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.
Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。
abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.
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:1-44Plant phenotyping relevance match · UnverifiedCrossref · checked 11 Sept 2026
Xylella fastidiosa (Xf) is among the most devastating phytosanitary threats to Mediterranean agriculture, causing Olive Quick Decline Syndrome (OQDS). Since containment efficacy depends on timely intervention, scalable in-field screening tools are needed. This study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling. Three replicate acquisitions were performed on 130 olive trees (390 measurements) across four cultivars (Cellina di Nardò, Ogliarola Salentina, Ogliarola Barese, and Leccino) in four Italian regions (Apulia, Calabria, Lazio, and Tuscany). Plant status was assigned from the official status of the sampling area (demarcated OQDS focus versus Xf-free area) and supported by real-time quantitative PCR (qPCR) on every plant; within demarcated sites, plants with undetectable DNA in sampled twigs were retained as Xf+ following phytosanitary criteria, giving 216 infected and 174 healthy samples. The multidimensional sensor signals were processed with an optimized Shallow Neural Network. Under plant-grouped 80/20 validation, keeping each plant’s replicates in the same subset, the model achieved (93.3 ± 4.0)% accuracy, (97.7 ± 3.5)% sensitivity, and (88.2 ± 8.3)% specificity (mean ± SD). A feature-importance analysis revealed a reproducible, though not chemically resolved, VOC-related response pattern. This low-cost, portable Internet of Things (IoT) device offers a proof-of-concept screening approach for Xf surveillance, pending plant-level and external validation.
Why it matches plant phenotyping methods植物の感染状態をVOCセンサーとAIで直接推定する現地スクリーニング手法を開発・評価しており、植物病害状態の取得が研究の中心である。
abstractThis study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling.
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.
Why it matches plant phenotyping methodsOliveIDを用いて炭化オリーブ内果皮の輪郭からサイズ、対称性、曲率、形状記述子を抽出し、デジタル画像形態計測の適用可能性と再現性を評価している。植物器官の形質抽出法が研究の中心である。
abstractThis 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.
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:311-320Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 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.
Why it matches plant phenotyping methods植物ストレス症状を画像から認識する深層学習手法を開発し、複数の植物画像ベンチマークで検証しているため、表現型取得・抽出法が中心である。
Abstract In the era of climate change, drought is defined as one of the most severe natural catastrophes that affects the environment, crop growth, and water resources, leading to economic losses, migration, and risks to human life. Since 2019, Morocco has suffered one of the most severe droughts in its recording history, coinciding with a broader period of precipitation deficit across the Mediterranean basin, resulting in a significant reduction in reservoir storage levels and the suspension of irrigation provided by dams in some areas due to low or absent rainfall, making drought a serious challenge to natural resources in this country. This study focused on semi-arid regions, especially on the Tensift basin in Morocco, and was conducted between 2018 and 2024. The effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area. The analyses indicated that arboriculture cover decreased markedly, from 11.17% in 2020 to 7.40% in 2023. From the analyses, it was evident that the cover of this culture had significantly declined from 11.17% in 2020 to 7.40% in 2023. From 2018 to 2024, more than half of the area suffered from drought in the agriculture of varying intensity levels, from moderate to severe. The meteorology of the evaluations confirmed the above observations by showing that there was a considerable decline in precipitation levels from about 350 mm in 2019 to less than 50 mm in 2024, coupled with continuously negative SPI-6 indices. Moreover, it was established that there was a significant effect on tree crops such as olives and citrus. Degradation of land was at its worst during 2021 when it affected more than 3,000 hectares of olives and 2,250 hectares of citrus. Similarly, 80% of farmers engaged in the production of citrus registered a decline in yield from 37% to 41%. Consequently, this study provides new information concerning the need for comprehensive evaluation and monitoring of agricultural drought in Morocco, thereby emphasizing the importance of essential factors.
Why it matches plant phenotyping methodsリモートセンシングと複数の植生・水ストレス指数を中核に、オリーブ・柑橘樹の植生健康状態や干ばつ影響を評価しており、植物状態の抽出手法の実質的応用に該当する。
abstractThe effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area.
Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R2p = 0.86), OCDM (R2p = 0.86), and M (R2p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R2p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring.
Why it matches plant phenotyping methods低コスト multispectral センサーとANNによるオリーブ果実の品質形質推定システムを開発・外部検証しており、植物形質取得法が中心的である。
abstractwe propose a methodology using a custom-made, low-cost multispectral device.
Olive is a major agricultural crop extensively cultivated throughout the Mediterranean region. However, olive trees are vulnerable to several diseases that can negatively affect productivity and yield. One of the most widespread foliar diseases is olive leaf peacock spot, caused by the fungus Cycloconium oleaginum. Early detection of this disease is essential for preventing leaf drop, limiting disease spread, maintaining tree health, and reducing treatment costs before the infection reaches an advanced stage. In this study, a multimodal hybrid deep learning framework is developed to detect peacock spot disease in olive leaves and assess disease severity based on visual and numerical features. The proposed framework integrates olive leaf images with soil conditions, environmental conditions, and vegetation and stress indices to provide a more comprehensive disease analysis than image-only approaches. A ResNet50-based convolutional neural network is used to extract visual features from leaf images, while a multilayer perceptron processes the numerical sensor-based and index-based data. These features are then fused within a unified learning framework to classify disease stages and estimate leaf damage severity, including lesion coverage and yellowing percentage. The performance of the proposed model was evaluated using standard performance metrics suitable for both classification and regression tasks. For classification, the model was evaluated on 494 testing samples and achieved an overall accuracy of 97.77 %, with a macro F1-score of 0.9809 and a weighted F1-score of 0.9776. In addition, the model achieved low regression errors, with mean absolute errors of 1.16 % for lesion coverage and 1.42 % for yellowing estimation. These results demonstrate the effectiveness of the proposed multimodal framework for accurate peacock spot detection and severity assessment, supporting its potential use in smart agricultural monitoring and disease management.
Why it matches plant phenotyping methods画像とセンサーデータを統合し、オリーブ葉の病斑被覆率・黄化率という植物の病害状態を推定する深層学習手法が研究の中心であるため。
abstracta multimodal hybrid deep learning framework is developed to detect peacock spot disease in olive leaves and assess disease severity based on visual and numerical features
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.
Why it matches plant phenotyping methodsオリーブ核の画像から形態形質を抽出する専用コードと、検証用ベンチマークデータセットを開発・提示しており、植物形質取得法が中心である。
abstractSuch 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.
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:381-404Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
This study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs. Unlike prior research restricted to narrow genotypic ranges or single-image modalities, we analyzed 65 genetically diverse olive cultivars from the Tarom Olive Research Station (Zanjan, Iran). We employed a dual-image phenotyping approach, integrating high-resolution imagery of both fruits and kernels with quantitative weight metrics. This methodology enabled the extraction of critical morphological traits—including eccentricity, solidity, and shape factors—to train and validate seven Machine Learning (ML) algorithms. Our comparative analysis of Discriminant Analysis (DA), Support Vector Machine (SVM), Neural Networks (NN), and ensemble methods reveals that the DA model achieves superior performance, attaining a recall and precision of 0.98 when integrating fruit, kernel, and weight data. This significantly outperforms standard models like KNN and Naive Bayes in this domain. These findings demonstrate that combining multi-view imaging with morphological feature extraction provides a highly accurate, cost-effective tool for managing olive genetic resources and accelerating crop improvement.
Why it matches plant phenotyping methods果実・核の画像から形態形質を抽出し、機械学習モデルを比較検証する高スループット植物フェノタイピング手法が研究の中心である。
abstractThis study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs.
Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R2 = 0.82 and 0.91) and tree height (R2 = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R2 = 0.73 and 0.68) and crown volume (R2 = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R2 = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions.
Why it matches plant phenotyping methodsUAV画像から樹体形状・LAIなどの植物形質を自動推定し、圃場基準値との比較検証を行う手法が研究の中心である。
abstractUAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach.
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.
Why it matches plant phenotyping methodsオリーブ葉のハイパースペクトルデータから植物状態に関わる植生指数を推定する、低コストな機械学習・スペクトル測定フレームワークの開発と検証が中心である。
abstractwe proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data
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:266-293Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Reliable aboveground biomass (AGB) estimates for woody crops are required for carbon accounting and MRV; however, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed orchards. We benchmarked four LiDAR modalities across three Mediterranean woody-crop sites in Córdoba (Spain), IFAPA, Doña María, and Villaseca using open national airborne laser scanning (PNOA/ALS), Riegl ALS, unmanned laser scanning (ULS), and mobile laser scanning (MLS). The field inventory used 58 fixed-area plots (20×50 m; 0.1 ha) collected in December 2024-January 2025 (1,867 trees) and species-specific allometries based on D2r to derive tree and plot AGB; carbon was computed using wood carbon fractions (0.445 olive; 0.457 almond) and CO2e via IPCC conversion. Plot-level LiDAR metrics (e.g., mean height, p95, maximum height, and cover proxies) were extracted from normalized point clouds and modeled with Random Forest, XGBoost, and an ensemble under an 80/20 train-test split. Mean field AGB differed among sites (33.89, 30.94 and 12.76 Mg ha−1 for Villaseca, Doña María, and IFAPA). In the provided summaries, XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). The results support cross-platform LiDAR for orchard AGB mapping and identify conditions under which open national LiDAR can enable scalable MRV. In addition, we evaluated TreeQSM-based quantitative structure models (QSMs) as an independent tree-level 3D reconstruction approach and examined their site-dependent agreement with field inventory estimates.
Why it matches plant phenotyping methods複数のLiDARモダリティと解析手法を比較・ベンチマークし、樹木およびプロットの地上部バイオマスを推定する技術評価が研究の中心であるため。
abstractWe benchmarked four LiDAR modalities across three Mediterranean woody-crop sites
Estimating olive (Olea europaea L.) leaf area is an important aspect of monitoring plant health and evaluating growth processes in agriculture. Accurate estimation of leaf area allows for a better understanding of processes such as water and nutrient utilization, photosynthesis efficiency, respiration, and yield potential. This study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves. Additionally, the predictive performances of multiple linear regression (MLR) and artificial neural network (ANN) were compared. A total of 1320 leaf samples collected from 22 olive cultivars were used in the study. Leaf length and width were taken as input parameters, and both MLR and ANN models were developed for each cultivar. Both multiple linear regression (MLR) and artificial neural network (ANN) models demonstrated high predictive accuracy for olive leaf area estimation across 22 cultivars. The MLR models explained up to 96% of the variation in leaf area using leaf length (LL) and leaf width (LW), with low root mean square errors, indicating strong reliability. When cultivar identity was modeled as a categorical factor through dummy encoding, the model captured significant cultivar-specific effects without altering the overall predictive performance. The ANN models achieved slightly higher accuracy, with determination coefficients exceeding 0.99 and minimal prediction errors, confirming their superior ability to model nonlinear relationships. Across both approaches, leaf width contributed more strongly to leaf area than leaf length. Cultivar-specific differences were statistically significant for only a few genotypes, while most cultivars exhibited comparable patterns after adjustment for multiple testing. In conclusion, both MLR and ANN models demonstrated high accuracy in predicting olive leaf area, with ANN models showing slightly superior performance. However, MLR models also yielded highly reliable results, indicating that both approaches are viable for practical applications in olive cultivation. These predictive models can be effectively used for rapid, non-destructive phenotyping, growth monitoring, and precision management in olive breeding and production systems.
Why it matches plant phenotyping methodsオリーブ葉面積という植物形質を対象に、葉長・葉幅からMLRとANNで推定モデルを開発・比較しており、非破壊フェノタイピング手法が中心です。
abstractThis study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves.
OliveMicroscopyCell / cellular structureVisualization / data management
The pollen tube is widely recognized as a suitable model for investigating the structure and spatial organization of cell wall components during polarized growth. This chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins, using specific monoclonal antibodies from the JIM series. JIM5 and JIM7 were employed to detect de-esterified and esterified homogalacturonan regions of pectin, respectively, while JIM8 and JIM13 were used to label distinct epitopes of arabinogalactan proteins. The protocol includes pollen germination, paraformaldehyde fixation, enzymatic digestion with cellulysin (for arabinogalactan protein detection only), and sequential antibody incubation, followed by confocal microscopy imaging using FITC filter settings. This approach enables precise visualization of the distribution patterns of pectins and arabinogalactan proteins in the pollen tube wall and provides a reliable framework for further studies on cell wall architecture in plant reproductive tissues.
