Plant diseases have continued to threaten agricultural productivity, while manual inspection methods have remained inefficient and prone to subjectivity. This study proposed and assessed a hybrid framework integrating a Convolutional Neural Network (CNN) with a Large Language Model (LLM) to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations. EfficientNetV2-M was employed as the visual backbone and trained on 11 selected classes of apple, grape, and potato leaf images derived from the PlantVillage dataset. A structured data splitting strategy was applied to ensure reliable model validation and unbiased testing. The classification capability of the CNN component was examined through standard multi-class evaluation indicators, including class-wise predictive consistency and error distribution analysis. Experimental results indicated that the model delivered highly consistent predictions, reaching a peak test accuracy of 99.79%, reflecting its robustness in distinguishing visually similar disease patterns. To overcome the black-box limitation, prediction outputs were transformed into structured prompts and processed by GPT-4o to generate contextual explanations. The generated narratives systematically described observable symptoms, highlighted distinguishing characteristics, and suggested initial management actions. Overall, the proposed hybrid system demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN・LLM統合手法を開発・評価しており、病害分類と説明生成が研究の中心であるため。
abstractThis study proposed and assessed a hybrid framework integrating a Convolutional Neural Network (CNN) with a Large Language Model (LLM) to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations.
Reproduction assets foundThe paper uses the public PlantVillage color dataset (11 apple/grape/potato classes, 9,385 images) and explicitly points to it in the DATA AVAILABILITY statement as the replication package data. The authors also provide a public Streamlit demonstration of their hybrid CNN–LLM system. No author analysis code or trained-Dataset · publicaper. The research was conducted for academic purposes, and no financial, commercial, or
personal relationships influenced the study design, data analysis, interpretation of results, or
preparation of the manuscript.
DATA AVAILABILITY
The data associated with this study are publicly available online in the replication package.
[https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color]
AUTHOR CONTRIBUTIONS
Frenky Riski Gilang Pratama: Conceptualization; Programming and coding
implementation; Methodology; Writing-Original Draft. Sugiarto Surono: Conceptualization;
Methodology; Supervision; Writing-Review & Editing. Aris Thobirin: Proofreading Paper;
Writing-Review & Editing; FunOpen asset ↗https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/colorpdf-raw-page:10 lines:1-52Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This dataset contains images of Manalagi apple diseases from Indonesia. Data collection was conducted from August 2024 to June 2026. Data were collected in apple orchards. All images were captured under natural environmental conditions. A total of 1168 unique Manalagi apple specimens were successfully documented. The specimens consisted of both healthy and diseased fruit. This dataset comprises four classes: Healthy, Anthracnose, Black Pox, and Powdery Mildew. Each specimen was observed and photographed directly. The documentation process yielded approximately 5100 raw images. The images were captured using various smartphone cameras and DSLR cameras. Each device has different camera specifications. The image size depends on the device used. Images that passed quality inspection were selected for the next stage. Each fruit specimen is cropped from the selected raw image. Each image was then labeled according to its disease class. The image size was standardized to 1024 × 1024 pixels. All images were saved in JPEG format. The curation process yielded 482 images. Each image represents a distinct fruit specimen.
Why it matches plant phenotyping methodsリンゴ果実の健全・病害状態を画像で記録し、分類用データセットとして構築・キュレーションした研究であり、植物病害表現型の取得方法と再利用可能なデータセットが中心です。
titleImage dataset of manalagi apple fruits for multi-class disease classification using deep learning.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository of Manalagi apple fruit disease images (raw, curated, and augmented), directly usable for plant disease phenotyping/classification.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/9zgkwwv9j8.6
Direct URL to data: https://data.mendeley.com/datasets/9zgkwwv9j8/6Open asset ↗Mendeley Data · 10.17632/9zgkwwv9j8.6html-lines:97-124Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published23 Jul 2026Journal of Intelligent Decision Making and Information ScienceCited by 0 · OpenAlex ↗
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effectiveness of disease detection and facilitates well-informed decision-making. But conventional manual inspection techniques are still tedious, unpredictable, and prone to errors. In order to overcome these constraints, this research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time. The proposed YOLOv12-CropNet approach makes use of the Convolutional Block Attention Module (CBAM) for adaptive attention, the Content-Aware Reassembly of Features (CARAFE) up-sampling module to preserve fine-grained disease characteristics, the YOLOv12 architecture improved with Ghost Convolution for effective feature extraction, and Involution layers to capture spatially specific patterns. Inspection techniques are still laborious, arbitrary, and prone to mistakes. A substantial set of data of 38 classes of both healthy and sick leaves from a variety of crops, including tomato, potato, apple, grape, corn, mango and sugarcane, was put together for training and evaluation. Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection. Accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances. The suggested technique provides a scalable and field-deployable way to assist effective identification of diseases and precision agricultural decision-making. The proposed YOLOv12-CropNet model exhibits better performance than the other evaluated models, attaining a 98.45% peak accuracy, 98.10% precision ,98.20 % sensitivity and a 98.18% F1 score, thereby highlighting its efficacy in multi-crop leaf disease detection.
Why it matches plant phenotyping methods複数作物の葉の病徴を画像から検出・分類する深層学習手法を開発し、データセットと性能評価を伴うため、植物病害状態のフェノタイピング手法が中心である。
abstractthis research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time.
Reproduction assets foundThe paper uses public Kaggle datasets as its phenotyping image inputs: the PlantVillage dataset (38 crop-disease classes) and the Sugarcane Leaf Disease dataset, both cited with explicit public URLs. No author code, models, or checkpoints are reported as publicly available.Dataset · public[37] PlantVillage Dataset. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗pdf-page:22 lines:1-61Dataset · public[39] Sugarcane leaf Disease Dataset available online: https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-Open asset ↗pdf-page:22 lines:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Agricultural leaf disease detection is crucial for early intervention and yield protection in precision agriculture. Among representative economic crops, such as apples, leaf lesions are typically small and appear in complex backgrounds, making accurate detection performed on resource-constrained embedded devices challenging. To address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n. A novel Dual-Aspect Feature Complementary Mapping (DAFCM) module type is embedded in their backbone to recover lost semantic and spatial information, while the original YOLO11n's neck is replaced by an Efficient Enhanced Cross-Scale Feature Fusion (EE-CSFF) module, which incorporates Gated Differential Convolutional Fusion (GDCF) modules to strengthen cross-scale information flow and small-object representation. Experimental results obtained on the ALDSOD dataset show that, compared with the YOLO11n baseline, DCL-YOLO improves recall from 81.9% to 84.6%, mAP50 from 86.8% to 88.4%, and mAP50:95 from 47.0% to 47.8%, while also reducing the parameter count from 2.58 M to 1.91 M and Giga Floating-Point Operations (GFLOPs) from 6.3 to 5.5. After applying Layer-Adaptive Magnitude-based Pruning (LAMP), the parameter count and GFLOPs are further reduced to 0.75 M and 2.7, respectively, with mAP50 and mAP50:95 still exceeding the baseline by 1.2 and 0.5 percentage points, respectively. When deployed on an embedded device, the pruned model achieved 15.2 FPS and 139 msec per image, confirming its applicability in real-time scenarios. Furthermore, cross-domain validation, performed on the Global Wheat Head Detection (GWHD) dataset, indicates the stable generalization capabilities of the proposed models across environmental domain shifts. The DCL-YOLO's source code is publicly available at: https://github.com/q123-code/dcl-yolo.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する軽量モデルを開発し、データセットで性能比較・クロスドメイン検証・組込み機器での実装評価を行っており、植物の病害状態推定手法が研究の中心です。
abstractTo address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n.
Reproduction assets foundThe paper's constructed ALDSOD apple-leaf disease detection dataset is publicly available via Zenodo DOI, and the authors' DCL-YOLO source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData Availability: The constructed ALDSOD dataset used in this study is available for download from the following DOI: https://doi.org/10.5281/zenodo.17198053 .Open asset ↗zenodo · 10.5281/zenodo.17198053lines:148-159Code · publicThe DCL-YOLO’s source code is publicly available at: https://github.com/q123-code/dcl-yolo .Open asset ↗github · q123-code/dcl-yololines:148-159Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Zero-shot visual anomaly detection in complex textured domains remains a fundamental challenge for building adaptive, self-organizing cyber-physical systems. Conventional deep learning approaches often rely on closed-set assumptions, require prohibitive pixel-level annotation costs, and suffer severe performance degradation under cross-domain shifts---limiting their deployability in real-world agricultural CPS where novel disease types and unseen crop species continuously emerge. To address these issues, we present TopoLeaf, a training-free and annotation-free anomaly detection framework. By leveraging the robust semantic representations of foundation models (specifically DINOv2), our method introduces two complementary scoring mechanisms: a geometric anomaly score based on local KNN distance in a stability-selected feature subspace, and a topological anomaly score derived from local persistent homology. The topological score effectively captures subtle structural deviations and micro-texture mutations that geometric distances often miss. Extensive experiments on cross-species plant disease benchmarks (3,100+ images across 40 source--target pairs) demonstrate that TopoLeaf achieves highly competitive and structurally robust zero-shot performance, providing a robust perception layer for closed-loop agricultural cyber-physical systems that must maintain diagnostic stability under previously unseen perturbations. Under well-aligned domains, our geometric score achieves near-perfect detection (e.g., 0.994 AUROC on Strawberry). The method exhibits informative failure modes on structurally isolated domains such as Corn (0.169 AUROC), revealing fundamental structural properties of the foundation model's feature manifold. Furthermore, the topological score demonstrates structural complementarity, achieving 0.542 AUROC on the challenging Corn-to-Apple pair where geometric scoring degenerates to 0.221. Module ablation studies confirm that stability-based dimensionality selection consistently improves cross-domain generalization.
Why it matches plant phenotyping methods植物病害の視覚的異常（植物の病徴・状態）を推定する新規画像解析手法を開発し、複数種の病害ベンチマークで検証しているため、植物フェノタイピング手法が中心である。
abstractwe present TopoLeaf, a training-free and annotation-free anomaly detection framework.
Reproduction assets foundThe paper publicly releases its complete TopoLeaf source code (implementation, baselines, evaluation scripts) under the MIT License on GitHub, and all experimental image data derives from the publicly available PlantVillage dataset, which is the leaf-image input used for the paper's anomaly-detection phenotyping and isCode · public427 7.4 Consent to Publish
428 Not applicable.
429 7.5 Data Availability
430 All experimental data used in this study is derived from the publicly available
431 PlantVillage dataset [17], which can be accessed at https://github.com/spMohanty/
432 PlantVillage-Dataset.
433 7.6 Code Availability
434 The complete source code, including implementation of TopoLeaf, baseline com-
435 parisons, and evaluation scripts, is publicly available at https://github.com/
436 Shutong-Hou/TopoLeaf under the MIT License.
437 7.7 Funding
438 This research received no specificOpen asset ↗Shutong-Hou/TopoLeafpdf-layout-page:26 lines:1-44Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
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:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Three dimensional (3D) instance segmentation is essential for precision characterization of tree architecture at the branch level, which supports both tree fruit crop breeding and the development of robotic systems for orchard management. Existing methods usually use sparse convolution-based operation, which requires a coordinate quantization preprocess to generate sparse tensors, risking the loss of geometric details for fine-grained downstream phenotyping tasks. To overcome this challenge, we developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees. Our hybrid DSPVFormer architecture maximizes the use of raw point features by dynamically mapping and aggregating the raw point features into the sparse voxel embeddings, capturing strong geometric features that may be discarded during quantization. Evaluations demonstrate that DSPVFormer achieved statistically significant improvements over baseline models on most instance segmentation metrics, which are further translated into more accurate phenotyping evaluation including branch counting and pruning map generation. These advances directly benefit downstream applications in plant phenotyping and robotic pruning for tree crops such as apples. Meanwhile, experimental results on phenotyping tasks suggested that phenotyping-specific evaluation metrics should be prioritized over upstream computer vision performance metrics to realize the full potential of high-throughput phenotyping for real-world applications.
Why it matches plant phenotyping methodsリンゴ樹の3D点群から枝レベル形質を抽出するセグメンテーション手法を開発・評価し、枝数や剪定マップへの応用まで検証しており、植物フェノタイピング手法が中心である。
abstractwe developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees.
Reproduction assets foundThe paper states that its data and code (including the DSPVFormer analysis pipeline built on Plant Segmentation Studio) are publicly available at the authors' PSS GitHub repository. The COS dataset of 98 dormant apple tree point clouds is also referenced as accessible via this repository/statement. Other URLs (spconv,mCode · publicThe data and code are available at the PSS GitHub repository: https://github.com/perrydoremi/PlantSegStudio .Open asset ↗https://github.com/perrydoremi/PlantSegStudiolines:383-408Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early and accurate detection of apple leaf diseases is critical for sustainable agriculture, yet manual diagnosis remains time-consuming and error-prone. This study introduces a novel deep learning framework centered on a custom ContinuousLayer, a spatially adaptive convolutional layer designed to overcome the limitations of standard CNNs. This architecture automates the classification of apple leaf diseases Black rot, rust, scab, and healthy leaves with high precision. The model addresses dataset imbalance through strategic resampling, achieving uniform class distribution. The ContinuousLayer introduces spatial feature modulation using trainable Gaussian basis functions, enhancing feature extraction while penalising kernel irregularities through a hybrid composite loss function. Trained on a dataset of 3,164 images balanced via bicubic up-sampling, and evaluated on a held-out test set of 10% of the data, the model attains a 98.63% test accuracy, with F1-scores ranging from 0.98 to 1.00 across classes. Visual analysis of the confusion matrix reveals minimal misclassification, predominantly between rust and scab. Comparative evaluation against baseline architectures demonstrates the efficacy of the ContinuousLayer in capturing disease-specific spatial patterns. These results underscore the potential of integrating mathematically inspired layers into CNNs for plant pathology applications, offering a highly accurate tool for precision agriculture in controlled environments.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から分類する新規深層学習層と解析手法を開発・比較評価しており、植物病害状態の表現型抽出が中心である。
abstractThis study introduces a novel deep learning framework centered on a custom ContinuousLayer, a spatially adaptive convolutional layer designed to overcome the limitations of standard CNNs.
Reproduction assets foundThe paper's apple leaf disease image dataset (3,164 images) is explicitly stated to be publicly available on Kaggle, matching an allowed URL. No author code or model checkpoints are reported as available.Dataset · publicThe datasets analysed during the current study is publicly available in the Kaggle repository at https://www.kaggle.com/datasets/mhantor/apple-leaf-diseases.Open asset ↗Kaggle · mhantor/apple-leaf-diseaseslines:595-613Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 × 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.
Why it matches plant phenotyping methodsリンゴ果実の硬度とBrixという植物器官形質を、ハイパースペクトル画像と深層学習で非破壊推定するデータセット・モデル・汎化性能評価が研究の中心である。
abstractThis study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness.
Reproduction assets foundThe paper explicitly states that the hyperspectral apple dataset (5756 apples, firmness/Brix/starch measurements) is deposited in the University of Essex research data repository and that the data cleaning, model training, and analysis code is on GitHub, both with public URLs.Dataset · publicThe datasets generated during and analysed during the current study are available in the University of Essex repository ( https://researchdata.essex.ac.uk/228/ )Open asset ↗researchdata.essex.ac.uk · 228lines:192-220Code · publicthe code used for data cleaning, model training and analysis are available on GitHub: ( https://github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaging.git )Open asset ↗github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaginglines:192-220Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Apple leaf disease segmentation is critical for yield and quality preservation in what is globally one of the most economically significant fruit crops. Despite recent advances in deep learning, real-world orchard environments present three primary challenges: (1) low contrast between lesions and background textures, which hinders accurate localization; (2) leaf overlap and occlusion, leading to incomplete feature representation and increased false negatives; and (3) the inherent limitations of unimodal RGB imagery in capturing subtle pathological features, which constrains generalization and accuracy. To address these issues, we proposed Language-Infused Visual Mamba (LViM), a dual-path U-Net architecture that integrates Mamba and Transformer modules for semantic-visual feature fusion. LViM achieves robust segmentation in complex environments through three core innovations: (1) A U-shaped Multimodal Transformer (MTT) branch integrated with AMBERT, which leverages inter-modal semantic relationships to enhance textual feature extraction and provide high-level semantic cues, thereby improving lesion-background discriminability; (2) a U-shaped Visual State Space (VMamba) branch that employs 2D Selective Scanning (SS2D) and Visual State Space (VSS) blocks to capture global context and fine-grained details, mitigating the impact of occlusion; and (3) Cross-Attention Gate Fusion (CAGF) and Linguistic Cross-Nested (LCN) modules that facilitate efficient cross-modal alignment and hierarchical feature modeling to better identify subtle lesions. Experimental results demonstrate that LViM consistently outperforms the VM-UNet baseline, yielding improvements of 4.05% in Precision, 4.25% in Dice coefficient, 4.49% in mIoU, and 4.23% in Recall.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から分割する手法を開発し、複雑な環境での性能を評価しており、植物病害状態の取得・推定が研究の中心である。
abstractApple leaf disease segmentation is critical for yield and quality preservation
Reproduction assets foundThe paper's curated multimodal apple leaf disease dataset (image-text pairs with pixel-level annotations for four disease types) is explicitly stated as publicly released in the authors' LViM GitHub repository. Code/models are only promised 'upon acceptance,' so the dataset asset qualifies as public, while the code is.Dataset · publicThe curated multimodal apple leaf disease dataset constructed in this study has been publicly released at https://github.com/csuft1906ll/LViMOpen asset ↗csuft1906ll/LViMlines:273-283Code / dataset availability confirmedCrossref · checked 13 Sept 2026
AppleStrawberryGreenhouseLiDAR / point cloudRGB / grayscaleFruitSegmentationTracking
Accurate and consistent fruit monitoring over time is a key step towards automated agricultural production systems. However, this task is inherently difficult due to variations in fruit size, shape, occlusion, orientation, and the dynamic nature of orchards where fruits may appear or disappear between observations. In this article, we propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time. Our approach directly operates on dense colored point clouds, capturing fine-grained 3D spatial detail. We segment individual fruits using a learning-based instance segmentation method applied directly to the point cloud. For each segmented fruit, we extract a compact and discriminative descriptor using a 3D sparse convolutional neural network. To track fruits across different times, we introduce an attention-based matching network that associates fruits with their counterparts from previous sessions. Matching is performed using a probabilistic assignment scheme, selecting the most likely associations across time. We evaluate our approach on real-world datasets of strawberries and apples, demonstrating that it outperforms existing methods in both instance segmentation and temporal re-identification, enabling robust and precise fruit monitoring across complex and dynamic orchard environments. • We propose a new performant approach to autonomous fruit tracking in real greenhouses. • It segments fruits using learning-based instance segmentation and RGB 3D point clouds. • Segmented fruits are encoded by a 3D CNN and matched via attentive data association. • Experiments on real strawberry and apple datasets show our method outperforms others. • Our approach enables precise temporal fruit monitoring in real and complex scenarios.
