Brown Rot (Monilinia spp.) and Leaf Curl (Taphrina deformans) are principal fungal diseases affecting peach (Prunus persica L. Batsch) production, lacking validated AI-based diagnostic tools in tropical highland orchards. This study presents a compact Convolutional Neural Network (3658 trainable parameters), applied identically to fruit and leaf classification, integrated with a background-removal preprocessing pipeline and evaluated through stratified 5-fold cross-validation on 800 in-situ images from four orchards in Cómbita and Choachí, Colombia. The proposed architecture achieved mean accuracies of 82.8% (fruit) and 95.3% (leaf), with AUC values of 0.87 and 0.98, and a trained model footprint of approximately 100 KB, supporting storage- and bandwidth-efficient deployment. Benchmarked against ImageNet-pretrained MobileNetV3-Small and MobileNetV2 under an identical protocol, the proposed architecture matched or exceeded MobileNetV3-Small on leaf classification despite a 257-fold smaller parameter count, and achieved comparable or lower inference latency than both larger backbones. To our knowledge, this is the first validated system for simultaneous detection of both pathogens in Prunus persica under real field conditions, combining a compact, deployment-ready architecture with an ablation-verified preprocessing pipeline. The proposed model was deployed in the DurAPP web platform, giving peach growers in tropical highland regions a practical, low-footprint diagnostic tool suited to smallholder farming conditions.
Why it matches plant phenotyping methodsモモ果実・葉の病徴を画像から分類するCNN、前処理、交差検証、他モデル比較、実地検証、プラットフォーム展開が中心であり、植物病害状態の画像ベースフェノタイピング手法に該当する。
abstractThis study presents a compact Convolutional Neural Network (3658 trainable parameters), applied identically to fruit and leaf classification, integrated with a background-removal preprocessing pipeline and evaluated through stratified 5-fold cross-validation on 800 in-situ images from four orchards
Abstract In modern agriculture, it is essential to identify the early symptoms of plant diseases and to accurately maintain the productivity of the crop and reduce economic losses. Foliar diseases are a special concern in peach because they can cause yield as well as quality if not timely detected. Artificial intelligence, machine learning and deep learning are some of the advanced technologies that are gaining great importance in today's agriculture, especially with the image analysis applications. In this study, a deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data. The dataset were taken at different phenological and disease stages under temperate conditions in Kashmir with four classes Healthy, Leaf Curl, Shot hole and Rust. Three convolutional neural networks (CNNs) architectures were applied, VGG-16, ResNet 50 and Xception were trained using transfer learning and Inception-V4 was trained from scratch for a comparative study of the learning strategies. Data augmentation techniques were applied to improve generalization. Results show that all models were able to learn disease specific features well. The result of Inception-V4 was found to be highest with 97.50%, followed by ResNet-50 with 94.49%, VGG-16 with 92.04% and Xception with 75.95%. The results of transfer learning-based architectures were also good and competitive but the best results obtained from the Inception-V4 architecture reveal its capability in modelling complex visual patterns. The results highlight the potential of deep learning techniques for early detection of diseases in peach, supporting precision agriculture and better disease management.
Why it matches plant phenotyping methodsモモ葉の画像から病害症状を自動検出・分類する深層学習手法が研究の中心であり、植物の病害状態を直接推定しているため。
abstracta deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data.
Background: Plant diseases significantly threaten global food security, reducing potential harvests and sometimes causing total crop failure. Traditional detection methods, which rely on manual inspection and laboratory testing, are time-consuming, costly and prone to human error. Methods: To address these challenges, this study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection. A comparative analysis of nine pretrained models, VGG16, VGG19, ResNet50, ResNet101V2, MobileNetV2, InceptionV3, DenseNet121, InceptionResNetV2 and Xception was conducted on the PlantVillage dataset, focusing on apple, potato and peach leaf images. Result: Results show that DenseNet121 and ResNet101V2 achieved the highest accuracy, particularly for potato leaves with 98.5%, while MobileNetV2 also performed well with up to 99% accuracy for apple and peach leaves. The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法を比較評価しており、病害表現型の取得・分類が研究の中心である。
abstractthis study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection.
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Abstract Purpose Capturing rapid changes in water status is key to optimizing deficit irrigation in Mediterranean orchards, but thermal remote sensing is constrained by the availability of high-spatial-resolution data. This study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards. Methods An experiment was conducted in two commercial orchards in south-eastern Spain, where mild water stress was induced by withholding irrigation for four days. High-resolution hyperspectral and thermal imagery were acquired concurrently with stem water potential measurements (ψ stem ). Structural, pigment-related, and water-sensitive indices were evaluated at high (20–50 cm) and medium (30 m) spatial resolutions to analyze the effects of pixel size on stress detection. The Crop Water Stress Index (CWSI), derived from thermal imagery, served as a reference indicator. Results Those optical indices based on SWIR reflectance at 1240 nm, the Normalized Difference Water Index (NDWI₁₂₄₀) and the Simple Ratio Water Index (SRWI), showed the strongest sensitivity to ψ stem variability (R² = 0.63, p
Why it matches plant phenotyping methods桃樹の水ストレス状態を高解像度ハイパースペクトル・熱画像とスペクトル指標で推定し、茎水ポテンシャルを用いて検証しており、植物表現型取得手法が中心である。
abstractThis study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards.
Pseudomonas syringae pv. syringae ( Pss ) is the causal agent of bacterial canker, a disease that can result in yield losses, aerial tissue damage, and tree mortality in stone fruits worldwide. Peach, one of the major stone fruit crops, experiences significant yield losses and tree mortality attributed to bacterial canker in the United States. As the second-largest peach-producing state, South Carolina faces direct and significant impacts due to Pss . Early evaluations of peach scion responses to Pss infection have relied primarily on circumstantial field observations in rootstock trials. Although laboratory evaluations in peach have been reported, these studies primarily focused on pathogen virulence testing or small accession sets and did not establish a standardized, scalable detached twig protocol for systematic germplasm phenotyping. The absence of a clearly described laboratory assay has limited reproducible and large-scale evaluation of bacterial canker tolerance in peach. To address this gap, a detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach. Dormant shoots from nine peach accessions were cut into 10 cm segments, surface-sterilized, and inoculated with a Pss suspension prepared in 10 mM MgCl 2 buffer or with the buffer alone. After six weeks of incubation, inner bark lesion size was evaluated visually and quantified using ImageJ. A newly developed visual rating scale was established and compared with quantitative lesion measurements. Spearman correlation analysis showed strong positive correlations between visual disease scores and ImageJ-based lesion measurements across two independent replicates (ρ = 0.80-1.00, p < 0.01), while shoot segment diameter showed weak-to-moderate negative correlations with disease severity. This adapted and consolidated dormant twig assay provides a practical, reproducible, and scalable method for phenotyping bacterial canker tolerance in peach and supports future germplasm screening and breeding efforts.
Why it matches plant phenotyping methodsモモの細菌性潰瘍病抵抗性を評価するため、切離枝アッセイを適応・最適化し、病斑測定と視覚評価を検証した中心的な表現型測定法の研究。
abstracta detached dormant twig assay, previously developed for cherry, was adapted and optimized for peach
PeachMicroscopyFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traits
Fruit size and shape, which influence horticultural quality, are determined by the number and the size of the cells in the local region. In fruit trees, however, the difficulty of applying molecular genetic approaches has hindered a detailed understanding of the localization and orientation of cell division in developing fruit tissues. In this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops, peach ( Prunus persica ), Japanese apricot ( P. mume ) and the interspecific hybrid Japanese apricot ( P. salicina x P. mume ), providing clear insight into the spatial distribution and orientation of dividing cells. We systematically optimized a 5-ethynyl-2′-deoxyuridine (EdU) labeling protocol for thick ovary tissues by adjusting infiltration conditions and fixation methods. In addition, electron microscopy combined with wide-view tiling visualization was applied to directly identify dividing cells, including those undergoing chromosome segregation and cell plate formation. By combining with machine learning-based detection, we efficiently and objectively identified dividing cells. Using these complementary approaches, we found that cell division activity was broadly distributed throughout pre-anthesis ovaries in all three crops, without pronounced spatial restriction. In contrast, analysis of division orientation revealed region-specific patterns: cells in the outermost exocarp divided predominantly anticlinally, whereas cells in the mesocarp divided largely periclinally, consistent with subsequent ovary (fruit) enlargement. The integrated framework presented here provides a foundation for understanding the spatial and three-dimensional regulation of fruit development and for future studies in fruit morphogenesis and horticulture.