Why it matches plant phenotyping methods植物花粉管細胞壁の成分分布を共焦点免疫蛍光で可視化するプロトコルが研究の中心であり、植物組織の空間的状態を測定する方法として扱える。
abstractThis chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins
Vegetation indices (VIs) offer potential for non-destructive olive fruit quality moni-toring, yet their performance across diverse germplasm remains uncharacterized. This exploratory screening systematically evaluated 87 VIs for predicting chlorophyll and polyphenol content across 31 cultivars at four ripening stages, prioritizing genetic diver-sity to establish species-level biochemical-spectral relationships through integration of hyperspectral data (380-1080 nm) with biochemical analyses. Modified Chlorophyll Absorption Ratio Index and Transformed Chlorophyll Ab-sorption achieved 91 strong correlations (|r| ≥ 0.9) across 124 cultivar-stage combina-tions. High-performing indices incorporated 550 nm with red/red-edge bands (670-710 nm) and non-linear formulations. Moderate inter-cultivar variability (CV = 19.7-21.3%) in-dicated cultivar-specific calibrations may be necessary. Principal component analysis captured 99.8% of variance, revealing three biochemi-cal clusters: high-chlorophyll cultivars (n=5; 450/4078 mg/kg chlorophyll/polyphenols), typical-range cultivars (n=23; 70/4750 mg/kg), and elite cultivars (n=3; 855/6260 mg/kg), demonstrating VI capacity for cultivar discrimination. Chlorophyll degradation exhibited conserved patterns (p < 0.001), supporting uni-versal tracking models. Conversely, polyphenol dynamics displayed marked geno-type-dependency, with cultivars showing positive, negative, or minimal variation, yield-ing non-significant population-level effects (p = 0.969) despite robust cultivar-specific trends.
Why it matches plant phenotyping methodsハイパースペクトルデータと植生指数によるオリーブ果実のクロロフィル・ポリフェノール推定を、多数の品種と成熟段階で系統的に評価しており、非破壊的な表現型取得法の性能検証が中心である。
abstractThis exploratory screening systematically evaluated 87 VIs for predicting chlorophyll and polyphenol content across 31 cultivars at four ripening stages
Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U2-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was WA = 0.0025D1.943H0.690 (R2 = 0.912), the model for belowground biomass (BGB) was WB = 0.012D1.231H0.525 (R2 = 0.693), the model for CA-D was D = 4.31427C0.513 (R2 = 0.751), CA-H model was H = 226.51939C0.268 (R2 = 0.500). (2) The optimal AGB model for CA single-parameter was WA = 1.80901C1.181 (R2 = 0.845), and the model for BGB was WB = 1.25043C0.772 (R2 = 0.741). (3) The R2 of Olea europaea L. biomass, as estimated by CA derived from the U2-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U2-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.
Why it matches plant phenotyping methodsUAV-RGB画像とU2-Netを統合し、個体からプロット規模でオリーブのバイオマスという植物形質を非破壊推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。
abstractAn innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
An innovative, non-destructive, and rapid method was introduced to quantify hydroxytyrosol content in olive leaves by means of advanced photonic technologies and chemometrics. At the heart of this approach is a custom-made, pocket-sized fluorometer with LED excitation at 375 nm and a miniaturized spectrometer for emission detection in the visible range, designed to measure intact olive leaves with ease and precision. Four Italian olive cultivars, namely Frantoio, Leccino, Leccio del Corno, and Moraiolo, were studied, with nine leaf samplings conducted over the 2022–2024 period. Simultaneously, hydroxytyrosol concentration was measured using HPLC-DAD-MS on the same samples to establish the reference data. Chemometric analysis was applied to the spectroscopic and HPLC-DAD-MS results. The Orthogonal Partial Least Squares (OPLS) regression method was successfully used to build a predictive model to quantify hydroxytyrosol concentrations ranging from 200 to 7000 mg/kg. The model achieved very good regression coefficients of 0.9 for calibration and 0.83 for cross-validation, with root mean square errors of 600 mg/kg and 720 mg/kg, respectively. This non-destructive method, combined with the portability of the pocket-sized fluorometer, is an innovation for high-throughput screening of olive leaves. It offers significant potential for evaluating olive leaves before their use in tea production or as raw material for dietary supplements, making it highly appealing for both the agriculture and health industries.
Why it matches plant phenotyping methodsオリーブ葉の化合物含量という植物器官形質を、携帯型蛍光計とケモメトリクスで非破壊推定する手法を開発し、HPLC-DAD-MSで検証しており、測定法が研究の中心である。
abstractAn innovative, non-destructive, and rapid method was introduced to quantify hydroxytyrosol content in olive leaves by means of advanced photonic technologies and chemometrics.
Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.
Why it matches plant phenotyping methodsUAV熱画像とSfMによる3D熱画像から、樹冠温度・水ストレスなどのオリーブ樹の状態を抽出する手法が中心であり、2D手法との比較を含む実質的なフェノタイピング手法研究である。
abstractThis study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture.
The identification of archaeological fruits and seeds is crucial for understanding the relationships between humans and plants within the cultural and biological history of both wild and cultivated species. We compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allows to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (barley, olive, date palm and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. Sample sizes ranged from 473 to 1,769 seeds per class, which constitute a relatively small dataset for training CNNs models yet typical within archaeobotanical research. On these eight datasets, we compared the performance of CNN and EFT coupled with linear discriminant analyses. Our objectives were twofold: i) to test whether CNN can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For the CNN network, we deliberately used a candid approach relying on pre-parameterised VGG19 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: our CNN used bare photographs where EFT used outline coordinates. This "pre-distilled" geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that our CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNNs for archaeobotany, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.
Why it matches plant phenotyping methods種子・果実石の画像形態を対象に、CNNと幾何学的形態計測を比較し、分類性能と必要サンプル数を検証する方法中心の研究である。植物器官の形状という観測可能な形質の抽出・識別を扱う。
abstractWe compared the relative performance of a deep learning approach, namely convolutional neural networks (CNN), and outline analyses via geometric morphometrics using elliptical Fourier transforms (EFT) at identifying pairs of plant taxa.
Accurate olive cultivar identification is critical for ensuring quality control and traceability in the olive oil industry. The International Olive Council (IOC) and the International Union for the Protection of New Varieties of Plants (UPOV) have established standardized protocols for varietal characterization. Over the past two decades, two-dimensional image analysis techniques have been increasingly employed for olive variety identification, utilizing various morphological parameters and machine learning approaches. This study investigates olive varietal classification through three-dimensional morphological analysis of fruits and stones using X-ray microtomography. The research evaluates the discriminative power of different trait combinations using both Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to contribute to an optimized protocol for cultivar identification. Five autochthonous olive cultivars from the Campania region (Southern Italy) were analyzed. A preliminary comparison of classification performance between continuous and discrete morphological olive data revealed superior effectiveness of the continuous ones. Integrating quantitative morphometric traits with selected visual discrete UPOV characteristics yielded optimal overall classification accuracy of 88.41% using LDA with 84.4% for Ravece, 81.5% for Ortice, 100% for Frantoio, 81.3% for Rotondella, and 90.9% for Minucciola olive varieties. The best variety prediction rates, based on an olive sample not used for training, were provided by SVM, obtaining 70.0% for Ravece, 87.5% for Ortice, 54.5% for Frantoio, 60.0% for Rotondella, and 66.7% for Minucciola. Quantification of varietal overlap through Bhattacharyya coefficients identified Ortice and Ravece as the most phenotypically similar varieties, while Rotondella and Minucciola exhibited the most distinctive fruit morphology. Notably, all varieties showed at least one misclassification with the Frantoio variety. Morphological analysis demonstrated that endocarp surface traits provided the most discriminative power, and internal cavity characteristics also contributed significantly to varietal differentiation. These findings suggest two key implications: potential updates of UPOV guidelines for distinctness evaluation protocols and promising applications in authenticity verification for high-quality olive products.
Why it matches plant phenotyping methodsX線マイクロトモグラフィーによる果実・核の3次元形態計測と、形態形質を用いた分類性能評価が研究の中心であり、品種識別用の植物表現型取得・解析手法に該当する。
abstractThis study investigates olive varietal classification through three-dimensional morphological analysis of fruits and stones using X-ray microtomography.
This study presents the application of advanced radio frequency (RF) sensors for non-invasive, plant structure-specific water stress monitoring in olive trees (Olea europaea L.), focusing on the cultivars Frantoio and Leccino, known for their differing water-use strategies. The sensing system comprises circular and double-layer rectangular spiral RF sensors, optimised to maximise the quality factor (Q-factor) for enhanced sensitivity. The double-layer design, where one layer is “left-handed” and the other “right-handed,” allows for an increased magnetic field and detection reliability, especially on small branches where signal stability can be challenging. Throughout an 88-day experimental period, olive trees were subjected to full irrigation (FI) and deficit irrigation (DI) treatments. RF sensors were placed on the olive plants trunks and branches to capture plant structure-specific stress responses, with measurements recorded weekly. In the Frantoio cultivar, resonance frequency shifts were pronounced under DI, especially in the trunk and large branches, where notable physiological changes were observed. Correlations were established between resonance frequency data and morpho-physiological indicators such as trunk diameter increment (SDI) and fresh water content (FWC), validating the sensor’s sensitivity to dielectric property variations due to water stress. Anatomical analyses further revealed tissue adaptations in Frantoio under DI, including increased bark and cortex thickness and intensified sclerenchyma fibre formation, indicative of structural changes to support water transport. In contrast, the Leccino cultivar showed minimal frequency variations and lacked significant anatomical alterations, reflecting its conservative water-use strategy and limited sensitivity to stress. This research confirms RF sensors’ potential as precise tools for early water stress detection in olive trees, with an emphasis on sensor placement on main plant structures and sensitivity optimization to enhance accuracy. These findings support the use of RF sensing systems in precision agriculture for sustainable irrigation management, especially in water-limited environments and conditions.
Why it matches plant phenotyping methodsRFセンサーによるオリーブ樹の水ストレス検出法を開発・最適化し、植物構造別の測定と形態・生理指標との相関で妥当性を検証しているため、フェノタイピング手法が中心である。
abstractThis study presents the application of advanced radio frequency (RF) sensors for non-invasive, plant structure-specific water stress monitoring in olive trees (Olea europaea L.)
Tree architecture, defined as the arrangement in space of the elements above the ground, is closely related to the biological and physiological processes of the tree. In particular, the quantitative study of the branch consists of classifying branches into orders, estimating lengths, insertion angles, diameters and volumes. In the case of the olive tree (Olea europaea L.), the knowledge of its architecture is important to determine which varieties are suitable for high-density planting systems and to guide canopy pruning, which allow simplification of field management and reduction of costs. Up to date, measurements are mainly done manually, using measuring tape and caliper, with high time expense and operator-related uncertainty. In this study, the analysis is extended to the three-dimensional case using photogrammetry. Next, the point cloud is processed using a modified version of the open-source code TreeQSM (version 2.4.1). Moreover, a new methodology, based on photogrammetry and branch segmentation using TreeQSM, is proposed to measure not only average branch diameter, but also node diameter and internodal distance along the principal axis of the twig point cloud. The main characteristics of the principal axis of the twig are obtained to prove the validity of the proposed method. The node average diameter is 2.94 mm with a standard deviation of 1.20 mm while the average internode is 14.38 mm with a standard deviation of 7.78 mm.
Why it matches plant phenotyping methodsオリーブ樹の枝構造・節径・節間距離を、フォトグラメトリ、点群処理、TreeQSMによって自動抽出する植物表現型計測法を開発・検証しており、方法が研究の中心である。
abstracta new methodology, based on photogrammetry and branch segmentation using TreeQSM, is proposed to measure not only average branch diameter, but also node diameter and internodal distance along the principal axis of the twig point cloud.
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.
Why it matches plant phenotyping methods衛星画像のセグメンテーションと感染・枯死オリーブ樹のラベル化による、植物病害状態の検出・検証用データセットが研究の中心である。
abstractThree very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees.
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
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1. Value of the Data
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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 ).
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It can support rOpen asset ↗figshare · 10.6084/m9.figshare.28191245.v4lines:95-140Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像による作物水分状態と収量の推定が題名の中心であり、植物状態・収量を抽出するセンシング手法の応用に該当する。
titleUAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions
Carbon stock (CS) is an important indicator of the structure and function of forest ecosystems, and plays an important role in mitigating climate change, maintaining ecological system balance, promoting carbon trading, and other socioeconomic and ecological values. Olea europaea L. is a species of high economic and ecological value, and its excellent nutritional composition, strong drought tolerance, sustainable production characteristics, and promotion of agrodiversity make it important in guaranteeing food security. Accurately estimating the CS of Olea europaea L. offers a reliable reference for its artificial breeding and yield prediction. Firstly, an independent estimation model of Olea europaea L. CS was constructed, while a compatibility model of Olea europaea L. unitary and binary CS was constructed using nonlinear metric error. Secondly, in the CS compatibility model system, the total CS model of Olea europaea L. was constructed by the Logarithmic Nonlinear Seemingly Unrelated Regression (LNSUR) method with D and D2H as independent variables. The results show: (1) The independent model of Aboveground CS (AGCS) was C = 0.0014D1.92876H0.67174 (R2 = 0.909), and the independent model of Belowground CS (BGCS) was C = 0.00723D1.23578H0.48553 (R2 = 0.686). The AGCS compatibility model effectively addresses the issue of component sums not equaling the total, while maintaining a low RMSE (1.918); (2) The LNSUR model improved the accuracy of the BGCS model more significantly (R2 = 0.787), and the estimated total CS also had a smaller RMSE (0.241~0.418); (3) Whole-plant CS of Olea europaea L. in 15 sample plots was estimated using the CS independent model and the LNSUR model with an R2 of 0.964. This study is the first attempt to construct a CS estimation model for Olea europaea L., which provides a scientific and technological basis for the monitoring of its economic and ecological value indicators, such as yield and carbon sink capacity.