Why it matches plant phenotyping methods果実を個体単位で3D点群からセグメンテーションし、時系列追跡する画像解析手法の開発・評価が研究の中心であり、植物器官の状態を抽出するため適格。
abstractwe propose a novel method for fruit instance segmentation and re-identification on 3D terrestrial point clouds collected over time
Reproduction assets foundThe paper explicitly states that the authors' implementation of the fruit matching method (IRIS3D) is publicly available on GitHub, which is the computational analysis code for this paper's fruit segmentation and re-identification phenotyping pipeline.Code · publicThe implementation of our fruit matching method is publicly available at https://github.com/PRBonn/IRIS3D .Open asset ↗PRBonn/IRIS3Dlines:72-99Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plant diseases pose a major threat to global food security, significantly reducing agricultural yields. Therefore, timely diagnosis of plant diseases can help prevent food losses and support economic stability. This study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis. The feature extraction layers of each model were modified to incorporate DropBlock layers, while preserving pretrained weights from the ImageNet dataset. Images from the Plant Pathology 2021 dataset were used to fine-tune the models for multi-label classification, targeting five disease categories and a healthy label. Three experiments were conducted to evaluate model performance on the test set. First, the ResNet50 model was used to determine optimal Dropout and DropBlock probabilities. Second, these parameters were applied across all models to identify those with the best performance. Finally, twenty-three Swarm Optimization Algorithms were used to optimize classifier thresholds, improving accuracy and F1-scores. A DropBlock probability of 0.05 and a Dropout probability of 0.2 yielded superior results. Among the models, SwinV2T attained an accuracy of 90.7%, while SwinV2S achieved the highest F1-score of 91.7%, slightly outperforming the ConvNeXtT and ConvNeXtS architectures. The results demonstrated the effectiveness of DropBlock regularization and optimized classifier thresholds, highlighting the superior performance of recent architectures and optimization algorithms over their older counterparts. These findings suggest that such networks hold substantial promise for accurately identifying and diagnosing apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を分類・診断する深層学習手法を比較評価し、正則化や閾値最適化による性能改善も検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis.
Reproduction assets foundThe paper's apple leaf disease phenotyping is based on the public Plant Pathology 2021 (FGVC8) Kaggle image dataset and an authors' reorganized multi-label version publicly deposited on GitHub; both are explicitly linked in the Data availability statement. No author analysis code or trained model checkpoints are statedDataset · publicFor this research, the dataset was reorganized and extended into a multi-label format. The complete modified dataset is publicly available at: https://github.com/soroushtou/Plant-Pathology-2021---MultiLabel-Dataset.Open asset ↗Plant-Pathology-2021---MultiLabel-Datasetlines:239-262Dataset · publicThe original dataset used in this study is the publicly available Plant Pathology 2021 dataset from the FGVC8 competition available at: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8.Open asset ↗lines:239-262Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
AppleField / plotRGB / grayscaleFruitClassificationObject detectionSegmentationGrowth / development / phenology
This article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red). The dataset serves as a resource for detecting and segmenting apples across different developmental phases. Each image includes pixel-level instance segmentation masks annotated in JSON format using the VGG Image Annotator (VIA), ensuring compatibility with deep learning frameworks. The dataset's real-world variability-spanning lighting conditions, occlusions, and clustered fruit arrangements-enhances its utility for training generalizable computer vision models in precision agriculture. It supports tasks such as fruit detection, segmentation and growth-stage classification, addressing the scarcity of annotated data for transitional maturity phases. With 2574 annotated apple instances, this dataset facilitates research on maturity grading and transfer learning for agricultural robotics. By standardizing annotations and incorporating diverse field conditions, this dataset reduces preprocessing overhead and accelerates the development of deployable AI solutions for orchard management. It is particularly valuable for improving model robustness in heterogeneous environments, thereby advancing data-driven horticultural practices.
Why it matches plant phenotyping methodsリンゴ果実の発育段階・成熟度という植物器官の状態を対象に、画素単位アノテーション付き画像データセットを構築しており、観測・抽出手法の再利用可能な基盤が中心である。
abstractThis article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red).
Reproduction assets foundThe paper is a data descriptor for a public apple image dataset (1406 RGB images, pixel-level instance segmentation masks in JSON) deposited on Mendeley Data with a direct URL and DOI, matching the allowed URL exactly.Dataset · publicRepository name: Wang, Dandan; Wang, Bo (2026), “A Multi-Stage, Pixel-Level Annotated Apple Dataset for Precision Agriculture Research”, Mendeley Data, V4
Data identification number: 10.17632/gfcmdbvw65.4
Direct URL to data:https://data.mendeley.com/datasets/gfcmdbvw65/4Open asset ↗Mendeley Data · 10.17632/gfcmdbvw65.4html-lines:1-97Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published12 May 2026Journal of Advanced College of Engineering and ManagementCited by 0 · OpenAlex ↗
Apple cultivation is a crucial agricultural activity in various mountainous regions, playing a vital role in supporting the local economy and sustaining the livelihoods of farmers. Several prominent mountain districts are known for leading apple production. However, apple orchards in these areas are often threatened by numerous diseases that reduce fruit yield and quality. In this research, we suggest a machine learning-based technique to automate the detection and classification of common apple diseases based on images of apple leaves collected from various regions. Through the use of Convolutional Neural Networks (CNN), the system can classify diseases with 97.36% precision. For post hoc explainability, Grad-CAM is used, which highlights the important regions that influenced CNN’s decision. The automated disease detection tool provides farmers in Nepal’s rural mountain areas with an affordable real time solution to monitor orchard health, minimize crop loss, and improve apple production. The dataset used in this study is originally derived from the United States based PlantVillage dataset, which is widely used for apple leaf disease classification research. Although the dataset is not collected from Nepal, the visual characteristics of apple leaf diseases remain largely consistent across regions due to similar biological infection patterns. Therefore, the model trained on this dataset is applicable to Nepali apple cultivation environments as well. At present, a publicly available or annotated Nepali specific apple leaf disease dataset is not available, which limits region-specific training and evaluation.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を分類するCNNベースの手法とGrad-CAMによる解釈を中心に扱うため、植物病害フェノタイピング手法として該当する。
abstractwe suggest a machine learning-based technique to automate the detection and classification of common apple diseases based on images of apple leaves collected from various regions.
Reproduction assets foundThe paper's apple leaf disease image dataset (9,696 images, four classes) is publicly available on Kaggle and explicitly cited by the authors as the dataset used for training and evaluation. No author code, trained model, or other paper-specific assets are reported.Dataset · publicIn this study, the dataset used for apple leaf disease classification was obtained from
Kaggle [20]. The dataset contains a total of 9,696 images of apple leaves, which include
both diseased and healthy samples.Open asset ↗Kagglepdf-raw-page:4 lines:1-39Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Accurate plant disease segmentation is often constrained by the availability of large, finely annotated datasets, particularly for rare diseases. This work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation. Procedurally-generated leaf geometries are built in the 3D modelling package Blender to provide exact ground-truth masks, after which style-transfer is applied using Stable Diffusion, fine-tuned with Low-Rank Adaptation (LoRA) and guided by ControlNet conditioning to both preserve leaf structure and enforce correct lesion placement. The approach is evaluated on apple leaf diseases using a deliberately restricted subset of the PlantVillage dataset, simulating a controlled low-data-resource environment. Downstream task effectiveness is measured through leaf disease segmentation. The results show that combining data from the pipeline with limited real data leads to consistent improvements in segmentation performance.
Why it matches plant phenotyping methods植物病斑の画像セグメンテーション性能向上を目的に、3D葉モデルと拡散モデルによる合成データ生成パイプラインを開発・評価しており、植物病害状態の画像ベース推定が中心である。
abstractThis work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation.
Reproduction assets foundThe authors publicly deposited the paper's annotated PlantVillage subset (75 images with segmentation masks) plus 300 synthetic images with ground-truth masks on Zenodo, directly reproducing this paper's phenotyping/segmentation data.Dataset · publicThis annotated subset of PlantVillage is available at https://doi.org/10.5281/zenodo.18659728 . The repository contains the 75 images from the restricted dataset with the corresponding segmentation masks along with 100 synthetic images per disease generated using Blender and Stable Diffusion, each with corresponding ground truth masks.Open asset ↗zenodo · 10.5281/zenodo.18659728lines:314-325Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Apple surface diseases are crucial factors affecting the quality and yield of apples. Traditional manual inspection methods suffer from low efficiency and poor real-time performance. To address these issues, this paper proposes an apple surface disease detection method based on an improved YOLOv11s. Firstly, three groups of GAM attention mechanisms are integrated into the neck structure of the YOLOv11s to enhance the efficiency of feature fusion and the capability of semantic information transmission. Secondly, the original convolutional downsampling in the backbone network is replaced with a Haar-based feature downsampling module, enabling the model to retain more high-frequency detail information during the downsampling process. In addition, the WFU module is introduced to realize the dynamic allocation of feature weights, enhancing the model's ability to recognize multi-scale defect features. Finally, the PIOUv2 loss function is adopted to optimize bounding box regression, improving the model's detection performance for tiny defect spots. In addition, various data augmentation methods for small datasets are employed to improve the model training performance and effectively avoid the problem of data overfitting. The experimental results demonstrate that the F1-score of the proposed model is increased by 4.2%, and the mAP@50:95 is boosted by 2.4%. The detection performance outperforms various comparative models, which verifies the effectiveness and superiority of the proposed method.
Why it matches plant phenotyping methodsリンゴ表面の病斑・欠陥を画像から検出する改良YOLO手法の開発と比較検証が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractthis paper proposes an apple surface disease detection method based on an improved YOLOv11s.
Reproduction assets foundThe paper uses a hybrid apple disease image dataset whose public portion is explicitly cited as reference [27] with a public Baidu Netdisk URL (the only allowed URL), matching the paper's apple surface disease detection dataset. The self-built portion and raw data are available only on request, and no author analysis代码Dataset · public3390/foods12061352.
26. Liu J., Zhao G., Liu S., Liu Y., Yang H., Sun J., Yan Y., Fan G., Wang J., Zhang H. New progress in intelligent picking: Online detection of apple maturity and fruit diameter based on machine vision. Agronomy. 2024;14:721. doi: 10.3390/agronomy14040721.
27. [(accessed on 4 April 2026)]. Available online: https://pan.baidu.com/s/1pfsr3yPczEJywNwwDUFliw?pwd=98te .
28. Géron A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc.; Sebastopol, CA, USA: 2022.
29. Apicella A., Isgrò F., Prevete R. Don’t push the button! exploring data leakage risks in machine learning and transfer learning. Artif. Intell. Rev. 2025;58:339. doi: 10.1007/s1Open asset ↗lines:424-433Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional methods of diagnosis are time-consuming, error-prone, and inefficient. With the rapid development of AI, deep learning (DL), and IoT, there is increasing demand for combined solutions that jointly address plant health monitoring and harvest optimization in a reproducible and deployment-oriented manner. This study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages to optimize yield quality and minimize agricultural losses. The work explicitly targets improved classification reliability, broader class evaluation, rigorous validation and generation of decision-ready outputs for precision agriculture. AgroDualNet comprises two modules. The crop-disease prediction module integrates ResNet50 with a Convolutional Block Attention Module (CBAM), and a Sequential Minimal Optimization (SMO)-based SVM classifier to enhance feature learning and classification performance Several different architectural designs are benchmarked and the resultant model is tested on both a dedicated 3-class subset and a large multi-class model of the PlantVillage dataset with leakage safe protocol(augmentation applied only on training data), cross-validation, statistical significance testing as well as ablation. The fruit-ripeness module employs YOLOv8 for real-time fruit localization and MobileNetV2 for lightweight ripeness classification suitable for edge deployment and a prototype decision-support layer maps predictions to actionable recommendations. That is able to run on the edge. Experiments show that the hybrid CBAM + ResNet50 + SMO model achieves 99.6% accuracy for crop disease classification on a three-class configuration of the PlantVillage dataset and maintains consistently higher accuracy than strong baseline in a 38-class setting, with statistically significant results confirmed by McNemar's test (p < 0.001) outperforming baseline and intermediate architectures in accuracy, precision, Recall and F1-Score The fruit ripeness pipeline achieves 98.88% classification accuracy across four ripeness stages (unripe, semi-ripe, ripe, over-ripe) on a combined Kaggle and real-field apple dataset with low inference time, confirming its suitability for near real-time deployment on edge devices. Cross-validation, Statistical significance tests and ablation studies collectively validate the robustness and significance of these gains and the decision-support layer demonstrates the feasibility of converting raw predictions into interpretable, recommendation-oriented outputs. AgroDualNet provides an efficient and unified system for monitoring plant diseases and evaluating fruit ripeness, with statically validated performance across both focused and full multi-class settings, addressing two critical challenges in precision agriculture with a single extensible framework. The dual-module design of AgroDualNet, which combines disease prediction with ripeness analysis and a preliminary decision-support prototype offers a more comprehensive and practically relevant AI-driven monitoring solution than conventional single-task models. By emphasizing multi-class validation on PlantVillage, leakage-aware experimentation, statistical verification, and system-level integration, this works supports real-time, precise and automated guidance to reduce crop losses, improve harvest timing, and enable smarter farm-level decision making.
Why it matches plant phenotyping methods植物病害状態と果実成熟度を画像から推定する深層学習パイプラインの開発・比較検証が中心であり、PlantVillageおよび実圃場データで交差検証、アブレーション、統計検定を実施しているため、植物フェノタイピング手法として含める。
abstractThis study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages
Reproduction assets foundThe paper's Data availability statement names two public datasets used directly in the phenotyping experiments: the PlantVillage crop-disease dataset and a Kaggle apple fruit-ripeness dataset, both with explicit Kaggle URLs matching allowed_urls. The statement also mentions implementation files, trained weights, and a Dataset · public. and V.V. wrote the main manuscript text, and K.N. prepared figures. All authors reviewed the manuscript.
Funding
There is no funding received from any organization for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are opeOpen asset ↗Kaggle · plantvillage-datasetlines:583-665Dataset · publiction for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are openly available at: 10.5281/zenodo.19051520.
Declarations
Competing interests
The authors declare no competing interests.
References
1.
George R Thuseethan S RagelOpen asset ↗Kaggle · fruit-image-dataset-22-classeslines:583-665Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Abstract. Accurate and timely identi cation of plant diseases is essential for improving crop productivity and ensuring sustainable agricultural practices. This paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops: Apple, Banana, Citrus, Guava, Mango, and Papaya. The dataset comprises high-quality RGB images representing both healthy and diseased samples, with disease symptoms including spots, lesions, discoloration, blight, rot, and fungal and bacterial infections captured under diverse real-world conditions. Variations in illumination, background complexity, viewing angles, growth stages, and symptom severity are intentionally included to enhance the robustness and generalizability of learning models developed using this data. The dataset is structured in a class-wise manner and preprocessed to support direct integration with deep learning frameworks. It is extensively used to train, validate, and evaluate deep learning based plant disease classi cation models, enabling automatic feature learning from raw images without manual intervention. Experimental usage demonstrates that the dataset is well suited for convolutional neural networks and attentionbased architectures, facilitating e ective discrimination between multiple disease categories across di erent crops and plant organs. By providing a uni ed multi-crop, multi-disease benchmark, this dataset aims to accelerate research in automated crop disease diagnosis, precision agriculture, and intelligent decision-support systems for sustainable farming.
Why it matches plant phenotyping methods植物の葉・果実の病徴画像を収録したデータセット／ベンチマークであり、病害状態の画像ベース推定を中心的に扱うため。
abstractThis paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops
Reproduction assets foundThe paper's core asset is the ABCGMP fruit and leaf disease image dataset, publicly deposited on Mendeley Data, with author analysis code also stated to be available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:33 lines:1-56Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:33 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model's decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .
Why it matches plant phenotyping methods果樹葉の病斑領域を画像から分割し、複数病害の重症度を定量推定する深層学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractthis research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves.
Reproduction assets foundThe paper's authors publicly release source code and trained models on GitHub, and the study analyzes two public Kaggle plant-image datasets (DiaMOS Plant and PlantVillage) used directly for the multi-disease severity phenotyping experiments.Code · publicThe source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .Open asset ↗https://github.com/mqasim0787/Multi-Disease-Severity · mqasim0787/Multi-Disease-Severitylines:1-23Dataset · publicThe datasets analyzed during the current study are available publicly in the Kaggle repository, DiaMOS dataset (1) and PlantVillage Dataset (2) 0.1. [https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset]Open asset ↗https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset · diamos-plant-datasetlines:964-977Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate flower-load assessment is critical for informed thinning strategies in orchard management. UAV-based deep learning automated counting offers efficiency advantages, yet precise counting is heavily dependent on abundant annotated data, which is scarce and costly to obtain in agricultural settings. While semi-supervised learning alleviates dependency on manual annotation, its application to UAV-based orchard imagery faces challenges: complex backgrounds and small target sizes, which undermine pseudo-label reliability. To address these challenges, this study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages. First, a color-SAM flower extractor (CSAM-FE) is proposed to preprocess images using a strategy combining color thresholding with the Segment Anything Model (SAM), suppressing background noise and extracting high-quality flower clusters, thereby providing purified inputs for the subsequent counting network. Second, an uncertainty-guided semi-supervised flower counting network (USCount-Net) is proposed for accurate stage-specific flower counting with limited labeled data. The USCount-Net incorporates two key components: an adaptive pseudo-label filtering (PLF) mechanism based on frequent forward uncertainty estimation (FFUE) is designed to dynamically suppress noisy gradient backpropagation, mitigating error propagation from unreliable pseudo-labels; and a noise-sensitive adaptive gated fusion (AGF) module is introduced to fuse cross-scale features without redundancy, addressing significant scale variations across phenological stages and observation angles. Comparative experiments on a self-built apple flower counting dataset demonstrate that USCount-Net achieves lower MAE and RMSE than state-of-the-art methods at 10%, 30%, and 50% labeling ratios. The results demonstrate that the proposed methodology serves as methodological support for rapid and precise apple flower counting in low-annotation agricultural scenarios.
Why it matches plant phenotyping methodsリンゴ花の画像抽出・計数手法と半教師あり解析ネットワークを開発し、データセット上で比較評価しているため、植物表現型取得が中心である。
abstractthis study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages.
