raw data.Open asset ↗lines:97-173Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code on GitHub (sandysan42/Zoysia3DModel), which reproduces the 3D-model-based phenotyping analysis (height, area, color, volume). No public phenotype dataset or trained model checkpoint is stated; supporting information is generic.Code · publicYAPAIBOON ET AL.
AC K N OW L E D G M E N T S
A part of this study is supported by JST CREST grant number
JPMJCR16O1.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Zoysia3DModel/.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154
RyoAkashi https://orcid.org/0000-0002-5651-8285
R E F E R E N C E S
Bienert, A., Hess, C., Maas, H.-G., & Von Oheimb, G. (2014). A voxel-
based technique to estimate the volume of trees from terrestriOpen asset ↗sandysan42/Zoysia3DModelpdf-raw-page:10 lines:1-78Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset ( n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets ( n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.
Reproduction assets foundThe paper's own drone image datasets, DGPS ground survey points, and sward height measurements are deposited publicly on Dryad, as stated in the Data Availability Statement. No author analysis code is explicitly deposited.Dataset · publicData available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.q7c400k (Forsmoo et al., 2019 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.q7c400klines:57-85