Why it matches plant phenotyping methodsオリーブ個体の全体炭素蓄積量という植物状態を推定する回帰モデルを構築・比較しており、炭素蓄積量の取得・推定手法が研究の中心である。
abstractFirstly, an independent estimation model of Olea europaea L. CS was constructed, while a compatibility model of Olea europaea L. unitary and binary CS was constructed using nonlinear metric error.
Proximal plant-based monitoring provides high-resolution data about trees, leading to more precise orchard management and in-depth knowledge about the tree physiology. The present work focuses on continuous real-time monitoring of olive cv. 'Ascolana Tenera' on an hourly time span during the third stage of fruit growth (mesocarp cell expansion) under mild water stress conditions (ψStem above -2 MPa). This is achieved by mounting dendrometers on the root, trunk, branch, and fruit to assess and model the behavior of each organ. The diameter variation of each organ at various time intervals (daily, two-weeks, and entire experiment), as well as their hysteretic patterns relative to each other and vapor pressure deficit, has been demonstrated. The results show different correlations between various organs, ranging from very weak to strongly positive. However, the trend of fruit versus root consistently shows a strong positive relationship throughout the entire experiment (R² = 0.83) and good across various two-week intervals (R² ranging from 0.54 to 0.93). Additionally, different time lags in dehydration and rehydration between organs were observed, suggesting that the branch is the most reactive organ, regulating dehydration and rehydration in the tree. Regarding the hysteretic pattern, different rotational patterns and characteristics (shape) were observed among the organs and in relation to vapor pressure deficit. This research provides valuable insight into the flow dynamics within a tree, models plant water relations and time lags in terms of water storage and transport and could be implemented for precise olive tree water status detection.
Why it matches plant phenotyping methods樹体各器官にデンドロメータを装着し、直径変動を連続・高頻度に取得して水分状態や器官間の応答をモデル化することが研究の中心であり、植物フェノタイピング手法の実質的な適用に該当する。
abstractThe present work focuses on continuous real-time monitoring of olive cv. 'Ascolana Tenera' on an hourly time span during the third stage of fruit growth
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.
Why it matches plant phenotyping methodsオリーブ樹冠の画像検出・セグメンテーションと幾何形状解析を可能にする公開データセットが研究の中心であり、植物の樹冠形態を抽出する再利用可能な基盤に該当する。
abstractThis article introduces OliveTreeCrownsDb, a comprehensive dataset of high-resolution images captured by a DJI Phantom 4 RTK drone.
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
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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:
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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.
Why it matches plant phenotyping methods植物のマルチスペクトル・ハイパースペクトル画像と葉の水分状態・化学形質を含む評価用データセットを構築しており、フェノタイピング手法開発のためのベンチマークが中心である。
abstractThis 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
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:1-35Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees' canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove located in Tenute di Cesa Farm, in the east of Tuscany, Italy, characterized by a 4x6 m planting layout and by developed plants. LiDAR data were used to measure canopy volumes, height, and diameter, and the generated point cloud was studied to assess the difference in density between treatments. Ten plants were selected for the study. To validate the LiDAR results, manual measurements of the canopy height and diameter dimensions of the plants were taken. The pruning weights of the monitored plants were obtained to assess the correlation with the canopy characterization data. The results obtained showed that pruning did not affect the results of the multispectral sensor, and the potential variation in canopy density and porosity did not lead to different results with this instrument. Plant volumes, height, and diameters calculated with the LiDAR sensor correlated well with the values of manual measurements, while volume differences between before and after pruning obtained good correlations with pruning weights (Pearson correlation coefficient: 0.66-0.83). The study of point cloud density in canopy thickness and height showed different shapes before and after pruning, especially in the former case. Correlations between point cloud density obtained from LiDAR and multispectral sensor results were not statistically significant. Even if more studies are necessary, the results obtained can be of interest in pruning management.
Why it matches plant phenotyping methodsLiDARとマルチスペクトルセンサーでオリーブ樹冠の体積・高さ・径・密度を測定し、手動測定および剪定重量との相関で技術検証しているため、植物フェノタイピング手法が中心です。
abstractThe 2D LiDAR was used to assess its ability to discriminate volume before and after pruning.
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.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis 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
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:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Published4 Nov 2024ISPRS annals of the photogrammetry, remote sensing and spatial information sciencesCited by 3 · OpenAlex ↗
Abstract. Rapid urban growth makes green space management crucial to improve citizens’ well-being. Urban olive trees characterize the Italian landscapes and their culture. This study explores different methodologies for urban tree assessment in this context, using an iPhone 14 Pro Max. These included: 1) its integrated Light Detection and Ranging (LiDAR) sensor using the Recon3D app, 2) its camera with Structure from Motion (SfM) techniques, and 3) its camera for generating 3D models using Neural Radiance Fields (NeRF). Additionally, a professional Mobile Laser Scanner (MLS), was used for comparison. Total height (H), canopy base height (CBH) and canopy volume (CV) measurements were extracted using both CloudCompare and allometric formulas. The main aim of this paper is to compare the 3D models of olive trees obtained from low-cost sensors with those generated from the MLS, which is a more accurate device but comes with significantly higher costs. The results, in terms of RMSE (iPhone LiDAR - H: 0.46 m, CBH: 0.12 m, CV: 15.66 m3; iPhone-SfM - H: 0.95 m, CBH: 0.19 m, CV: 25.85 m3; iPhone-NeRF - H: 1.26 m, CBH: 0.31 m, CV: 33.79 m3), bias and volume differences, reveal that the smartphone, in all the methodologies, tends to underestimate measurements as the size of the trees increases. This is due to the higher MLS range of acquisition. Despite these limitations, low-cost solutions like smartphone-based methods can be a viable alternative given their economic accessibility.
Why it matches plant phenotyping methodsスマートフォンLiDAR・SfM・NeRFによるオリーブ樹の3D形状から樹高、樹冠基部高、樹冠体積を抽出し、MLSと精度比較・検証しており、植物形質取得手法が中心である。
abstractThe main aim of this paper is to compare the 3D models of olive trees obtained from low-cost sensors with those generated from the MLS, which is a more accurate device but comes with significantly higher costs.
Dimensional characterization of trees plays important roles in phytoremediation project of precision agriculture and environmental protection. The dimensional characterization can be evaluated by using UAV-based geomatic surveys. The work in this study applies low-cost UAV photogrammetry for tree height estimation, particularly for a phytoremediation project on contaminated soils. Two locations that had differing mean tree heights (7m and 4m) were used for the purpose of study. Three different UAV flights were carried out at 40m, 50m, and 60m altitudes in Area 1, an olive grove, and two different flights at 45m and 52m altitudes in Area 2, which has poplar species. The Structure from Motion (SfM) method, Vegetation Filter (VF), Digital Surface Models (DSMs), and Raster computational tool were used to process the UAV point clouds in order to produce Canopy Height Models (CHMs) for a local maximum driven extraction of tree height. Relatively, the results obtained from the tree height estimation experiment for the locations using UAV are higher than the results obtained using in-field measurement, thereby justifying the suitability of UAV photogrammetric data for tree height estimation.
Why it matches plant phenotyping methodsUAVフォトグラメトリとSfM、CHM、画像点群処理を用いて樹高を推定し、圃場測定と比較検証しているため、植物形質取得法が研究の中心です。
abstractThe work in this study applies low-cost UAV photogrammetry for tree height estimation
A complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained using micro-computed tomography. In the case of the olive fruit fly (Bactrocera oleae), complete reconstruction of the galleries was achieved. The galleries were colour-coded according to the size of the internal lumens produced by larval instars. In the case of the olive moth (Prays oleae), we confirmed that the larvae only consume olive stones, leaving pulp tissue intact. This study revealed the evolutionary defensive adaptation developed by larvae, creating entrance/exit gallery in the form of a zigzag with alternating angles to avoid the action of possible parasitoids. In the case of olive fruit rot, caused by fungal infection transmitted by the midge (Lasioptera berlesiana), microtomography revealed the infection cavity, which was delimited by a protective layer of tissue produced by the plant to isolate the infection zone, which contained fungal hyphae and reproductive organs of the fungus. Two ovoid cavities were observed below a single external orifice in the concave necrotic depression. These results were interpreted as successive ovipositions of B. oleae, followed by the parasitoid L. berlesiana. High-resolution 3D rendered images are included as well as supplementary videos that could be useful tools for future research and teaching aids.
Why it matches plant phenotyping methodsマイクロCTを用いてオリーブ果実内部の食害・感染損傷を3次元的に取得・可視化する手法が研究の中心であり、植物の損傷状態を測定している。
abstractA complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained using micro-computed tomography.
In the Baluchistan province of Pakistan, Ziarat and Sherani districts hold significant economic potential for plant cultivation, yet they face severe environmental challenges, including illegal tree cutting, forest fires, and plant diseases. The first part of our comprehensive study, using advanced technology like Landsat 8–9 Operational Land Imager (OLI) imagery from 2013 and 2022 and the Normalized Difference Vegetation Index (NDVI) has revealed alarming statistics: 99% of the vegetation land cover consists of dead plants, 97% are categorized as unhealthy, and very healthy plants are extinct. Projections indicate that moderately healthy plants will disappear in 3 years in Sherani and 6 years in Ziarat. The second part of our study focuses on early disease detection, especially for exotic tree species like olive as Ziarat and Sherani districts are rich in exotic tree species such as pine nut, juniper, and wild olive. We utilized advanced deep-learning techniques and a dataset comprising 5,334 olive leaf images, including those affected by Aculus Olearius and Olive Peacock Spot diseases, in addition to healthy leaves. Innovative transfer learning models such as Inception V3, Inception Resnet V2, MobileNet, and Convolutional Neural Networks (CNN) have been applied to enhance disease identification accuracy. The results highlight the promise of these technologies in early disease detection, with MobileNet demonstrating exceptional performance by reducing execution time through the strategic use of fewer training epochs, achieving a 99% accuracy rate for binary classification and 97.6% for multiclass classification, along with the highest F1 score of 99.4. These findings underscore the urgent need to preserve plant health, protect vegetation, and safeguard species, highlighting the importance of biodiversity and forest conservation in critical regions. Keywords: Environmental challenges, Vegetation health, Disease detection, Deep learning models, Biodiversity conservation
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法を複数比較し、精度やF1スコアを評価しており、植物表現型取得・推定が中心的である。NDVIによる地域植生監視部分は対象外だが、病害推定部分が収載基準を満たす。
abstractThe second part of our study focuses on early disease detection
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.
Why it matches plant phenotyping methods植物の葉画像から病害・害虫状態を推定するAI搭載スマートフォン／市民科学プラットフォームを開発し、野外試験と検出精度評価まで行っており、植物状態の取得・抽出法が中心である。
abstractAn innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests
The rational fertilisation of olive trees, based on adding exclusively the nutrients that are actually needed, is important from both the economic and environmental sustainability points of view. This paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression. The paper shows the convenience of addressing the problem as a Multiple Instance Regression, and compares two strategies of data aggregation and different choices of feature vectors derived from the raw multispectral data. The models provided good results for N, P and K (r² = 0.76, r² = 0.87 and r² = 0.91, respectively for the Hojiblanca model, and r² = 0.79, r² = 0.80 and r² = 0.80 for the Picual model). The rest of nutrients studied also offered good results for both the Picual and Hojiblanca models, ranging from r² = 0.69 for B to r² = 0.93 for Cu. The results indicate a robust performance of the models and a potential for improvement with the addition of more data, along with an advantage of considering individual models for each cultivar variety. Overall, these results are very promising for the estimation of the leaf nutrient content of olives trees and the detection of spatial variability in the fertilisation needs of orchards.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータからオリーブ葉の栄養含量を推定するモデルを開発・比較しており、植物の生理形質取得が研究の中心である。
abstractThis paper employs UAV-obtained multispectral data collected from five different orchards located in Southern Spain to build a set of models for the prediction of the leaf nutrient content of olive trees using Support Vector Regression.