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' USCount-Net source code on GitHub and the self-built apple flower counting dataset (UAV images, annotations, flower cluster images) on Google Drive.Code · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗USCount-Netlines:681-780Dataset · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗lines:681-780Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX . This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別するCNN手法を開発し、複数データセット・交差検証・ベースライン比較で性能を評価しており、植物病害表現型の取得・推定が中心である。
Reproduction assets foundThe paper uses two public Kaggle apple leaf disease image datasets as its phenotyping inputs; both are directly cited with public URLs. No author analysis code, trained model checkpoints, or supplementary code repository is deposited — the data availability statement only offers contact with corresponding authors.Dataset · publicThis research utilizes the Apple Tree Leaf Disease dataset, collected from Kaggle and made available by Nirmal (Kaggle, 2025).Open asset ↗Kagglehtml-lines:128-184Dataset · publicDhar S. (2023). Apple leaf disease classification dataset. Available online at: https://www.kaggle.com/datasets/showravdhar/apple-disease-dataset (Accessed November 1, 2023).Open asset ↗Kaggle · showravdhar/apple-disease-datasethtml-lines:1449-1484Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precision management in high-density orchards requires individual-tree, nondestructive monitoring of canopy nitrogen concentration (CNC), but hyperspectral applications are limited by two factors: unmodeled vertical stratification of CNC within 3D canopies and mixed-pixel effects near canopy boundaries. We develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards. With this framework, we quantified layer-specific nitrogen-spectral relationships and assessed mixed-pixel effects across canopy positions. Stratified sampling, continuous wavelet transform (CWT), and partial least squares regression (PLSR) with variable importance in projection (VIP)-based band selection were used for spectral optimization, and K-means was applied to isolate representative canopy pixels. Field experiments over two consecutive years (2023-2024) revealed consistent CNC gradients, with the lower canopy exceeding the upper by 0.5-9.5 % across fertilization treatments. CWT-2 delivered the most accurate and robust performance across years. VIP-PLSR indicated layer-dependent CNC-informative wavelengths spanning the visible, red-edge, and near-infrared regions, with scale-dependent cross-layer overlap after CWT. Pixel clustering revealed distinct spatial structure: canopy-interior pixels exhibited characteristic vegetation spectra and achieved R 2 val of 0.69-0.76, substantially outperforming boundary-affected pixels with R 2 val of 0.48-0.57. These results demonstrate that coupling spectral feature optimization with layer-specific modeling and clustering-based pixel screening improves the accuracy of tree-level CNC estimation in complex canopies. The proposed framework provides a mechanistic and operational basis for robust biochemical retrieval in structurally complex orchard systems.
Why it matches plant phenotyping methodsUAVのRGB・ハイパースペクトルデータを統合し、個体樹の樹冠窒素濃度という植物形質を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心です。
abstractWe develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards.
Reproduction assets foundThe paper's data availability statement explicitly deposits the apple canopy nitrogen concentration dataset and canopy original-reflectance validation dataset in a public GitHub repository, which is a paper-specific, publicly actionable phenotyping asset. No author analysis code or trained models are explicitly stated.Dataset · publicThe apple CNC dataset and the canopy OR independent validation dataset are available at https://github.com/Chenb94115/Plant-Phenomics . Additional supporting data are available from the corresponding author upon reasonable request.Open asset ↗Chenb94115/Plant-Phenomicslines:278-377Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
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:151-171Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are $\leq95\%$ occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methodsリンゴの姿勢推定アノテーションを大幅に効率化する3D再構成・自動ラベル投影パイプラインを開発し、姿勢推定性能も評価しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
Reproduction assets foundThe paper explicitly provides two paper-specific public assets: the authors' phenotyping/pose-estimation pipeline code on GitHub and the collected apple orchard image dataset on a 4TU DOI. Both are directly used for the paper's measurements and analysis.Dataset · publicIn total, 367 images were collected. The dataset is available at https://doi.org/10.4121/976c94f2-028f-4291-adfd-20eb82b0f647Open asset ↗10.4121/976c94f2-028f-4291-adfd-20eb82b0f647lines:92-108Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture.
Why it matches plant phenotyping methods植物葉の病徴領域を画像から検出・分類する深層学習手法の開発が中心であり、植物の病害状態を直接推定するため、方法論文として採用する。
abstractwe used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases.
Reproduction assets foundThe paper's phenotyping inputs are PlantVillage apple leaf disease images (leaf spot, mosaic, rust, brown spot), explicitly cited with a public GitHub URL; no author analysis code or trained model checkpoints are released.Dataset · publicPlantVillege Dataset. Available online: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color (accessed
on 1 December 2024).Open asset ↗PlantVillage-Dataset · raw/colorpdf-page:21 lines:1-59Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate detection of apple leaf diseases remains a critical challenge in precision agriculture, where complex field conditions and subtle symptom variations often degrade model performance. This paper introduces a novel hybrid architecture combining enhanced spatial attention with edge-aware feature extraction to improve disease classification robustness. The proposed model integrates a multi-scale feature fusion module to capture both local lesion patterns and global contextual cues, while a lightweight attention mechanism dynamically prioritizes disease-relevant regions. Experiments on a curated dataset of 12,350 apple leaf images demonstrate the effectiveness of proposed approach, achieving 96.7% classification accuracy across six disease categories - a significant improvement over baseline models like EfficientNet-B4 (94.1%) and ResNet-50 (93.8%). The system particularly excels in detecting early-stage infections, showing 15% higher precision for subtle scab lesions compared to existing methods. With only 3.2 million parameters, the model maintains practical deployment potential for edge devices in orchard environments. These advances address key limitations in current vision-based plant disease detection systems while balancing accuracy and computational efficiency for real-world agricultural applications.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を推定するCNN手法の開発と評価が研究の中心であり、植物病害表現型の画像ベース計測に該当する。
abstractThis paper introduces a novel hybrid architecture combining enhanced spatial attention with edge-aware feature extraction to improve disease classification robustness.
Reproduction assets foundThe paper's Data Availability statement points to the public Kaggle Apple Leaf Disease Dataset (ALDD-v2) used for all training/evaluation, matching an allowed URL. No author code or model checkpoints are shared.Dataset · publicThe datasets analysed during the current study is publicly available in the Kaggle repository at https://www.kaggle.com/dsv/2068940.Open asset ↗Kagglehtml-lines:505-539Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.
Why it matches plant phenotyping methodsリンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。
abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
Reproduction assets foundThe authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.Dataset · publicData accessibility
Repository name: Github
Data identification number: DOI 10.5281/zenodo.15600557
Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.Open asset ↗GitHub · DOI 10.5281/zenodo.15600557html-lines:84-123Code · publicAll data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].Open asset ↗GitHubhtml-lines:1-83Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published19 Nov 2025ISPRS Journal of Photogrammetry and Remote SensingCited by 3 · OpenAlex ↗
Accurate fruit shape reconstruction under real-world field conditions is essential for high-throughput phenotyping, sensor-based yield estimation, and orchard management. Existing approaches based on 2D imaging or explicit 3D reconstruction often suffer from occlusions, sparse views, and complex scene dynamics as a result of the plant geometries. This paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards. The proposed method integrates (1) Grounded-SAM2 for multi-object tracking and segmentation (MOTS), (2) photogrammetric structure-from-motion for 3D scene reconstruction, and (3) DeepSDF, an implicit neural representation, for completing occluded fruit geometries with a neural network. We furthermore propose a new MOTS evaluation protocol to assess tracking performance without requiring ground truth annotations. Experiments conducted in both controlled laboratory conditions and an operational apple orchard demonstrate the accuracy of our 3D fruit reconstruction at the centimeter level. The Chamfer distance error of the proposed shape completion method using the DeepSDF shape prior reduces this to the millimeter level, and outperforms the traditional method, while Grounded-SAM2 enables robust fruit tracking across challenging viewpoints. The approach is highly scalable and applicable to real-world agricultural scenarios, offering a promising solution to reconstruct complete fruits with visibility higher than 10% for precise 3D fruit phenotyping at a large scale under occluded conditions.
Why it matches plant phenotyping methods果実形状を対象とするUAV画像・3D再構成・形状補完法を開発し、実験で精度評価しており、植物表現型取得が研究の中心である。
abstractThis paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards.
Reproduction assets foundThe paper's authors publicly release their UAV orchard video data, lab 3D apple scans, and analysis code via a GitHub repository explicitly stated in the text. A Zenodo deposit (10.5281/zenodo.15635994) is also mentioned for the data, but its URL is not among the allowed URLs, so only the GitHub asset is reported.Code · publicing in orchard environments,(2) to propose a novel method to evaluate
MOTS without any annotations, and (3) to provide a highly accurate
3D apple dataset collected in a laboratory environment, along with
UAV-captured high-resolution videos in the field. The dataset and codes
for this research are publicly available at: https://github.com/Kaiwen-Robotics/Mono3DOrchard.2. Study area and materials
This study contains two data collection areas: field data collection
and laboratory data collection.
2.1. Field data collection
2.1.1. Study area
The field data collection was conducted within an apple orchard
located in Randwijk, Overbetuwe, the Netherlands (51.9376, 5.703057
in WGS84 UTM 31U), as shOpen asset ↗Kaiwen-Robotics/Mono3DOrchard.2pdf-raw-page:2 lines:75-128Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するデータセットが研究の中心であり、植物病害フェノタイピング用の画像データセットとして適格です。
abstractThe PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
None
1
Value of the Data
•
The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Plant diseases pose a significant threat to global food security and agricultural productivity. In this work, we propose a novel deep convolutional neural network (CNN) model enhanced with Squeeze-and-Excitation (SE) blocks and Attention Gates (AGs) for multi-class plant disease classification across five crops: apple, maize, grape, potato, and tomato. Leveraging a large image dataset and a comprehensive training regime, the proposed model achieves high performance across all metrics, including 99% accuracy, 0.99 F1-score, and strong specificity. Evaluation includes feature visualization and Grad-CAM interpretability. The model's robustness and interpretability make it a compelling solution for practical agricultural applications.
Why it matches plant phenotyping methods植物画像から病害状態を分類するCNN手法の開発と性能評価が中心であり、植物病害フェノタイピングに該当する。
titleAttention-Based Deep Convolutional Neural Networks for Plant Disease Classification
Reproduction assets foundThe paper's plant disease classification experiments are built directly on the public PlantVillage Kaggle image dataset (21 classes across five crops), which is the paper-specific image input for its phenotyping measurements. No author analysis code, trained model checkpoints, or other paper-specific assets are stated.Dataset · publicThe dataset used in this study is a curated subset of the publicly available PlantVillage dataset [21], originally hosted on
Kaggle.Open asset ↗Kagglepdf-page:9 lines:1-26Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
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:151-171Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する統計手法であり、複数種・多数形質で性能評価され、Rパッケージも開発されているため、表現型推定法が研究の中心です。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:6 lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Apple leaf diseases severely affect the quality and yield of apples, and accurate classification is crucial for reducing losses. However, in natural environments, the similarity between backgrounds and lesion areas makes it difficult for existing models to balance lightweight design and high accuracy, limiting their practical applications. In order to resolve the aforementioned problem, this paper introduces a lightweight converged attention multi-branch network named LCAMNet. The network integrates depthwise separable convolutions and structural re-parameterization techniques to achieve efficient modeling. To avoid feature loss caused by single downsampling operations, a dual-branch downsampling module is designed. A multi-scale structure is introduced to enhance lesion feature diversity representation. An improved triplet attention mechanism is utilized to better capture deep lesion features. Furthermore, a dataset named SCEBD is constructed, containing multiple common disease types and interference factors under natural environments, realistically reflecting orchard conditions. Experimental results show that LCAMNet achieves 92.60% accuracy on the SCEBD and 95.31% on a public dataset, with only 0.03 GFLOPs and 1.30M parameters. The model maintains high accuracy while remaining lightweight, enabling effective apple leaf disease classification in natural environments on devices with limited resources.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から分類する軽量モデルを開発し、自然環境データセットを構築・評価しており、植物病害状態の画像ベース表現型推定が中心である。
abstractthis paper introduces a lightweight converged attention multi-branch network named LCAMNet.
Reproduction assets foundThe paper's data availability statement links three public image datasets directly used in its experiments: the FGVC8 Plant Pathology 2021 Kaggle dataset, the AppleLeaf9 GitHub dataset, and the ATLDSD dataset on ScienceDB. No author analysis code or trained model is released, and the self-constructed SCEBD has no own公开Dataset · publicce Foundation Project (No. 2024MS06002), the Inner Mongolia Autonomous Region universities innovative research team project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources,Open asset ↗plant-pathology-2021-fgvc8 · plant-pathology-2021-fgvc8lines:727-753Dataset · publicomous Region universities innovative research team project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources, Writing – review & editing. XF: Project administration, SupervisOpen asset ↗JasonYangCode/AppleLeaf9 · JasonYangCode/AppleLeaf9lines:727-753Dataset · publicteam project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources, Writing – review & editing. XF: Project administration, Supervision, Writing – review & editing. BW: Project aOpen asset ↗0e1f57004db842f99668d82183afd578lines:727-753Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely disease detection methods. The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. The integration of these models has witnessed a notable improvement in the accuracy of leaf disease classification. GCN has been widely used for learning from graph-structured data, and GAT enhances this by incorporating attention mechanisms to focus on the most important neighbors. The methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features. The robustness of the model is further enhanced by the edge augmentation technique. The edge augmentation technique in the context of graph has introduced a significant degree of generalization in the detection capabilities of the model as analyzed on apple, potato, and sugarcane leaves. To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. The results indicate that the model is effective and consistent in identifying leaf diseases in plants.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類するGAT-GCNモデルを開発・比較評価しており、病害表現型の抽出手法が中心である。
abstractThe methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features.
Reproduction assets foundThe paper evaluates its GAT-GCN hybrid leaf disease classifier on three public leaf image datasets. Two of them (apple and potato) are cited with explicit Kaggle URLs that match allowed_urls entries; the sugarcane dataset is cited without a public URL. No author code or model release is mentioned.Dataset · publicAdvanced Comput. Sci. Appl.
10 ( 8 ), 486 – 492 . doi:
10.14569/IJACSA.2019.0100863
Alsayed A. Alsabei A. Muhammad A.
( 2021 ).
Classification of apple tree leaves diseases using deep learning methods
. Int. J. Comput. Sci. Network Secur.
21 , 324 – 330 .
Antor M. H.
( 2020 ). Apple leaf diseases dataset . Available online at: https://www.kaggle.com/datasets/mhantor/apple-leaf-diseases (Accessed October 13, 2024 ).
Bansal P. Kumar R. Kumar S.
( 2021 ).
Disease detection in apple leaves using deep convolutional neural network
. Agriculture
11 , 617 . doi:
10.3390/agriculture11070617
Bera A. Bhattacharjee D. Krejcar O.
( 2024 ).
Pnd-net: plant nutrition deficiency and disease classification usOpen asset ↗Kaggle · mhantor/apple-leaf-diseaseslines:548-708Dataset · publicnt. J. Res. Eng.
5 , 516 – 523 . doi:
10.21276/ijre.2018.5.9.4
Peng Y. Wang Y.
( 2022 ).
Leaf disease image retrieval with object detection and deep metric learning
. Front. Plant Sci.
13 , 963302 . doi:
10.3389/fpls.2022.963302
, PMID:
36176678
PMC9513793
Putra M. A.
( 2020 ). Potato leaf disease dataset . Available online at: https://www.kaggle.com/datasets/muhammadardiputra/potato-leaf-disease-dataset (Accessed October 13, 2024 ).
Rao S. U. M. Sreekala K. Rao P. Srinivas Shirisha N. Srinivas G. Sreedevi E.
( 2024 ).
Plant disease classification using novel integration of deep learning cnn and graph convolutional networks
. Indonesian J. Electrical Eng. Comput. Sci.
36 , 1721 – 1730 .
RathOpen asset ↗Kaggle · muhammadardiputra/potato-leaf-disease-datasetlines:709-821Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of 97% based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments.
Why it matches plant phenotyping methodsリンゴ樹を個体別に画像分割し、育種・品種試験向けの表現型情報抽出を可能にする手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractComputer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput.
Reproduction assets foundThe paper's apple REFPOP image dataset (RGB orchard images with manual tree/trunk annotations used for the phenotyping segmentation task) is publicly deposited on Zenodo via DOI 10.57745/DZBMAM, stated in both the Supplementary Materials and Data Availability Statement. Other URLs (Ultralytics, FrontVeg, arXiv) are forDataset · publicon.
Grouding approach
Approach linking text as a prompt or description to localize
objects or regions in an image.
Latent embedding
Low-dimensional vector representation at the end of a neural
network that captures the key features of input data.
Supplementary Materials
The following supporting information can be downloaded at: https://doi.org/10.57745/DZBMAM , https://www.napari-hub.org/plugins/frontveg (accessed on 10 July 2025).
Author Contributions
H.M.: Conceptualization, Methodology, Software, Investigation, Data Curation, Writing—Original Draft, Writing—Review and Editing. D.R.: Conceptualization, Writing—Review and Editing, Supervision, Administration. P.R., J.L. and H.D.: SOpen asset ↗10.57745/DZBMAMlines:536-601Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Objective The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques. Material An open-source collection of fruit disease images, comprising both diseased and healthy samples from the first five fruit types, was used in this study. The data was split into 70% training, 15% validation, and 15% testing. A 5-fold cross-validation was used to maintain the generalizability and stability of the model's performance. Models For performance comparisons of these models on the dataset, we benchmarked state-of-the-art pre-trained convolutional neural network (ConvNet) models, including Swin Transformer (ST), EfficientNetV2, ConvNeXt, YOLOv8, and MobileNetV3. A new model, the Dual-Branch Attention-Guided Vision Network (DBA-ViNet), was introduced. A hybrid with two branches of DBA-ViNet can efficiently integrate global and local features for improved disease identification accuracy. Grad-CAM was used to visualize the regions that contributed to each prediction, helping to interpret the model. These heatmaps verified that DBA-ViNet can correctly direct its attention to disease-specific symptoms, thereby increasing trust and transparency in the classification results. Results The proposed DBA-ViNet achieved a high testing classification accuracy of 99.51%, specificity of 99.42%, recall of 99.61%, precision of 99.30% and F1 score of 99.45% outperforming baseline models in all evaluation metrics. While the improvements were consistent, statistical significance testing was not performed and will be explored in future work. Conclusion These results confirm the effectiveness of the proposed DBA-ViNet architecture in fruit disease detection, suggesting that incorporating both global and local feature extraction into the design of the double-branch attention mechanism for classification can achieve high accuracy and reliability. It is potentially practical in smart agriculture and the automated crop health monitoring system.
Why it matches plant phenotyping methods果実画像から植物の病害状態を推定する深層学習モデルを開発し、複数モデルとの性能比較・検証を行っており、植物フェノタイピング手法が中心である。
abstractThe primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset used in this study consists of 7,639 images representing healthy and diseased samples of five common fruits: apple, guava, mango, orange, and pomegranate https://www.kaggle.com/datasets/saravanansri/apple-guava-mangoe-pomegranate-orange-datasetOpen asset ↗Kagglelines:110-130Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して植物形質を推定する統計手法として開発・検証され、Rパッケージも提供されているため、形質取得・推定手法が中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning. To address class imbalance and improve generalization, the study adopts a holistic training strategy that integrates data augmentation, stratified data splitting, and class weighting, alongside transfer learning. The model is evaluated on the PlantVillage (PV) dataset and a curated Apple PV (APV) dataset and compared against EfficientNet-B0, EfficientNet-B3, Inception-v3, ResNet50, and VGG16 models. The fine-tuned model demonstrates outstanding test accuracies of 99.69% and 99.78% for classifying plant diseases using the APV and PV datasets, respectively. The fine-tuned model outperforms EfficientNet-B0, EfficientNet-B3, and VGG16 on both datasets and shows superior performance compared to Inception-v3 and ResNet-50 on the PV dataset. Both EfficientNet-B0 and the fine-tuned model demonstrate the lowest memory consumption and floating-point operations per second (FLOPs). Also, as compared to the EfficientNet-B0 model, the fine-tuned model achieves an 11% increase in accuracy on the APV dataset and a 49.5% accuracy improvement on the PV dataset, with approximately a 7-8% increase in both memory usage and FLOPs. The fine-tuned model thus emerges as an effective solution for plant leaf disease classification, delivering outstanding accuracy with optimized memory consumption and FLOPs, making it suitable for resource-constrained environments. This study demonstrates that fine-tuned CNN approaches, when combined with transfer learning, advanced data pre-processing, and architectural optimizations, can significantly enhance the accuracy of diseased leaf classification in crops with efficient implementation in limited-resource settings.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から分類するCNN手法の開発・比較評価が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractThis paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe PV dataset used for this research work is taken from: https://data.mendeley.com/datasets/tywbtsjrjv/1Open asset ↗tywbtsjrjv/1lines:334-374Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
AppleLaboratory / benchtopMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration ( % Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.