Why it matches plant phenotyping methods植物組織内の細胞分裂という発生状態を可視化・定量する統合フレームワークを開発し、EdU標識、電子顕微鏡、広視野タイリング、機械学習検出を組み合わせて検証・適用しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2026Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Soluble solids content (SSC) is an important indicator for determining the commercial value of peaches. Visible/near-infrared (Vis/NIR) spectroscopy combined with chemometric methods is a primary technique for predicting peach SSC. However, the interference of fruit color with spectral signals makes it challenging to accurately detect SSC across different varieties. This study explored the feasibility of fusing spectral and image data to achieve accurate SSC prediction for multiple peach varieties. Diffuse reflectance spectra and images of three peach varieties (‘Hujing’, ‘Jinqiuhong’, and ‘Dongxue’) were collected. Multiple feature-level fusion strategies for spectral and image data were proposed. Partial least squares regression (PLSR) and support vector regression (SVR) models were developed based on the multimodal fusion data to predict the SSC of individual and multiple varieties, respectively. Their predictive performance was compared with that of models established using spectral data alone. To further improve the generalization ability of the multi-variety models, a spectrum-image fusion network (SIFNet) was proposed by extracting and leveraging high-level image features and integrating them with spectral information. The results showed that the SIFNet achieved superior performance in predicting the SSC of multi-variety peaches, with RP2, RMSEP, and RPDP of 0.8235, 1.0514, and 2.5208, respectively.
Why it matches plant phenotyping methodsスペクトル・画像融合とSIFNetを開発し、個々のモモ果実のSSCという器官形質を予測する方法が研究の中心である。
abstractThis study explored the feasibility of fusing spectral and image data to achieve accurate SSC prediction for multiple peach varieties.
Spatial frequency domain imaging (SFDI) is a non-invasive optical imaging technique widely used for the quantitative determination of fruit tissue optical properties, specifically absorption coefficient (μₐ) and reduced scattering coefficient (μₛ’). However, traditional SFDI methods rely on multiple frequency and phase images, limiting real-time imaging capabilities. To address this issue, we present a novel rapid prediction method based on a two-stage deep neural network architecture, termed FSGOP (Frequency-Spatial Attention UNet and GAN-based two-stage network for optical properties prediction). Compared with conventional three-phase demodulation SFDI, this method reduces the acquisition time by approximately 5/6 and requires only 0.21 s for inference. In the first stage, a UNet network enhanced by Frequency-Spatial Attention (FSA) is employed to effectively decouple the multi-frequency components. In the second stage, a Generative Adversarial Network (GAN) is utilized to predict the optical properties, thereby enabling the simultaneous extraction of μₐ and μₛ’ maps under different frequency conditions from a single multi-frequency mixed fringe image. In experiments on apples, pears, and peaches, the method yielded normalized mean absolute errors of 0.10 (f₁) and 0.09 (f₂) for μₛ’, and 0.07 and 0.06 for μₐ, respectively. The results revealed significant complementary information in the optical property maps at different frequencies, with lower frequencies being more sensitive to subsurface damage and higher frequencies revealing surface texture features more effectively. This method enhances information utilization and real-time performance in multi-frequency imaging, offering a rapid, accurate, and low-cost solution for optical property extraction and quality inspection of agricultural products.
Why it matches plant phenotyping methods果実の光学特性を単一画像から推定する画像・深層学習手法を開発し、取得時間と精度を評価しており、植物器官の状態計測が中心である。
abstractwe present a novel rapid prediction method based on a two-stage deep neural network architecture, termed FSGOP
Abstract This study introduces a robust early disease detection system for peach crops, leveraging computer vision and artificial intelligence to address significant economic losses caused by Brown Rot (Monilinia spp.) and Leaf Curl (Taphrina deformans). The methodology comprises a structured approach, starting with the collection of a high-quality dataset of 640 images captured under real-world field conditions. These images, representing healthy and diseased fruits and leaves, underwent a rigorous preprocessing pipeline that included background removal, color space conversion, resizing, and contour detection to optimize them for model training. A Convolutional Neural Network (CNN) was developed and validated using k-fold cross-validation, achieving an outstanding accuracy of 90.28\% for fruit disease detection and 96.43\% for leaf disease detection during the validation phase. The model's final performance, evaluated with a confusion matrix, demonstrated a remarkable 100\% precision for Brown Rot in fruits and 96.4\% precision for Leaf Curl in leaves. These results confirm the system's reliability and its potential for practical application in precision agriculture. The project culminates in a functional web application, showcasing the viability of deploying deep learning solutions as accessible tools for farmers to facilitate timely and proactive crop management.
Why it matches plant phenotyping methods桃の果実・葉の病徴を画像から検出・分類するコンピュータビジョン手法を開発し、データセット、前処理、CNN、交差検証、性能評価まで中心的に扱っているため、植物病害フェノタイピング手法に該当する。
abstractThis study introduces a robust early disease detection system for peach crops, leveraging computer vision and artificial intelligence
The rapid advancement of technologies such as artificial intelligence (AI), deep learning, and precision agriculture tools is driving the development of efficient, data-driven crop management solutions. These innovations are increasingly critical in modern agriculture, where early and accurate detection of plant diseases plays a vital role in securing crop yields and sustainability. Agronomists, agriculturists, and local farmers continue to face significant economic losses due to delayed diagnosis or misclassification of diseases affecting high-value crops, key contributors to the global market. Failure to identify and manage such diseases in time can severely impact both agricultural productivity and global food supply chains. To achieve the United Nations’ sustainable development goals of zero hunger, climate change, good health, and well-being, early and timely disease detection is critical to ensure increased apple-related production, damage control, and reduced application of inappropriate herbicides that pollute the environment. Despite the availability of various methods for early disease detection and classification, how early signs of green attacks can be identified remains uncertain. Using the Turkey Plant Pests and Diseases (TPPD) dataset with 4,447 images categorized into 15 diverse classes, this research implements ResNet-9 to detect and classify the commonly known pests and diseases of six plants, including Malus pumila, Prunus armeniaca, Prunus padus, Prunus persica L. Batsch., Pyrus communis L., and Juglans regia. A laborious hyperparameter tuning, hyperparameter optimization, and augmentation procedure on the training set was done for some imbalanced dataset classes. Testing results of the proposed model demonstrated accuracy, precision, recall, and F1-score values of 97.4%, 96.4%, 97.09%, And 95.7%, respectively, which is a significant leap in comparison to other existing research. This study further elucidates and enhances the interpretability of the proposed model by making saliency maps available using SHapley Additive exPlanations (SHAP) that efficiently illustrate the rationale behind the model’s prediction capabilities. The study further tested the statistical significance of the model, the Area Under the receiver operating characteristic curve (AUC-ROC), and the confidence interval (CI). Critical observations revealed that the model uses several visual cues for disease detection and classification, including (i) edge contours and shape structures that help define lesion boundaries, (ii) texture and color variations that signal symptom type and severity, and (iii) high-activation regions that indicate areas of strong feature relevance. These cues collectively guide the model in distinguishing between visually similar disease patterns across different plant parts. The application of SHAP saliency maps further enabled interpretation by visually localizing and quantifying the influence of these features on the model’s predictions.
Why it matches plant phenotyping methods植物の病害・害虫状態を画像から分類する深層学習手法を開発・評価しており、病徴の局在化と重症度に関連する視覚特徴も解析しているため、植物フェノタイピング手法が中心である。
abstractthis research implements ResNet-9 to detect and classify the commonly known pests and diseases of six plants
PeachAerial / UAVField / plotFlowerClassificationSegmentationGrowth / development / phenology
A timely and accurate assessment of flowering characteristics is vital for tracking floral phenology in agricultural management and peach breeding. In this study, an unmanned aerial vehicle (UAV) equipped with a high-resolution camera was integrated with deep learning techniques to monitor peach flowering across multiple varieties. An instance segmentation model, PeFloSEG, was proposed for the accurate detection of peach flowers and buds. Based on the YOLOv5-seg framework, PeFloSEG integrates an enhanced detection head for improved feature representation and a modified loss function—Focal Efficient Intersection over Union (Focal-EIoU)—to optimize bounding box regression. To boost model efficiency, a network slimming algorithm was applied, significantly reducing model size while maintaining high accuracy. PeFloSEG achieved strong results, with mean average precision (mAP@0.5) scores of 0.876 for detection and 0.825 for segmentation, outperforming state-of-the-art algorithms by 0.5 %–24.9 % in detection and 5.7 %–28.1 % in segmentation. Three flowering indices derived from PeFloSEG outputs—flowering intensity (FI) indices (FI1 and FI2) and single-tree flowering ratio (SFR)—were evaluated. Linear correlation analysis revealed strong relationships between these indices and ground truth values, with R² values of 0.964 (FI1), 0.961 (FI2), and 0.986 (SFR). These indices were further used to assess flowering dynamics over time and to distinguish phenological stages, achieving an overall classification accuracy of 91.7 %. They also enabled effective variety classification, facilitating the exploration of flowering characteristics across different peach varieties. Overall, the proposed approach offers a scalable and efficient solution for high-throughput phenotyping and provides valuable tools for peach breeding and germplasm resource evaluation.