Abstract A complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained by micro-computed tomography. In the case of the olive fruit fly ( Bactrocera oleae ), a complete reconstruction of the galleries was obtained. The galleries were colour-coded according to the internal lumen, corresponding to the size of the larval instars. In the case of the olive moth ( Prays oleae ), it was confirmed that the larvae only consume olive stones, leaving the pulp tissue intact. This study revealed the evolutionary defensive adaptation that the larva has developed by making the entrance/exit gallery in the form of a zigzag with alternating angles to avoid the action of possible parasitoids. In the case of olive fruit rot, caused by a fungal infection transmitted by the midge ( Lasioptera berlesiana ), microtomography revealed the infection cavity, delimited by a protective layer of tissue produced by the plant to isolate the infection zone, full of fungal hyphae and the reproductive organs of the fungus. Below and near the single external orifice present in the concave necrotic depression, two ovoid cavities were observed. These results were interpreted as successive ovipositions of B. oleae and its parasitoid L. berlesiana . High-resolution 3D rendered images are included as well as supplementary videos that could be a useful tool for future research and a valuable teaching aid.
Why it matches plant phenotyping methodsマイクロCTによるオリーブ果実内部の損傷・感染状態の3D取得と再構成が研究の中心であり、植物器官の病害・食害状態を画像化する実質的な表現型計測である。
abstractA complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained by micro-computed tomography.
The bacterial plant pathogen Xylella fastidiosa causes disease in several globally important crops. However, some cultivars harbour reduced bacterial loads and express few symptoms. Evidence considering plant species in isolation suggests xylem structure influences cultivar susceptibility to X. fastidiosa. We test this theory more broadly by analysing high‐resolution synchrotron X‐ray computed tomography of healthy and infected plant vasculature from two taxonomic groups containing susceptible and resistant varieties: two citrus cultivars (sweet orange cv. Pera, tangor cv. Murcott) and two olive cultivars (Koroneiki, Leccino). Results found the susceptible plants had more vessels than resistant ones, which could promote within‐host pathogen spread. However, features associated with resistance were not shared by citrus and olive. While xylem vessels in resistant citrus stems had comparable diameters to those in susceptible plants, resistant olives had narrower vessels that could limit biofilm spread. And while differences among olive cultivars were not detected, results suggest greater vascular connectivity in resistant compared to susceptible citrus plants. We hypothesize that this provides alternate flow paths for sustaining hydraulic functionality under infection. In summary, this work elucidates different physiological resistance mechanisms between two taxonomic groups, while supporting the existence of an intertaxonomical metric that could speed up the identification of candidate‐resistant plants.
Why it matches plant phenotyping methods高解像度X線CT画像から植物の木部血管形態・連結性を抽出し、耐病性候補指標として比較することが研究の中心であり、植物状態の画像ベース表現型解析に該当する。
titleA high‐throughput analysis of high‐resolution X‐ray CT images of stems of olive and citrus plants resistant and susceptible to Xylella fastidiosa
In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.
Why it matches plant phenotyping methodsUAV画像・SfM・DSM/CHMを用いて樹高を抽出する手法を開発・検証しており、植物形質の取得が研究の中心です。
abstractThis paper focuses on the use of low-cost UAV photogrammetry to estimate tree height
Early diagnosis of plant diseases is one of the key elements determining plant productivity. The productivity and quality of plants are significantly reduced when plant diseases are not identified and prevented in a timely manner, which results in major financial losses for producers. Olive is a plant with high added value. While the fruit and oil of olive are consumed as food, its oil is used in cosmetics, medicine, etc. It is also used in industries. In addition, active substances such as oleuropein, triterpene, maslinic acid, and flavonoid found in olive leaves are also used in the pharmaceutical industry. Considering all these valuable uses of olive, the importance of productivity is understood. Plant diseases are one of the most significant factors affecting the yield of olives. Among these diseases, fungal disease called peacock eye can spread to the whole tree through the leaves. This disease causes reduced crop production, defoliation, and rot of tree branches. In this study, an efficient method was developed to detect peacock eye disease from olive leaves. In the first stage, an original dataset of healthy and diseased leaves was created. Then, by extracting deep features from this dataset with CNN models, diseased and healthy leaf classification was performed with the transfer learning approach. As a result of the experiments, very satisfactory results were obtained around 98.63%.
Why it matches plant phenotyping methodsオリーブ葉の病害状態を画像から分類する深層特徴抽出法を開発しており、植物病害表現型の取得・推定が研究の中心である。
abstractIn this study, an efficient method was developed to detect peacock eye disease from olive leaves.
The easy and accurate identification of varieties is fundamental in agriculture, especially in the olive sector, where more than 1200 olive varieties are currently known worldwide. Varietal misidentification leads to many potential problems for all actors in the sector: farmers and nursery workers may establish the incorrect variety, leading to maladaptation in the field; olive oil and table olive producers may label and sell a nonauthentic product; consumers may be misled; and breeders may commit errors during targeted crossings between different varieties. To date, the standard for varietal identification and certification consists of two methods: morphological classification and genetic analysis. The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp. In contrast, different methods for genetic classification exist (RAPDs, SSR, and SNP). Both classification methods present advantages and disadvantages. Visual morphological classification requires highly specialized personnel and is prone to human error. Genetic identification methods are more accurate but incur a high cost and are difficult to implement. This paper introduces OliVaR, a novel approach to olive varietal identification. OliVaR used a teacher–student deep learning architecture to learn the defining characteristics of the endocarp of each specific olive variety and perform varietal classification. We construct what is, to the best of our knowledge, the largest olive variety dataset to date, comprising image data for 131 varieties from the Mediterranean basin. We thoroughly test OliVaR on this dataset and show that it correctly predicts olive varieties with over 86% accuracy.
Why it matches plant phenotyping methodsオリーブ内果皮画像から品種を識別する深層学習手法を開発・評価し、大規模画像データセットも構築しているため、植物形質・状態の画像解析手法が中心です。
abstractThis paper introduces OliVaR, a novel approach to olive varietal identification.
Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 ( Xf ), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf -infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.
Why it matches plant phenotyping methodsHSRとケモメトリクスにより、無症状オリーブ葉からXylella感染状態を推定する手法を中心に扱っており、植物病害状態の非破壊センシング手法開発に直接つながる。
abstractAsymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics.
Accurate and early olive-fruit yield estimation is a greatly desired objective in oliviculture as a tool to increase profitability and sustainability of the exploitations. This paper presents the design and testing of a novel methodology that, unlike previous proposals using models fed with indirect variables, exploits computer vision techniques to estimate yield from visual features of existing visible fruit, as traditionally done by field experts. The design comprised a neural network fed with 16 descriptors, 8 calculated per canopy face, including the number of visible fruits (1 descriptor), the area of exposed fruit (1 descriptor), and other analytical descriptors aimed at mathematically modelling fruit dispersion (2 descriptors) and aggregation (4 descriptors). The methodology was trained on a set of 37 sample points (74 images), and externally validated on a set of 10 (20 images), manually taken in a super-intensive olive orchard of the Picual Olea europaea L. variety located in Elvas (Portugal), two months prior to fruit harvesting. The total actual yield manually measured corresponding to the external validation set was 173.23kg, providing the methodology an estimated yield of 177.80kg, what implied a root-mean-square-error of 0.9914kg per sample point, and an overestimation of 2.64%.
Why it matches plant phenotyping methods可視果実の画像特徴からオリーブ果実収量という植物形質を推定するコンピュータビジョン手法を設計・訓練し、外部検証しており、フェノタイピング手法が中心である。
abstractThis paper presents the design and testing of a novel methodology
Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.
Why it matches plant phenotyping methods高解像度の衛星・航空ハイパースペクトル・熱画像を用いて、植物病害の症状、病勢、植物形質を検出・評価し、センサー間の精度比較も行うことが中心であるため、植物フェノタイピング手法研究に該当する。
abstractHere, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards
Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIₙ, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.
Why it matches plant phenotyping methods高解像度衛星・航空ハイパースペクトル・熱画像を用いて植物病害症状を検出し、精度比較・評価しており、植物の病害状態を推定するセンシング手法が研究の中心です。
abstractHere, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards
To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R 2 values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R 2 , other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.
Why it matches plant phenotyping methodsオリーブ果実のRGB画像から油分・フェノール濃度を推定する画像表現型取得とニューラルネットワーク解析が研究の中心で、性能評価も実施している。
abstractthe combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations
Spectral indices can be used as fast and non-destructive indicators of plant water status or stress. It is the objective of the present study to evaluate the feasibility of using several spectral indices including water index (WI) and normalized spectral water indices 1-5 (NWI 1-5) to estimate water status in olive trees in arid regions in Iran. The experimental treatments involved two olive cultivars (Koroneiki and T2) and four irrigation regimes (irrigated with 100%, 85%, 70%, and 55% estimated crop evapotranspiration [ETc]). The results obtained showed that olive trees subjected to the different irrigation regimes of 85%, 70%, and 55% ETc experienced soil water content (SWC) deficits by 4.5%, 12%, and 20.5% that of the control, respectively. Significant differences were observed among the treatments with respect to measured relative water content (RWC), SWC, and the spectral indices of WI and NWI 1-5. The normalized spectral indices combining NIR and NIR wavelengths were found more effective in tracking changes in RWC and SWC than those that combine NIR and VIS or VIS and VIS wavelengths, respectively. Spectral indices were closely and significantly associated with RWC (.63**<R2<.77**) and SWC (.51**<R2<.67**). Among all the spectral indices investigated, NWI-2 showed the least consistent associations with RWC (ranging from 4-15% lower than the other indices examined) and SWC (ranging from 1-23% lower than the others). Based on the pooled data on spectral indices, RWC, and SWC collected during the study period, WI, NWI-1, NWI-4, and NWI-5 showed stronger correlations with RWC and SWC than did NWI-3 and NWI-2. In conclusion, the spectral indices of WI and NWI 1-5 measured at the leaf level are found useful as fast and non-destructive estimators of plant water stress in arid regions.
Why it matches plant phenotyping methodsオリーブの水分状態という植物生理形質を、葉レベルのスペクトル指数で非破壊推定し、複数指数の有効性と相関を評価しているため、センサー型フェノタイピング手法の実質的な検証・適用に該当する。
abstractIt is the objective of the present study to evaluate the feasibility of using several spectral indices including water index (WI) and normalized spectral water indices 1-5 (NWI 1-5) to estimate water status in olive trees in arid regions in Iran.
Xylella fastidiosa is a xylem‐limited plant pathogen infecting many crops globally and is the cause of the recent olive disease epidemic in Italy. One strategy proposed to mitigate losses is to replant susceptible crops with resistant varieties. Several genetic, biochemical and biophysical traits are associated to X. fastidiosa disease resistance. However, mechanisms underpinning resistance are poorly understood. We hypothesize that the susceptibility of olive cultivars to infection will correlate to xylem vessel diameters, with narrower vessels being resistant to air embolisms and having slower flow rates limiting pathogen spread. To test this, we scanned stems from four olive cultivars of varying susceptibility to X. fastidiosa using X‐ray computed tomography. Scans were processed by a bespoke methodology that segmented vessels, facilitating diameter measurements. Though significant differences were not found comparing stem‐average vessel section diameters among cultivars, they were found when comparing diameter distributions. Moreover, the measurements indicated that although vessel diameter distributions may play a role regarding the resistance of Leccino, it is unlikely they do for FS17. Considering Young–Laplace and Hagen–Poiseuille equations, we inferred differences in embolism susceptibility and hydraulic conductivity of the vasculature. Our results suggest susceptible cultivars, having a greater proportion of larger vessels, are more vulnerable to air embolisms. In addition, results suggest that under certain pressure conditions, functional vasculature in susceptible cultivars could be subject to greater stresses than in resistant cultivars. These results support investigation into xylem morphological screening to help inform olive replanting. Furthermore, our framework could test the relevance of xylem geometry to disease resistance in other crops.
Why it matches plant phenotyping methodsX線CT画像から木部道管をセグメンテーションし、直径分布を測定する手法が研究の中心であり、病害抵抗性に関わる植物形態形質の取得・解析を実施している。
abstractScans were processed by a bespoke methodology that segmented vessels, facilitating diameter measurements.