Why it matches plant phenotyping methodsリンゴの甘度・成熟度・品種を推定するマルチスペクトル撮像システムと、注釈付き大規模画像データセットを中心に構築しており、植物器官の品質・状態を定量化する再利用可能なフェノタイピング手法に該当する。
abstractThe study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system.
Reproduction assets foundThe article is a Data in Brief describing a public multi-spectral apple image dataset (sweetness/Brix, ripeness over 18 days, variety) deposited on Mendeley Data with DOI 10.17632/y5h6v8w6ms.2 and a direct URL, explicitly stated as publicly accessible. This is the paper's own phenotyping image dataset. The MATLAB code,Dataset · publicme environment using a custom-built multi-spectral imaging chamber . The imaging conditions were carefully maintained to ensure consistency. The dataset is securely stored for research and study purposes.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/y5h6v8w6ms.2
Direct URL to data: https://data.mendeley.com/datasets/y5h6v8w6ms/2
Instructions for accessing these data:
Dataset Title: Dataset of Apples for Grading by Sweetness, Ripeness, and Variety
Public Access: The dataset titled ``Dataset of Apples for Grading by Sweetness, Ripeness, and Variety'' is publicly available on Mendeley Data and can be accessed via the following DOI:
https://doi.org/Open asset ↗Mendeley Data · 10.17632/y5h6v8w6ms.2lines:40-82Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Humans need food to sustain their lives. Therefore, agriculture is one of the most important issues in nations. Agriculture also plays a major role in the economic development of countries by increasing economic income. Early diagnosis of plant diseases is crucial for agricultural productivity and continuity. Early disease detection directly impacts the quality and quantity of crops. For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. Disease classification was performed using seven state-of-the-art pretrained convolutional neural network architectures: VGG16, ResNet50, SqueezeNet, Xception, ShuffleNet, DenseNet121 and MobileNetV2. A simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification. GAPNet was designed to be a lightweight and fast model with 337.872 parameters. To address the data imbalance between classes, oversampling was carried out using the synthetic minority oversampling technique. The proposed model achieves accuracy rates of 99.72%, 99.53%, and 99.83% for grape, apple and potato leaf disease classification, respectively. A success rate of 99.64% was achieved in multiplant leaf disease classification when the grape, apple and potato datasets were combined. Compared with the state-of-the-art methods, the lightweight GAPNet model produces promising results for various plant species.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する軽量CNN手法を開発・評価しており、病害表現型の抽出が研究の中心です。
abstractIn this study, a simple and effective leaf disease classification method was developed.
Reproduction assets foundAuthors publicly release GAPNet implementation code via GitHub and Zenodo, and the paper's leaf image datasets (PlantVillage, New Plant Disease, Plant Pathology 2020) are publicly available at listed URLs.Code · publiccle, and approved the final draft.
Asuman Günay Yılmaz conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data and code are available at GitHub and Zenodo:
- https://github.com/ozgenurr/GAPNet.git .Open asset ↗ozgenurr/GAPNetlines:727-762Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases uOpen asset ↗PlantVillage-Datasetlines:763-789Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing SympOpen asset ↗lines:763-789Dataset · publicGAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing Symposium (IDAP-2023) 2023:67–74. doi: 10.53070/bbd.1349566.
BanjaOpen asset ↗lines:763-789Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases. The approach begins with enhancing the CycleGAN-M network using a multi-scale attention mechanism to generate synthetic samples, improving model robustness and generalization by mitigating imbalances in disease-type representation. Next, an improved YOLOv8s-KEF model is introduced to overcome limitations in feature extraction, particularly for small lesions and complex textures in natural environments. The model's backbone replaces the standard C2f structure with C2f-KanConv, significantly enhancing disease recognition capabilities. Additionally, we optimize the detection head with Efficient Multi-Scale Convolution (EMS-Conv), improving the model's ability to detect small targets while maintaining robustness and generalization across diverse disease types and conditions. Incorporating Focal-EIoU further reduces missed and false detections, enhancing overall accuracy. The experiment results demonstrate that the YOLOv8s-KEF model achieves 95.0% in accuracy, 93.1% in recall, 95.8% in precision, and an F1-score of 94.5%. Compared to the original YOLOv8s model, the proposed model improves accuracy by 7.2%, precision by 6.5%, and F1-score by 5.0%, with only a modest 6MB increase in model size. Furthermore, compared to Faster RCNN, ResNet50, SSD, YOLOv3-tiny, YOLOv6, YOLOv9s, and YOLOv10m, our model demonstrates substantial improvements, with up to 30.2% higher precision and 18.0% greater accuracy. This study used CycleGAN-M and YOLOv8s-KEF methods to enhance the detection capability of apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉の病斑・病害を画像から検出する深層学習モデルの改良と性能比較が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractThis study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits a portion of the apple leaf disease dataset and source code in a public GitHub repository, which directly supports this paper's CycleGAN-M augmentation and YOLOv8s-KEF detection experiments.Code · publicA portion of the dataset and source code is available on GitHub at https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection .Open asset ↗https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection · Lijun-Gao/Apple-Leaf-Disease-Detectionlines:158-169Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes. Second, we distinguish fresh and rotten apples with Apple Fruit Quality Categorization (AFQC), which has 2,320 photos. Third, an Apple Diseases Extensive Collection (ADEC), comprised of 2,976 images with seven classes, was offered. Fourth, following the state of the art, we develop an Optimized Apple Orchard Model (OAOM) with a new loss function named measured focal cross-entropy (MFCE), which assists in improving the proposed model's efficiency. The proposed OAOM gives the highest performance for apple varieties identification with AFVC; accuracy was 93.85%. For the apples rotten recognition with AFQC, accuracy was 98.28%. For the identification of the diseases via ADEC, it was 99.66%. OAOM works with high efficiency and outperforms the baselines. The suggested technique boosts apple system automation with numerous duties and outstanding effectiveness. This research benefits the growth of apple's robotic vision, development policies, automatic sorting systems, and decision-making enhancement.
Why it matches plant phenotyping methodsリンゴ画像から腐敗状態や病害を推定するデータセットと深層学習モデルを開発・評価しており、植物状態の画像ベース推定が中心である。
abstractwe first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes.
Reproduction assets foundThe paper's three apple image datasets (AFVC, ADEC, AFQC) are explicitly released with free public access via the authors' GitHub repositories, and the Data Availability statement confirms all data is available at these URLs. These are paper-specific image datasets used directly for the paper's apple variety, disease,,Dataset · public7)
2,682
294
2,976
Fig 5
The Apple Fruit Varieties Collection (AFVC) distributions through 85 classes.
Fig 6
The Apple Fruit Varieties Collection (AFVC) measurement was split through 85 classes; the overall training was 26,775, and the testing was 2,975 samples.
The second collection, Apple Fruit Quality Categorization (AFQC) [ https://github.com/mustafa20999/AFQC ], was collected from the orchard ( Table 1 ). The study area was Beijing City, Huairou District, Beijing Shengshiguowang, with a mean temperature of 76°C − 19°C and an average monthly rainfall of 51.2 mm. Data was collected at two different periods between October 1st, 2023, and October 10th, 2023: in the morning, when shootinOpen asset ↗mustafa20999/AFQClines:66-100Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-enabled vision transformer (ViT) architecture to identify the diseases in plants. The proposed Inception-enabled ViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently use computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model archives an accuracy rate of 99.17% for the apple leaf dataset, 99.32% for the rice dataset, 96.89% for the ibean dataset, 75.42% for the cassava leaf dataset, and 99.33% for the plantvillage dataset.
Why it matches plant phenotyping methods植物病害の画像から病害状態を推定するコンピュータビジョン手法を開発・比較しており、植物表現型（病害状態）の抽出が中心です。
abstractIn this paper, we have proposed an Inception-enabled vision transformer (ViT) architecture to identify the diseases in plants.
Reproduction assets foundThe paper evaluates an Inception-enabled ViT model on five publicly available plant disease image datasets. The Data Availability section explicitly lists Kaggle URLs for the apple, bean (ibean), rice/wheat-rust, PlantVillage, and cassava datasets. These are public, paper-specific image datasets directly used for the模型Dataset · publicThe datasets generated and/or analysed during the current study
are available in Kaggle repository at:
https://www.kaggle.com/datasets/piantic/plantpathology-apple-datasetOpen asset ↗Kaggle · piantic/plantpathology-apple-datasetpdf-page:22 lines:1-48Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can help to automate this process. To do so, they need to be able to perceive the surrounding environment to identify target objects such as trees and plants. In this paper, we introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors. Our approach is able to simultaneously provide semantic segmentation, instance segmentation of trunks and fruits, and instance segmentation of trees (a trunk with its fruits). This allows us to identify relevant information such as individual plants, fruits, and trunks, and capture the relationship among them, such as precisely estimate the number of fruits associated to each tree in an orchard. To efficiently evaluate our approach for hierarchical panoptic segmentation, we provide a dataset designed specifically for this task. Our dataset is recorded in Bonn, Germany, in a real apple orchard with a variety of sensors, spanning from a terrestrial laser scanner to a RGB-D camera mounted on different robots platforms. The experiments show that our approach surpasses state-of-the-art approaches in 3D panoptic segmentation in the agricultural domain, while also providing full hierarchical panoptic segmentation. Our dataset is publicly available at https://www.ipb.uni-bonn.de/data/hops/. The open-source implementation of our approach is available at https://github.com/PRBonn/hapt3D.
Why it matches plant phenotyping methodsリンゴ樹・果実・幹を3Dセグメンテーションし、樹ごとの果実数を推定する手法と専用データセットを中心に開発・評価しており、植物の器官形態・収量関連形質の取得に該当する。
abstractwe introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors.
Reproduction assets foundThe paper introduces the HOPS dataset of annotated 3D apple orchard point clouds (TLS, UAV, UGV, SfM) for hierarchical panoptic segmentation, publicly available at the authors' IPB Bonn page, and releases the open-source implementation (hapt3D) on GitHub. Both are paper-specific, public, and actionable.Code · publicThe open-source implementation of our approach is available at https://github.com/PRBonn/hapt3D .Open asset ↗PRBonn/hapt3Dlines:1-59Code / dataset availability confirmedCrossref · checked 6 Sept 2026
The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R2) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.
Why it matches plant phenotyping methods植物病害画像から病斑割合と病害重症度を推定する画像セグメンテーション手法を開発し、野外データセット、精度、速度を評価しており、植物表現型取得法が中心である。
abstractThe segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity.
Reproduction assets foundThe paper's field-scenario plant disease image dataset (SBB, WSR, CAR with three-color pixel labels) is publicly released on Kaggle via DOI, as stated in the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicData Availability Statement: The original data presented in this study are openly available in Kaggle
at https://doi.org/10.34740/kaggle/ds/6620728, accessed on 9 March 2025.Open asset ↗Kaggle · 10.34740/kaggle/ds/6620728pdf-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する計算手法として開発され、複数作物・多数形質で検証されている。Rパッケージも提供され、表現型推定が研究の中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The cuticle is a polymeric membrane covering all plant aerial organs of primary origin. It regulates water loss and defends against environmental stressors and pathogens. Despite its significance, understanding of the micro-mechanical properties of the cuticle (cuticular membrane; CM) remains limited. In this study, non-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit. The BLS signal arises from the photon interaction with thermally induced pressure waves and allows for imaging with mechanical contrast. The derived loss tangent showed significant differences with wax extraction from the CM and further with carbohydrate extraction from the DCM, consistent with tensile test results. Spatial heterogeneity between anticlinal and periclinal regions was observed by BLS microscopy of CM and DCM, but not in CU. The key conclusions are: (1) BLS is sensitive to micro-mechanical variations, particularly the strain-stiffening effect of the cutin framework, offering insights into the CM's micro-mechanical behavior and underlying chemical structures; (2) CM and DCM exhibit spatial micro-mechanical heterogeneity between periclinal and anticlinal regions.
Why it matches plant phenotyping methodsリンゴ果実のクチクラの微力学特性を、BLS顕微鏡による非侵襲的イメージングで測定・比較しており、植物器官の物性形質取得が研究の中心である。
abstractnon-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit.
Reproduction assets foundThe paper deposits its underlying Brillouin light scattering measurement data (primary and supplementary figures) in a public LUIS repository (DOI 10.25835/xvsi5g6m). The Brillouin analysis python script is only available on request, so it is not a public code asset.Dataset · publicThe underlying data for all the primary and Supplementary Figs. has been deposited in a publicly accessible repository [ https://doi.org/10.25835/xvsi5g6m ] 67 . Raw data may be obtained from the authors upon reasonable request.Open asset ↗10.25835/xvsi5g6mlines:177-254Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. The predictive ability of these models depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic prediction. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Results Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out cross-validation, reflecting the application of genomic prediction to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to 10-fold cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic prediction approach is impractical. Conclusions Extending the training set using germplasm related with the target breeding material is crucial to improve the predictive ability of genomic prediction in apple. RADseq is a viable alternative to SNP array genotyping, while phenomic prediction is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.
Why it matches plant phenotyping methodsリンゴ育種におけるNIR分光データを用いたフェノミック予測を、ゲノム予測と多数のシナリオで比較評価しており、植物形質推定ワークフローの技術的検証が中心的です。
titleEvaluation of genomic and phenomic prediction for application in apple breeding.
Reproduction assets foundThe paper's own phenotypic, genomic, and near-infrared spectroscopy (NIRS) data acquired in this study are publicly deposited in the ETH Research Collection, directly reproducing the paper's phenotyping measurements and phenomic/genomic prediction analysis inputs. The NCBI SRA deposit contains only raw RADseq reads (m-Dataset · publicThe phenotypic, genomic, and near-infrared spectroscopy data acquired in this study are available in the ETH Research Collection at https://doi.org/10.3929/ethz-b-000699803 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000699803lines:180-211Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する深層学習手法の開発・汎化評価が中心であり、植物表現型（病徴・病害状態）の取得方法に該当する。
abstractThis paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods.
Reproduction assets foundThe paper's web-sourced plant disease image dataset (the paper-specific data contribution, combined with PlantDoc for experiments) is publicly deposited on Zenodo per the Data Availability Statement. PlantDoc is a cited third-party dataset, not a paper-specific asset, and no author analysis code or trained model is de-Dataset · publicData Availability Statement: Data used in paper are available at: https://zenodo.org/records/14051480Open asset ↗zenodo · 14051480pdf-page:21 lines:1-57Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model's superior performance, achieving over 99% accuracy and significantly improving F 1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification.
Why it matches plant phenotyping methods植物葉の画像から病徴・病害状態を分類する新規Vision Transformer手法を開発し、複数データセットと既存モデルで性能比較しているため、植物フェノタイピング手法が中心です。
abstractThis study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model.
Reproduction assets foundThe paper's plant disease classification experiments use a publicly available Kaggle leaf image dataset, explicitly named in the Data Availability Statement. No author code or model checkpoints are disclosed.Dataset · publicThe data that support the findings of this study are available in the
“New Plant Diseases Dataset” at Kaggle, accessible through https://www.kaggle.com/datasets/Open asset ↗New Plant Diseases Datasetpdf-page:13 lines:1-58Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
In this paper, we present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans. Our goal was to enhance the precision of radiative transfer models for modelling tree shading by using highly resolved 3D tree models. The approach was tested on a single apple tree (Malus domestica (Suckow) Borkh.) in a peri-urban setting and was validated by utilising an open-source radiative transfer model and comparing the simulation output with in-situ measurements of photosynthetically active radiation (PAR) as well as simulations utilizing turbid voxels of 0.2 m and 1 m edge length. The in-situ measurements of 60 PAR sensors showed a correlation coefficient (r) of 0.92 with the simulated light intensities for the reconstructed polygons which was higher than for the voxel-based approaches (0.2 m: r = 0.85, 1 m: r = 0.73). We were able to demonstrate that our approach effectively simulates light extinction through the canopy. This innovative method has the potential to easily provide detailed insights into high resolution radiation patterns within forests, which are connected to multiple ecosystem functions like species and habitat diversity.
Why it matches plant phenotyping methodsTLSデータから樹木の木部・樹皮・葉の3D形状を抽出する手法を開発し、PARシミュレーションとの比較で検証しており、植物形態の取得が中心的です。
abstractwe present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans.
Reproduction assets foundThe authors state that all study data (TLS-derived point clouds, PAR measurements) and the full R code for the leaf/wood polygon reconstruction are openly available in their GitHub repository, archived as Frey & Kröner 2024 (JulFrey/dotshadow, Zenodo DOI 10.5281/zenodo.14204435, cited in the references). The Zenodo URLCode · publicAll data relevant to the study and the full R code for the reconstruction of the leaves and woody
compartments can be found at our GitHub repository under open source license (Frey and Kröner
2024).Open asset ↗pdf-page:10 lines:1-56Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Deep learning-based methods have proven to be effective for various purposes in the agricultural sector. However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. In this study, we used images of healthy and diseased grape leaves as training datasets and utilized the latent diffusion model, known for its superior performance in image generation, to generate images of diseased apple leaves that were not included in this dataset. Image-to-image generation was utilized to preserve the original healthy leaf features, which enabled the appropriate image generation of diseased apple leaves. To ascertain whether the generated diseased apple leaf images could be used to detect leaf diseases, a deep learning-based classification model was trained to discriminate between diseased and healthy apple leaves from a dataset with a mixture of actual and generated images. Results showed that leaves were accurately classified, indicating that diseased apple leaves not included in the training data could be used to identify the actual diseased apple leaves. Our approach opens up new avenues for improving plant disease detection methods.
Why it matches plant phenotyping methods植物病害画像を生成して未見クラスの葉病害を検出する手法の開発が中心であり、葉の病害状態を画像から推定する植物フェノタイピング研究に該当する。
abstractwe propose the use of a latent diffusion model for plant disease detection by generating unseen class images.
