Miho H, Barranco Navero D, Trujillo Navas I, Morello Parra P, Koubouris G, Trapero Ramirez C, López-Bernal Á, Oueslati A, Flores Mesas D, García López MT, Valverde Caballero P, Deger RE, Meca E, Perri E, Santilli E, Moral Moral J, Estudillo Cazorla C, Cabello Pozo D, Yousef Yousef M, Acar S, Gurbuz-Veral M, Priego Capote F, Zahiri A, Oulbi S, Márquez Pérez MI, M. Díez C.
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
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Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction This study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations. Conventional morphometric analyses of olive endocarps largely rely on manual measurements, limiting reproducibility and quantitative comparison. Methods Ten archaeological endocarps were selected from previously published archaeological assemblages based on the integrity of their outlines, apex-base morphology and overall preservation quality. Quantitative descriptors describing endocarp size, symmetry, curvature and contour geometry were extracted using the OliveID software and compared with a modern morphometric reference database comprising Greek and international olive cultivars. Results Reliable contour extraction and quantitative descriptor computation were successfully achieved for all archaeological specimens despite carbonization. Preliminary comparison of representative morphometric descriptors showed that the archaeological specimens were positioned within the morphometric variation observed among the modern reference collection. Hierarchical clustering consistently associated the archaeological endocarps with the modern Throumbolia morphotype, while distinguishing them from elongated, globular and mucro-bearing cultivars. Discussion These findings demonstrate the feasibility of applying digital image-based morphometric analysis to sufficiently preserved archaeological carbonized olive endocarps and indicate a similar morphometric affinity between the analyzed archaeological material and the modern Throumbolia cultivar. This proof-of-concept study highlights the potential of digital morphometric approaches for quantitative archaeobotanical investigations of archaeological olive remains, while emphasizing the need for larger archaeological datasets and standardized image acquisition to further validate the observed morphometric similarity.
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。Supplement · publicThe quantitative measurements of the archaeological specimens are presented in Supplementary Table 2 , whereas the corresponding mean values and standard errors for the modern cultivars are provided in Supplementary Table 3 .Open asset ↗lines:311-320Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant stress recognition plays a vital role in precision agriculture by enabling the early detection of diseases, insect infestations, and nutrient deficiencies that adversely affect crop productivity. Although deep learning models have achieved promising results, existing CNN-, Transformer-, and hybrid architectures often struggle to capture complex spatial dependencies, distinguish visually similar stress symptoms, and handle class imbalance in multi-label classification. To address these challenges, this paper proposes Q-TriLSTM-Vision, a novel hybrid deep learning framework that integrates an EfficientNet-B0 visual encoder, a tri-stream long short-term memory (TriLSTM) network, and a lightweight quantum-inspired interference gate. Unlike quantum computing-based approaches, the proposed interference mechanism is implemented entirely using classical neural operations to enhance feature representation without requiring quantum hardware. The model further employs an entanglement-inspired attention fusion module, focal binary cross-entropy loss with label smoothing, weighted sampling, and per-class threshold calibration to improve discriminative learning and minority-class recognition. The proposed framework was evaluated on the OLID-I dataset and further validated on the PlantVillage and PlantDoc benchmark datasets. Experimental results demonstrate that Q-TriLSTM-Vision achieved Macro-F1 scores of 0.9127, 0.9624, and 0.8975 on OLID-I, PlantVillage, and PlantDoc, respectively, outperforming representative CNN-, Transformer-, and hybrid deep learning models while achieving lower Hamming loss and improved recall. Cross-validation, ablation studies, and statistical significance analysis further confirm the robustness and effectiveness of the proposed framework. Overall, Q-TriLSTM-Vision provides an accurate, computationally efficient, and reliable solution for intelligent plant stress recognition in precision agriculture.
Reproduction assets foundThe paper's plant-phenotyping measurements are based on the public OLID-I plant stress image dataset hosted on Kaggle, which the authors explicitly identify as the data source for training and evaluating Q-TriLSTM-Vision. No author analysis code, trained models, or supplementary assets with explicit public availabilityDataset · publicusion method for incorporating the multi-directional representations. This combination is unprecedented in any other existing hybrid plant stress recognition model.