Precision agriculture is a management strategy to improve resource efficiency, production, quality, profitability and sustainability of the crops. In recent years, olive tree management is increasingly focused on determining the correct health status of the plants in order to distribute the main resource using different technologies. In the olive grove, the focus is often on the use of multispectral information from UAVs (Unmanned Aerial Vehicle), but it is not known how important spectral and biometric information actually is for the agronomic management of the olive grove. The aim of this study was to investigate the ability of multispectral data acquired from a UAV platform to predict nutritional status, biometric characteristics, vegetative condition and production of olive orchard as tool to DSS. Data were collected on vegetative characteristics closely related to vigour such as trunk cross-sectional area (TCSA), Nitrogen concentration of the leaves, canopy area and canopy volume. The production was collected for each plant to create an accurate yield map. The flight was carried out with a UAV equipped with a multispectral camera, at an altitude of 50 m and with RTK correction. The flight made it possible to determine the biometric condition and the spectral features through the normalized difference vegetation index (NDVI). The NDVI map allowed to determine the canopy area. The Structure for Motion (SfM) algorithm allow to determine the 3D canopy volume. The experiment showed that the NDVI was able to determine with high accuracy the vegetative characteristic as canopy area (r = 0.87 ***), TCSA (r = 0.58 ***) and production (r = 0.63 ***). The vegetative parameters are closely correlated with the production, especially the canopy area (r = 0.75 ***). Data clustering showed that the production of individual plants is closely dependent on leaf nitrogen concentration and vigour status.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、NDVI、SfMによる樹冠面積・体積などの植物形質推定を評価し、実測値との相関で検証しているため、フェノタイピング手法が中心である。
abstractThe aim of this study was to investigate the ability of multispectral data acquired from a UAV platform to predict nutritional status, biometric characteristics, vegetative condition and production of olive orchard as tool to DSS.
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.
Why it matches plant phenotyping methodsオリーブ果実を対象とした圃場ハイパースペクトル画像データセットであり、画像取得、正規化、アルゴリズムによるスペクトル特徴抽出、データ保存が中心的に記述されているため、植物フェノタイピング手法・データセットとして収録する。
abstractThis study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September.
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
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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:1-52Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance since the early pre-Roman era. In 2019, Al-Jouf region in a Kingdom of Saudi Arabia's north achieved global prominence by breaking a Guinness World Record for having more number of olive trees in a world. Unmanned aerial systems (UAS) were increasingly being used in aerial sensing activities. However, sensing data must be processed further before it can be used. This processing necessitates a huge amount of computational power as well as the time until transmission. Accurately measuring the biovolume of trees is an initial step in monitoring their effectiveness in olive output and health. To overcome these issues, we initially formed a large scale of olive database for deep learning technology and applications. The collection comprises 250 RGB photos captured throughout Al-Jouf, KSA. This paper employs among the greatest efficient deep learning occurrence segmentation techniques (Mask Regional-CNN) with photos from unmanned aerial vehicles (UAVs) to calculate the biovolume of single olive trees. Then, using satellite imagery, we present an actual deep learning method (SwinTU-net) for identifying and counting of olive trees. SwinTU-net is a U-net-like network that includes encoding, decoding, and skipping links. SwinTU-net's essential unit for learning locally and globally semantic features is the Swin Transformer blocks. Then, we tested the method on photos with several wavelength channels (red, greenish, blues, and infrared region) and vegetation indexes (NDVI and GNDVI). The effectiveness of RGB images is evaluated at the two spatial rulings: 3 cm/pixel and 13 cm/pixel, whereas NDVI and GNDV images have only been evaluated at 13 cm/pixel. As a result of integrating all datasets of GNDVI and NDVI, all generated mask regional-CNN-based systems performed well in segmenting tree crowns ( F 1-measure from 95.0 to 98.0 percent). Based on ground truth readings in a group of trees, a calculated biovolume was 82 percent accurate. These findings support all usage of NDVI and GNDVI spectrum indices in UAV pictures to accurately estimate the biovolume of distributed trees including olive trees.
Why it matches plant phenotyping methodsUAV・衛星画像と深層学習を用いて、個体樹冠の分割、樹木数、およびオリーブ樹のバイオボリュームを推定する手法を開発・評価しており、植物形質の取得が中心である。
abstractAccurately measuring the biovolume of trees is an initial step in monitoring their effectiveness in olive output and health.
Marking the tree canopies is an unavoidable step in any study working with high-resolution aerial images taken by a UAV in any fruit tree crop, such as olive trees, as the extraction of pixel features from these canopies is the first step to build the models whose predictions are compared with the ground truth obtained by measurements made with other types of sensors. Marking these canopies manually is an arduous and tedious process that is replaced by automatic methods that rarely work well for groves with a thick plant cover on the ground. This paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera, regardless of the plant cover density between canopies. The method is based on the relative spatial information between canopies.The planting pattern used by the grower is computed and extrapolated using Delaunay triangulation in order to fuse this knowledge with that previously obtained from spectral information. It is shown that the minimisation of a certain function provides an optimal fit of the parameters that define the marking of the trees, yielding promising results of 77.5% recall and 70.9% precision.
Why it matches plant phenotyping methodsオリーブ樹冠を高解像度UAV画像から自動検出・標識する手法の開発が中心であり、植物の樹冠形態・位置を抽出する画像ベースのフェノタイピング手法に該当する。
abstractThis paper develops a standard method for the detection of olive tree canopies from high-resolution aerial images taken by a multispectral camera
The Olive (Olea europaea L.) is a typical fruit tree of Mediterranean areas characterized by high-quality oil production and high tolerance to water deficit. Due to worldwide water scarcity in Mediterranean regions, it becomes indispensable to monitor plant water status, in example, through xylem water potential (Ψx). Unfortunately, measurement is difficult to perform with high spatial resolution at field scale (> 50 measurements per hectare), due to the large amount of manpower required in the prosses which turned this technique into a high-cost solution. This situation drastically hinders its applicability in large production areas. Thus, the objective of this research is implementing a spatial prediction model of plant water status in an olive orchard, using a single Ψx measurement performed in a reference site over the orchard. The experimental site was established in 2.2 hectares of commercial olive trees in the Pencahue valley located in the Maule region (Chile) during the 2013/14 growing season. Measurements of Ψx were performed at key phenological stages of olive trees. The proposed methodology allowed to estimate the behavior of Ψx in unsampled olive trees from reference site measurements, with an average spatial error less than ±0.6 MPa and correlation of 0.8 (R2) ratifying the high spatial dependence between different sites sampled at field scale. Therefore, distribution of spatial variability would be adequate for the application of irrigation in homogeneous management zones, facilitating water management practices in clearly identified zones within the olive orchard under study.
Why it matches plant phenotyping methodsオリーブの植物水分状態（Ψx）という生理形質を空間予測するモデルを開発・評価しており、形質推定手法が研究の中心である。
abstractThus, the objective of this research is implementing a spatial prediction model of plant water status in an olive orchard, using a single Ψx measurement performed in a reference site over the orchard.
An efficient management of water relies on the correct estimation of tree water requirements and the accurate monitoring of tree water status and canopy growth. This study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment. We used mature olive trees of two cultivars subjected to full irrigation, deficit irrigation (41–44% of full irrigation), or rainfed conditions. Deficit irrigation had limited or no effect on fruit and oil yield. There was a significant relationship between the remotely sensed crop water stress index derived from thermal images and the stem water potential (R2 = 0.83). The RGB images by UAV allowed to estimate tree canopy volume and were able to detect differences in canopy growth across irrigation regimes. A significant relationship between canopy volume and LAI was found for both cultivars (R2 of 0.84 and 0.88 for Frantoio and Leccino, respectively). Our results confirm the positive effects of deficit irrigation strategies to save relevant volumes of water and show that aerial images from UAV can be used to monitor both tree water stress and its effects on canopy growth and yield.
Why it matches plant phenotyping methodsUAVの可視・熱画像を用いてオリーブ樹の水ストレス、樹冠体積、成長を推定し、地上測定やLAIとの関係で検証しており、植物形質取得手法が研究の中心です。
abstractThis study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment.
Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires detailed pre-flight planning in order to generate the desired 3D-products needed for ITD. In this study, we aimed to find the most optimal flight parameters (flight altitude and image overlap) and processing options (smoothing window size) for the detection of taxus trees in Belgium. Next, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece. We found that the processing parameters had a larger effect on the accuracy and precision of ITD than the flight parameters. In particular, a smoothing window of 3 × 3 pixels performed best (F-scores of 0.99) compared to no smoothing (F-scores between 0.88 and 0.90) or a window size of 5 (F-scores between 0.90 and 0.94). Furthermore, the results show that model transferability can still be a bottleneck as it does not capture management induced characteristics such as the typical crown shape of olive trees (F-scores between 0.55 and 0.61).
Why it matches plant phenotyping methodsUAV画像の飛行・処理条件とマーカー制御セグメンテーションを開発・検証し、樹冠の個体検出精度と他樹種への転用性を評価しており、植物画像計測手法が研究の中心である。
abstractNext, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece.
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.
Why it matches plant phenotyping methodsオリーブ葉の病徴を画像から分類する深層学習手法の開発・評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。
abstractwe developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model.
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:203-253Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days.
Why it matches plant phenotyping methodsオリーブ樹園の自動インベントリと、衛星画像による樹冠被覆率（FCC）推定手法の開発・検証が中心であり、植物の樹冠状態を定量化している。
abstractdevelop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques
Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.
Why it matches plant phenotyping methods植物の病徴を目視検査する手法の検出性能を疫学的に評価しており、病害状態のフェノタイピング法の検証が中心です。
abstractIn the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen
Unmanned aerial vehicles (UAVs) are one of the various aerial remote sensing platforms with ease of use and cost-effectiveness it can deliver high-resolution imaging, obtained using a variety of sensors. Photogrammetric data is derived by the use of unmanned aerial systems (UAS, which consists of a UAV, sensor(s), and base station). As a result of these types, vegetation monitoring is conceivable. Deep neural networks have had a lot of success with image classification tasks, especially in the remote sensing field. In this paper, we demonstrate how deep neural networks can be used to classify olive trees status from aerial images. We have addressed a multi-class classification problem. In this work five different neural network architectures: VGG16, ResNet50, MobileNet, Xception, and VGG19 had been compared. Transfer learning had been accomplished using training of the fully connected layer(s) at the end of the deep learning layers. We used metrics such as accuracy, precision, recall, and confusion metric to evaluate the results. With accuracy, our model achieves the best results using ResNet50 with an accuracy is (97.2%).
Why it matches plant phenotyping methodsUAV画像からオリーブ樹の状態を分類する深層学習手法を比較・評価しており、植物の状態推定が研究の中心です。
abstractIn this paper, we demonstrate how deep neural networks can be used to classify olive trees status from aerial images.
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.
Why it matches plant phenotyping methodsオリーブ果実・内果皮の形態形質を対象に、2D/3Dイメージングを比較・検証し、3Dスキャンによる新規形質を抽出しており、フェノタイピング手法が中心である。
abstractThe present study compares two- and three-dimensional imaging systems for phenotyping a large dataset of 50 olive varieties
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:179-197Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Background: Olive (Oleo europaea L.) cultivars are widely cultivated all over the world. However, they are often attacked by pests and pathogens. This deteriorates the quality of the crop, leading to less yield of olive oil. The different infections that cause comparable disease symptoms on olive leaves can be classified using image processing techniques. Objective: The olive has established itself as a superfood and a possible source of medicine, owing to the rapid increase in the availability of data in the field of nutrigenomics. The goal of this review is to underline the importance of applying image processing techniques to detect and classify diseases early. Method: PubMed, ScienceDirect, and Google Scholar were used to conduct a systematic literature search using the keywords olive oil, pest and pathogen of olives, and metabolic profiling. Results: Infections caused by infectious diseases frequently result in significant losses and lowquality olive oil yields. Early detection of disease infestations can safeguard the olive plant and its yield. Results: This strategy can help protect the crop from disease spread, and early detection and classification of the disease can aid in prompt prophylaxis of diseased olive plants before the disease worsens. Protecting olive plants from pests and pathogens can help keep the yield and quality of olive oil consistent.
Why it matches plant phenotyping methodsオリーブ葉の病害を画像処理で早期検出・分類する方法を扱うレビューであり、植物の病徴という状態の取得・推定が中心です。
abstractThe goal of this review is to underline the importance of applying image processing techniques to detect and classify diseases early.
The presence of olive external damages influences consumers perception in the case of table olive, lowering consumers acceptance and willingness to purchase. Defects cause a decrease of extra virgin olive oil quality and its shelf-life. Indeed, fruit external quality represents an important factor for marketing and oil quality characteristics. In this context, RGB image processing systems can potentially support the production of high-quality products through the automatic and rapid classification into different qualitative classes of both lots for oil production and table olives. The neural networks known as Deep Neural Networks represent a kind of artificial intelligence that demonstrated very high levels of accuracy in different application fields. The aim of the present study regards the rapid classification through RGB images and advanced Convolutional Neural Network modeling (YOLO, You Only Look Once) for olives selection on the base of defects and color. The model was trained, tested and evaluated for the future realization of an optomechanical RGB sorting system for real-time olive classification into different classes (e.g., ripening, defects, etc.) through the simultaneous extraction of parameters and dedicated features. The images acquisition was carried out with a high-resolution RGB camera equipped on a laboratory conveyor belt. The algorithm was trained using two datasets: the first made of 1500 oil olive images (i.e., Carboncella, Frantoio and Leccino cultivars), the second one of 930 table olive images (i.e., Bella di Cerignola cultivar). The classification accuracy resulted to be above 95% for both datasets, as required by the high-efficiency standards of a selection prototype.