Why it matches plant phenotyping methodsUAV画像とインスタンスセグメンテーションモデルを開発し、花数・開花強度・単木開花率などの植物表現型を定量化することが研究の中心であるため。
abstractAn instance segmentation model, PeFloSEG, was proposed for the accurate detection of peach flowers and buds.
Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance.
Why it matches plant phenotyping methodsリンゴ・モモの画像から霜害・高温害という植物の状態を検出する新規異常検出モデルを開発し、複数モデルと比較検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance.
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較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。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Recently, the classification of plant leaf diseases has become a critical research area to improve the agricultural productivity.Early and accurate identification of diseases is needed to prevent the disease transmission and reduce the crop losses.Deep learning approaches enables to learn complex meaningful patterns within the various leaves.In the paper, enhanced convolutional neural network with attention mechanism (ECNNA) model is proposed to extract the discriminative features and classify the types of leaf diseases.The ECNNA comprises of prior feature extraction with convolution layers and maximum pooling layers, feature enhancement with attention mechanism, classification using SoftMax classifier.Additionally, the model utilizes data augmentation techniques to increase dataset diversity and improve generalization ability of model.The attention layer is incorporated in convolutional neural network to improve the performance of system that increase crop yield and quality.The system classifies the various diseases categories.Experimental results demonstrate that the proposed ECNNA model achieves the classification accuracy of 99.99% on Potato dataset, 97.27% on Corn dataset, 97.95% on Apple dataset, 99.14% on Grape dataset, 99.62% on Peach dataset, 99.31% on six classes dataset, and 98.90% on eight classes dataset.The classification results are also compared with previous studies, indicating the higher classification rate.This research contributes to the early detection and diagnosis of plant leaf disease for supporting sustainable agriculture and food security.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類する深層学習手法を開発し、複数データセットで性能比較・評価しており、病害表現型の取得・判定が中心です。
abstractenhanced convolutional neural network with attention mechanism (ECNNA) model is proposed to extract the discriminative features and classify the types of leaf diseases.
PeachField / plotLiDAR / point cloudLeafMorphology / geometry measurementLeaf traits
Fruit tree canopy leaf area is an important metric for calculating airflow and pesticide dose for accurate variable-rate applications (VRAs) in orchard air-assisted spraying. Existing canopy leaf area calculation models have been established based on the data of a single growth period and the whole canopy leaf area, and it is difficult to meet the precise VRA needs of orchard spraying during whole growth period of fruit trees. In this study, a feature information detection system for fruit tree canopies was designed based on light detection and ranging (LiDAR). Canopy leaf area and LiDAR point cloud detection tests were carried out on peach trees during their whole growth period. The changes of area of individual leaves, leaf number, LiDAR point clouds, and section-based canopy leaf areas and volumes at different growth stages were obtained. Based on least squares regression (LSR) Gaussian fitting and backpropagation (BP) neural network methods, the online calculation models of section-based canopy leaf area was established, and a model modification method was proposed. The R² values of the LSR and BP models increased from 0.865 and 0.863 to 0.906 and 0.898, respectively, and the root-mean-square error (RMSE) decreased from 5110.65 cm² and 5208.74 cm² to 4325.37 cm² and 4600.74 cm², respectively. The accuracy of the model constructed by LSR Gaussian fitting was relatively high, and it was easier to deploy in VRA programs. Compared with those of the existing calculation models, the calculation accuracy and generality of the model constructed in this paper are improved, thus providing model support for the research and development of airflow and pesticide dose on-demand control systems for orchard precision variable-rate spraying.
Why it matches plant phenotyping methodsLiDARを用いた果樹キャノピーの葉面積検出システムと、成長期間全体で葉面積をオンライン推定するモデルを開発・検証しており、植物形質の取得・抽出手法が研究の中心である。
abstractIn this study, a feature information detection system for fruit tree canopies was designed based on light detection and ranging (LiDAR).
AppleMangoPeachPearPlumField / plotNeRF / 3D Gaussian SplattingRGB / grayscaleFruitWhole plant / canopy / plot / field
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards. Our work is based on FruitNeRF [6], which employs a neural semantic field combined with a fruit-specific clusteringapproach. The requirement for adaptation for each fruit type limits the applicability of the method, and makes it difficult to use in practice. To lift this limitation, we design a shape-agnostic multi-fruit counting framework, that complements the RGB and semantic data with instance masks predicted by a vision foundation model. The masks are used to encode the identity of each fruit as instance embeddings into a neural instance field. By volumetrically sampling the neural fields, we extract apoint cloud embedded with the instance features, which can be clustered in a fruit-agnostic manner to obtain the fruit count. We evaluate our approach using a synthetic dataset containing apples, plums, lemons, pears, peaches, and mangoes, as well as a real-world benchmark apple dataset. Our results demonstrate that FruitNeRF++ is easier to control and compares favorably to other state-of-the-art methods.
Why it matches plant phenotyping methods果実を対象とした画像ベースの汎用カウント手法を開発し、合成および実データで評価しているため、植物形質（果実数）の取得・推定が研究の中心です。
abstractWe introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards.
Accurate and precise spraying in orchards is paramount for optimized agricultural practices, ensuring efficient pesticide utilization, minimized environmental impact, and enhanced crop yield by targeting specific areas with the right amount of treatment. The asymmetrical distribution of foliage and flowers in peach orchards poses a formidable challenge to achieving precise spray accuracy, impeding the uniform application of treatments and compromising the overall efficacy of pest and disease control measures. In response to the prevailing challenges in achieving accurate spray application caused by the asymmetrical distribution of foliage and flowers in peach orchards, this paper introduces a novel deep neural network to map the RGB image and corresponding depth to the density map of peach flowers or foliage. The model consists of components: (1) two backbones based on ResNet-50 that extract contextual features from the RGB image and depth features from depth data at multiple scales and levels; (2) an optimized depth-enhanced module that effectively fuses the distinct features extracted from the two input streams; and (3) a two-stage decoder that aggregates the high-level cross-modal features to regress the coarse density map and subsequently integrates it with the low-level cross-modal features for final density map prediction. To evaluate the performance of our model, we collected 493 frames (206,095 instances) of peach flowers and 475 frames (350,833 instances) of foliage from the peach orchards utilizing our sprayer prototype equipped with stereo cameras. The proposed method outperforms state-of-the-art models on our datasets, demonstrating the superiority and efficacy for encoding canopy characteristics in the form of flower and foliage density maps for blossom and cover sprays. It attains significant computational efficiency, exhibiting a frame rate of 20 FPS, and showcases exceptional accuracy with a WMAPE of 12.11% for peach flowers and a WMAPE of 13.37% for leaves.
Why it matches plant phenotyping methods桃の花・葉の密度という植物器官の形質をRGB-D画像から推定する手法を開発・評価しており、散布制御への応用を超えてフェノタイピング手法自体が中心です。
abstractthis paper introduces a novel deep neural network to map the RGB image and corresponding depth to the density map of peach flowers or foliage
Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in developing countries. Symptoms such as chlorosis, structural deformities, and wilting, characterize these diseases. However, early identification can be challenging due to symptoms similarity. Researchers using artificial intelligence (AI) for plant disease classification, challenges like data imbalance, symptom variability, real-time performance, and costly annotation hinder accuracy and adoption. This work introduced a novel approach using the You Only Look Once (YOLO) deep learning model, chosen for its exceptional accuracy and speed. The study focuses on analyzing YOLO models, specifically YOLOv3 and YOLOv4, to identify fruit plant diseases. This work examines healthy peach and strawberry leaves, as well as peach leaves affected by bacterial spots and strawberry leaves with scorch disease. These models underwent thorough training using data from the publicly accessible Plant Village dataset. The simulation results were highly promising, numerically YOLOv3 model achieved 97% accuracy and a Mean Average Precision (mAP) of 92%, within a total detection time of 105 s. In comparison, the YOLOv4 model outperformed, with a 98% accuracy and an impressive mean average precision of 98%, all while completing the detection process in just 29 s. YOLOv4 demonstrated lower complexity, significantly faster, and more precise performance, especially in detecting multiple items. Serving as an efficient real-time detector, it holds the potential to transform plant disease diagnosis and mitigation strategies, ultimately leading to increased agricultural productivity and enhanced financial outcomes for developing nations.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像から識別するYOLO手法の開発と性能比較が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis work introduced a novel approach using the You Only Look Once (YOLO) deep learning model
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Plant Village dataset on Kaggle (4,222 peach/strawberry leaf images across four classes), used to train and evaluate YOLOv3/YOLOv4 disease-detection models. No author analysis code, trained model checkpoints, or paper-specific supplements are publicly statedDataset · publicThis study utilizes data from the publicly available Plant Village dataset 41 , accessible on Kaggle.Open asset ↗Kagglelines:139-155Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample preparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods. Here, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device. The needle sampling device helped release specific VOCs from the studied vegetables that usually require mechanic stimulation, while maintaining the vegetable viability. On the other hand, the colorimetric sensor array was optimized for sulfur compound-based VOCs with a limit of detection (LOD) in the 1-25 ppm range, and classified fourteen different vegetable VOCs, including sulfoxides, sulfides, mercaptans, thiophenes, and aldehydes. By combining principal components analysis (PCA) analysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demonstrates the capability to distinguish between different types of tested fruits and vegetables, including garlic, green pepper, and nectarine. This rapid and minimally invasive sensing technology holds great promise for conducting field-based vegetable quality monitoring.
Why it matches plant phenotyping methods野菜のVOCを低侵襲に取得し、センサーアレイとスマートフォンで分類する測定プラットフォームの開発が研究の中心であり、野菜の品質・表現型分類に直接用いられている。
abstractHere, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device.
Firmness is a critical indicator for predicting fruit ripeness, optimal harvest date, and shelf life. In this study, a novel fruit acoustic real-time detection prototype device and a conventional visible near-infrared (Vis/NIR) spectroscopy real-time detection device were used to collect acoustic and spectral signals from yellow flesh peaches to jointly predict their firmness. The acoustic and optical signals were generated into one- and two-dimensional feature data by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT) and Gramian angular field (GAF) data processing methods. Based on these data, a variety of yellow flesh peach firmness prediction models were constructed in this study, including partial least square (PLS), support vector regression (SVR), Swin Transformer (SwinT), and SwinT-PLS/SVR. The experimental results showed that the SwinT-PLS model based on the fusion of competitive adaptive re-weighted sampling (CARS)-acoustic image features and CARS-Vis/NIR spectral features showed the best prediction performance (R²P = 0.951, the RMSEP = 0.443 N/mm, RPDP = 4.339), and the prediction performance is significantly higher than that of the prediction model based on single acoustic and Vis/NIR spectral data. The method proposed can fast, non-destructively, accurately predict fruit firmness and has excellent prospects for commercial real-time fruit sorting applications.
Why it matches plant phenotyping methods桃果実の硬度という植物器官形質を、音響・Vis/NIRセンサーと画像化・機械学習で非破壊推定する新規リアルタイム手法の開発が中心であり、性能評価も行っている。
abstracta novel fruit acoustic real-time detection prototype device and a conventional visible near-infrared (Vis/NIR) spectroscopy real-time detection device were used to collect acoustic and spectral signals from yellow flesh peaches to jointly predict their firmness.
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.
Why it matches plant phenotyping methods植物画像から病徴を分類するCNN-SVM手法の開発・比較・検証が中心であり、植物病害状態の画像ベース表現型推定に該当する。
abstractThis research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans.
Abstract This study investigated the use of on-site calibrated soil and plant based sensors for irrigation control in a nectarine (Prunus persica) , orchard on heavy clay soil. Irrigation was applied according to predetermined mid-day stem water potential (SWP) thresholds for each of the three phenological stages of fruit development. Calibration of sensors relative to SWP was done in a drying and wetting plot in a separate part of the orchard. Irrigation was withheld in that plot for several periods of time during two seasons, allowing SWP of six nectarine trees to reach values of moderate water stress. The readings of continuous tensiometers, soil volumetric water content sensors and dendrometers were regressed on those of SWP at that time. Water stress thresholds were then calculated from the regressions and subsequently used for irrigation scheduling. The irrigation aimed to keep non-limiting conditions in stages Ⅰ and Ⅲ by keeping SWP at ~-0.9 MPa, and moderate water stress in stage Ⅱ by keeping SWP at ~-1.5 MPa. Adjustments were made weekly in five treatment plots for each sensor type, according to the thresholds. Results showed that tensiometers could be used for stages Ⅰ and Ⅲ, as they were highly sensitive to small changes in soil wetness. However, when stress was applied soil water tension exceeded the range of the tensiometer sensors at 30 cm depth.
Why it matches plant phenotyping methods植物・土壌水分センサーを茎水ポテンシャルに対して校正し、連続的な植物水分状態の測定性能と閾値利用を評価しており、単なる灌漑試験ではなくセンサーによる生理形質取得が中心です。
titleUsing continuous soil and plant water status sensors calibrated against stem water potential for irrigation scheduling – nectarine as a test case
Accurate detection and segmentation of fruit is a key factor in the development of smart farming. Problems such as light variation, fruit overlap and leaf shading create a complex environment in orchards and have a significant impact on the development of smart farming. Many current deep learning-based segmentation methods do not make full use of edge information, resulting in inadequate sharpening of the fruit edges obtained from segmentation. To address this problem, an edge-guided based fruit segmentation method (EdgeSegNet) in complex environments is proposed by us. The method first performs feature extraction through the ResNet model as the backbone network, then integrates and refines the high-level semantic and spatial information through the Global Localization Module (GLM) and localizes potential targets in the target region with the help of the proposed Multi-Scale Localization Block (MSLB). Then Boundary Aware Module (BAM) sharpen the edges of potential targets by integrating the feature information of high and low layers, and finally get the accurate segmented image. The principle of the model is blurred positioning, precise sharpening, edge guiding. The experimental results showed that the method achieved an average MIoU of 0.909 and 0.942 on the apple and peach datasets of three different sizes, large, medium and small, respectively, outperforming several other state-of-the-art models in terms of accuracy and complexity as well as inference time.
Why it matches plant phenotyping methods果実を対象とした画像セグメンテーション手法を開発し、複数データセットで精度・複雑性・推論時間を比較評価しているため、植物表現型取得法が中心である。
abstractan edge-guided based fruit segmentation method (EdgeSegNet) in complex environments is proposed by us.
Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から樹冠面積・植生指数などの樹木状態を抽出し、害虫による植物症状を予測する手法が研究の中心であるため。
abstractTherefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing.
Introduction Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する深層学習手法を開発しており、植物の病徴・病害状態の取得と推定が研究の中心であるため。
abstractThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases.
Reproduction assets foundThe paper's plant leaf disease classification experiments are built entirely on two public Kaggle image datasets explicitly cited by the authors: the PlantVillage Dataset (eight-class experiment) and the Plant Disease Prediction Dataset (three-class experiment). No author code, trained model, or supplementary deposit (Dataset · publicWe verified the effectiveness and robustness of the DeepPlantNet model by using images from the publicly available Kaggle “PlantVillage Dataset” dataset ( Dataset : https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ).Open asset ↗Kaggle · abdallahalidev/plantvillage-datasetlines:355-366Dataset · publicWe validated our model using another common, publicly accessible Kaggle dataset, “Plant Disease Prediction Dataset,” to assess and estimate the generalizability and performance of the DeepPlantNet model ( Dataset : https://www.kaggle.com/datasets/shuvranshu/plant-disease-prediction-dataset ).Open asset ↗Kaggle · shuvranshu/plant-disease-prediction-datasetlines:729-756Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions. .
Why it matches plant phenotyping methodsLiDARとUAV写真測量を用いてブドウ樹冠の高さ・幅・体積を抽出し、両手法を定量比較・評価しており、植物形質取得法が研究の中心である。
abstracttwo systems were compared to characterise a 50 m long vineyard row section
The first major contribution of the paper is the proposal of using an improved DEtection Transformer network (named R2N-DETR) and Kinect-V2 camera for detecting multiple-size peaches under orchards with varied illumination and fruit occlusion. R2N-DETR model first employed Res2Net-50 to extract a fused low-high level feature map containing fine spatial features and precise semantic information of multi-size peaches from Red-Green-Blue-Depth (RGB-D) images. Second, the encoder-decoder was performed on the feature map to obtain the global context. Finally, all detected objects were detected according to each object’s global context. For the detection of 1101 RGB-D images (imaged from two orchards over three years), the R2N-DETR model achieves an average precision of 0.944 and an average detecting time of 53 ms for each image. The developed system could provide precise visual guidance for robotic picking and contribute to improving yield prediction by providing accurate fruit counting.
Why it matches plant phenotyping methodsRGB-D撮像と改良物体検出モデルを開発・評価し、モモ果実の検出とカウントという植物器官形質を抽出する方法が中心である。
abstractThe first major contribution of the paper is the proposal of using an improved DEtection Transformer network (named R2N-DETR) and Kinect-V2 camera for detecting multiple-size peaches under orchards with varied illumination and fruit occlusion.
Plant diseases pose a significant threat to agricultural productivity and food security in Bangladesh. In this research, we address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models. We curated a diverse dataset comprising 18 categories of plant leaf images, including Bell pepper Bacterial spot, Bell pepper Healthy, Peach Healthy, Potato Early Blight, Rice Leaf Blast, Rice Healthy, Rice Brown Spot, Potato Healthy, Peach Bacterial spot, Corn Blight, Potato Late blight, Corn Healthy, Tomato Bacterial spot, Strawberry Leaf Scorch, Tomato Early blight, Tomato Early blight, Strawberry Healthy, and Tomato Healthy. The dataset represents the most prevalent plant diseases observed in the Bangladeshi context. We employed three state-of-the-art deep learning algorithms, EfficientNetV2M, VGG-19, and NASNetLarge, to develop robust plant disease detection models. Through transfer learning, these pre-trained models were fine-tuned on our specialized dataset to adapt them for the task at hand. The performance evaluation revealed impressive results, with EfficientNetV2M achieving an accuracy rate of 99%, VGG-19 achieving 93%, and NASNetLarge attaining 83% accuracy. The high accuracy of EfficientNetV2M showcases its exceptional capability in accurately classifying plant diseases prevalent in Bangladesh. The success of these deep neural models in detecting various plant diseases signifies their potential in revolutionizing plant disease management and enhancing agricultural practices. Our research contributes valuable insights into the effective use of transfer learning for plant disease detection and emphasizes the significance of dataset curation for improved model performance. The developed models hold promise in providing timely and precise disease diagnosis to farmers and agricultural professionals, thereby facilitating prompt interventions and minimizing crop losses. Future research can explore the integration of these deep neural models into practical agricultural tools, enabling real-time disease detection and offering substantial benefits to the agricultural industry in Bangladesh.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルの開発・性能評価が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractwe address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models.
The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.
Why it matches plant phenotyping methodsUAV-DAPとMTLS-LiDARによる果樹キャノピー形状計測を比較・検証し、高スループット表現型解析への適用可能性を評価しているため、植物形質取得法が中心である。
abstractA comparison between both kinds of measurements was performed
Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) scanners have been adopted as a promising instrument for plant parameter estimation in agricultural studies recently. However, accurate LiDAR data registration typically requires expensive external navigation devices such as survey-grade global navigation satellite systems (GNSSs) and tactical-grade inertial measurement units (IMUs). Although algorithmic point cloud registration can be an alternative method, the lack of unique landmarks in agricultural fields might bring much difficulty to accurate aerial LiDAR data alignment. In this study, we developed a UAV-LiDAR system employing UAV’s built-in navigation units, and proposed a novel approach for registering UAV-LiDAR data of level agricultural fields utilizing a colored iterative closest point (ICP) algorithm and GNSS location and IMU orientation information from the UAV. The proposed algorithm was tested in a peach tree parameter estimation experiment in comparison to GNSS and IMU-based georeferencing. Using manually measured crown widths in two perpendicular dimensions and heights of 11 trees as evaluation metrics, our proposed algorithm achieved a root mean square error (RMSE) range of 0.05 to 0.2 m depending on the tree parameter and flight altitude, and it was able to register tree point clouds up to 67% more accurately in terms of the extracted tree parameters than the georeferencing method. The results demonstrated the potential of the proposed algorithm being a low-cost solution to crop inspection using single-pass aerial LiDAR point clouds from straight-pathed flights, yet future work is still needed to improve the algorithm’s adaptability to multi-pass LiDAR data of complex landscapes from flights with curved paths.
Why it matches plant phenotyping methodsUAV-LiDAR点群登録アルゴリズムを開発し、モモ樹の樹冠幅・樹高という植物形質の推定精度で検証しており、フェノタイピング取得・抽出法が中心である。
abstractwe developed a UAV-LiDAR system employing UAV’s built-in navigation units, and proposed a novel approach for registering UAV-LiDAR data of level agricultural fields utilizing a colored iterative closest point (ICP) algorithm and GNSS location and IMU orientation information from the UAV.
In the winter pruning operation of deciduous fruit trees, the number of pruning branches and the structure of the main branches greatly influence the future growth of the fruit trees and the final harvest volume. Terrestrial laser scanning (TLS) is considered a feasible method for the 3D modeling of trees, but it is not suitable for large-scale inspection. The simultaneous localization and mapping (SLAM) technique makes it possible to move the lidar on the ground and model quickly, but it is not useful enough for the accuracy of plant detection. Therefore, in this study, we used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees. Then, we compared and analyzed these models and further proposed a method to distinguish branches from 3D point clouds by spatial point cloud density. The results showed that the 3D lidar SLAM technique had a shorter modeling time and higher accuracy than UAV-SfM for the winter pruning period of peach trees. The method had the smallest RMSE of 3084 g with an R2 = 0.93 compared to the fresh weight of the pruned branches. In the branch detection part, branches with diameters greater than 3 cm were differentiated successfully, regardless of whether before or after pruning.
Why it matches plant phenotyping methods桃樹の3D形状取得と枝検出・重量推定法を開発し、UAV-SfMと3D LiDAR SLAMを比較評価しており、植物形質取得手法が研究の中心である。
abstractwe used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees.
Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models.
Why it matches plant phenotyping methodsモモ葉の病徴状態を画像から分類する深層学習手法を開発・比較評価しており、植物病害表現型の取得・推定が中心です。
abstractwe proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images.
Abstract Background Statistical analysis of root architectural parameters is necessary for development and exploration of root structure representations and their resulting anchorage properties. Three-dimensional (3D) models of orchard tree root systems, Lovell (from seed, prunus persica ), Marianna (from cutting, prunus cerasifera ), Myrobalan (from cutting, also prunus cerasifera ), that were extracted from the ground by vertical pullout are reconstructed through photogrammetry, and then skeletonized as nodes and root branch segments. Combined analyses of the 3D models and skeletonized models enable detailed examination of basic bulk properties and quantification of architectural parameters divided into simple root segment classifications— trunk root, main lateral root, and remaining roots. Results The patterns in branching and diameter distributions show significant difference between the trunk and main laterals versus the remaining lateral roots. In general, the branching angle decreases with branching order. The main lateral roots near the trunk show significant spreading while the lateral roots near the end tips grow roughly parallel to the parent root. For branch length, the roots branch more frequently near the trunk than further from the trunk. The root diameter decays at a higher rate near the trunk than in the remaining lateral roots, while the total cross-sectional area across a bifurcation node remains mostly conserved. The histograms of branching angle, and branch length and thickness gradient can be described using lognormal and exponential distributions, respectively. Conclusions Statistical measurements of root system architecture upon hierarchy provide a basis for representation and exploration of root system structure. This unique study presents data to characterize mechanically important structural roots, which will help link root architecture to the mechanical behaviors of root structures.
Why it matches plant phenotyping methodsフォトグラメトリによる根系3D再構成とスケルトン化を中心に、根系構造形質を定量化しており、植物フェノタイピング手法が研究の中核である。
abstractCombined analyses of the 3D models and skeletonized models enable detailed examination of basic bulk properties and quantification of architectural parameters
Spatially resolved spectroscopy (SRS) provides a new approach to the measurement of optical scattering and absorption properties and quality assessment of horticultural products. In this research, spatially resolved (SR) spectra over 550 – 1,650 nm were acquired for 600 peaches using a recently developed SRS system covering the light source-detector (S-D) distances from 1.5 to 36 mm with 30 fibers of three sizes (i.e., 50 µm, 105 µm and 200 µm). A new method of calculating spectral differences, between the first S-D distance and the remaining 14 S-D distances was proposed to enhance firmness and soluble solids content (SSC) predictions. Partial least squares (PLS) regression models based on SR and difference reflectance (DR) spectra were developed and compared for firmness and SSC prediction. Results showed that when using SR spectra, prediction results for firmness and SSC varied greatly with the S-D distance; SSC prediction results became worse with the increasing S-D distance, while larger S-D distances of 12–32 mm resulted in better firmness predictions. DR spectra gave consistently better results for both firmness and SSC predictions than SR spectra, with the best correlation coefficients of 0.853 and 0.839 for firmness and SSC prediction, respectively. Overall, SRS coupled with the spectral difference technique can enhance the prediction of firmness and SSC for peach fruit.
Why it matches plant phenotyping methods桃果実の硬度と可溶性固形分という植物器官形質を、空間分解分光法と新規スペクトル差分手法で推定することが研究の中心であり、予測モデルの比較評価も行っている。
abstractA new method of calculating spectral differences, between the first S-D distance and the remaining 14 S-D distances was proposed to enhance firmness and soluble solids content (SSC) predictions.
Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches ( Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University's Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.
Why it matches plant phenotyping methodsUAV RGB・LiDAR画像の撮像角度を最適化し、桃樹の樹高と樹冠容積という建築形質の抽出精度を評価する方法研究であり、フェノタイピング手法が中心です。
abstractthe goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images.
The accurate large-scale measurement of peach crowns is vital in horticultural science and the optimization of orchard management. Nowadays, numerous crown parameters (e.g., crown area, height, and volume) can be obtained via the analysis of point clouds or photographs. Current laser-based sensors provide the required reliable and accurate information; however, they are costly and time-consuming. Therefore, a simpler approach for crown measurement is required. For this purpose, this study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images of a peach orchard in Southeast China. Considering the limitation that the original aerial image dataset contains little information, a data augmentation process is adopted, and an efficient deep learning architecture based on conditional generative adversarial networks (cGANs) was designed to extract the crown area. Then, the shape of the crown area was clustered using an edge detection process and a$k$-means algorithm. Finally, an ellipsoid volume method (EVM) was applied to estimate the crown volume. Five indicators—namely,$Q_{\mathrm {seg}}$,$S_{\mathrm {r}}$, Precision, Recall, and F-measure—were employed to evaluate the crown extraction effects, and the average results for testing samples were 0.832, 0.847, 0.851, 0.828, and 0.846, respectively. Compared with other approaches—namely, fully convolutional network (FCN), U-Net, SegNet21, the excess green index (ExG), and the color index of vegetation extraction (CIVE)—the proposed cGAN model performs better, achieving an accuracy improvement of 5%–25%. For the estimation of crown volume, using measurements from a light detection and ranging (LIDAR) scanner as a reference, the correlation coefficient and relative-root-mean-square error (R-RMSE) were found to be 0.836% and 14.93%, respectively. Overall, the results demonstrate that the proposed method is feasible for measuring peach tree crowns. The wide application of such technology would facilitate applied research in plant phenotyping and precision horticulture.
Why it matches plant phenotyping methodsUAV画像から桃樹冠の面積・形状・体積を抽出・推定する深層学習パイプラインを開発し、他手法およびLiDAR基準で技術評価しており、植物表現型取得が中心です。
abstractthis study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images
The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.
Why it matches plant phenotyping methods植物の水ストレス状態を画像から自動検出・分類する深層学習手法を開発し、複数データセットで性能評価しており、植物フェノタイピング手法が中心である。
titleA Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress
Throughout history, pomologists have developed various trainings systems for temperate fruit trees to improve light interception, fruit yield, and fruit quality. To achieve this, these training systems enforce certain branch and canopy morphologies upon the tree. Quantifying architecture could aid the selection for trees that require less pruning or naturally excel in specific growing/training system conditions. Tree architecture is also directly associated with resource optimization, funneling what nutrients the plant absorbs into the most efficient, high-yielding configuration possible. In peaches [Prunus persica (L.) Batsch], branching indices (BIs) have been developed in attempts to quantify tree architecture. BIs can effectively focus on a particular area of tree architecture (e.g., an index focused on branching density, or BDi), producing quantitative measurements that can accurately represent a tree’s unique architecture. However, the required branching data to develop these indices is hard to collect. Historically, branching data has been collected manually. Often this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing 3D remote imaging technology, such as terrestrial LiDAR scanning (tLiDAR). To test this, young peach trees were scanned with 3D scanners and modeled using TreeQSM. This allowed us to collect branching data with which to calculate BDi values. Statistical analyses of BDi measurements from the 4 young trees will create a methodological pipeline with which mature and complex trees architectures may be simulated. These BDi values, either in young or adult trees, will be used to better phenotype trees’ architecture and to better select trees for further breeding and selection (i.e. future genomic studies - GWAS and novel QTL identification). Keywords: Plant Breeding, Computational Biology, Phenomics, Phenotyping, Bioinformatics, 3D modelling, tLiDAR
Why it matches plant phenotyping methodstLiDARとTreeQSMによる3D取得・モデル化から枝分かれ指標を算出する、植物形態フェノタイピングの方法論的パイプラインが中心である。
abstractTree architecture is also directly associated with resource optimization
Unmanned aerial vehicle (UAV) remote sensing has become a readily usable tool for agricultural water management with high temporal and spatial resolutions. UAV-borne thermography can monitor crop water status near real-time, which enables precise irrigation scheduling based on an accurate decision-making strategy. The crop water stress index (CWSI) is a widely adopted indicator of plant water stress for irrigation management practices; however, dependence of its efficacy on data acquisition time during the daytime is yet to be investigated rigorously. In this paper, plant water stress captured by a series of UAV remote sensing campaigns at different times of the day (9h, 12h and 15h) in a nectarine orchard were analyzed to examine the diurnal behavior of plant water stress represented by the CWSI against measured plant physiological parameters. CWSI values were derived using a probability modelling, named ‘Adaptive CWSI’, proposed by our earlier research. The plant physiological parameters, such as stem water potential (ψstem) and stomatal conductance (gs), were measured on plants for validation concurrently with the flights under different irrigation regimes (0, 20, 40 and 100 % of ETc). Estimated diurnal CWSIs were compared with plant-based parameters at different data acquisition times of the day. Results showed a strong relationship between ψstem measurements and the CWSIs at midday (12 h) with a high coefficient of determination (R2 = 0.83). Diurnal CWSIs showed a significant R2 to gs over different levels of irrigation at three different times of the day with R2 = 0.92 (9h), 0.77 (12h) and 0.86 (15h), respectively. The adaptive CWSI method used showed a robust capability to estimate plant water stress levels even with the small range of changes presented in the morning. Results of this work indicate that CWSI values collected by UAV-borne thermography between mid-morning and mid-afternoon can be used to map plant water stress with a consistent efficacy. This has important implications for extending the time-window of UAV-borne thermography (and subsequent areal coverage) for accurate plant water stress mapping beyond midday.
Why it matches plant phenotyping methodsUAV熱画像からCWSIを算出してネクタリン樹の水ストレスを推定し、取得時刻依存性を生理指標で検証することが研究の中心である。
titleDependence of CWSI-Based Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard
The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動推定する深層学習・クラウド実装を開発・評価しており、植物フェノタイピング手法が中心です。
abstractAutomated identification and classification of plant leaf diseases is, therefore, essential
Reproduction assets foundThe paper's Data Availability statement explicitly links a public Kaggle plant-disease image dataset used for training/testing and an authors' GitHub code repository. The TensorFlow plant_village catalog URL is a generic mirror of the same public dataset rather than a paper-specific deposit.Dataset · publicData Availability: Dataset is available from the below link: https://www.kaggle.com/emmarex/plantdiseaseOpen asset ↗kaggle · emmarex/plantdiseaselines:123-130Code · publicGithub Code Repo Link: https://github.com/umairnawazz/Plant-Disease-DetectionOpen asset ↗github · umairnawazz/Plant-Disease-Detectionlines:123-130Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published26 Nov 20202020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)Cited by 13 · OpenAlex ↗
Recognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of 3×3 and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves.
Why it matches plant phenotyping methods植物葉画像から健全・罹病状態を推定するCNN分類手法の性能評価が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。
abstractThis work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased.
PeachChlorophyll fluorescenceFruitClassificationGrowth / development / phenologyPigment / colour / senescence
Technology for rapid, non-invasive and accurate determination of fruit maturity is increasingly sought after in horticultural industries. This study investigated the ability to predict fruit maturity of yellow peach cultivars using a prototype non-destructive fluorescence spectrometer. Collected spectra were analysed to predict flesh firmness (FF), soluble solids concentration (SSC), index of absorbance difference (I AD ), skin and flesh colour attributes (i.e., a* and H°) and maturity classes (immature, harvest-ready and mature) in four yellow peach cultivars-'August Flame', 'O'Henry', 'Redhaven' and 'September Sun'. The cultivars provided a diverse range of maturity indices. The fluorescence spectrometer consistently predicted I AD and skin colour in all the cultivars under study with high accuracy (Lin's concordance correlation coefficient > 0.85), whereas flesh colour's estimation was always accurate apart from 'Redhaven'. Except for 'September Sun', good prediction of FF and SSC was observed. Fruit maturity classes were reliably predicted with a high likelihood ( F1 -score = 0.85) when samples from the four cultivars were pooled together. Further studies are needed to assess the performance of the fluorescence spectrometer on other fruit crops. Work is underway to develop a handheld version of the fluorescence spectrometer to improve the utility and adoption by fruit growers, packhouses and supply chain managers.
Why it matches plant phenotyping methods蛍光分光計を用いてモモ果実の成熟度や品質関連形質を非破壊推定し、複数品種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す。
abstractusing a prototype non-destructive fluorescence spectrometer
Reproduction assets foundThe paper reports peach fluorescence-spectra phenotyping and PLS/LDA modelling. The only qualifying paper-specific public asset is the authors' sample analysis code (simulated annealing wavelength band selection with PLS), explicitly stated as freely available as a Jupyter Notebook. No public phenotype dataset, spectraCode · publicSample scripts of the algorithms are freely available as a Project Jupyter Notebook [ 45 ].Open asset ↗lines:45-57Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on AWS DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0.349s, providing disease information to the user in less than a second.
Why it matches plant phenotyping methods植物葉の病徴を画像から自動検出・分類する手法と、AWS DeepLens/SageMakerによるリアルタイム実装・評価が研究の中心であり、植物病害状態のフェノタイピングに該当します。
abstractOur proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases
AppleGrapevinePeachRootClassificationMorphology / geometry measurementRoot system architecture
Premise While root-order approaches to fine-root classification have shown wide utility among wild plants, they have seen limited use for perennial crop plants. Moreover, inadequate characterization of fine roots across species of domesticated perennial crops has led to a knowledge gap in the understanding of evolutionary and functional patterns associated with different fine-root orders. Methods We examined fine-root traits of common horticultural fruit and nut crops: Malus ×domestica, Prunus persica, Vitus vinifera, Prunus dulcis, and Citrus ×clementina. Additional roots were sampled from 33 common perennial horticultural crops, native to tropical, subtropical, and temperate regions, to examine variation in 1st- and 2nd-order absorptive roots. Results First-order roots of grape and 1st- and 2nd-order roots of apple and peach were consistently thin, nonwoody, mycorrhizal, and had high N:C ratios. In contrast, 4th- and 5th-order roots of grape and 5th-order roots of apple and peach were woody, nonmycorrhizal, had low N:C ratios, and were thicker than lower-order roots. Among the 33 horticultural species, diameter of 1st- and 2nd-order roots varied about 15-fold, ranging from 0.04 to 0.60 mm and 0.05 to 0.89 mm respectively. This variation generally was phylogenetically conserved across plant lineages. Conclusions Collectively, our research shows that root-order characterization has considerably more utility than an arbitrary diameter cutoff for identifying roots of different functions in perennial horticultural crops. In addition, much of the variation in root diameter among species can be predicted by evolutionary relationships.
Why it matches plant phenotyping methods根の次数分類法を用いて複数の多年生園芸作物の根機能関連形質を比較し、直径閾値法に対する有用性を評価しているため、根形質取得・分類法の検証が中心です。
abstractConclusions Collectively, our research shows that root-order characterization has considerably more utility than an arbitrary diameter cutoff for identifying roots of different functions in perennial horticultural crops.
The development of precise and reliable near infrared spectroscopy (NIRS)-based non-destructive tools to assess physicochemical properties of fleshy fruit has been challenging. A novel crop load × fruit developmental stage protocol for multivariate NIRS-based prediction models calibration to non-destructively assess peach internal quality and maturity was followed. Regression statistics of the prediction models highlighted that dry matter content (DMC, R 2 = 0.98, RMSEP = 0.41%), soluble solids concentration (SSC, R 2 = 0.96, RMSEP = 0.58%) and index of absorbance difference (I AD , R 2 = 0.96, RMSEP = 0.08) could be estimated accurately with a single scan during fruit growth and development. Thus, the impact of preharvest factors such as crop load and canopy position on peach quality and maturity was evaluated. Large-scale field validation showed that heavier crop loads reduced peach quality (DMC, SSC) and delayed maturity (I AD ) and upper canopy position advanced both mainly in the moderate crop loads. This calibration protocol can enhance NIRS adaptation across tree fruit supply chain.
Why it matches plant phenotyping methods桃果実の内部品質・成熟度を単一スキャンで非破壊推定するNIRSモデルの開発、校正、検証が研究の中心であり、植物器官の形質測定法に該当する。
abstractThe development of precise and reliable near infrared spectroscopy (NIRS)-based non-destructive tools to assess physicochemical properties of fleshy fruit has been challenging.
This research measured the optical absorption (µ a ) and reduced scattering (μ s ') properties in peaches during quality deterioration, and determine the relationships of the optical parameters with select structural and biochemical parameters. Spatially resolved reflectance was measured for healthy and fungal infected peaches, followed by physical (the size and tissue color), structural [membrane permeability and SEM], and biochemical (Vc, soluble sugar, titratable acid, chlorophyll, total phenolic content) measurements. Both µ a and µ s ' were correlated well with the cellulosic structural and biochemical parameters of peaches, and they had the best correlations with those quality parameters at 675 nm. The correlation of μ s ' with membrane permeability was the highest from -0.962-0.743, while μ a had the best correlation with the chlorophyll content at 675 nm which is an indicator of plant maturation and senescence. These findings would be useful for further development of an effective optical technique for early disease detection of peach fruit.
Why it matches plant phenotyping methods桃果実の健全・感染状態を対象に、空間分解反射による光学特性測定を行い、病害・成熟関連の植物状態との関係を評価しており、光学的フェノタイピング手法が中心です。
abstractSpatially resolved reflectance was measured for healthy and fungal infected peaches
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
The reduction-oxidation (redox) environment of the phytobiome (i.e., the plant–microbe interface) can strongly influence the outcome of the interaction between microbial pathogens, commensals, and their host. We describe a noninvasive method using a bacterial bioreporter that responds to reactive oxygen species and redox-active chemicals to compare microenvironments perceived by microbes during their initial encounter of the plant surface. A redox-sensitive variant of green fluorescent protein (roGFP2), responsive to changes in intracellular levels of reduced and oxidized glutathione, was expressed under the constitutive SP6 and fruR promoters in the epiphytic bacterium Pantoea eucalypti 299R (Pe299R/roGFP2). Analyses of Pe299R/roGFP2 cells by ratiometric fluorometry showed concentration-dependent responses to several redox active chemicals, including hydrogen peroxide (H 2 O 2 ), dithiothreitol (DTT), and menadione. Changes in intracellular redox were detected within 5 min of addition of the chemical to Pe299R/roGFP2 cells, with approximate detection limits of 25 and 6 μM for oxidation by H 2 O 2 and menadione, respectively, and 10 μM for reduction by DTT. Caffeic acid, chlorogenic acid, and ascorbic acid mitigated the H 2 O 2 -induced oxidation of the roGFP2 bioreporter. Aqueous washes of peach and rose flower petals from young blossoms created a lower redox state in the roGFP2 bioreporter than washes from fully mature blossoms. The bioreporter also detected differences in surface washes from peach fruit at different stages of maturity and between wounded and nonwounded sites. The Pe299R/roGFP2 reporter rapidly assesses differences in redox microenvironments and provides a noninvasive tool that may complement traditional redox-sensitive chromophores and chemical analyses of cell extracts.
Why it matches plant phenotyping methods植物表面の酸化還元状態を非侵襲的に測定する細菌バイオレポーターを開発・検証し、花弁や果実の成熟・損傷による表面環境の差を評価しているため、植物状態の取得法が中心である。
abstractWe describe a noninvasive method using a bacterial bioreporter that responds to reactive oxygen species and redox-active chemicals to compare microenvironments perceived by microbes during their initial encounter of the plant surface.
PeachFruitClassificationGrowth / development / phenology
In regions with the predominance of agriculture, an inspection of the quality and fruit maturity index in the orchard is usually analyzed by the farmer’s experience, which can be subject to errors and generate a greater cost of time and money. Thus, monitoring equipment that generates a rapid and accurate response to the growth cycle of the peaches in the crop is desirable, together with a low marketing cost. For this purpose, electronic noses prove to be the most suitable equipment, since it allows online monitoring of the VOCs (Volatile Organic Compounds) generated by the crop. In this context, a prototype was developed to perform the classification of the fruit growth cycle (pre-harvest and post-harvest). Models with the 13 gas sensors made with a metal oxide semiconductor (MOS) and the reduction to 7 sensors were studied with the aid of the Pearson’s Chi-square test, for comparison. Samples with 4 growth stages were used for the training and construction of the model. The accuracy of 99.23% in the validation step and 98.08% in the sample test step using the Random Forest method with linear discriminant analysis for the reduced data set for 7 sensors shows that the device is promising for monitoring of areas with an intense emission of VOCs.
Why it matches plant phenotyping methods桃果実の成長段階・成熟状態をVOCセンサーで推定する電子鼻プロトタイプを開発し、センサー構成と分類精度を検証しており、植物状態の取得法が研究の中心である。
abstracta prototype was developed to perform the classification of the fruit growth cycle (pre-harvest and post-harvest).
Continuous assessment of plant water status indicators provides the most precise information for irrigation management and automation, as plants represent an interface between soil and atmosphere. This study investigated the relationship of plant water status to continuous fruit diameter (FD) and inverse leaf turgor pressure rates ( p p ) in nectarine trees [ Prunus persica (L.) Batsch] throughout fruit development. The influence of deficit irrigation treatments on stem ( Ψ stem ) and leaf water potential, leaf relative water content, leaf stomatal conductance, and fruit growth was studied across the stages of double-sigmoidal fruit development in 'September Bright' nectarines. Fruit relative growth rate (RGR) and leaf relative pressure change rate (RPCR) were derived from FD and p p to represent rates of water in- and outflows in the organs, respectively. Continuous RGR and RPCR dynamics were independently and jointly related to plant water status and environmental variables. The independent use of RGR and RPCR yielded significant associations with midday Ψ stem , the most representative index of tree water status in anisohydric species. However, a combination of nocturnal fruit and leaf parameters unveiled an even more significant relationship with Ψ stem , suggesting a changing behavior of fruit and leaf water flows in response to pronounced water deficit. In conclusion, we highlight the suitability of a dual-organ sensing approach for improved prediction of tree water status.
Why it matches plant phenotyping methods果実径と葉の膨圧を連続センシングし、水分状態指標を抽出・予測する手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractContinuous assessment of plant water status indicators provides the most precise information for irrigation management and automation
The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.
Why it matches plant phenotyping methodsネクタリン果実の内部品質、硬さ、裂果障害を hyperspectral transmittance imaging と解析モデルで評価しており、表現型取得法の開発・検証が研究の中心である。
abstractThe internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging.
In recent years, using multispectral cameras on UAVs has provided an opportunity to capture separate bands that offer the extraction of spectral features used for early detection of diseased plants. One of the main steps in disease detection is radiometric calibration that converts digital numbers to reflectance values commonly using white reference panels. This paper focused on the necessity of radiometric calibration to distinguish disease trees in orchards based on aerial multi-spectral images. For this purpose, two study sites with various climate conditions and tree species as well as different disease types were selected where multispectral images were taken using a multirotor UAV. The impact of radiometric correction on plant disease detection was assessed in two ways: 1) comparison of separability between the healthy and diseased classes using T-test and entropy distances; 2) radiometric calibration effect on the accuracy of classification. The experimental result showed the insignificant effect of radiometric calibration on separability criteria. Furthermore, based on T-test and entropy distances criteria, NIR and R spectral features made highest distances between healthy and Greening infected citrus trees, respectively, at the first study site while NDRE and BNDVI spectral features made highest distances between healthy and peach leaf curl infected trees, respectively, at the other study site. In the second strategy, the experimental result showed that radiometric calibration had no effect on the accuracy of classification. As a result, the overall accuracy and kappa values for both un-calibrated and calibrated orthomosaic classifications of the citrus orchard were 96.6% and 0.94%, respectively, using five spectral bands as well as DVI, NDRE, NDVI and GNDVI vegetation indices using a random forest classifier. The experimental results were also similar at the other study site. Therefore, the overall accuracy and kappa values for both the un-calibrated and calibrated orthomosaic classifications were 96.1%, 0.92, respectively, using five spectral bands as well as NDRE, BNDVI, GNDVI, DVI, and NDVI vegetation indices.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植物病害状態の検出について、放射測定校正の必要性と分類性能を比較評価しており、表現型取得・抽出手法の検証が中心である。
abstractThis paper focused on the necessity of radiometric calibration to distinguish disease trees in orchards based on aerial multi-spectral images.
In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m ( R 2 = 0.99) and 0.15 m ( R 2 = 0.93) for the two orthogonal crown widths, and 3.87 m 2 for CPA ( R 2 = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m ( R 2 = 0.91), 0.51 m ( R 2 = 0.74), and 4.96 m 2 ( R 2 = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.
Why it matches plant phenotyping methodsUAV画像とSfM/MVS、画像分割によりモモ樹の樹冠幅・樹冠投影面積を抽出し、手動 delineation と圃場測定で精度検証しており、植物表現型取得法が研究の中心である。
abstractwe propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information
Reproduction assets foundThe paper explicitly states that source codes and sample data for the peach crown characterization method are available at the authors' public GitHub repository, which matches an allowed URL.Code · publicThe crown geometry is derived using two kinds of DSM (bare-branch DSM and foliated DSM) by image analysis techniques in the following five steps (source codes and sample data are available at our surpport page: https://github.com/UTokyo-FieldPhenomics-Lab/Characterization-of-peach-tree-crown):Open asset ↗UTokyo-FieldPhenomics-Lab/Characterization-of-peach-tree-crownlines:46-69Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA). This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
Why it matches plant phenotyping methodsリンゴ・モモ育種プロジェクトのレビューで、複雑形質の新しい表現型測定法の開発とデータベース化を明示的に扱っており、フェノタイピング手法・データが中心的な構成要素の一つである。
abstractThis review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
Reproduction assets foundThe paper describes the FruitBreedomics database storing the project's apple/peach phenotypic and genotypic data, publicly accessible at the tecnoparco URL, and the HapAg (HaploblockAggregator) software developed for the apple linkage map analysis, publicly available at the Wageningen URL. Both are paper-specific,公开,和可Dataset · publicAll data are accessible through a stable and well-maintained interface, available at the address http://bioinformatics.tecnoparco.org/fruitbreedomics .Open asset ↗lines:39-45Code · publicThe dedicated software HapAg was developed in support to this approach, which has been made public available at http://www.wageningenur.nl/en/show/HaploblockAggregator.htm .Open asset ↗lines:62-69Plant phenotyping relevance match · UnverifiedCrossref · checked 10 Sept 2026
The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific (‘adaptive’ hereafter) lower and higher temperature references (Twet and Tdry respectively). Direct measurements of stem water potential (SWP, ψstem) and stomatal conductance (gs) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψstem and gs with determination coefficients (R2) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops.
Why it matches plant phenotyping methodsUAV熱画像から作物の水分ストレスを推定する適応型CWSIを開発し、茎水ポテンシャルと気孔コンダクタンスで検証しており、植物生理状態の取得手法が研究の中心である。
abstracthigh-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability
The aim of this study was to develop and validate a standard area diagram set (SADs) to assess the severity of peach rust, caused by Tranzschelia discolor. The proposed SADs includes ten images of leaves with a range of severity (0.1, 0.5, 1.0, 2.0, 5.0, 10, 15, 20, 25 and 30%). The SADs was validated by 14 raters who had no experience in plant disease severity estimation. In the first step of the validation, the raters made severity estimates of 50 leaves with a range of rust severity without using SADs. In the second step, the same raters estimated severity of rust on the same 50 leaves using the SADs to aid estimation. Lin’s concordance correlation analysis showed that both precision and accuracy improved when the raters used the SADs compared to the assessments made without SADs. Accuracy, as measured by the coefficient of bias (C b) improved from 0.70 to 0.98, without and with SADs, respectively, and precision measured by the correlation coefficient (r) improved from 0.85 to 0.90, without and with SADs, respectively. Overall agreement, measured by Lin’s concordance correlation coefficient (ρ c), improved from 0.59 to 0.88 without and with SADs, respectively. Furthermore, estimates were more reliable when using SADs: the coefficient of determination (R²) was 0.60 without and 0.73 with SADs; and the intra-class correlation coefficient (ρ) was 0.72 without, and 0.86 with SADs. Thus, the use of SADs improved the precision, accuracy and reliability of visual estimates of severity of peach rust.
Why it matches plant phenotyping methodsモモさび病の葉面積重症度を評価する標準面積図セットを開発し、評価者実験で精度・正確度・信頼性を検証しており、植物病徴の表現型取得法が中心である。
abstractThe aim of this study was to develop and validate a standard area diagram set (SADs) to assess the severity of peach rust, caused by Tranzschelia discolor.
One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.
Why it matches plant phenotyping methodsモモ花の検出という植物器官の状態を、UASマルチスペクトル画像と植生指数に基づく画像処理アルゴリズムで取得する方法を開発・評価しており、フェノタイピング手法が中心である。
abstractThis paper presents the development of an image processing algorithm to detect peach blossoms on trees.
In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ѰL) throughout the season. The feasibility of mapping within-orchard spatial variability of ѰL from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. ѰL relationships. For a specific given CWSI value, ѰL was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive ѰL maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise ѰL mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m2.
Why it matches plant phenotyping methods航空熱画像とCWSIを用いて果樹の水分状態を推定・マッピングする手法を開発し、葉水ポテンシャルとの関係および画素サイズの感度を評価しており、植物表現型取得法が中心である。
abstractThe CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ѰL) throughout the season.