3
Materials and methods
3.1
Data collection
For this study, the data were gathered from the publicly available OLID-I plant stress image dataset hosted on Kaggle: ( https://www.kaggle.com/datasets/raiaone/olid-i ). We chose it mainly because it includes leaf images from multiple vegetable crops, and those images show both healthy and stressed states, too. In the implemented code, the dataset is pulled in through the KaggleHub package, while the folder structure is scanned in an automatic way. During this scan, the script tries toOpen asset ↗Kaggle · raiaone/olid-ilines:51-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Image analysis of pits and grains provide alternative routes for overcoming the invasive approach of genomic tools in the investigation of archaeological or modern plant material, which is only seldom a viable option due to the complex and laborious methodologies required. Nevertheless, any investigation of pit morphology and cultivar interpretation requires a high quality, comprehensive dataset for comparison. Such a benchmark dataset for the morphology of olive (Olea europaea) pits is presented in this paper, designed to facilitate similar research and establish a base for future investigations. The dataset was established by image analysis of pits of 18 olive cultivars that were photographed in both lateral and dorsal positions. A dedicated MATLAB® code was developed to extract the silhouettes of each pit and to calculate 16 morphometric traits of each view of the pit. Altogether, a total of 1008 photos of 504 pits of the 18 cultivars, together with their detailed morphometric description and statistical analysis are available here. These were used to test the accuracy of the dataset and the new approach in representing the different cultivars.
Reproduction assets foundThe paper's olive pit images (1008 photos of 504 pits) and morphometric trait data (16 parameters per view) are openly deposited on Zenodo, along with the authors' MATLAB 'PitAnalyzer' software used for silhouette extraction and trait calculation. Both are paper-specific, public, and directly actionable via the Zenodo.Dataset · publicAll the images are available on a dedicated Zenodo repository17. The file name of each image comprises an abbreviation of the cultivar name (Table 1), tree number (a, b or c), pit number (1–30) and the pit position (VD VL for dorsal and lateral, respectively).Open asset ↗Zenodohtml-lines:220-292Code · publicThe code that was used in this work is compiled as a stand-alone software based on MATLAB “PitAnalyzer”. The software is available to download at the following repository, where any use of it should be attributed appropriately to this publication (https://zenodo.org/records/18789307).Open asset ↗Zenodohtml-lines:381-404Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Accurate plant health monitoring relies on hyperspectral imagery to extract vegetation spectral signatures and compute vegetation indices (VIs), which are critical for phenotyping and crop condition assessment. However, the requirement for high spectral resolution significantly increases the cost and complexity of data acquisition. In this study, we proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data. The aim was to reduce the dependency on high-resolution spectral imagery without compromising prediction accuracy. The framework integrated correlation-based feature selection with four regression models to identify and utilize the most informative spectral bands from coarsely sampled data. The system was trained and validated using a data set consisting of 555 spectral signatures collected from olive leaves at five stages of dehydration, with spectral resolutions ranging from 1 to 100 nm. A total of 25 vegetation indices, commonly used in the estimation of water stress, chlorophyll, and nitrogen, were predicted on various sampling scales. Experimental results show that even with 100 nm spectral resolution, the proposed framework achieves high prediction accuracy, with coefficients of determination reaching 0.99 for RVSI, VOPT, and SPADI indices. These findings demonstrate that accurate vegetation index estimation is achievable with significantly fewer spectral bands, offering a cost-effective solution for large-scale plant health monitoring. This framework lays the groundwork for the development of low-cost, data-efficient remote sensing systems for precision agriculture, especially in crops such as olives, where health dynamics are sensitive to water and nutrient status.
Reproduction assets foundThe paper's Data Availability statement points to a Figshare deposit (DOI 10.6084/m9.figshare.26950660.v2), which per the statement hosts the study's data — the 555 olive-leaf hyperspectral signatures and vegetation index measurements underlying the phenotyping analysis. This is a paper-specific, publicly accessible,直接Dataset · publicnm.
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Inclusivity in global research questionnaire.
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Acknowledgments
The authors thank the Advanced Center of Electric and Electronic Engineering - AC3E ANID. The authors acknowledge the support provided by Universidad Técnica Federico Santa María and the Direction of Post-Grade programs DDP.
Data Availability
https://doi.org/10.6084/m9.figshare.26950660.v2 .
Funding Statement
This work was funded by the ANID FB240002 basal center AC3E, and ANID national doctorate scholarship, folio N°21231129. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Ruiz-Carrasco B, Fernández-Lobato L, López-Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2lines:266-293Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The bacterium Xylella fastidiosa (Xf) is a plant pathogen first identified in Europe in 2013, specifically in olive groves in the Apulia region (south-eastern Italy). It is now spreading across the Mediterranean basin and poses a serious threat to the local economy by causing branch desiccation and the rapid death of olive trees, a condition known as olive quick decline syndrome (OQDS). Several studies have investigated the potential of remote sensing (RS) technology to monitor OQDS over time and space; however, accurate and reliable data on OQDS occurrence remain scarce. To enhance the distribution data of Xf-infected trees in the Apulia region, we investigated an infection hotspot of 25 km² area in the province of Brindisi, where records of infections were documented in 2019 and 2020. Three very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees. Through visual interpretation, 2340 trees were identified most likely as either infected or removed due to OQDS. This dataset provides a valuable resource for developing or validating RS techniques for early detection of OQDS. Furthermore, it could support studies aimed to evaluate spectral bands or indices most correlated with infection presence. Finally, the dataset can be integrated with other Xf-infection presence data to support species distribution model studies.
Reproduction assets foundThe paper is a Data in Brief article describing a public Figshare dataset (OQDS-Insight) containing WorldView-2 satellite raster imagery (RGB and NDVI GeoTIFFs) and a shapefile of 76,637 olive tree points with OQDS infection labels — directly the paper's phenotyping measurements.Dataset · publicsouth-eastern Italy. The extent (EPSG:32633) is from 706164.541 N to 713395.999 N, and from 4508710.411 E to 4513574.414 E.
Data are stored at the Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Italy.
Data accessibility
Repository name: OQDS-Insight
Data identification number: https://doi.org/10.6084/m9.figshare.28191245.v4
Direct URL to data: https://doi.org/10.6084/m9.figshare.28191245.v4
Related research article
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Open in a new tab
1. Value of the Data
•
The dataset provides a detailed record of OQDS olive groves within an infection hotspot in the province of Brindisi, Apulia region (south-eastern Italy) ( Fig. 1 ).
•
It can support rOpen asset ↗figshare · 10.6084/m9.figshare.28191245.v4lines:95-140Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article introduces OliveTreeCrownsDb, a comprehensive dataset of high-resolution images captured by a DJI Phantom 4 RTK drone. The dataset includes 46 images covering an entire olive farm, focusing on the detection and analysis of olive tree crowns and supporting segmentation tasks. Each image is accompanied by detailed metadata, such as focal distance, capture altitude, GPS coordinates, and other essential parameters for accurate tree mapping and localization. OliveTreeCrownsDb is publicly accessible, promoting research in precision agriculture, including tree crown detection, segmentation, geometric shape analysis, automation, yield estimation, and computer vision applications. It facilitates the development of innovative algorithms to optimize resource allocation and improve crop management. By enabling studies on tree crown analysis and farm monitoring, OliveTreeCrownsDb advances agricultural technologies and enhances management practices in olive cultivation.
Reproduction assets foundThe paper's own UAV olive tree crown dataset (images, annotations, point cloud, DEM) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public/ Town / Region: Meknas farm site
Country: Morocco
The GPS coordinates of the olive farm are 33°53′17"N 5°25′22"W, or in decimal format: 33.88802°N, -5.42281°W.
Data accessibility
Repository name: OliveTreeCrownsDb
Data identification number : doi: 10.17632/xym8rd2srf.2
Direct URL to data:
Instructions for accessing these data: https://data.mendeley.com/datasets/xym8rd2srf/2
Related research article
none
1.
Value of the Data
The OliveTreeCrownsDb dataset is a valuable resource for research in computer vision and precision agriculture. Here are the key aspects that highlight its importance:
•
Unique and Specialized Source: OliveTreeCrownsDb offers an exclusive high-resolution dataset specificOpen asset ↗10.17632/xym8rd2srf.2lines:1-54Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Reproduction assets foundThe paper's multispectral images, hyperspectral reflectance, and trait measurements (weight, chlorophyll, nitrogen, fuel moisture) are publicly deposited on Figshare with an explicit DOI. The authors' sample Matlab code is included within that dataset. The MicaSense imageprocessing repository is a generic third-party工具Dataset · publicAll the data is available at this repository DOI: https://doi.org/10.6084/m9.figshare.26950660.v2.Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2pdf-page:14 lines:1-35Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and 'n' rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.
Reproduction assets foundThe paper is a data descriptor whose hyperspectral olive dataset (raw/normalised HSIs and processed spectral signatures) is publicly deposited in Mendeley Data with DOI and direct URL given in the article.Dataset · publicolive field in a city on the north-west side of Seville in the south of Spain.
• City/Town/Region: Espartinas, Seville province
• Country: Spain
• Latitude and longitude: 37.394327, -6.121881
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/8xvhcsdvst.1
Direct URL to data: https://data.mendeley.com/datasets/8xvhcsdvst/1
Value of the Data
•
In smart agro applications, there are technological approaches that use artificial intelligence or traditional statistical methods such as ANOVA or PLS [1] , [2] , [3] , which require the use of data. In this regard, data are essential for both artificial intelligence and stochastic approaches. There Open asset ↗Mendeley Data · 10.17632/8xvhcsdvst.1lines:1-52Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task. It is estimated that people have been cultivating olive trees for 6000 years, making it one of the most useful and profitable fruit trees in history. Symptoms that appear on infected leaves can vary from one plant to another or even between individual leaves on the same plant. Because olive groves are susceptible to a variety of pathogens, including bacterial blight, olive knot, Aculus olearius , and olive peacock spot, it has been difficult to develop an effective olive disease detection algorithm. For this reason, we developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model. The goal of this method is to detect and classify diseases that can affect olive leaves. In addition, binary and multiclassification systems based on deep convolutional models were used to categorize olive leaf disease. The results are encouraging and show how effectively CNN and vision transformer models can be used together. Our model outperformed the other models with an accuracy of about 96% for multiclass classification and 97% for binary classification, as shown by the experimental results reported in this study.
Reproduction assets foundThe paper's olive leaf disease image dataset (3,400 images) is explicitly stated as publicly deposited on the authors' GitHub repository. The Keras ViT example and TensorFlow Keras applications URLs are generic libraries/tutorials, not paper-specific assets.Dataset · publicThe Olive dataset used to support the findings of this study has been deposited in the https://github.com/sinanuguz/CNN_olive_dataset .Open asset ↗sinanuguz/CNN_olive_datasetlines:203-253Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Morphological characterization of olive (Olea europaea L.) varieties to detect desirable traits has been based on the training of expert panels and implementation of laborious multiyear measurements with limitations in accuracy and throughput of measurements. The present study compares two- and three-dimensional imaging systems for phenotyping a large dataset of 50 olive varieties maintained in the National Germplasm Depository of Greece, employing this technology for the first time in olive fruit and endocarps. The olive varieties employed for the present study exhibited high phenotypic variation, particularly for the endocarp shadow area, which ranged from 0.17−3.34 cm2 as evaluated via 2D and 0.32−2.59 cm2 as determined by 3D scanning. We found significant positive correlations (p < 0.001) between the two methods for eight quantitative morphological traits using the Pearson correlation coefficient. The highest correlation between the two methods was detected for the endocarp length (r = 1) and width (r = 1) followed by the fruit length (r = 0.9865), mucro length (r = 0.9631), fruit shadow area (r = 0.9573), fruit width (r = 0.9480), nipple length (r = 0.9441), and endocarp area (r = 0.9184). The present study unraveled novel morphological indicators of olive fruits and endocarps such as volume, total area, up- and down-skin area, and center of gravity using 3D scanning. The highest volume and area regarding both endocarp and fruit were observed for ‘Gaidourelia’. This methodology could be integrated into existing olive breeding programs, especially when the speed of scanning increases. Another potential future application could be assessing olive fruit quality on the trees or in the processing facilities.
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。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11111501/s1 , Table S1: Endocarp 3D morphological traits of 50 olive varieties.; Table S2: Fruit 3D morphological traits of 50 olive varieties.Open asset ↗lines:179-197