Why it matches plant phenotyping methodsオリーブ果実の色・外観損傷という器官形質をRGB画像とYOLOで自動抽出・分類し、選別システム用に学習・評価した研究であり、画像取得・解析手法が中心的です。
abstractThe aim of the present study regards the rapid classification through RGB images and advanced Convolutional Neural Network modeling (YOLO, You Only Look Once) for olives selection on the base of defects and color.
Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.
Why it matches plant phenotyping methodsUAV画像と深層学習によりオリーブ樹冠の個数・面積を抽出する手法を開発し、複数モデルとの比較検証を行っており、植物形態形質の取得が研究の中心である。
abstractwe propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net
The olive tree (Olea europaea L.) is one of the main crops of the Mediterranean region which suffers from drought and soil salinization. We assessed the photosynthetic rate, leaf water content and antioxidative enzyme activity (APX, GPX, SOD and CAT) of three Greek olive cultivars (‘Amfisis’, ‘Mastoidis’ and ‘Lefkolia Serron’) subjected to drought and salinity stresses. Hyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements. The photosynthetic rate and water content of stressed plants decreased, while enzyme activity had an increasing tendency. ‘Amfisis’ was more resistant to drought and salinity stress than ‘Mastoidis’ and ‘Lefkolia Serron’. The NDVI appeared to have the highest correlation with the photosynthetic rate, followed by the PRI. APX enzyme activity was the most highly correlated with the 1150–1370 nm range, with an additional peak at 1840 nm. CAT enzyme activity resulted in the highest correlation with the visible part of the spectrum with two peaks at 1480 nm and 1950 nm, while GPX enzyme activity appeared to have a strong correlation within all the available spectral ranges except for 670–1180 nm. Finally, SOD activity showed high correlation values within 1190–1850 nm. This is the first time the correlation of hyperspectral imagery with photosynthetic rate and antioxidant enzyme activities was determined, providing the background for high-throughput plant phenotyping through a drone with a hyperspectral camera. This progress would provide the possibility of early stress detection in large olive groves and assist farmers in decision making and optimizing crop management, health and productivity.
Why it matches plant phenotyping methodsハイパースペクトル計測と回帰・判別モデルにより、光合成速度、水分状態、抗酸化酵素活性という植物生理形質を推定し、高スループット表現型解析への応用を明示しているため、方法が中心的です。
abstractHyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements.
The olive tree (Olea europaea L.) is one of the main crops of the Mediterranean region which suffers from drought and soil salinization. We assessed the photosynthetic rate, leaf water content and antioxidative enzyme activity (APX, GPX, SOD and CAT) of three Greek olive cultivars (‘Amfisis’, ‘Mastoidis’ and ‘Lefkolia Serron’) subjected to drought and salinity stresses. Hyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements. The photosynthetic rate and water content of stressed plants decreased, while enzyme activity had an increasing tendency. ‘Amfisis’ was more resistant to drought and salinity stress than ‘Mastoidis’ and ‘Lefkolia Serron’. The NDVI appeared to have the highest correlation with the photosynthetic rate, followed by the PRI. APX enzyme activity was the most highly correlated with the 1150–1370 nm range, with an additional peak at 1840 nm. CAT enzyme activity resulted in the highest correlation with the visible part of the spectrum with two peaks at 1480 nm and 1950 nm, while GPX enzyme activity appeared to have a strong correlation within all the available spectral ranges except for 670–1180 nm. Finally, SOD activity showed high correlation values within 1190–1850 nm. This is the first time the correlation of hyperspectral imagery with photosynthetic rate and antioxidant enzyme activities was determined, providing the background for high-throughput plant phenotyping through a drone with a hyperspectral camera. This progress would provide the possibility of early stress detection in large olive groves and assist farmers in decision making and optimizing crop management, health and productivity.
Why it matches plant phenotyping methodsハイパースペクトル計測と回帰・判別モデルにより、光合成速度や抗酸化酵素活性などの植物生理状態を推定し、高スループット表現型解析への応用可能性を評価しているため、手法が中心的です。
abstractHyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements.
Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.
Why it matches plant phenotyping methodsドローン orthophoto の画像分割からオリーブ樹冠半径を自動抽出し、収量推定に用いる手法を開発・検証しており、植物形質の取得と予測が研究の中心である。
abstractcombining image analysis techniques with light drone images acquisition and photogrammetric reconstruction
Remote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials and, consequently, to reduce the time and cost of the field work. In this study, we assessed the ability of a UAV equipped with RGB-NIR cameras in highlighting differences in geometrical and spectral canopy characteristics between eight olive cultivars planted at different planting distances in a hedgerow olive orchard. The relationships between measured and estimated canopy height, projected canopy area and canopy volume were linear regardless of the different cultivars and planting distances (RMSE of 0.12 m, 0.44 m2 and 0.68 m3, respectively). A good relationship (R2 = 0.95) was found between the pruning mass material weighted on the ground and its volume estimated by aerial images. NDVI measured in February 2019 was related to fruit yield per tree measured in November 2018, whereas no relationships were observed with the fruit yield measured in November 2019 due to abiotic and biotic stresses that occurred before harvest. These results confirm the reliability of UAV imagery and structure from motion techniques in estimating the olive geometrical canopy characteristics and suggest further potential applications of UAVs in early discrimination of yield efficiency between different cultivars and in estimating the pruning material volume.
Why it matches plant phenotyping methodsUAV画像とStructure from Motionを用いたオリーブ樹冠形質の推定を中心に、実測値との精度検証も行っているため、植物表現型解析手法として適格。
abstractRemote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials
This work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH), an important 3D structural parameter in forest inventory and biomass estimation. The study areas were an agricultural field located in the province of Málaga, Spain, where a small group of olive trees was chosen for the UAV surveys, and an open woodland area in the outskirts of Sofia, the capital of Bulgaria, where a 10 ha area grove, composed mainly of birch trees, was overflown. A DJI Phantom 4 Pro quadcopter UAV was used for the image acquisition. We applied structure from motion (SfM) to generate 3D point clouds of individual trees, using Agisoft and Pix4D software packages. The estimation of DBH in the point clouds was made using a RANSAC-based circle fitting tool from the TreeLS R package. All trees modeled had their DBH tape-measured on the ground for accuracy assessment. In the first study site, we executed many diversely designed flights, to identify which parameters (flying altitude, camera tilt, and processing method) gave us the most accurate DBH estimations; then, the resulting best settings configuration was used to assess the replicability of the method in the forested area in Bulgaria. The best configuration tested (flight altitudes of about 25 m above tree canopies, camera tilt 60°, forward and side overlaps of 90%, Agisoft ultrahigh processing) resulted in root mean square errors (RMSEs; %) of below 5% of the tree diameters in the first site and below 12.5% in the forested area. We demonstrate that, when carefully designed methodologies are used, SfM can measure the DBH of single trees with very good accuracy, and to our knowledge, the results presented here are the best achieved so far using (above-canopy) UAV-based photogrammetry.
Why it matches plant phenotyping methodsUAV画像測量とSfMによる樹木DBH推定手法を、飛行設計・処理条件の比較および地上実測との精度検証を通じて評価しており、植物形態形質の取得方法が中心である。
abstractThis work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH)
Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca ( Xfp ) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp -infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.
Why it matches plant phenotyping methodsオリーブ葉のハイパースペクトルデータ解析を用いて、感染植物を早期識別する診断手法の性能を評価しており、植物の病害状態を測定する方法が中心である。
abstractThe aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources.
The elucidation of plant health status requires quantifying multiple molecular metabolism markers. Until now, the extraction of these biomarkers is performed independently, with different extractions and protocols. This approach is inefficient, since it increases laboratory time, amount of sample, and could introduce biases or difficulties when comparing data. To limit these drawbacks, we introduce a versatile protocol for quantifying seven of the most commonly analysed biomarkers (photosynthetic pigments, free amino acids, soluble sugars, starch, phenolic compounds, flavonoids and malondialdehyde) covering substantial parts of plant metabolism, requiring only a minimum sample amount and common laboratory instrumentation. The procedures of this protocol rely on classic methods that have been updated to allow their sequential use, increasing reproducibility, sensibility and easiness to obtain quantitative results. Our method has been tested and validated over an extended diversity of organisms (Arabidopsis thaliana, Solanum lycopersicum, Olea europaea, Quercus ilex, Pinus pinaster and Chlamydomonas reinhardtii), tissues (leaves, roots and seeds) and stresses (cold, drought, heat, ultraviolet B and nutrient deficiency). Its application will allow increasing the number of parameters that can be monitored at once while decreasing sample handling and consequently, increasing the capacity of the laboratory.
Why it matches plant phenotyping methods植物の健康状態に関わる複数の生理・生化学的形質を、少量試料から連続的に定量する統合プロトコルを開発し、多様な植物・組織・ストレスで検証しており、測定法自体が中心である。
abstractwe introduce a versatile protocol for quantifying seven of the most commonly analysed biomarkers
Harvest timing in olive orchards has a strong effect on the quality and quantity of oil yield, but many farmers still lack simple and affordable quantitative tools for rationally deciding appropriate harvest dates. This study presents and tests a conceptual model for predicting fruit oil content (Of, g oil fruit⁻¹) from inexpensive measurements of fruit dry weight (wf). The model presents two physiologically relevant parameters, the fruit dry weight at the onset of the oil accumulation phase (wf₀) and the ratio of accumulated oil per unit of fruit dry weight increase during the oil accumulation period (β), the latter assumed invariable throughout ripening. A compilation of data on wf and Of dynamics collected from four experiments including six olive cultivars and contrasting conditions of water supply and crop load was used to test the model. Our results suggest that β could be fairly independent of crop load or watering regime and, probably, genetically controlled. By contrast, wf₀ is clearly affected by both the cultivar and the availability of assimilates for fruit growth preceding oil accumulation, which makes it orchard- and year-specific. According to those premises, once cultivar-specific β values are available wf₀ could be easily calibrated by either a single determination of Of and wf at any time during the oil accumulation phase (Approach A) or by directly measuring wf₀ if the date for the onset of oil accumulation can be estimated (Approach B). Validation tests with an independently calibrated β showed an excellent performance for reproducing Of patterns from wf data using Approach A. Approach B satisfactory predicted oil accumulation rates, but absolute estimates of Of were less reliable. Regardless of the calibration approach, the model is easy to implement and has a minimal cost, which satisfies the demand for inexpensive tools for monitoring oil accumulation dynamics.
Why it matches plant phenotyping methodsオリーブ果実の乾物重から油含量を推定するモデルを開発し、独立データで性能検証しており、果実形質の取得・推定手法が研究の中心である。
abstractThis study presents and tests a conceptual model for predicting fruit oil content (Of, g oil fruit⁻¹) from inexpensive measurements of fruit dry weight (wf).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
A comprehensive characterization of water stress is needed for the development of automated irrigation protocols aiming to increase olive orchard environmental and economical sustainability. The main aim of this study is to determine whether a combination of continuous leaf turgor, fruit growth, and sap flow responses improves the detection of mild water stress in two olive cultivars characterized by different responses to water stress. The sensitivity of the tested indicators to mild stress depended on the main mechanisms that each cultivar uses to cope with water deficit. One cultivar showed pronounced day to day changes in leaf turgor and fruit relative growth rate in response to water withholding. The other cultivar reduced daily sap flows and showed a pronounced tendency to reach very low values of leaf turgor. Based on these responses, the sensitivity of the selected indicators is discussed in relation to drought response mechanisms, such as stomatal closure, osmotic adjustment, and tissue elasticity. The analysis of the daily dynamics of the monitored parameters highlights the limitation of using non-continuous measurements in drought stress studies, suggesting that the time of the day when data is collected has a great influence on the results and consequent interpretations, particularly when different genotypes are compared. Overall, the results highlight the need to tailor plant-based water management protocols on genotype-specific physiological responses to water deficit and encourage the use of combinations of plant-based continuously monitoring sensors to establish a solid base for irrigation management.
Why it matches plant phenotyping methods複数の連続植物センサーによる葉の膨圧、果実成長、樹液流の測定を用い、軽度水ストレス検出の有効性・感度・測定頻度の限界を評価しており、植物状態の取得方法が中心です。
abstractThe main aim of this study is to determine whether a combination of continuous leaf turgor, fruit growth, and sap flow responses improves the detection of mild water stress
Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the most widely applied technologies in metabolomics, as it provides a blend of rapid, sensitive, and selective qualitative and quantitative analyses with the ability to identify metabolites. The purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves. Results obtained via multivariate analysis through an HPLC-ESI HRMS platform (LTQ-Orbitrap from Thermo Scientific) show a clear separation between HP and OP samples. Among the differentially expressed metabolites, 18 different organic compounds highly expressed in the OP group were annotated; results obtained by this metabolomic approach could be used as a fast and reliable method for the biochemical characterization of OQDS and to develop targeted MS approaches for OQDS detection by foliage analysis.