Reproduction assets foundThe paper's plant-disease phenotyping analysis is built on the public PlantVillage leaf image dataset, which the authors explicitly used as training/input data. No authors' analysis code, generated image sets, or trained model checkpoints are stated to be publicly available.Dataset · publicspMohanty PlantVillage-Dataset. Available online: https://github.com/spMohanty/PlantVillage-Dataset (accessed on 29 March
2024).Open asset ↗PlantVillage-Datasetpdf-page:10 lines:1-26Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Fruit size is crucial for growers as it influences consumer willingness to buy and the price of the fruit. Fruit size and growth along the seasons are two parameters that can lead to more precise orchard management favoring production sustainability. In this study, a Python-based computer vision system (CVS) for sizing apples directly on the tree was developed to ease fruit sizing tasks. The system is made of a consumer-grade depth camera and was tested at two distances among 17 timings throughout the season, in a Fuji apple orchard. The CVS exploited a specifically trained YOLOv5 detection algorithm, a circle detection algorithm, and a trigonometric approach based on depth information to size the fruits. Comparisons with standard-trained YOLOv5 models and with spherical objects were carried out. The algorithm showed good fruit detection and circle detection performance, with a sizing rate of 92%. Good correlations (r > 0.8) between estimated and actual fruit size were found. The sizing performance showed an overall mean error (mE) and RMSE of + 5.7 mm (9%) and 10 mm (15%). The best results of mE were always found at 1.0 m, compared to 1.5 m. Key factors for the presented methodology were: the fruit detectors customization; the HoughCircle parameters adaptability to object size, camera distance, and color; and the issue of field natural illumination. The study also highlighted the uncertainty of human operators in the reference data collection (5–6%) and the effect of random subsampling on the statistical analysis of fruit size estimation. Despite the high error values, the CVS shows potential for fruit sizing at the orchard scale. Future research will focus on improving and testing the CVS on a large scale, as well as investigating other image analysis methods and the ability to estimate fruit growth.
Why it matches plant phenotyping methods果実サイズという植物器官形質を、深度カメラ・物体検出・円検出・三角測量で推定するコンピュータビジョン手法を開発・検証しており、フェノタイピング手法が中心である。
abstracta Python-based computer vision system (CVS) for sizing apples directly on the tree was developed
Reproduction assets foundThe paper's RGB-D apple dataset (RGB/depth frames, annotations, and reference caliper measurements) is explicitly released as open source on GitHub with a Zenodo DOI. The YOLOv5 base model is a generic third-party library, not a paper-specific asset; no author analysis code repository is stated.Dataset · publicThe obtained dataset is open source and available at
https://github.com/ECOPOM/OpenAcces_RGBD_apple_dataset (Bortolotti et al., 2024).Open asset ↗ECOPOM/OpenAcces_RGBD_apple_datasetpdf-page:3 lines:1-52Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge. Therefore, in this study, a lightweight deep learning model is designed from scratch to identify the apple leaf condition. The developed framework comprises two stages. First, the designed 37-layer model was employed to assess the condition of apple leaves (healthy or diseased). Second, transfer learning was used for further subclassification of the disease class (e.g., rust, complex, scab, and frogeye leaf spots). The trained lightweight model was reused because the model trained with correlated images facilitated transfer learning for further classification of the disease class. A dataset available online was used to validate the proposed two-stage framework, resulting in a classification rate of 98.25% for apple leaf condition identification and an accuracy of 98.60% for apple leaf disease diagnosis. Furthermore, the results confirm that the proposed model is lightweight and involves relatively fewer learnable parameters in comparison with other pre-trained deep learning models.
Why it matches plant phenotyping methodsリンゴ葉画像から健全・罹病状態および病害クラスを推定する軽量深層学習手法を開発し、精度検証しており、植物病害表現型の取得・分類が中心である。
abstracta lightweight deep learning model is designed from scratch to identify the apple leaf condition
Reproduction assets foundThe paper's phenotyping input is the publicly available Kaggle 'Plant Pathology 2021 - FGVC8' apple leaf image dataset (18,632 images), explicitly cited with its public URL. No author analysis code, trained model checkpoints, or supplementary code/model deposit is mentioned in the supplied blocks.Dataset · publicThe dataset used in this study is publicly available at Kaggle “Plant Pathology 2021 - FGVC8” ( https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8/data , accessed on August 20, 2024). The dataset contains 18,632 images captured using a Canon Rebel T5i DSLROpen asset ↗Kaggle · Plant Pathology 2021 - FGVC8lines:535-876Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction In response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This method is based on the LCNet(A Lightweight CPU Convolutional Neural Network) and GSConv(Group Shuffle Convolution) module modified YOLO(You Only Look Once) framework. Methods Firstly, the lightweight LCNet is utilized to reconstruct the backbone network, with the purpose of reducing the number of parameters and computations of the model. Secondly, the GSConv module and the VOVGSCSP (Slim-neck by GSConv) module are introduced in the neck network, which makes it possible to minimize the number of model parameters and computations while guaranteeing the fusion capability among the different feature layers. Finally, coordinate attention is embedded in the tail of the backbone and after each VOVGSCSP module to improve the problem of detection accuracy degradation issue caused by model lightweighting. Results The experimental results show the LCGSC-YOLO can achieve an excellent detection performance with mean average precision of 95.5% and detection speed of 53 frames per second (FPS) on the mixed datasets of Plant Pathology 2021 (FGVC8) and AppleLeaf9. Discussion The number of parameters and Floating Point Operations (FLOPs) of the LCGSC-YOLO are much less thanother related comparative experimental algorithms.
Why it matches plant phenotyping methodsリンゴ葉の病害症状を画像から検出する軽量な深層学習手法を開発し、精度と処理速度を評価しており、植物表現型取得が中心的貢献である。
abstractthe paper proposes a lightweight method and names it LCGSC-YOLO.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe links to the datasets used in this study are provided below: https://drive.google.com/drive/folders/1MRfK5eOm5-6KZTngPzpzjp9gx1NyEvZY?usp=sharing .Open asset ↗lines:388-395Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract The agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. Grad-CAM, or gradient-weighted class activation mapping, creates heatmaps that highlight significant regions for prediction, which enhances the interpretability of the model.To increase robustness and accuracy, techniques such as spectral-shifted adversarial perturbation for data augmentation and spectrally-weighted global average pooling for feature aggregation are used. This technique provides 99.75\% accuracy with minimal processing needs, making it acceptable for real-time applications in agricultural situations. This considerably improves apple disease management.
Why it matches plant phenotyping methodsリンゴ果実の病徴を画像から推定するCNNベースの疾病検出手法を開発しており、植物の病害状態を直接評価する方法が研究の中心です。
abstractThis article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models
Reproduction assets foundThe paper's Data availability declaration points to the authors' public Kaggle dataset (MADCB-DS), the apple fruit disease image dataset used for all experiments in this study.Dataset · publicalgorithm to large-scale datasets, ensuring scalability without compromising
accuracy. This line of inquiry is vital for transitioning the model from experimental
frameworks to real-world, practical implementations.
Declarations
• Funding: No Funds wwere obtained to help with the development of this paper.
• Data availability: https://www.kaggle.com/datasets/anilsandhii/improved-fruit-disease-dataset-with-class-balance
• Author Contribution: Dr. Rejeev Kumar guided to conduct complete research.
References
[1] Khan, M. A. et al. An optimized method for segmentation and classification of
apple diseases based on strong correlation and genetic algorithm based feature
selection. IEEE Access 7, 46Open asset ↗Kaggle · anilsandhii/improved-fruit-disease-dataset-with-class-balancepdf-raw-page:44 lines:1-86Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract: Preserving plant health and early detection of diseases are crucial in modern agriculture. Artificial intelligence techniques, particularly deep learning networks, are employed for this purpose. In this study, disease recognition was conducted using leaf images from various plant species. The study encompassed important agricultural products such as apples, strawberries, grapes, corn, peppers, and potatoes among the plant species considered. Among the deep learning networks, popular architectures like AlexNet, Vgg16, MobileNetV2, and Inception were compared. The Inception V3 model achieved the highest success rate of 92%, followed by the AlexNet architecture with a success rate of 91%. Among these networks, the InceptionV3 model yielded the best results. The InceptionV3 model effectively learned from plant leaf images and accurately distinguished between diseased and healthy leaves. These findings demonstrate that AI-based systems can be efficiently utilized for disease recognition and prevention in the agriculture sector. In this study, the performance of the InceptionV3 model in disease recognition on plant leaves was analyzed in detail, emphasizing the role of deep learning networks in agricultural applications.
Why it matches plant phenotyping methods植物葉画像から健全・罹病状態を推定する深層学習手法を比較・評価しており、植物病害表現型の取得・分類が研究の中心です。
abstractIn this study, disease recognition was conducted using leaf images from various plant species.
Reproduction assets foundThe paper's plant-disease classification experiments were performed on the public New Plant Diseases Dataset (Kaggle), which the authors explicitly state is openly accessible via a Kaggle URL. This is a paper-specific, public, actionable phenotype image dataset. No author analysis code or trained models are reported asDataset · publicsector. Suggestions for
future research include the use of larger and more
diverse datasets and the application of federated learning
techniques, which can improve the performance of the
model and provide security.
Dataset Access: The dataset used in this study is open
and can be accessed from the relevant source link.
Access:
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetpdf-raw-page:11 lines:97-114Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
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:74-108Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight ( Erwinia amylovora ) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1-6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with Erwinia amylovora , including cultivars, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.
Why it matches plant phenotyping methodsRGB画像から植物病徴を検出するための注釈付きデータセットを開発・提示しており、植物病害状態の画像ベース表現型評価が中心である。
abstracta digital disease monitoring system for fire blight based on RGB images was developed for orchards.
Reproduction assets foundThe paper is a data descriptor for the ERWIAM dataset of annotated RGB images of fire blight symptoms, publicly deposited on Mendeley Data with a direct URL and DOI given in the text.Dataset · publicTolerance located in Quedlinburg (Germany) [51°46ʹ22″N 11°08ʹ41″E] and at the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz (Germany) [51°00ʹ01″N 13°53ʹ12"E].
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/fpmnncmg84.1
Direct URL to data: https://data.mendeley.com/datasets/fpmnncmg84/1
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In the experimental greenhouse of the JKI-Quedlinburg Institute, around 2000 different genotypes of apple breeding material were artificially inoculated with Erwinia amylovora in 2021 and 2022, which could be used to record fire blight symptoms. The JKI-Dossenheim Institute has a heterogeneous appleOpen asset ↗Mendeley Data · 10.17632/fpmnncmg84.1lines:42-82Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
By means of a unique, low vibration circular conveyor system, plant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees, with seven Malus x domestica Borkh. 'Gala' apple trees in each block. Measurements took place four times during the season. Additionally at harvest, diurnal courses were recorded with 18 readings during three days. The data are provided as [i] raw data (3D point clouds of 3 blocks of trees scanned from right and left sides and thermal images), [ii] processed 3D point clouds of canopies annotated with temperature data from the thermal camera, and [iii] manually segmented 3D point clouds of fruit, representing the spatially-resolved fruit surface temperature (FST). Manual FST readings are provided on each measuring date and during diurnal courses. The fruit data are capturing 1236 FST, providing temperature distribution as 3D point cloud and one manually recorded reference FST per fruit. Additionally, fruit size and colour were measured for each fruit, despite for the first date, when fruit were too small for colour readings. Weather data are provided from a station located in the orchard. Usage of data could be (a) in developing methodology for 3D point cloud processing based on raw data, accomplished with reference FST data. Furthermore, (b) the pre-processed point clouds of fruit surface temperature can be reused in ecophysiological studies related to global warming, optimizing fruit production systems, and other. Because the sensors and trees were measured from the same angle and distance, time series analysis of the canopies would be possible.
Why it matches plant phenotyping methodsLiDARと熱画像を統合し、果実表面温度を3D点群として取得・注釈化した再利用可能なデータセットであり、植物表現型取得手法とデータ提供が中心です。
abstractplant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees
Reproduction assets foundThe paper is a Data in Brief article describing a public Zenodo deposit containing the paper's own phenotyping measurements: raw LiDAR point clouds, thermal images, temperature-annotated 3D point clouds of apple canopies, 1236 manually segmented fruit point clouds with FST reference readings, fruit size/colour data, anDataset · publicocation
The conveyor system is located 52.4673340479, 12.9606589643 in the experimental station of Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany (ATB). Data repository is stored on Zenodo server [ 1 ]
Data accessibility
Repository name: Zenodo
Doi: https://doi.org/10.5281/zenodo.10792723
url: https://zenodo.org/records/10792723
Related research article
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The stationary conveyor system enabled repeated readings of apple tree canopies, with minimum vibration due to electric engine of the conveyor, and equal geometry between sensors and samples in all measurements. The value of 3D point clouds obtained with LiDAR sensor was enhanced bOpen asset ↗Zenodo · 10.5281/zenodo.10792723lines:46-71Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.
Why it matches plant phenotyping methods植物画像向け超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子の計数性能を評価しており、表現型取得・抽出法が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper's authors publicly released their analysis code (PlantSR super-resolution model and experiments) on GitHub, and also deposited the PlantSR dataset and HR soybean images on figshare; however, only the GitHub URL is among the allowed URLs, so only the code asset is reported.Code · publicThe source code is available at https://github.com/SkyCol/PlantSR (accessed on 28 November 2023).Open asset ↗SkyCol/PlantSRlines:94-293Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類するCNN手法の開発と性能比較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。
abstractMultiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters.
Reproduction assets foundThe paper trains and evaluates its E-CNN plant disease detection models on the public PlantVillage Kaggle dataset (apple, corn, potato leaf images). No author-generated datasets, code, or models are released; the data availability statement says no datasets were generated or analysed. The only qualifying paper-specificDataset · publicPlantVillage Dataset (2023) [Online]. Available: https://www.kaggle.com/datasets/abdallahalidev/plantOpen asset ↗Kaggle · PlantVillage Datasetpdf-page:28 lines:1-67Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model's classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別する最適化CNNを提案し、学習条件・背景・データセットで性能比較と精度評価を行っており、植物表現型取得法が中心である。
abstractThis study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases.
Reproduction assets foundThe paper's apple leaf disease dataset fuses authors' field-collected images (not publicly deposited) with the public Kaggle Plant Pathology 2020 (FGVC7) dataset, which is directly used in this paper's phenotyping/classification measurements. No author analysis code, trained model checkpoints, or data deposit is statedDataset · publich period from apple tree germination to harvesting, and each disease image sample contained different stages of onset.
Considering the insufficient number of collected apple leaf disease images and the fact that the background and category of the apple disease leaf images in the “Plant Pathology Challenge” for CVPR 2020-FGVC7 ( https://www.kaggle.com/c/plantpathology-2020 fgvc7, accessed on 20 December 2023) [ 17 ] are consistent with the collected image data, the two were fused to form the dataset used in this study. Additionally, we collaborated with professional scholars to screen, classify, and organize the above two image types one by one, establishing a relatively reliable dataset of aOpen asset ↗Kaggle · plantpathology-2020lines:34-43Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. Results Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. Conclusions The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.
Why it matches plant phenotyping methodsリンゴ果実の画像取得・コンピュータ解析・重量測定を統合した装置を開発し、測定速度、再現性、誤差、適切なサンプルサイズを評価しており、果実形態形質の取得法が研究の中心である。
abstractHere we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit.
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw fruit images, extracted datasets, and R scripts in the ETH Research Collection (doi:10.3929/ethz-b-000590509), and the Matlab/R image-analysis scripts (apple fruit feature extractor, affe) on SourceForge. Both are paper-specific, public, and actionable.Dataset · publicSupplementary files for this article, which include raw images in tif format, datasets extracted from the images and R scripts used in this study are available from the ETH Research collection under following doi: https://doi.org/10.3929/ethz-b-000590509Open asset ↗ETH Research collection · 10.3929/ethz-b-000590509lines:125-170Code · publicThe Matlab- and R-scripts are available via sourceforge, project apple fruit feature extractor (affe), https://sourceforge.net/projects/affeOpen asset ↗sourceforge · affelines:125-170Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. Results In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Conclustion Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.
Why it matches plant phenotyping methodsリンゴ果肉の赤色形質をRGB画像と色解析で定量する手法を開発し、遺伝子型間の識別に実質的に適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicScript designed for image analysis is public and can be found at: https://github.com/pibouillon/colour_val/blob/main/colour_val.pyOpen asset ↗https://github.com/pibouillon/colour_val · colour_val.pylines:166-220Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.
Why it matches plant phenotyping methods植物画像の超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子のカウント性能を改善する手法を検証しており、植物器官数の画像ベース推定が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the PlantSR plant image dataset (figshare), and the high-resolution soybean seed images used for transfer learning (figshare). The P2PNet-Soy repository is cited prior work, not a paper-specific asset.Code · publicThe source codes and associated
datasets are available at https://github.com/SkyCol/PlantSR.Open asset ↗SkyCol/PlantSRpdf-page:2 lines:1-63Code / dataset availability confirmedOpenAlex · Crossref · checked 7 Sept 2026
Phenotyping of genetic resources is an important prerequisite for the selection of resistant varieties in breeding programs and research. Computer vision techniques have proven to be a useful tool for digital phenotyping of diseases of interest. One pathogen that is increasingly observed in Europe is Diplocarpon coronariae, which causes apple blotch disease. In this study, a high-throughput phenotyping method was established to evaluate genetic apple resources for susceptibility to D. coronariae. For this purpose, inoculation trials with D. coronariae were performed in a laboratory and images of infested leaves were taken 7, 9 and 13 days post inoculation. A pre-trained YOLOv5s model was chosen to establish the model, which was trained with an image dataset of 927 RGB images. The images had a size of 768 × 768 pixels and were divided into 738 annotated training images, 78 validation images and 111 background images without symptoms. The accuracy of symptom prediction with the trained model was 95%. These results indicate that our model can accurately and efficiently detect spots with acervuli on detached apple leaves. Object detection can therefore be used for digital phenotyping of detached leaf assays to assess the susceptibility to D. coronariae in a laboratory.
Why it matches plant phenotyping methodsリンゴ葉の病斑をYOLOv5で画像から検出し、病害感受性を評価する高スループット表現型計測法を確立・検証しており、フェノタイピング手法が中心である。
abstractIn this study, a high-throughput phenotyping method was established to evaluate genetic apple resources for susceptibility to D. coronariae.
Reproduction assets foundThe authors explicitly state that the image dataset used for model training (image_dataset_2023, 927 RGB images with 4167 annotations) and the YOLOv5s detection workflow with instructions are available in an open-source GitHub repository. This is a paper-specific, public, actionable asset directly reproducing the phenyDataset · publicThe image dataset for the model training is available in the open-source
GitHub repository (https://github.com/digijkizo/Apple_blotch_detection/tree/master,
accessed on 29 April 2024).Open asset ↗https://github.com/digijkizo/Apple_blotch_detection/tree/masterpdf-page:5 lines:1-59Code · publicThe image dataset for the model training and the detection workflow
with instructions are available in the open-source GitHub repository (https://github.com/digijkizo/
Apple_blotch_detection/tree/master, accessed on 29 April 2024).Open asset ↗pdf-page:10 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential, forming the foundation for precision apple management. Traditionally, 2D imaging technologies were employed to delineate the architectural traits of apple trees, but their accuracy was hampered by occlusion and perspective ambiguities. This study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization, utilizing point clouds collected by a terrestrial laser scanner (TLS). The pipeline consisted of four modules: (a) data preprocessing module, (b) tree instance segmentation module, (c) tree structure segmentation module, and (d) architectural trait extraction module. The developed pipeline was used to analyze 84 trees of two representative apple cultivars, characterizing architectural traits such as tree height, trunk diameter, branch count, branch diameter, and branch angle. Experimental results indicated that the established pipeline attained an R 2 of 0.92 and 0.83, and a mean absolute error (MAE) of 6.1 cm and 4.71 mm for tree height and trunk diameter at the tree level, respectively. Additionally, at the branch level, it achieved an R 2 of 0.77 and 0.69, and a MAE of 6.86 mm and 7.48° for branch diameter and angle, respectively. The accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding programs. Moreover, bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.