Why it matches plant phenotyping methodsオリーブ葉の代謝プロファイルから感染状態を識別するHPLC-HRMS測定・解析法の開発が中心であり、植物病害状態の生理的フェノタイプ取得に該当する。
abstractThe purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves.
Thermal stress indicators are one of the most accurate indices for sensing plant water status that can be remotely measured by the means of infrared thermography. In addition to the canopy temperature, these indices need to access the wet and dry reference temperatures which refer to the temperatures of the canopy at well-watered and fully stressed conditions, respectively. The main goal of this study is to measure the canopy as well as reference temperatures automatically by the means of a single thermal image, captured from an olive tree. The temperatures of artificial reference surfaces were extracted by the means of an object detection method based on the edge detection and morphological processes. The temperatures of sunlit and shaded canopy portions were also detected, using a Fuzzy C-means clustering of thermal images with the wet and dry reference temperatures as thresholds. The algorithm was successfully detected the references in 90% of the images and the automatic extracted canopy temperatures were significantly correlated with the manual ones.
Why it matches plant phenotyping methods熱画像から作物の樹冠温度と水ストレス指標用の基準温度を自動抽出する画像処理手法を開発・検証しており、植物状態の取得が研究の中心である。
abstractThe main goal of this study is to measure the canopy as well as reference temperatures automatically by the means of a single thermal image, captured from an olive tree.
Accurate yield estimation is a greatly desired objective in oliviculture due to the high economic value of its production. This paper presents a methodology aimed at achieving that end. It comprises an artificial-vision algorithm able to detect visible olives in digital images of olive trees captured directly in the field, at night-time and with artificial illumination. These images were taken in an intensive olive orchard of the Picual Olea europaea L. variety in September 2018 (two months prior to harvesting). Regarding the methodology, first, the images are pre-processed to generate a set of sub-images with high probability of containing an olive, thus reducing the search space by a magnitude of 10³. Next, these sub-images are classified by a convolutional neural network (CNN) as olive, if they are centred in an olive fruit, or as other in any other case (even if they contain peripheral fruits). To train and validate the CNN, a special database called OLIVEnet was compiled with two disjoint sets integrating these sub-images. A training and a validation set was built with 234,168 and 299,946 olive and other sub-images, respectively. Five different CNN topologies were tested, correctly classifying the best performing one in 83.13% of olive instances, with a precision of 84.80%, and 99.12% of other instances; measured accuracy and F₁ Score were 0.9822 and 0.8396, respectively. As far as the authors' knowledge goes, this article presents the first image analysis approach to automatically identify olive fruits in an image of the entire tree directly taken in the field. The obtained results constitute a first and solid step towards the implementation of an automatic system for yield estimation of olive orchards.
Why it matches plant phenotyping methods圃場画像からオリーブ果実を自動検出し、収量推定に用いる画像解析・CNN手法を開発および検証しており、植物器官の表現型取得が中心である。
abstractThis paper presents a methodology aimed at achieving that end. It comprises an artificial-vision algorithm able to detect visible olives in digital images of olive trees captured directly in the field
Xylella fastidiosa ( Xf ) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sørensen-Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives.
Why it matches plant phenotyping methodsUAVによる可視・マルチスペクトル画像と新規セグメンテーション／分類手法で、オリーブ樹の病徴を検出する方法が研究の中心であり、性能指標も提示されている。
abstractimage processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees.
Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a "data fusion" procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans.
Why it matches plant phenotyping methodsUAV・近接センサー・目視によるオリーブ樹の病徴／感染状態を統合し、植物病害の感染リスクを確率推定するデータ融合手法が研究の中心であるため。
abstractThe main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.
Why it matches plant phenotyping methods低コストの3D画像ベース植物フェノタイピング基盤を開発・性能評価し、植物形質の再構成精度と処理時間を検証しているため、方法が研究の中心です。
abstractThis study aims to test the performances of a low-cost and automatic phenotyping platform
About 1.5 Mha of olive orchards are found in the southern Spanish region of Andalusia, representing over 15% of the world olive surface. Some of the most critical rates of soil erosion in Mediterranean agriculture have been found in the local steep slopes of olive orchards (> 61 t ha-1 year-1), where soil is frequently tilled to avoid crop-weeds competition. Conservation agriculture has been proposing alternative strategies such as the use of inter-row cover crops (CC), sown or indigenous, during the period of lowest evaporative demand, with effective (chemical or mechanical) control in spring to avoid significant inter-specific competition for water during the critical period. However, despite the efforts of policy making and scientific research, the use of CC has not been fully adopted yet and a high variability regarding the fraction of ground cover is still observed in the region. In this sense, a better understanding on the main factors driving such variability is required and the development of an up-scaled methodology for mapping and analyzing CC dynamics in olive orchards could considerably contribute to it. In this light, we developed and tested a ‘big data’ approach trained to quantify the fractional green canopy cover (FGCC) as a key diagnostic variable of CC dynamics. We started by collecting the time-series of summer vegetation signals in order to represent FGCC in the absence of CC, assuming that the fraction of bare soil was maximum in summer as CC was controlled before the maximum evaporative period. Therefore, the FGCC of olive trees was directly derived from summer imagery and inter-row FGCC (%CC) was calculated as the difference between 'real time' and summer FGCC (assumed as constant for mature plants in the absence of pruning or other canopy-reducing factors). A validation dataset (N=1600) was built from Deimos-2 satellite data (4x4m), assessed with an image processing package (Fiji Image-J) and based on a binary classification according to the structure of each pixel brightness histogram. Different machine learning (ML) methods taking into account all satellite bands were tested against standard vegetation indices (NDVI, EVI, BI). A higher robustness in predicting FGCC was achieved when using ML methods rather than vegetation indexes, especially for the case of PLS regression, Bayesian Ridge or Multiple Linear Regression Models (MLR). A model based on PLS was tested on Sentinel-2 data for more than 16.500 plots and evaluated with both the Deimos-2 validation dataset and field observations. The PLS model revealed a satisfactory potential to be used from crop field (10x10m) to landscape scale, with a temporal resolution of 5-10 days in cloud-free conditions. Pixel classification showed higher accuracy when distinguishing between higher CC densities (high from >60 to medium
Why it matches plant phenotyping methods衛星画像と機械学習によりオリーブ園の植生被覆率（FGCC）を推定する手法を開発・検証しており、植物状態の定量的取得が研究の中心である。
abstractwe developed and tested a ‘big data’ approach trained to quantify the fractional green canopy cover (FGCC) as a key diagnostic variable of CC dynamics.
3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.
Why it matches plant phenotyping methods3D・マルチスペクトル画像の融合と自動解析により、個体ごとの樹高・体積・スペクトル形質・健康状態を抽出する手法が研究の中心である。
abstractwe propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard.
Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と3D点群、形態学的画像解析により、樹木の分割・計数・位置推定を行う植物フェノタイピング手法を開発・検証しており、手法が研究の中心である。
abstracta methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed.
Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r²VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.
Why it matches plant phenotyping methodsオリーブ樹のXylella感染症状・感染率を、3D放射伝達モデル、航空ハイパースペクトル画像、Sentinel-2時系列から推定する手法を開発し、3000本超の樹木で検証しており、植物状態の取得・推定が中心である。
abstractWe developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations.
The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders.
Why it matches plant phenotyping methodsオリーブ育種試験向けに、UAV画像とOBIAによる樹体形態形質の高スループット取得システムを開発し、実験試験で精度評価しているため、植物フェノタイピング手法が中心である。
abstractan UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions.
A comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article. The purpose was to validate the suitability of unmanned aerial vehicle (UAV) photogrammetry and 3D modeling techniques with respect to manual and traditional methods of TRV detection. The use of UAV photogrammetry can reduce the amount of estimated biomass and, therefore, reduce the volume of pesticides to be used in the field by means of more accurate prescription maps. The presented comparison of methodologies was performed on an adult super-high-density olive orchard, planted with a density of 1660 trees per hectare. The first method (TRV1) was based on close-range photogrammetry from UAVs, the second (TRV2) was based on manual in situ measurements, and the third (TRV3) was based on a formula from the literature. The comparisons of TRV2-TRV1 and TRV3-TRV1 showed an average value of the difference equal to +13% (max: +65%; min: −11%) and +24% (max: +58%; min: +5%), respectively. The results show that the TRV1 method has high accuracy in predicting TRV with minor working time expenditure, and the only limitation is that professionally skilled personnel is required.
Why it matches plant phenotyping methodsUAVフォトグラメトリと3Dモデリングによるオリーブ樹列体積（TRV）推定法を、手作業・従来法と比較検証しており、植物形態・バイオマス関連形質の取得手法が中心である。
abstractA comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article.
We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.
Why it matches plant phenotyping methodsUAV搭載マルチスペクトルカメラと植生指数を用いて、オリーブ園の生育不均一性・灌漑不均一性を検出する手法を開発しており、植物状態の取得方法が研究の中心である。
abstractWe have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV).
OliveField / plotGreenhouseLeafGrowth / time-series analysisGrowth / development / phenology
The abundance of scientific papers dealing with olive reproductive phenology contrasts with the scarce information available in relation to the winter dormant state of olive vegetative structures. In this study, three experiments with young olive trees were performed in Southern Spain, aiming to provide insight into some features of the winter rest period in this evergreen species. Experiment 1 evaluated the environmental cues triggering dormancy induction by measuring leaf appearance rates in trees subjected to different conditions of temperature and daylength over the course of the 2012 autumn. In Experiment 2, several sets of plants were placed into a greenhouse at different dates along the 2013/2014 winter, testing the ability of dormant plants to resume growth upon the return of favourable temperatures. Finally, Experiment 3 was carried out during the autumns of 2016 and 2017 in two locations, and was devoted to assess differences between five cultivars in the onset of dormancy under natural conditions. Our findings revealed that dormancy induction is not controlled by photoperiod, but by low temperatures. The subsequent winter rest state seems to be easily reversed after 1–2 weeks of exposure to warm conditions, irrespective of the initial date of exposure. With regard to cultivar variability, differences in the timing of growth cessation was found to be rather small. Finally, two simple models for predicting the onset of dormancy based on the accumulation of a certain amount of chilling (either considering or not a reversal of chilling by warm temperatures) are presented. Calibration and validation was performed with independent datasets from Experiments 1, 2 and 3. Validation tests highlighted the reliability of both models in reproducing the date of growth cessation.
Why it matches plant phenotyping methodsオリーブの休眠開始という植物状態を予測するモデルを開発し、独立データセットで較正・検証しており、単なる生物学的測定にとどまらず予測手法が中心である。
abstractFinally, two simple models for predicting the onset of dormancy based on the accumulation of a certain amount of chilling (either considering or not a reversal of chilling by warm temperatures) are presented.
Monitoring Xylella fastidiosa is critical for eradicating or at least containing this harmful pathogen. New low-cost and rapid methods for early detection capability are very much needed. Metabolomics may play a key role in diagnosis; in fact, mobile metabolites could avoid errors in sampling due to erratically distributed pathogens. Of the various different mobile signals, we studied dicarboxylic azelaic acid (AzA) which is a key molecule for biotic stress plant response but has not yet been associated with pathogens in olive trees. We found that infected Olea europaea L. plants of cultivars Cellina di Nardò (susceptible to X. fastidiosa) and Leccino (resistant to the pathogen) showed an increase in AzA accumulation in leaf petioles and in sprigs by approximately seven- and sixfold, respectively, compared with plants negative to X. fastidiosa or affected by other pathogens. No statistically significant variation was found between the X. fastidiosa population level and the amount of AzA in either of the plant tissues, suggesting that AzA accumulation was almost independent of the amount of pathogen in the sample. Furthermore, the association of AzA with X. fastidiosa seemed to be reliable for samples judged as potentially false-negative by quantitative polymerase chain reaction (cycle threshold [C t ] > 33), considering both the absolute value of AzA concentration and the values normalized on negative samples, which diverged significantly from control plants. The accumulation of AzA in infected plants was partially supported by the differential expression of two genes (named OeLTP1 and OeLTP2) encoding lipid transport proteins (LTPs), which shared a specific domain with the LTPs involved in AzA activity in systemic acquired resistance in other plant species. The expression level of OeLTP1 and OeLTP2 in petiole samples showed significant upregulation in samples positive to X. fastidiosa of both cultivars, with higher expression levels in positive samples of Cellina di Nardò compared with Leccino, whereas the two transcripts had a low expression level (C t > 40) in negative samples of the susceptible cultivar. Although the results derived from the quantification of AzA cannot confirm the presence of the erratically distributed X. fastidiosa, which can be definitively assessed by traditional methods, we believe they represent a fast and cheap screening method for large-scale monitoring.