Why it matches plant phenotyping methodsTLS点群を用いて樹体構造を分割し、樹高・幹径・枝数・枝径・枝角度を抽出する3D表現型計測パイプラインの開発と精度評価が中心である。
abstractThis study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization, utilizing point clouds collected by a terrestrial laser scanner (TLS).
Reproduction assets foundThe paper's AppleQSM pipeline source code is explicitly stated as publicly available on the authors' GitHub repository. Raw TLS point cloud data are only available upon reasonable request, so they do not qualify as a public asset. TreeQSM and FLIP_main repositories are cited prior work/tools, not paper-specific assets.Code · publicThe source code is available at the project GitHub repository ( https://github.com/suptimq/Apple_Crop_Potential_Prediction/tree/master ). Raw data used in this study will be shared upon reasonable request.Open asset ↗https://github.com/suptimq/Apple_Crop_Potential_Prediction/tree/masterlines:293-327Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
Great diversity of shape, size, and skin color is observed among the fruits of different apple genotypes. These traits are critical for consumers and therefore interesting targets for breeding new apple varieties. However, they are difficult to phenotype and their genetic basis, especially for fruit shape and ground color, is largely unknown. We used the FruitPhenoBox to digitally phenotype 525 genotypes of the apple reference population (apple REFPOP) genotyped for 303,148 single nucleotide polymorphism (SNP) markers. From the apple images, 573 highly heritable features describing fruit shape and size as well as 17 highly heritable features for fruit skin color were extracted to explore genotype-phenotype relationships. Out of these features, seven principal components (PCs) and 16 features with the Pearson's correlation r < 0.75 (selected features) were chosen to carry out genome-wide association studies (GWAS) for fruit shape and size. Four PCs and eight selected features were used in GWAS for fruit skin color. In total, 69 SNPs scattered over all 17 apple chromosomes were significantly associated with round, conical, cylindrical, or symmetric fruit shapes and fruit size. Novel associations with major effect on round or conical fruit shapes and fruit size were identified on chromosomes 1 and 2. Additionally, 16 SNPs associated with PCs and selected features related to red overcolor as well as green and yellow ground color were found on eight chromosomes. The identified associations can be used to advance marker-assisted selection in apple fruit breeding to systematically select for desired fruit appearance.
Why it matches plant phenotyping methodsFruitPhenoBoxを用いた画像ベースのデジタル表現型解析が、525遺伝子型から果実形状・サイズ・色の特徴を抽出する中心的方法として明示されている。
abstractWe used the FruitPhenoBox to digitally phenotype 525 genotypes of the apple reference population (apple REFPOP)
Reproduction assets foundThe paper's data availability statement explicitly deposits the FruitPhenoBox apple images, the raw image-derived phenotypic features, supplementary phenotypic data, and the authors' R analysis code at public repositories with resolvable DOIs and a GitLab URL. SNP genotypic deposits were excluded as molecular genomics/Code · publicThe R code can be accessed through the following link https://gitlab.ethz.ch/kellebea/fruitphenobox .Open asset ↗gitlab.ethz.ch · kellebea/fruitphenoboxlines:460-609Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published10 Apr 2024International Journal For Innovative Engineering and Management ResearchCited by 5 · OpenAlex ↗
Plant diseases cause significant crop losses globally, posing challenges to agricultural productivity.Detecting these diseases is difficult due to the lack of expert knowledge.Deep learning-based models offer promising solutions using leaf images, but issues like the need for larger training sets and computational complexity persist.To address this, we propose a convolutional neural network (CNN) with fewer layers, reducing computational burden.Augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to expand the training set without capturing more images.As agriculture remains crucial for nourishing about half of the global population, increasing production by 50-60% is urgent, especially in regions with rapid population growth.Despite an expanding cultivation area, apple crop production in India faces challenges, with minimal growth in yield.In Himachal Pradesh, a major apple-producing state, fungal diseases significantly impact fruit quality.Our project addresses these challenges by employing deep learning models, including pre-trained ones, and utilizing YOLO series models for efficient disease detection in apples.By leveraging image processing and AI, timely and accurate disease diagnosis is ensured.This project has the potential to revolutionize disease detection in apple plants, enhancing food security globally.Farmers stand to benefit from prompt intervention, safeguarding their crops and ensuring increased yields, thereby contributing to overall food security for the growing global population.
Why it matches plant phenotyping methodsリンゴ葉画像から植物病害を検出するCNNを開発しており、病徴・病害状態の画像ベース推定が研究の中心である。
abstractwe propose a convolutional neural network (CNN) with fewer layers, reducing computational burden.
Reproduction assets foundThe paper explicitly provides a public Kaggle dataset link (PlantVillage-based apple leaf disease images) used for its classification experiments. The Roboflow link is only a format-conversion tool, not a paper-specific asset, and no author code or trained models are shared.Dataset · public4.
[30] J. W. Orillo, J. Dela Cruz, L. Agapito, P. J.
Satimbre, and I. Valenzuela, Identication of
diseases in Rice plant (oryzasativa) using back
propagationarticial neural network, in Proc. Int.
Conf. Humanoid, Nanotech nol., Inf. Technol.,
Commun. Control, Environ. Manage. (HNICEM),
2014, pp. 16.
Dataset link
Classification
:https://www.kaggle.com/datasets/lavaman151/plan
tifydr-dataset
Detection :https://roboflow.com/convert/labelbox-json-to-yolov5-pytorch-txtOpen asset ↗Kagglepdf-raw-page:11 lines:1-96Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases. Methods To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information. Results The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet. Discussion The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.
Why it matches plant phenotyping methodsリンゴ葉および病斑の画像セグメンテーション手法を開発し、既存手法と比較検証しているため、植物病害状態の画像ベース表現型計測が中心である。
titleRAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss.
Reproduction assets foundThe paper's apple leaf disease image datasets (ALDD) are publicly available via a Google Drive link provided in the Data availability statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/file/d/1qV3zZCNh8FhrMwQwZds9rRkm9SUQXV5P/view?usp=sharing .Open asset ↗lines:783-804Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Like other crops, different types of diseases affect apple trees. These diseases cause ugly cosmetic changes on the fruit and hence reduce its shelf life and value. To eliminate their impact, they need to be detected well in advance before any control measures are applied. The manual method of disease detection and subsequent classification has flaws as it involves manual scouting and analysis of the affected leaves through the naked eye. Besides, the manual method may result in wrong judgment as the knowledge of an expert limits the accuracy. Deep Learning Models have been successfully implemented for automated disease detection and classification. However, these models need massive datasets for training, testing and validation. This study proposes one such dataset that has been built indigenously by collecting images from the apple cultivation fields of Kashmir valley and subjecting it to cleaning and subsequent annotation by experts. Augmentation techniques have been used to enhance the size and quality of the dataset to prevent over-fitting of deep learning models.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から検出・分類するための注釈付きデータセットを構築しており、植物病害フェノタイピングの再利用可能な基盤が中心です。
abstractThis study proposes one such dataset that has been built indigenously by collecting images from the apple cultivation fields of Kashmir valley and subjecting it to cleaning and subsequent annotation by experts.
Reproduction assets foundThe paper is a Data in Brief article describing an indigenous apple leaf disease image dataset (Healthy, Alternaria, Apple-Mosaic) collected from Kashmir Valley, publicly deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL.Dataset · publicaptured using cameras and other handheld devices having different optical characteristics.
Data Source Location
The data was collected from apple cultivation fields in different regions of Kashmir Valley.
Data Accessibility
Repository name: Mendeley Data
Data identification number: doi: 10.17632/9m2dcb5mmr.2
Direct URL to data:
https://data.mendeley.com/datasets/9m2dcb5mmr/3
Instructions for accessing these data:
The images belonging to three classes are available in their individual directories
Related Research Article
https://www.taylorfrancis.com/chapters/edit/10.1201/9781003405573-28/optimized-model-apple-leaf-disease-detection-performance-comparison-state-art-techniques-using-indigenousOpen asset ↗Mendeley Data · 10.17632/9m2dcb5mmr.2lines:1-67Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Apple trees face various challenges during cultivation. Apple leaves, as the key part of the apple tree for photosynthesis, occupy most of the area of the tree. Diseases of the leaves can hinder the healthy growth of trees and cause huge economic losses to fruit growers. The prerequisite for precise control of apple leaf diseases is the timely and accurate detection of different diseases on apple leaves. Traditional methods relying on manual detection have problems such as limited accuracy and slow speed. In this study, both the attention mechanism and the module containing the transformer encoder were innovatively introduced into YOLOV5, resulting in YOLOV5-CBAM-C3TR for apple leaf disease detection. The datasets used in this experiment were uniformly RGB images. To better evaluate the effectiveness of YOLOV5-CBAM-C3TR, the model was compared with different target detection models such as SSD, YOLOV3, YOLOV4, and YOLOV5. The results showed that YOLOV5-CBAM-C3TR achieved mAP@0.5, precision, and recall of 73.4%, 70.9%, and 69.5% for three apple leaf diseases including Alternaria blotch, Grey spot, and Rust. Compared with the original model YOLOV5, the mAP 0.5increased by 8.25% with a small change in the number of parameters. In addition, YOLOV5-CBAM-C3TR can achieve an average accuracy of 92.4% in detecting 208 randomly selected apple leaf disease samples. Notably, YOLOV5-CBAM-C3TR achieved 93.1% and 89.6% accuracy in detecting two very similar diseases including Alternaria Blotch and Grey Spot, respectively. The YOLOV5-CBAM-C3TR model proposed in this paper has been applied to the detection of apple leaf diseases for the first time, and also showed strong recognition ability in identifying similar diseases, which is expected to promote the further development of disease detection technology.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から検出・分類する新規深層学習モデルを開発し、複数モデルとの比較評価も行っており、植物病害状態の表現型抽出が中心である。
abstractresulting in YOLOV5-CBAM-C3TR for apple leaf disease detection.
Reproduction assets foundThe paper's apple leaf disease detection experiments rely on a publicly available apple leaf pathology image dataset hosted on Baidu AI Studio, which the authors explicitly cite with a URL. This is a paper-specific, public, actionable image dataset used directly for the paper's phenotyping (disease detection) analysis.Dataset · publicof similar apple leaf diseases. As far as we know, this is the first time that the YOLOV5-CBAM-C3TR model has been used for the identification and localization of apple leaf diseases.
2.
Materials and methods
2.1.
Datasets
In this study, the images were collected from the publicly available apple leaf pathology image dataset ( https://aistudio.baidu.com/datasetdetail/11591 ). Disease images in natural environments in the dataset were obtained from a real apple orchard in Yantai, Shandong Province, China. A total of 390 high-quality images of three common apple leaf diseases were selected for study in this dataset. However, the original images cannot be trained, validated, and tested directlyOpen asset ↗aistudio.baidu.com · 11591lines:33-78Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
Why it matches plant phenotyping methods複数作物の果実・器官について、ハイパースペクトル画像から乾物含量を非破壊推定するデータセットを構築しており、形質取得・推定手法と再利用可能なベンチマークが研究の中心である。
abstractThe dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models.
Reproduction assets foundThe paper is a data descriptor for the SpectroFood hyperspectral dataset; all five Zenodo deposits (meta-dataset plus per-crop hyperspectral image data) are public, paper-specific phenotype/trait datasets with direct URLs in the Specifications Table.Dataset · publicce), Rc: corrected hyperspectral image.
Data source location
Data are stored at Agricultural University of Athens (AUA) premises. Iera Odos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https:/Open asset ↗Zenodo · 10.5281/zenodo.8362947lines:1-65Dataset · publicOdos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302Open asset ↗Zenodo · 10.5281/zenodo.10301753lines:1-65Dataset · publicdo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the sameOpen asset ↗Zenodo · 10.5281/zenodo.10302438lines:1-65Dataset · public8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and acrossOpen asset ↗Zenodo · 10.5281/zenodo.10302426lines:1-65Dataset · publicps://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
•
Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and across all four.
•
The dry matter content of the four crops is the common variable when considering the quality of theOpen asset ↗Zenodo · 10.5281/zenodo.10302386lines:1-65Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework’s performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework’s performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework’s applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.
Why it matches plant phenotyping methods植物葉画像から病変画素率を算出して病害重症度を推定する画像解析手法を開発し、データセット上で比較・統計検証しており、植物表現型取得が中心である。
abstracta lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances.
Reproduction assets foundThe paper's plant-phenotyping measurements (apple leaf disease detection and severity estimation) were performed on the publicly available Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset, which the authors link via a Science Data Bank deposit. No authors' analysis code or trained model checkpoints are explicitlydeDataset · publicrk of this research also includes the deployment of the PDSE-Lite framework on different IoT devices, such as Unmanned Aerial Vehicles (UAVs), to enable real-time monitoring of plant diseases in agricultural fields.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
PB: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing. PG: Conceptualization, Methodology, Software, Visualization, Writing – original draft. SM: Formal analysis, Resources, Writing – review & editing.
Funding Open asset ↗lines:501-513Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction In precision agriculture, accurately diagnosing apple frog-eye leaf spot disease is critical for effective disease management. Traditional methods, predominantly relying on labor-intensive and subjective visual evaluations, are often inefficient and unreliable. Methods To tackle these challenges in complex orchard environments, we develop a specialized deep learning architecture. This architecture consists of a two-stage multi-network model. The first stage features an enhanced Pyramid Scene Parsing Network (L-DPNet) with deformable convolutions for improved apple leaf segmentation. The second stage utilizes an improved U-Net (D-UNet), optimized with bilinear upsampling and batch normalization, for precise disease spot segmentation. Results Our model sets new benchmarks in performance, achieving a mean Intersection over Union (mIoU) of 91.27% for segmentation of both apple leaves and disease spots, and a mean Pixel Accuracy (mPA) of 94.32%. It also excels in classifying disease severity across five levels, achieving an overall precision of 94.81%. Discussion This approach represents a significant advancement in automated disease quantification, enhancing disease management in precision agriculture through data-driven decision-making.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から分割し、病害重症度を定量化・分類する深層学習手法が研究の中心であり、植物病態の表現型推定に該当する。
abstractwe develop a specialized deep learning architecture
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe apple leaf image dataset used in this study is sourced from the public dataset Plant Pathology 2021, which supplements the Plant Pathology 2020 dataset ( Thapa et al., 2020 ), originally provided by the 2020 Kaggle Plant Pathology competition.Open asset ↗Kagglelines:341-408Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
With the increasing integration of AI technology in the food industry, deep learning has demonstrated its immense potential in the domain of plant disease image recognition. However, there remains a gap in research between models capable of continual learning of new diseases and addressing the inherent catastrophic forgetting issue in neural networks. This study aims to comprehensively evaluate various learning strategies based on advanced computer vision models for multi-disease continual learning tasks in food disease recognition. To cater to the benchmark dataset requirements, we collected the PlantDiseaseCL dataset, sourced from the internet, encompassing diverse crop diseases from apples, corn, and more. Utilizing the Vision Transformer (ViT) model, we established a plant disease image recognition classifier, which, in joint learning, outperformed several comparative CNN architectures in accuracy (0.9538), precision (0.9532), recall (0.9528), and F1 score (0.9560). To further harness the potential of ViT in food disease defect recognition, we introduced a mathematical paradigm for crop disease recognition continual learning. For the first time, we proposed a novel ViT-TV architecture in the multi-disease image recognition scenario, incorporating a Total Variation (TV) distance-based loss (TV-Loss) to quantify the disparity between current and previous attention distributions, fostering attention consistency and mitigating the catastrophic forgetting inherent in ViT without prior task samples. In the incremental learning of the PlantDiseaseCL dataset across 3-Steps and 5-Steps, our strategy achieved average accuracies of 0.7077 and 0.5661, respectively, surpassing all compared Zero-Exemplar Approaches like LUCIR, SI, MAS, and even outperforming exemplar-based strategies like EEIL and ICaRL. In conclusion, the ViT-TV approach offers robust support for the long-term intelligent development of the agricultural and food industry, especially showcasing significant applicability in continual learning for crop disease image recognition.
Why it matches plant phenotyping methods植物病害画像から病害状態を推定する認識手法を開発・評価し、継続学習用データセットも構築しているため、植物フェノタイピング手法が中心である。
abstractwe collected the PlantDiseaseCL dataset, sourced from the internet, encompassing diverse crop diseases from apples, corn, and more.
Reproduction assets foundThe paper's PlantDiseaseCL dataset (30,863 crop disease images used for all phenotyping/recognition experiments) is explicitly made publicly available on Kaggle by the authors. No code or model checkpoint availability is stated in the supplied blocks.Dataset · publicFor the broader research community’s benefit, we have made the PlantDiseaseCL dataset publicly available on the Kaggle platform. It can be accessed at https://www.kaggle.com/datasets/gabrielwang01/leaf-disease-must (last accessed on 18 August 2023).Open asset ↗Kaggle · gabrielwang01/leaf-disease-mustlines:310-388Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15,335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub-set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research paper titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation" [1].
Why it matches plant phenotyping methodsリンゴ果実のRGB-D画像、アノテーション、サイズ正解値を含む公開データセットで、果実サイズ推定法の開発・評価を直接支援するため、植物フェノタイピング手法のデータ資源として中心的です。
abstractenabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation
Reproduction assets foundThe article is a Data in Brief describing the AmodalAppleSize_RGB-D dataset (RGB-D apple tree images with modal/amodal segmentation masks and fruit size ground truth), publicly deposited in Dataverse (CORA) with DOI 10.34810/data916 and a direct URL. This is the paper's own phenotyping data (images, annotations, callipDataset · publicData accessibility
Repository name: Dataverse
Data identification number: https://doi.org/10.34810/data916 [2]
Direct URL to data: https://dataverse.csuc.cat/dataset.xhtml?persistentId=doi:10.34810/data916Open asset ↗Dataverse · doi:10.34810/data916lines:43-67Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Abstract Farmers face a daunting challenge in meeting the escalating demands of a rapidly growing population for agricultural products, while plant diseases continue to exact a devastating toll on food production. Despite investing billions of dollars in disease management, agriculturists often struggle to achieve effective disease control without the support of advanced technology. The article explores a deep learning-based approach for disease detection. Specifically, it employs a Convolutional Neural Network (CNN) architecture for the detection. For the automated detection of plant disease, using plant images. This paper presents a new model for the early detection of plant detection based on processing plant images. And compare the in-depth performance analysis of hyper parameters in the context of plant disease detection by focusing on three distinct crops: (Apple, Corn, and Potato). Moreover, the data augmentation impact is analyzed. To enhance accessibility for farmers, our model is seamlessly integrated with a mobile application. The experimental results show the efficiency of our fine-tuned enhanced CNN model (E-CNN) achieving 98.17% accuracy on fungal classes. This research endeavors to pave the way for more effective plant disease management and ultimately to improve agricultural productivity in the face of mounting global challenges.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNを開発・評価し、ハイパーパラメータとデータ拡張の性能分析も行っているため、病害フェノタイピング手法が中心です。
abstractThe article explores a deep learning-based approach for disease detection.
Reproduction assets foundThe paper's plant disease detection experiments are built on the public PlantVillage image dataset, which the authors explicitly cite with a Kaggle URL. No author-generated code, trained model checkpoints, or supplementary data deposits are mentioned.Dataset · public[21] “PlantVillage Dataset.” Accessed: Dec. 05, 2023. [Online]. Available:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗Kaggle · plantvillage-datasetpdf-page:28 lines:1-61Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs. Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images, consistent with biological interpretation. While using substantial SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and dataset balance in terms of phenotypes for more precise outcomes.
Why it matches plant phenotyping methodsSNPからリンゴ画像を予測・再構成するGenoDrawingフレームワークを提案しており、果樹の視覚形質を推定する計算手法が研究の中心である。
abstractThis study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array
Reproduction assets foundThe authors publicly release their analysis code, notebooks, and trained model weights (autoencoder and embedding predictor) for the GenoDrawing framework in a GitHub repository. The apple images used for phenotyping are only available upon request from a prior study, so they do not qualify as public assets.Code · publicThe code repository including notebooks and models with their trained weights can be found in the following GitHub repository: https://github.com/Fedjurrui/GenoDrawingOpen asset ↗Fedjurrui/GenoDrawinglines:80-113Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
The rapid development of image processing technology and the improvement of computing power in recent years have made deep learning one of the main methods for plant disease identification. Currently, many neural network models have shown better performance in plant disease identification. Typically, the performance improvement of the model needs to be achieved by increasing the depth of the network. However, this also increases the computational complexity, memory requirements, and training time, which will be detrimental to the deployment of the model on mobile devices. To address this problem, a novel lightweight convolutional neural network has been proposed for plant disease detection. Skip connections are introduced into the conventional MobileNetV3 network to enrich the input features of the deep network, and the feature fusion weight parameters in the skip connections are optimized using an improved whale optimization algorithm to achieve higher classification accuracy. In addition, the bias loss substitutes the conventional cross-entropy loss to reduce the interference caused by redundant data during the learning process. The proposed model is pre-trained on the plant classification task dataset instead of using the classical ImageNet for pre-training, which further enhances the performance and robustness of the model. The constructed network achieved high performance with fewer parameters, reaching an accuracy of 99.8% on the PlantVillage dataset. Encouragingly, it also achieved a prediction accuracy of 97.8% on an apple leaf disease dataset with a complex outdoor background. The experimental results show that compared with existing advanced plant disease diagnosis models, the proposed model has fewer parameters, higher recognition accuracy, and lower complexity.
Why it matches plant phenotyping methods植物病害画像から病害状態を推定する軽量CNNモデルを開発し、複数データセットで精度・複雑度を評価しており、病害表現型の抽出手法が中心である。
abstracta novel lightweight convolutional neural network has been proposed for plant disease detection
Reproduction assets foundThe paper's plant disease identification experiments use three public image datasets (PlantVillage, Plant Pathology 2020-FGVC7 apple leaf, Pl@ntNet-300K), all explicitly linked in the data availability statement. No author code or model checkpoints are released.Dataset · publicice for plant disease diagnosis, deploying the proposed model on the device for practical applications in automatic plant disease diagnosis scenarios.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://zenodo.org/record/5645731#.YeGDOdvjKWh (Pl@ntNet-300K) , https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color (PlantVillage) , https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data (FGVC7 Apple Leaf) .
Author contributions
JW: Conceptualization, Funding acquisition, Writing – review & editing. SQ: Conceptualization, Methodology, Supervision, Visualization, Writing – original draft. ZJ: MethoOpen asset ↗PlantVillage-Datasetlines:592-612Dataset · publicapplications in automatic plant disease diagnosis scenarios.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://zenodo.org/record/5645731#.YeGDOdvjKWh (Pl@ntNet-300K) , https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color (PlantVillage) , https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data (FGVC7 Apple Leaf) .
Author contributions
JW: Conceptualization, Funding acquisition, Writing – review & editing. SQ: Conceptualization, Methodology, Supervision, Visualization, Writing – original draft. ZJ: Methodology, Supervision, Writing – review & editing. MY: Investigation, Methodology, Software,Open asset ↗lines:592-612Dataset · publicnal resources of the network, and develop a portable handheld device for plant disease diagnosis, deploying the proposed model on the device for practical applications in automatic plant disease diagnosis scenarios.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://zenodo.org/record/5645731#.YeGDOdvjKWh (Pl@ntNet-300K) , https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color (PlantVillage) , https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data (FGVC7 Apple Leaf) .
Author contributions
JW: Conceptualization, Funding acquisition, Writing – review & editing. SQ: Conceptualization, MethodologyOpen asset ↗lines:592-612Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
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:729-756Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
Why it matches plant phenotyping methodsリンゴ果実の欠陥を画像から画素単位で抽出する深層学習手法を開発・評価しており、植物器官の状態（欠陥）取得が中心的な方法論的貢献である。
abstractwe propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs)
Reproduction assets foundThe paper's authors publicly release the analysis code for their apple defect segmentation experiments via a GitHub repository, explicitly stated in the text. The apple image dataset itself is cited prior work (Kleynen et al.) and no separate dataset deposit by these authors is stated.Code · publicwe investigate the feasibility of using RGB images exclusively as input data instead of multi-spectral images. Encouragingly, the results show that the accuracy achieved in this scenario is nearly comparable to the multi-spectral approach. The experiments can be reproduced using the code made available at the following address: https://github.com/cimice15/Quasi_real-time_apple_defect_segmentation (accessed on 8 September 2023).
The paper is organized as follows: Section 2 presents related works, Section 3 presents the database used in our experiments and the method we propose. Section 4 presents evaluation metrics and experimental setups. Finally Section 5 discusses results of the proposed mOpen asset ↗cimice15/Quasi_real-time_apple_defect_segmentationlines:40-49Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15,335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits.
Why it matches plant phenotyping methodsRGB-D画像と深層学習を用いて果実の検出・セグメンテーションおよび直径推定法を開発し、アノテーションデータで性能評価しているため、果実形質の取得手法が中心である。
abstractThis work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks.
Reproduction assets foundThe authors explicitly state that the code for their multitask Mask R-CNN diameter-regression network was made publicly available together with the annotated RGB-D apple dataset (masks, diameter ground truth, spherical mask projections) at the GRAP-UdL publication page. This is a paper-specific, public, actionable codeCode · publice, which goes from
14 14 (default pooling resolution) to 28 28. After the
deconvolution, the data is flattened and fed to a linear layer
that predicts the diameter for that mask.
The developed network was implemented in the Pytorch
framework and the code has been made publicly available
jointly with the presented dataset at http://www.grap.udl.cat/en/publications/papple_rgb-d-size-dataset/.2.2.3. Network training and inference details
a) Weight initialisation: Mask ReCNN has a set of weight
initialisations pre-trained with different backbones on
ImageNet (Deng et al., 2009). In our case, the used
weights were pre-trained with a ResNet50 backbone.
However, during the course of this projecOpen asset ↗pdf-raw-page:6 lines:1-143Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Great diversity of shape, size, and skin color is observed among the fruits of different apple genotypes. These traits are critical for consumers and therefore interesting targets for breeding new apple varieties. However, they are difficult to phenotype and their genetic basis, especially for fruit shape and ground color, is largely unknown. We used the fruit FruitPhenoBox to digitally phenotype 506 genotypes of the apple reference population (apple REFPOP) genotyped for 303,148 single nucleotide polymorphism (SNP) markers. From the apple images, 573 highly heritable features describing fruit shape and size as well as 17 highly heritable features for fruit skin color were extracted to explore genotype-phenotype relationships. Out of these features, nine and four principal components (PCs) as well as 16 and eight uncorrelated features were chosen to carry out genome-wide association studies for fruit shape, size, and fruit skin color, respectively. In total, 69 SNPs scattered over all 17 apple chromosomes were significantly associated with round, conical, cylindrical, or symmetric fruit shapes and fruit size. Novel associations with major effect on round or conical fruit shapes and fruit size were identified on chromosomes 1 and 2. Additionally, 16 SNPs associated with PCs and uncorrelated features related to red over color as well as green and yellow ground color were found on eight chromosomes. The identified associations can be used to advance marker-assisted selection in apple fruit breeding to systematically select for desired fruit appearance.
Why it matches plant phenotyping methodsFruitPhenoBoxを用いた画像ベースのデジタル表現型計測が研究の中心で、リンゴ果実の形状・サイズ・色の特徴抽出を大規模に実施しているため、表現型手法の実質的応用に該当する。
abstractWe used the fruit FruitPhenoBox to digitally phenotype 506 genotypes of the apple reference population (apple REFPOP)
Reproduction assets foundThe paper deposits its raw supplementary phenotypic data (sorting-machine and visually scored traits used in the analysis) publicly on Recherche Data Gouv. The FruitPhenoBox raw imaging data are marked 'TBA' (not yet available), and the SNP genotype deposits are genotypic rather than phenotyping assets. No author code,Dataset · public459 phenotypic data are available at https://doi.org/10.15454/VARJYJ.Open asset ↗pdf-page:13 lines:1-53Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
In recent years, the term 'Precision agriculture' has gained tremendous attention due to its ability to improve crop yield while reducing its cost. This can be achieved by applying modern Machine learning and Computer vision techniques. In this paper, we propose a comprehensive study of the following deep learning models VGG16 and InceptionV3. Our approach involves training these models individually on a data-set of apple-tree leaf images, which include healthy and diseased trees,and evaluating their performance on a validation set. We also conduct experiments to compare the performance of the two models and find a distinguishable difference between their Validation accuracy. Our results show that both VGG16 and InceptionV3 achieve high accuracy in detecting apple tree diseases, with InceptionV3 outperforming VGG16 slightly. We believe that our method can be applied in real-world scenarios to help farmers detect and manage diseases in their apple trees, leading to higher crop yield and reduced costs.
Why it matches plant phenotyping methodsリンゴ葉画像から健全・罹病状態を推定する深層学習手法が研究の中心であり、VGG16とInceptionV3の性能比較・検証も行っているため、植物病害フェノタイピング手法に該当する。
abstractOur approach involves training these models individually on a data-set of apple-tree leaf images, which include healthy and diseased trees,and evaluating their performance on a validation set.
Reproduction assets foundThe paper's phenotyping measurements (apple leaf disease classification with VGG16/InceptionV3) are based on the public New Plant Diseases Dataset from Kaggle, which the authors explicitly state is freely available with no access restrictions. No author code, models, or checkpoints are deposited.Dataset · publicThe data is available on ‘kaggle’ which is a popular site to get datasets. We have used only a portion of
the dataset(only data that is related to apple tree diseases) which makes this project stand out since
there is no paper that uses this portion of data to train test and validate this data with such accuracies
.There are no restrictions to access the data and is freely available. To access the data. :
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-datasetOpen asset ↗Kaggle · vipoooool/new-plant-diseases-datasetpdf-page:7 lines:1-44Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.
Why it matches plant phenotyping methodsリンゴ樹の開花強度という植物形質をUAV RGB画像から推定するための3年間の画像・地上真値データセットであり、再利用可能なフェノタイピング基盤として中心的です。
abstractThe present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020.
Reproduction assets foundThis Data in Brief article describes the authors' own public Zenodo deposit containing the paper-specific UAV RGB images, flower cluster/floridity ground truth, and GCP files for the apple orchard flowering monitoring study, with direct download URL provided.Dataset · publicRepository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.6802308
Direct URL to data: https://zenodo.org/record/6802308#.YvvMFuxBz0pOpen asset ↗Zenodo · 10.5281/zenodo.6802308lines:1-51Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.
Why it matches plant phenotyping methodsリンゴ果実を画像から検出・追跡して収量（果実数）を推定する手法を開発・評価しており、植物表現型取得が研究の中心です。
titleFruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods.
Reproduction assets foundThe paper's apple detection/counting dataset was assembled from publicly available sources, and the Data Availability Statement explicitly links the public tropical fruit dataset of Pawara et al. used as image input. No author analysis code, trained model checkpoints, or paper-specific supplements are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.ai.rug.nl/~p.pawara/ (accessed on 23 May 2023).Open asset ↗lines:234-247Code / dataset availability confirmedCrossref · checked 8 Sept 2026
AppleArabidopsisLaboratory / benchtopMultispectral / hyperspectralLeafTissueClassificationObject detectionWater status / transpiration
Abstract Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana , and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Why it matches plant phenotyping methods植物組織培養におけるハイパーヒドリシティという植物状態を、分光計測と機械学習で自動検出・分類する手法を開発し、別のハイパースペクトル画像取得で妥当性検証しているため。
abstractThe objective of this study was to characterize optical features to allow an automated detection of HH.
Reproduction assets foundThe paper's RGB image dataset of hyperhydric in vitro explants (used for CNN-based HH detection) is publicly available on Roboflow, explicitly stated in the Data availability section and cited as Bethge (2023). Spectral datasets and trained spectral classifier are only available on request.Dataset · publicRGB image dataset analysed during the current study available in the Bethge ( 2023 ) repository, [ https://universe.roboflow.com/hains/hh-detection-in-vitro/dataset/8 ].Open asset ↗Roboflow · hh-detection-in-vitrolines:203-234Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.
Why it matches plant phenotyping methods果実の遮蔽に頑健な画像ベースのアモーダル分割と、リンゴ果径という植物形質の推定手法を開発・検証しており、方法が研究の中心である。
abstractThe predicted amodal masks were used to estimate the fruit diameters in pixels.
Reproduction assets foundThe paper's apple amodal segmentation dataset (RGB-D images, modal/amodal masks, calliper-measured diameters) and the authors' analysis code are both explicitly stated to be publicly available at the authors' GitHub repository GRAP-UdL-AT/Amodal_Fruit_Sizing.Dataset · publictain data from both maturity stages, of different fruit size and with
different fruit visibilities. The dataset split was performed randomly,
obtaining in each partition a similar distribution of diameters (Fig. 4.b)
and apples visibilities (Fig. 4.d) than in the original dataset. The dataset
has been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.The data used for the case study was acquired in an Elstar apple
orchard located in Randwijk (the Netherlands). Five different trees were
imaged at four different dates (Table 1), obtaining data at different
growth stages: BBCH75, BBCH77, BBCH78 and BBCH85 (Fig. 2b). To
have a complete representation of trees, images Open asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:3 lines:1-74Code · publicft, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The code and the dataset used to evaluate the method have been
made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.Acknowledgements
This work was partly funded by the Departament de Recerca i Uni
versitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the
Spanish Ministry of Science, Innovation and Universities (grants
RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00
[PAgPROTECT project] and PID2020-117142GOpen asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:12 lines:1-75Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · checked 7 Sept 2026
Abstract Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and improved dataset balance in terms of shape for more precise outcomes.
Why it matches plant phenotyping methodsSNPからリンゴ画像を予測・再構成するオートエンコーダ手法自体が中心であり、果実形状などの植物表現型推定に直接関係する。
abstractThis study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array
Reproduction assets foundThe paper's Data availability section states that the code repository including notebooks and trained models is publicly available on GitHub at https://github.com/Fedjurrui/GenoDrawing, which is a paper-specific asset containing the authors' analysis code and trained phenotyping model weights. The image and SNP phenotvCode · publicThe code repository including notebooks, and models with their trained weights can be found in the
following github repository:
https://github.com/Fedjurrui/GenoDrawingOpen asset ↗Fedjurrui/GenoDrawingpdf-page:13 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variability.
Why it matches plant phenotyping methodsリンゴ器官の成長形質を階層的・反復的に収録した再利用可能なデータセットであり、成長率やアロメトリーの推定・モデル較正に用いるデータ資源として方法論的価値がある。
titleA hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources.
Reproduction assets foundThe paper is a Data in Brief article describing its own apple tree growth phenotype dataset (shoot, fruit, trunk measurements) deposited publicly on Mendeley Data with DOI 10.17632/852r5dnzd5.1 and a direct URL. This is a paper-specific, public, actionable phenotype dataset.Dataset · publiccommercial orchard
City: Caldaro, Bolzano/Bozen province, Trentino Alto Adige region
Country: Italy
Latitude and longitude collected samples/data: 46° 21’ N, 11° 16’ E, Altitude 240 m
Period: May-November 2014
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/852r5dnzd5.1
Direct URL to data: https://data.mendeley.com/datasets/852r5dnzd5/1
Related research article
F. Reyes, T. DeJong, P. Franceschi, M. Tagliavini, D. Gianelle, Maximum growth potential and periods of resource limitation in apple tree, Frontiers in Plant Science 7 (2016). doi: 10.3389/fpls.2016.00233
Value of the Data
•
The dataset allows analysis of the impact of fruit load, on vegetative aOpen asset ↗Mendeley Data · 10.17632/852r5dnzd5.1lines:1-61Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO. Methods In the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. The aim is to reduce the number of parameters and floating point of operations (FLOPs) during feature extraction and feature fusion, thus increasing the operational efficiency of the network with less impact on detection performance. In addition, the coordinate attention module is embedded into the critical locations of the network to select the critical spot information and suppress useless information, which is to enhance the detection accuracy of diseases with various sizes from different scenes. Furthermore, the SIoU loss replaces CIoU loss as the bounding box regression loss function to improve the accuracy of prediction box localization. Results The experimental results indicate that the proposed method can achieve the detection performance of 95.5% on the mean average precision and a speed of 625 frames per second (FPS) on the apple leaf disease dataset (ALDD). Compared to the latest research method on the ALDD, the detection accuracy and speed of the proposed method were improved by 12.3% and 596 FPS, respectively. In addition, the parameter quantity and FLOPs of the proposed method were much less than other relevant popular algorithms. Discussion In summary, the proposed method not only has a satisfactory detection effect, but also has fewer parameters and high calculation efficiency compared with the existing approaches. Therefore, the proposed method provides a high-performance solution for the early diagnosis of apple leaf disease and can be applied in agricultural robots. The code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出するYOLOベース手法の開発・性能評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格。
abstractTherefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.
Reproduction assets foundThe paper open-sources its EADD-YOLO detection code on GitHub and provides sample annotation files for the ALDD images in the Frontiers supplementary material; both are paper-specific, public, and actionable.Code · publicThe code repository is open-sourced at https://github.com/AWANWY/EADD-YOLO .Open asset ↗AWANWY/EADD-YOLOlines:224-321Dataset · publicThe location of the annotation boxes for several representative images is presented in Figure 1 , and the corresponding annotation files for these images are provided in the supplementary material.Open asset ↗lines:328-370Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.
Why it matches plant phenotyping methods病斑分布という植物の病害表現型を対象に、教師あり・弱教師ありセマンティックセグメンテーション手法を開発・評価し、データセットも提供しているため、方法が中心的である。
abstractIn this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning.
Reproduction assets foundThe authors contributed a diseased-leaf dataset with pixel-level annotations (used directly for this paper's segmentation experiments) and deposited it publicly on Mendeley Data, with a Baidu Pan mirror. Source datasets (Plant Village, diseased apple leaves) are cited prior public datasets, not paper-specific assets,;Dataset · publicWe uploaded the images and the corresponding annotation to the Mendeley Data repository ( https://data.mendeley.com/datasets/tsfxgsp3z6 ).Open asset ↗Mendeley Data · tsfxgsp3z6lines:27-64Dataset · publicThe dataset is also available at https://pan.baidu.com/s/1y7K2dVpfkQ3HVOU1qEeChQ (password: ecff).Open asset ↗lines:27-64Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Spectroscopy data are useful for modelling biological systems such as predicting quality parameters of horticultural products. However, using the wide spectrum of wavelengths is not practical in a production setting. Such data are of high dimensional nature and they tend to result in complex models that are not easily understood. Furthermore, collinearity between different wavelengths dictates that some of the data variables are redundant and may even contribute noise. The use of variable selection methods is one efficient way to obtain an optimal model, andthis was the aim of this work. Taking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800-2500 nm were used to classify bruise damage in three apple cultivars, namely 'Golden Delicious', 'Granny Smith' and 'Royal Gala'. Six prominent machine learning classification algorithms were employed, and two variable selection methods were used to determine the most relevant wavelengths for the problem of distinguishing between bruised and non-bruised fruit. The selected wavelengths clustered around 900 nm, 1300 nm, 1500 nm and 1900 nm. The best results were achieved using linear regression and support vector machine based on up to 40 wavelengths: these methods reached precision values in the range of 0.79-0.86, which were all comparable (within error bars) to a classifier based on the entire range of frequencies. The results also provided an open-source based framework that is useful towards the development of multi-spectral applications such as rapid grading of apples based on mechanical damage, and it can also be emulated and applied for other types of defects on fresh produce.
Why it matches plant phenotyping methodsリンゴ果実の打撲損傷という植物器官の状態を、非接触FT-NIR分光と機械学習で分類し、波長選択とモデル性能を評価しているため、植物フェノタイピング手法が中心です。
abstractTaking advantage of a non-contact spectrometer, near infrared spectral data in the range of 800-2500 nm were used to classify bruise damage in three apple cultivars
Reproduction assets foundThe paper explicitly states that the analysis code, results, and data for the FT-NIR apple bruise classification are publicly available on Zenodo, along with a walk-through tutorial. These are paper-specific, public, and actionable assets.Code · publicThe code, together with the results, is available on Zenodo at https://zenodo.org/badge/latestdoi/478611734 ).Open asset ↗Zenodo · 478611734lines:235-328Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から検出するYOLOv5改良モデルを開発し、複数モデルとの性能比較で検証しているため、植物病害フェノタイピング手法が中心です。
abstractwe explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods.
Reproduction assets foundThe paper's apple leaf disease detection dataset (12,500 labeled images of five apple leaf diseases) is explicitly stated to be publicly available online at a Baidu AI Studio dataset URL in the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe apple leaf dataset is publicly available online at https://aistudio.baidu.com/aistudio/datasetdetail/11591 (accessed on 26 December 2022).Open asset ↗datasetdetail/11591lines:86-193Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that are representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses these novel data alongside the original high-fidelity data to produce low-fidelity images. A machine learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis.
Why it matches plant phenotyping methodsUAV画像と機械学習、GANを組み合わせ、植物画像から病害状態を推定する二段階手法が研究の中心であり、精度評価も実施しているため。
abstractThis paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy.
Reproduction assets foundThe paper's experiments were conducted on the Plant Pathology 2020 dataset, and the authors explicitly point to its public Kaggle URL in the Data Availability Statement. No author analysis code, models, or generated synthetic data are stated to be publicly available.Dataset · public.D.B.;
validation, Mathew Horak and W.D.B.; visualization, A.P. and N.M.; writing—original draft, A.P.,
N.M., M.H. and W.D.B.; writing—review and editing, M.H. and W.D.B. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: https://www.kaggle.com/c/plant-pathology-2020-fgvc7 (accessed on
8 August 2022).
Conflicts of Interest: The authors declare no conflict of interest.
References
1. FAO. Food and Agriculture Organization of the United Nations: International Plant Protection Convention. Available online:
https://www.fao.org/plant-health-2020/about/en (accessed on 17 September 2022).
2. BOpen asset ↗Kaggle · plant-pathology-2020-fgvc7pdf-raw-page:12 lines:1-52Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Tree architecture shows large genotypic variability, but how this affects water-deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high-throughput field phenotyping and genome-wide association studies (GWAS) on an apple tree (Malus domestica) core-collection. We used terrestrial light detection and ranging (T-LiDAR) scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)-by-SNP and multi-SNP methods. Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well-watered and water deficit conditions, suggesting control of water loss was largely genotype-dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait. We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted.
Why it matches plant phenotyping methods高スループット圃場フェノタイピングを中核として、T-LiDAR、マルチスペクトル・熱画像から樹体構造、光 interception、蒸散などの植物形質を測定しているため。
abstractwe combined high-throughput field phenotyping and genome-wide association studies (GWAS) on an apple tree (Malus domestica) core-collection.
Reproduction assets foundThe paper's raw phenotypes and BLUPs (T-LiDAR architectural traits, thermal/multispectral indices, water potentials) are publicly deposited on Portail Data INRAE at https://doi.org/10.15454/C8IPII, explicitly stated in the Data availability section. The SNP genotyping deposit (10.15454/F5XIVJ) is a molecular omics-typeDataset · publicRaw data and BLUPs of phenotypes together with the list of the 241 cultivars with the recently attributed MUNQ codes (for Malus UNiQue genotype code, Denancé et al ., 2020 ) are publicly available in Coupel‐Ledru et al . ( 2022 ) at this site: https://doi.org/10.15454/C8IPIIOpen asset ↗10.15454/C8IPIIlines:663-812Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
Why it matches plant phenotyping methods植物表現型データのデジタル記録・取得を目的とするオープンソースアプリの開発であり、表現型測定ワークフローが中心的です。
abstractTherefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses.
Reproduction assets foundThe paper describes PhenoApp, an open-source Android phenotyping app. Authors provide the app's source code (Gitea, archived on Zenodo) and underlying example input/output phenotype data files on Zenodo under CC0. The SHAPE II project website is a project page, not a paper-specific data deposit, and is excluded.Code · publice ‘in’ folder of the app main directory and no additional source data is required).
-
Output_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants fOpen asset ↗gitea.julius-kuehn.de · JKI/pheno-applines:333-433Code · publicput_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants from the German Federal Ministry of Education and Research to FS (SelWineQ, FKZ 031B0889Open asset ↗Zenodo · 10.5281/zenodo.5525779lines:333-433Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees ( Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees containing a total of 615 apples. The resulting point clouds were 3D segmented by identifying the 3D points corresponding to each apple (3D instance segmentation), obtaining a single point cloud for each apple. All segmented apples were labelled with ground truth diameter annotations. Since the data was acquired in field conditions and at different maturity stages, the set includes different fruit diameters -from 26.9 mm to 94.8 mm- and different fruit occlusion percentages due to foliage. In addition, 25 apples were photographed 360° in laboratory conditions, obtaining high resolution 3D point clouds of this sub-set. To the best of the authors' knowledge, this is the first publicly available dataset for apple size estimation in field conditions. This dataset was used to evaluate different fruit size estimation methods in the research article titled "In-field apple size estimation using photogrammetry-derived 3D point clouds: comparison of 4 different methods considering fruit occlusion" (Gené-Mola et al., 2021).
Why it matches plant phenotyping methodsリンゴ果実の3D画像・点群から果径を推定するための公開データセットであり、アノテーション、3Dセグメンテーション、サイズ推定評価が中心的な方法論的貢献である。
abstractThe PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees ( Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation.
Reproduction assets foundThe paper is a Data in Brief article describing the PFuji-Size dataset (raw images, 3D tree point clouds, apple segmentation masks, diameter/centre annotations), publicly deposited in Dataverse (CSUC) with DOI 10.34810/data141 and a direct URL. This is a paper-specific, public, actionable phenotyping dataset directly.Dataset · publicData accessibility
Repository name: Dataverse
Data identification number: https://doi.org/10.34810/data141
DOI: https://doi.org/10.34810/data141
Direct URL to data: https://dataverse.csuc.cat/dataset.xhtml?persistentId=doi:10.34810/data141Open asset ↗Dataverse · doi:10.34810/data141lines:54-87Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Diseases and pests have a profound effect on a yearly harvest and productivity in agriculture. A precise and accurate detection of the diseases and pests could facilitate timely treatment and management of the diseases and pests and lessen the resultant loss in economy and health. Herein, we propose an improved design of the disease detection system for plant images. Methods Built upon the two-stage framework of object detection neural networks such as Mask R-CNN, the proposed network involves three types of extensions, including the addition of additional level of feature pyramids to improve the exploration and proposal of candidate regions, the aggregation of feature maps from all levels of feature pyramids per candidate region to fully exploit the information from feature pyramids, and the introduction of a squeeze-and-excitation block to the construction of feature pyramids and the aggregated feature maps to improve the representation of feature maps. Results The proposed network was evaluated using 74 images of infected apple fruits. In 3-fold cross-validation, the proposed network achieved averaged precision (AP) of 72.26, AP at 0.5 threshold of 88.51 and AP at 0.75 threshold of 82.30. In the comparative experiments, the proposed network outperformed the other competing networks. The utility of the three extensions was also demonstrated in comparison to Mask R-CNN. Conclusions The experimental results suggest that the proposed network could identify and localize the symptom of the disease with high accuracy, leading to an early diagnosis and treatment of the disease, and thus holding the potential for improving crop yield and quality.
Why it matches plant phenotyping methods植物画像から病徴を識別・局在化するCNN手法を開発し、Mask R-CNNとの比較検証も行っており、植物病害状態の表現型抽出が中心である。
abstractwe propose an improved design of the disease detection system for plant images.
Reproduction assets foundThe paper's Data Availability statement explicitly makes the study's data (74 annotated apple fruit disease images used for phenotyping/disease detection) publicly available on the authors' GitHub repository, matching an allowed URL.Dataset · publicData Availability: We have made the data from our study publicly available ( https://github.com/QuIIL/Dataset-Region-Aggregated-Attention-CNN-for-Disease-Detection-in-Fruit-Images ).Open asset ↗QuIIL/Dataset-Region-Aggregated-Attention-CNN-for-Disease-Detection-in-Fruit-Imageslines:158-174Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.
Why it matches plant phenotyping methodsリンゴ果実の数とサイズを動画から自動抽出するフォトグラメトリ手法を開発し、実験で精度を検証しており、植物フェノタイピング手法が中心である。
abstractThis work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number.
Reproduction assets foundThe authors explicitly state that acquired data, labelled images, code, and network weights for the apple phenotyping pipeline are publicly available on the 3DOM-FBK GitHub account, with a concrete URL given in reference [56]. This is a paper-specific, public, actionable asset covering the Mask R-CNN retraining code/权重Code · publicData Availability Statement: Data acquired and used in the presented experiments, labelled im-
ages, code, and network weights, are available to the scientific community at 3DOM-FBK-GitHub
[56].Open asset ↗pdf-page:16 lines:1-58Dataset · publicAcquired
data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.Open asset ↗pdf-page:1 lines:1-67Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. In this paper, a novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves. The diagnosis of different kinds of diseases and the same disease with different grades was accomplished. First of all, to solve the problem of insufficient images of anthracnose and ring rot, Cycle-GAN algorithm was applied to achieve dataset expansion on the basis of traditional image augmentation methods. Cycle-GAN learned the image characteristics of healthy apples and diseased apples to generate anthracnose and ring rot lesions on the surface of healthy apple fruits. The diseased apple images generated by Cycle-GAN were added to the training set, which improved the diagnosis performance compared with other traditional image augmentation methods. Subsequently, DenseNet and Multi-scale connection were adopted to establish two kinds of models, Multi-scale Dense Inception-V4 and Multi-scale Dense Inception-Resnet-V2, which facilitated the reuse of image features of the bottom layers in the classification neural networks. Both models accomplished the diagnosis of 11 different types of images. The classification accuracy was 94.31 and 94.74%, respectively, which exceeded DenseNet-121 network and reached the state-of-the-art level.
Why it matches plant phenotyping methodsリンゴ葉・果実の病害画像と病害グレードを深層学習で診断する画像ベースの植物状態推定法が研究の中心であり、データ拡張、モデル構築、精度比較まで実施している。
abstracta novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves.
Reproduction assets foundThe paper's apple leaf disease images come from a public dataset (AI-Challenger Plant Disease Recognition on Gitee), explicitly cited with URL. The fruit images, Cycle-GAN generated images, and model code are not publicly deposited (data availability directs inquiries to authors), so only the public leaf image dataset,Dataset · publicented.
2. Materials and Methods
2.1. Dataset Preparation
2.1.1. Composition of Image Dataset
The dataset employed in our research includes diseased leaf images, diseased fruit images, healthy leaf images, and healthy fruit images, as shown in Figure 1 . The images of apple leaves come from Challenger-Plant-Disease-Recognition ( https://gitee.com/cheng_xiao_yuan/AI-Challenger-Plant-Disease-Recognition ). The leaf images are divided into six categories, including healthy apple leaf, general apple scab, serious apple scab, apple gray spot, general cedar apple rust, and serious cedar apple rust. The fruit images were collected in the field. These images include five categories, including healthyOpen asset ↗AI-Challenger-Plant-Disease-Recognition · https://gitee.com/cheng_xiao_yuan/AI-Challenger-Plant-Disease-Recognitionlines:32-94Code / dataset availability confirmedCrossref · checked 9 Sept 2026
In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2% and 95.9%, respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05% increase in precision and 7.6% increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.
Why it matches plant phenotyping methods画像ベースでリンゴ植物の病害状態を検出する深層学習手法を開発・評価しており、植物表現型（病害状態）の取得が中心的な研究目的である。
abstracta deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm
Reproduction assets foundThe paper's custom annotated dataset is only available upon request, but the underlying apple disease images come from the public Kaggle PlantPathology Apple Dataset, which the authors explicitly used to construct their dataset. LabelImg is a generic annotation tool, not a paper-specific asset, and no author analysis代码Dataset · publica total of 600 original images consisting of 200 images from each of the two apple diseases
(i.e., scab and rust) and 200 images containing both scab and rust have been collected from
the publicly available Kaggle PlantPathology Apple Dataset [58] to construct the single
datasetOpen asset ↗Kaggle PlantPathology Apple Datasetpdf-page:7 lines:1-65Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional "point-type" reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for "point-type" apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = ( R 640 -1 + R 800 -1 ) - R 678 -1 . Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.
Why it matches plant phenotyping methodsリンゴ果実のハイパースペクトル画像から組織損傷を検出する指標を開発・検証しており、植物状態の取得手法が研究の中心である。
abstractThe validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe sampling of the spectral data and rendering of the index images have been carried out using Gelion, the original software for processing hyperspectral images ( https://github.com/AlexanderMipt/Gelion ).Open asset ↗github.com/AlexanderMipt/Gelionlines:105-152Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
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:123-130Code / dataset availability confirmedCrossref · OpenAlex · checked 9 Sept 2026
The use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivity of these sensors to highly illuminated environments, it is necessary to know under which conditions RGB-D sensors are capable of operating. This work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features. The methodology was applied to evaluate the performance of the Microsoft Kinect v2 sensor in an apple orchard. The results show that sensor resolution and precision decreased significantly under middle to high ambient illuminance (>2000 lx). However, this effect was minimised when measurements were conducted closer to the target. In contrast, illuminance levels below 50 lx affected the quality of colour data and may require the use of artificial lighting. The methodology was useful for characterizing sensor performance throughout the full range of ambient conditions in commercial orchards. Although Kinect v2 was originally developed for indoor conditions, it performed well under a range of outdoor conditions.
Why it matches plant phenotyping methodsRGB-Dセンサーの性能を、果樹キャノピーの3D形状・色・NIR特徴の取得という植物フェノタイピング用途で、照明・距離条件下で評価する方法論が中心である。
abstractThis work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features.
Reproduction assets foundThe paper's Kinect Evaluation in Orchard conditions (KEvOr) dataset of RGB/NIR/point-cloud captures from an apple orchard is publicly deposited on Zenodo, and the authors' MATLAB analysis code for the sensor evaluation is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicA MATLAB® (R2020a, Math Works Inc., Natick, MA, USA) code was developed to analyse all
the data and provide the sensor evaluation results. This code has been made publicly available at
https://github.com/GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchards [35].Open asset ↗GitHub · GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchardspdf-page:6 lines:1-60Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Premise Apple orchards in the United States are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs and increased environmental and health impacts. Methods and results We have manually captured 3651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset of images, expert-annotated to create a pilot data set for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for the Plant Pathology Challenge as part of the Fine-Grained Visual Categorization (FGVC) workshop at the 2020 Computer Vision and Pattern Recognition conference (CVPR 2020). Participants were asked to use the image data set to train a machine learning model to classify disease categories and develop an algorithm for disease severity quantification. The top three area under the ROC curve (AUC) values submitted to the private leaderboard were 0.98445, 0.98182, and 0.98089. We also trained an off-the-shelf convolutional neural network on this data for disease classification and achieved 97% accuracy on a held-out test set. Discussion This data set will contribute toward development and deployment of machine learning-based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot data set for a larger, more comprehensive expert-annotated data set for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.
Why it matches plant phenotyping methodsリンゴ葉の病害症状画像データセットを構築し、病害分類と重症度定量化アルゴリズムを評価する研究であり、植物病害表現型の取得・推定が中心です。
abstractWe have manually captured 3651 high-quality, real-life symptom images of multiple apple foliar diseases
Reproduction assets foundThe paper's expert-annotated apple foliar disease image dataset (3651 images) is publicly available on Kaggle, and the authors' disease classification algorithm (ResNet50-based CNN) is available on GitHub, both explicitly stated in the Data Availability section.Dataset · publicThe image dataset is freely available to download on Kaggle ( https://www.kaggle.com/c/plant‐pathology‐2020‐fgvc7/data ).Open asset ↗Kaggle · plant‐pathology‐2020‐fgvc7lines:109-130Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefulness of the multi-view approach and the application of forced air flow to reduce the number of fruit occlusions. The data provided in this article is related to the research article entitled "Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow" [1].
Why it matches plant phenotyping methodsリンゴ果実の位置を含む3D LiDARデータセットを構築し、果実検出、マルチビュー、遮蔽低減の評価に利用できる再利用可能なフェノタイピング基盤であるため。
abstractThis article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth.
Reproduction assets foundThe paper's own LFuji-air dataset (annotated 3D LiDAR point clouds of Fuji apple trees with apple location ground truth) is publicly available at the authors' GRAP-UdL dataset pages, and the authors' point cloud generation and fruit detection code is publicly available on GitHub.Dataset · publicThe repository Lfuji-air dataset ( http://www.grap.udl.cat/en/publications/LFuji_air_dataset.html ) includes 3D LiDAR point clouds of 11 Fuji apple trees ( Malus domestica Borkh. Cv. Fuji) containing 1444 apples ( Fig. 1 ).Open asset ↗Lfuji-air dataset · Lfuji-air datasetlines:61-98Code · publicThe code used to process the row data and generate the georeferenced point clouds has been made publicly available at https://github.com/GRAP-UdL-AT/MTLS_point_cloud_generation .Open asset ↗GRAP-UdL-AT/MTLS_point_cloud_generation · GRAP-UdL-AT/MTLS_point_cloud_generationlines:61-98Code / 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:62-69