Why it matches plant phenotyping methods植物体内のアゼライン酸蓄積を感染状態の指標として評価し、PCRや他病原体対照との比較を通じて、Xylella fastidiosa感染植物の迅速スクリーニング法として検証している。単なる代謝測定ではなく、病害状態の判定法が中心である。
abstractwe believe they represent a fast and cheap screening method for large-scale monitoring.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Leaf analysis is a useful way of diagnosing the nutritional status of the plants and therefore fast methods of analysis are demanded to aid in fertilization management decisions. In this work, a strategy based on the combined use of near-infrared spectroscopy (NIR) and portable energy dispersive X-Ray Fluorescence (EDXRF) is proposed as a suitable cheap and rapid alternative to traditional wet analytical methodologies. The approach has the major benefit of minimal sample preparation since leaves need to be only dried and ground. The ability of both techniques individually and applying two strategies of data fusion for the prediction of the most important plant nutrients, namely N, P, K, Ca, Mg, Mn, Zn, and B was tested. Predictive models were constructed using Partial Least Squares (PLS) to correlate the spectra with the nutrient contents. Models of unequal prediction performance in terms of the ratio of predictive deviation (RPD) were obtained for the different parameters when considering both techniques separately. Low-level data fusion, which consists of a concatenation of the raw data from both techniques, showed little improvement and even decreased the predictive ability for some elements. Better results were obtained with mid-level data fusion, that is, merging data after a feature extraction step performed by means of Principal components analysis (PCA). The results show that a fair quantitative prediction is possible for Ca, K and Mn with RPDs ≥ 2 for external validation, whereas models for N and P allowed a semiquantitative estimation. Mg and B models were less satisfactory and can be used only for distinguish between low and high levels, while Zn content cannot be predicted. Finally, the potential of the fusion of FT-NIR and EDXRF spectroscopic data for the fast screening of olive crop nutritional status has been tested. Deficiencies in important elements like N and K has been successfully detected.
Why it matches plant phenotyping methodsNIRとEDXRFのデータ融合により、オリーブ葉の栄養素含量と作物栄養状態を推定する測定・予測手法を開発し、外部検証している。化学分析の単なる利用ではなく、植物状態の取得・推定法が中心である。
abstracta strategy based on the combined use of near-infrared spectroscopy (NIR) and portable energy dispersive X-Ray Fluorescence (EDXRF) is proposed as a suitable cheap and rapid alternative to traditional wet analytical methodologies.
This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains.
Why it matches plant phenotyping methodsオリーブ果実の質量・サイズという植物器官形質を、画像解析、数学的形態学、統計的閾値処理で推定する手法を開発し、外部検証しており、フェノタイピング手法が研究の中心である。
abstractThis paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques.
Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security 1 , ecosystem integrity and societies in general 2-5 . Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment 6 . Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa's geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage 7,8 . X. fastidiosa can infect more than 350 plant species worldwide 9 , and early detection is critical for its eradication 8 . In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.
Why it matches plant phenotyping methods航空画像分光・熱画像から植物機能形質を抽出し、オリーブ樹のXylella感染を可視症状前に検出する手法を中心的に開発・実証しているため、植物フェノタイピング手法研究に該当する。
abstractchanges in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible.
New super-high-density (SHD) olive orchards designed for mechanical harvesting using over-the-row harvesters are becoming increasingly common around the world. Some studies regarding olive SHD harvesting have focused on the effective removal of the olive fruits; however, the energy applied to the canopy by the harvesting machine that can result in fruit damage, structural damage or extra stress on the trees has been little studied. Using conventional analyses, this study investigates the effects of different nominal speeds and beating frequencies on the removal efficiency and the potential for fruit damage, and it uses remote sensing to determine changes in the plant structures of two varieties of olive trees (‘Manzanilla Cacereña’ and ‘Manzanilla de Sevilla’) planted in SHD orchards harvested by an over-the-row harvester. ‘Manzanilla de Sevilla’ fruit was the least tolerant to damage, and for this variety, harvesting at the highest nominal speed led to the greatest percentage of fruits with cuts. Different vibration patterns were applied to the olive trees and were evaluated using triaxial accelerometers. The use of two light detection and ranging (LiDAR) sensing devices allowed us to evaluate structural changes in the studied olive trees. Before- and after-harvest measurements revealed significant differences in the LiDAR data analysis, particularly at the highest nominal speed. The results of this work show that the operating conditions of the harvester are key to minimising fruit damage and that a rapid estimate of the damage produced by an over-the-row harvester with contactless sensing could provide useful information for automatically adjusting the machine parameters in individual olive groves in the future.
Why it matches plant phenotyping methodsオリーブ樹の収穫前後の構造変化をLiDARで評価し、加速度計と組み合わせて収穫による植物状態・損傷を測定している。センシングによる植物形態評価が研究の主要な技術的構成要素である。
abstractit uses remote sensing to determine changes in the plant structures of two varieties of olive trees
Olive growing is mainly based on traditional varieties selected by the growers across the centuries. The few attempts so far reported to obtain new varieties by systematic breeding have been mainly focused on improving the olive adaptation to different growing systems, the productivity and the oil content. However, the improvement of oil quality has rarely been considered as selection criterion and only in the latter stages of the breeding programs. Due to their health promoting and organoleptic properties, phenolic compounds are one of the most important quality markers for Virgin olive oil (VOO) although they are not commonly used as quality traits in olive breeding programs. This is mainly due to the difficulties for evaluating oil phenolic composition in large number of samples and the limited knowledge on the genetic and environmental factors that may influence phenolic composition. In the present work, we propose a high throughput methodology to include the phenolic composition as a selection criterion in olive breeding programs. For that purpose, the phenolic profile has been determined in fruits and oils of several breeding selections and two varieties ("Picual" and "Arbequina") used as control. The effect of three different environments, typical for olive growing in Andalusia, Southern Spain, was also evaluated. A high genetic effect was observed on both fruit and oil phenolic profile. In particular, the breeding selection UCI2-68 showed an optimum phenolic profile, which sums up to a good agronomic performance previously reported. A high correlation was found between fruit and oil total phenolic content as well as some individual phenols from the two different matrices. The environmental effect on phenolic compounds was also significant in both fruit and oil, although the low genotype × environment interaction allowed similar ranking of genotypes on the different environments. In summary, the high genotypic variance and the simplified procedure of the proposed methodology for fruit phenol evaluation seems to be convenient for breeding programs aiming at obtaining new cultivars with improved phenolic profile.
Why it matches plant phenotyping methodsオリーブ果実のフェノール組成を育種選抜に利用するための高スループット評価法を提案し、複数環境・遺伝子型で評価している。果実形質の取得手法が研究の中心であり、単なる生物学的測定ではない。
abstractIn the present work, we propose a high throughput methodology to include the phenolic composition as a selection criterion in olive breeding programs.
Addressing the spatial and temporal variability of crops for agricultural management requires intensive and periodical information gathering from the crop fields. Unmanned Aerial Vehicle (UAV) photogrammetry is a quick and affordable method for information collecting; it provides spectral and spatial information when required with the added value of Digital Surface Models (DSMs) that reconstruct the crop structure in 3D using “structure from motion” techniques. In the full process from UAV flights to image analysis, DSM generation is one bottle-neck due to its high processing time. Despite its importance, the optimization of the required forward overlap for saving time in DSM generation has not yet been studied. UAV images were acquired at 50 and 100 m flight altitudes over two olive orchards with the aim of generating DSMs representing the tree crowns. Several DSMs created with different forward laps (in intervals of 5–6% from 58 to 97%) were evaluated in order to determine the optimal generation time according to the accuracy of tree crown measurements computed from each DSM. Based on our results, flying at 100 m altitude and with a 95% forward lap reported the best configuration. From the analysis derived from this configuration, tree volume was estimated with 95% accuracy. In addition, computing time was 85% lower in comparison to the maximum overlap studied (97%). It allowed computing the 3D features of 600 trees in a 3-ha parcel in a highly accurate and quick (a few hours after the UAV flights) manner by using a standard computer.
Why it matches plant phenotyping methodsUAV画像からDSMを生成し、樹冠測定・樹体積を推定する処理条件を比較最適化しており、植物形態計測手法の技術評価が中心である。
abstractSeveral DSMs created with different forward laps (in intervals of 5–6% from 58 to 97%) were evaluated in order to determine the optimal generation time according to the accuracy of tree crown measurements computed from each DSM.
Background The morphological analysis of olive leaves, fruits and endocarps may represent an efficient tool for the characterization and discrimination of cultivars and the establishment of relationships among them. In recent years, much attention has been focused on the application of molecular markers, due to their high diagnostic efficiency and independence from environmental and phenological variables. Results In this study, we present a semi-automatic methodology of detecting various morphological parameters. With the aid of computing and image analysis tools, we created semi-automatic algorithms applying intuitive mathematical descriptors that quantify many fruit, leaf and endocarp morphological features. In particular, we examined quantitative and qualitative characters such as size, shape, symmetry, contour roughness and presence of additional structures such as nipple, petiole, endocarp surface roughness, etc.. Conclusion We illustrate the performance and the applicability of our approach on Greek olive cultivars; on sets of images from fruits, leaves and endocarps. In addition, the proposed methodology was also applied for the description of other crop species morphologies such as tomato, grapevine and pear. This allows us to describe crop morphologies efficiently and robustly in a semi-automated way.
Why it matches plant phenotyping methods植物器官の画像から形態形質を抽出・定量する半自動画像解析手法が研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractwe present a semi-automatic methodology of detecting various morphological parameters.
Aims To set up a practical method to detect latent infections of Colletotrichum sp., the causal agent of olive anthracnose, on olives before the onset of disease symptoms. Methods and results Freezing, sodium hydroxide (NaOH), ethanol and ethylene treatments were evaluated to detect latent infections on inoculated and naturally infected olive fruit by Colletotrichum sp. as non-hazardous alternatives to paraquat. Treatments were conducted using fruit of cultivars Arbequina and Hojiblanca. The disease incidence and T 50 were calculated. Dipping in NaOH 0·05% solution and the paraquat method were the most effective treatments on both inoculated and naturally infected fruit, although the value of T 50 was lower for the NaOH method than for the paraquat method in one of the experiments. Subsequently, the dipping time in NaOH 0·05% was evaluated. Longer dipping times in NaOH 0·05% were better than shorter ones in cultivar Arbequina, with 72 h being the most effective in cultivar Hojiblanca. Conclusions NaOH solution is a practical method to detect latent infections of Colletotrichum sp. on immature olive fruit. Significance and impact of the study This study is relevant because we set up a viable, non-hazardous alternative to paraquat to detect latent infections of Colletotrichum sp. using NaOH. The use of NaOH is a simple and eco-friendly tool that allows the determination of the level of latent infections by Colletotrichum in olives. Therefore, our method will be useful in decision-making processes for disease management before the appearance of the first visible symptoms.
Why it matches plant phenotyping methodsオリーブ果実の潜在感染という植物の病態を検出・定量する方法の設定と比較検証が研究の中心であり、単なる病理実験の routine 測定ではない。
abstractTo set up a practical method to detect latent infections of Colletotrichum sp., the causal agent of olive anthracnose, on olives before the onset of disease symptoms.
OliveX-ray / CTTissuePhysiological trait estimationWater status / transpiration
Xylem vulnerability to embolism represents an essential trait for the evaluation of the impact of hydraulics in plant function and ecology. The standard centrifuge technique is widely used for the construction of vulnerability curves, although its accuracy when applied to species with long vessels remains under debate. We developed a simple diagnostic test to determine whether the open-vessel artefact influences centrifuge estimates of embolism resistance. Xylem samples from three species with differing vessel lengths were exposed to less negative xylem pressures via centrifugation than the minimum pressure the sample had previously experienced. Additional calibration was obtained from non-invasive measurement of embolism on intact olive plants by X-ray microtomography. Results showed artefactual decreases in hydraulic conductance (k) for samples with open vessels when exposed to a less negative xylem pressure than the minimum pressure they had previously experienced. X-Ray microtomography indicated that most of the embolism formation in olive occurs at xylem pressures below -4.0 MPa, reaching 50% loss of hydraulic conductivity at -5.3 MPa. The artefactual reductions in k induced by centrifugation underestimate embolism resistance data of species with long vessels. A simple test is suggested to avoid this open vessel artefact and to ensure the reliability of this technique in future studies.
Why it matches plant phenotyping methods木部栓塞抵抗性という植物生理形質の測定法について、遠心法のアーティファクト診断テストを開発し、X線マイクロトモグラフィーで校正・検証しているため、方法が中心である。
abstractWe developed a simple diagnostic test to determine whether the open-vessel artefact influences centrifuge estimates of embolism resistance.
LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.
Why it matches plant phenotyping methods自作モバイルLiDARスキャナと点群解析アルゴリズムを開発し、樹冠容積・形状・構造という植物形質を推定、従来法との比較検証も行っており、フェノタイピング手法が中心である。
abstracta self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume.