Abstract Tomato leaf diseases (TLDs) such as Early Blight (EB), Late Blight (LB), and Leaf Mold (LM) have a negative impact on crop yield and quality, result-ing in economic losses in agriculture. Traditional diagnosis methods depend on the visual recognition of a disease, which are time-consuming, subjective and may be difficult to reach in rural settings. Keeping these drawbacks in mind, the present work introduces a Transfer Learning model for tomato leaf disease classification based on EfficientNetB3 network. A pretrained Ef-ficientNetB3 network, trained on ImageNet, is used to obtain discriminative features like lesion boundaries, discoloration, fungal textures, and infection spots. Images are cropped to 224 × 224 pixels and split into training, validation and test set. The proposed architecture employs Global Average Pooling, a dense layer with ReLU activation, drop out regularization and Softmax classifier for classification of tomato leaf images into four classes: Early Blight, Late Blight, Leaf Mold and Healthy. Experimental results show the high classification accuracy and low training, validation and test-ing losses. Inter-class misclassifications of the confusion matrix show very few, which supports good generalization. Moreover, Grad-CAM visualiza-tions can generate interpretable heat maps that point to the regions of the image affected by the disease, and multi-class ROC analysis gives high AUC values, which means that it has excellent class separability. The proposed framework provides a precise, reliable, and interpretable approach for auto-mated tomato leaf disease diagnosis, contributing to precision agriculture by assisting in early detection and prompt management of tomato diseases.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する深層学習手法が研究の中心であり、Grad-CAMによる病変領域の解釈と性能評価も行っているため、植物表現型計測手法として採択。
abstractthe present work introduces a Transfer Learning model for tomato leaf disease classification based on EfficientNetB3 network
Reproduction assets foundThe paper's tomato leaf disease classification uses a publicly available Kaggle dataset (PlantVillage-derived tomato leaf images, 4000 images across 4 classes), explicitly declared in the Data Availability statement with a public URL. No author code, trained models, or other paper-specific assets are disclosed.Dataset · publicitted in accordance with the Journal policies.
Permission to use third-party material
Images or figures are never published previously and did not take from any
internet resources.
Data Availability
The dataset used in this study is publicly available from the PlantVillage
tomato leaf disease dataset on Kaggle’s following link:
https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf
Acknowledgements
The authors would like to thank SR University, Warangal, Telangana,
INDIA and Sharda University, Greater Noida, Uttar Pradesh, INDIA for
providing research facilities and computational resources to carry out this
work.Open asset ↗Kaggle · kaustubhb999/tomatoleafpdf-raw-page:20 lines:1-18Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published17 Aug 2026International Journal of Drug Delivery TechnologyCited by 0 · OpenAlex ↗
Why it matches plant phenotyping methodsトマト葉の病害を画像と深層学習で検出する手法が題名上の中心であり、植物の病害状態を観察的に推定するフェノタイピング研究に該当する。
titleDetection of Tomato Leaf Disease in Leaves with Deep Learning MobileNetV2 with Gaussian and Gabor Preprocessing
Reproduction assets foundThe paper's phenotyping analysis is based on the publicly available PlantVillage plant leaf disease image dataset hosted on Kaggle (54,303 labeled leaf images across 38 classes), which the authors explicitly state was sourced from a publicly available Kaggle dataset. No author-specific code, models, or derived datasetsDataset · publicThe research incorporated PlantVillage dataset(24)
accessible on Kaggle that contains 54,303 plant leaf
images showing both healthy and diseased
conditions spanning across 38 specific categories.Open asset ↗Kagglepdf-raw-page:2 lines:1-105Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The tomato plant is considered one of the most important crops in the world, yet it is vulnerable to various diseases that affect crop quality and agricultural productivity. These challenges have driven the need for an efficient and intelligent plant disease detection system. With the development of computer vision and artificial intelligence, this proposed methodology based on deep learning for tomato leaf diseases has been presented. Two public datasets: Taiwan DS with nine classes and Tomato Leaf Diseases Detection Computer Vision Dataset (TLDDCV DS) with seven classes have been used to test this system. This system begins with plant image processing, which includes gamma correction and bilateral filtering, to enhance image quality and clarity while preserving key disease features. Then, a genetic metaheuristic algorithm was used to automatically select the most significant hyperparameters, further optimizing both processing time and accuracy. After that, the tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model. The YOLOv11n backbone is edited through a Data-efficient Image Transformer (DeiT) to improve the system's capacity for learning global contextual information and long-range dependencies. Experimental results demonstrate that the proposed system outperforms existing methods. It achieved an average mAP@50 of 97.8%, mAP@50-95 of 93.4%, precision of 97.3%, recall of 93.8%, and F1-score of 95.5% on the Taiwan dataset. Additionally, it achieved an average mAP@50 of 87%, mAP@50-95 of 48%, precision of 83.9%, recall of 70.3%, and F1-score of 76.4% on the TLDDCV dataset. These results demonstrate the generalizability and effectiveness of the proposed system in real-world agricultural situations.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・分類する深層学習手法の開発と2データセットでの性能評価が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractthe tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model.
Reproduction assets foundThe paper uses two public Roboflow tomato leaf disease image datasets and states its source code is publicly available on Zenodo, all with explicit availability statements and URLs.Dataset · publicThe first dataset is the Taiwan dataset, which can be found at the following link: (https://universe.roboflow.com/bryan-b56jm/tomato-leaf-disease-ssoha).Open asset ↗tomato-leaf-disease-ssohalines:317-328Dataset · publicThe second dataset is the TLDDCV dataset, which can be found at the following link: (https://universe.roboflow.com/sylhet-agricultural-university/tomato-leaf-diseases-detect)Open asset ↗tomato-leaf-diseases-detectlines:317-328Code · publicThe source code of the proposed framework, including the implementation of the proposed methodology and experimental setup, is publicly available in the Zenodo repository: https://doi.org/10.5281/zenodo.20777853 .Open asset ↗Zenodo · 10.5281/zenodo.20777853lines:317-328Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Precise and reliable diagnosis of leaf diseases in tomato is essential for enhancing crop cultivation and minimizing agricultural losses. While deep learning models have performed well on benchmark datasets, the majority of present techniques rely on flat multi-class classification, which predicts all disease categories simultaneously. Such formulations promotes inter-class confusion, particularly when biologically different diseases with similar visual symptoms are learned within a same model. To overcome this constraint, we propose a biologically structured hierarchical deep learning framework in this study. Instead of directly classifying 10 disease classes, the proposed method first classifies leaf images into meaningful biological groups such as bacterial, fungal, pest-associated and healthy using a vision transformer (ViT) model. Then, specialized convolutional neural network (CNN) experts perform fine-grained classification within each category. The proposed hierarchical model shows an overall accuracy of 97.8%, when validated on PlantVillage tomato dataset. A flat ViT model trained with class-weighted loss obtained 96.3% accuracy, whereas a flat CNN model reached 99.3% under clean conditions but decreased sharply to 41% under Gaussian perturbation ( σ = 0.05). On the other hand, the hierarchical model performed steadily under noise with 97.4% accuracy at the same perturbation level. These results indicate that adding biological structure to model design reduces confusion, helps prevent imbalance effects and increases robustness, providing a more trustworthy and interpretable solution for real-world agricultural disease diagnosis.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する深層学習手法を開発し、既存モデルとの比較およびノイズ下での技術検証を行っており、植物表現型（病害状態）の取得・推定が中心である。
abstractwe propose a biologically structured hierarchical deep learning framework in this study.
Reproduction assets foundThe paper's phenotyping/classification measurements are based on the public PlantVillage tomato leaf image dataset, which the authors explicitly state was analyzed and provide a public Kaggle URL. No author analysis code, trained models, or paper-specific supplementary assets are described with availability language.Dataset · publicIsabel Luna-Maldonado , Autonomous University of Nuevo León, Mexico
Reviewed by: Noredine Hajraoui , Moulay Ismail University, Morocco
Tri Handhika , Universitas Gunadarma Pusat Studi Komputasi Matematika, Indonesia
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/charuchaudhry/plantvillage-tomato-leaf-dataset .
Author contributions
HG: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. SR: Supervision, Validation, Writing – review & editing. BL: Supervision, Validation, Writing – review & editing.
Conflict of interest
The author(s) declared thaOpen asset ↗Kaggle · charuchaudhry/plantvillage-tomato-leaf-datasetlines:588-616Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoLiDAR / point cloudLeafMorphology / geometry measurementSegmentationLeaf traits
Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best F 1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09 cm, 1.78 cm and 8.98 cm 2 respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.
Why it matches plant phenotyping methodsトマト葉の3D画像取得、器官セグメンテーション、葉インスタンス識別、形態形質推定を統合した自動フェノタイピング手法を開発・評価しており、方法が研究の中心です。
abstractproposed a fully automatic pipeline for leaf phenotyping
Reproduction assets foundThe paper's tomato point cloud dataset (with semantic and leaf instance annotations used for the phenotyping pipeline) is publicly available on Kaggle via a footnote. NPEC website is a facility page, and Open3D is a generic library, so neither qualifies.Dataset · public2. ^ The dataset used in this study is available at: https://www.kaggle.com/datasets/xinbolai/vtc-tomatoOpen asset ↗Kaggle · xinbolai/vtc-tomatolines:545-624Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published23 Jul 2026Journal of Intelligent Decision Making and Information ScienceCited by 0 · OpenAlex ↗
The early and effective diagnosis of tomato leaf diseases is very important to enhance crop yield and reduce economic loss in precision agriculture. The conventional image-based methods are typically based on single architecture model, which cannot capture fine-grained lesion details and global contextual patterns simultaneously in the real-field. To this end, we introduce a deep hybrid Convolutional Neural Network (CNN) –Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) (as local feature extractor) and Swin Transformer (as global context encoder). The concatenated features vector is then fed to a shallow classifier to predict the disease. The model was tested on two datasets, namely a field dataset in agriculture areas from Madhya Pradesh (India) and a benchmark tomato leaf dataset. Experimental results revealed that the proposed scheme achieved accuracy of 92.83% on a primary dataset, and performance was significantly high with an accuracy of up to 95.65% in terms of generalization rate for computing technique models from various environmental conditions.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定するCNN–Transformer手法を提案し、複数データセットで性能検証しており、植物フェノタイピング手法が中心である。
abstractwe introduce a deep hybrid Convolutional Neural Network (CNN) –Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) (as local feature extractor) and Swin Transformer (as global context encoder).
Reproduction assets foundThe paper uses a public Tomato Leaves Dataset from GTS AI as its secondary/external validation dataset for tomato leaf disease classification. The primary field dataset from Madhya Pradesh is not stated as publicly available, and no author analysis code or trained model is reported as deposited.Dataset · publicSecondary Dataset: The Secondary dataset was extracted from the public Tomato Leaves Dataset
available at GTS AI platform. It involves various disease classes, such as bacterial spot, early blight,
late blight, leaf mold, powdery mildew, septoria leaf spot and spider mites (Figure 1) target spots are
present in tomato mosaic virus leaves yellow curl virus of tomato. This data set was employed as an
external validation to evaluate the generalization of proposed model in different conditions and
diseases types.
Source : https://gts.ai/dataset-download/tomato-leaves-dataset/Open asset ↗GTS AIpdf-page:20 lines:1-23Code / 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
Plant diseases significantly affect agricultural productivity and global food security, while accurate disease identification remains challenging because of uncertain and overlapping visual symptoms in leaf images. Existing deep learning approaches often require large annotated datasets and suffer from limited interpretability in practical agricultural environments. This study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset. The objective is to develop an interpretable and reliable classification model capable of handling uncertainty in plant disease patterns through feature-driven fuzzy similarity analysis. The methodology integrates image preprocessing, color and texture feature extraction, variance-based feature weighting, prototype generation using K-means clustering, and fuzzy similarity computation using Mahalanobis distance and Gaussian membership functions. RGB, HSV, and Gray-Level Co-occurrence Matrix (GLCM) features are extracted from standardized leaf images and evaluated within an improved fuzzy soft classification framework. Performance comparison is carried out using machine learning models including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) implemented in Python using Scikit-learn libraries. Experimental simulation results demonstrate that the proposed framework achieves competitive classification performance while preserving interpretability and robustness under uncertain feature distributions. Performance evaluation is conducted through accuracy analysis, ROC-AUC curves, confusion matrices, ablation studies, and Wilcoxon Signed-Rank statistical testing. The proposed Improved Fuzzy Soft model achieved an accuracy of 88.57% which is less than LDA (94.92%), Random Forest (97.78%) and SVM (97.94%) classifiers. However, in the cross data set validation, the proposed Improved Fuzzy Soft model achieved an accuracy of 67.35% which is greater than LDA (51.02%), Random Forest (51.02%) and SVM (55.10%) classifiers. Statistical validation using the Wilcoxon Signed-Rank Test produced a p-value of [Formula: see text], confirming that the performance difference between the Improved Fuzzy Soft framework and the Random Forest classifier is statistically significant under the current experimental setting.
Why it matches plant phenotyping methodsトマト葉画像から植物病害状態を推定する解釈可能な画像解析・分類フレームワークを開発し、複数モデル、交差データセット検証、アブレーション、統計検定で評価しており、病害表現型の取得・抽出手法が中心である。
abstractThis study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset.
Reproduction assets foundThe paper uses public tomato leaf image datasets (PlantVillage and PlantDoc from Kaggle) as phenotyping inputs and states the authors' Improved Fuzzy Soft Framework implementation is publicly available on Zenodo with source code and reproduction instructions.Dataset · publicThe dataset analyzed during the current study are available in the repository:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗kaggle.com/datasets/abdallahalidev/plantvillage-datasetpdf-page:24 lines:1-75Code · publicThe implementation of the proposed Improved Fuzzy Soft Framework is publicly available through the Zenodo repository:
https://doi.org/10.5281/zenodo.20570546
The repository contains the source code, documentation, and instructions required to reproduce the experiments reported in
this study.Open asset ↗zenodo · 10.5281/zenodo.20570546pdf-page:25 lines:1-74Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Plant disease detection under complex climatic conditions and cross-crop scenarios remains a critical challenge. To address this, we propose a novel SimAM-Inception-StyleRandomization (SIS)-YOLOv11 algorithm based on YOLOv11n for early/late blight detection on potato and tomato leaves. Our core innovations are: 1) A C3k2-SSI module integrating Style Randomization, Inception architecture, and SimAM attention to enhance cross-crop generalization; 2) A Fusion-InceptionConv module for fine-grained feature extraction under rainfall/haze noise; 3) SPPF-Inception and C2PSA-IS modules to optimize multi-scale feature fusion; 4) DepGraph pruning to reduce 47.82% parameters while improving performance. Experiments show that the pruned SIS-YOLOv11 outperforms YOLOv11n by 3.7% in precision, 6.6% in recall, 5.4% in mAP50, and 7.9% in mAP50-95, and surpasses mainstream models (Faster R-CNN, SSD, etc.). This study provides a robust, lightweight solution for automated cross-crop disease detection in complex agricultural environments.
Why it matches plant phenotyping methodsジャガイモとトマト葉の病害状態を画像から検出する新規アルゴリズムを開発し、性能比較・軽量化まで行っており、植物フェノタイピング手法が中心である。
abstractwe propose a novel SimAM-Inception-StyleRandomization (SIS)-YOLOv11 algorithm based on YOLOv11n for early/late blight detection on potato and tomato leaves.
Reproduction assets foundThe paper's image dataset (potato/tomato leaf disease images with annotations and climate-noise augmentation) is explicitly declared publicly available on Baidu AI Studio. No author code or trained model deposit is stated.Dataset · publicData Availability: All image datasets used and analyzed in this study are publicly available from the Baidu AI Studio dataset repository at the URL: https://aistudio.baidu.com/datasetdetail/245434 .Open asset ↗Baidu AI Studio · 245434lines:1-133Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Plant diseases affecting leaves and fruits cause substantial yield and economic losses worldwide, particularly in horticultural crops cultivated under diverse agro-climatic conditions. Early and accurate disease diagnosis is essential for effective crop management; however, manual inspection is time-consuming, subjective, and often infeasible at large scale. In this work, we present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition. The dataset comprises labeled RGB images of healthy and diseased leaves and fruits from three economically important crops—tomato, chilli, and papaya—captured under real-field and semi-controlled environments, reflecting significant variability in illumination, background complexity, and disease severity. To demonstrate the applicability of the dataset, several commonly used convolutional neural network (CNN) architectures, including VGG, ResNet, DenseNet, MobileNet, and EfficientNet models, were trained and evaluated on the TCP dataset using transfer learning. Experimental results show that deep CNN models can effectively learn discriminative visual features corresponding to disease-specific patterns such as leaf spots, lesions, discoloration, curling, and fruit surface abnormalities. Lightweight models such as MobileNet achieve competitive performance with reduced computational cost, while deeper architectures provide improved accuracy at the expense of higher complexity. The results highlight the importance of dataset diversity for robust model generalization across multiple crops and plant organs. The TCP dataset provides a challenging benchmark for single-crop and multi-crop disease classification and supports the development of advanced deep learning, attention-based, and explainable AI models for precision agriculture. By enabling reproducible research and realistic performance evaluation, this dataset contributes toward scalable and practical AI-driven plant disease diagnosis systems aimed at reducing yield losses and supporting sustainable agriculture.
Why it matches plant phenotyping methods植物の葉・果実の病徴を画像から評価する大規模データセットとベンチマークを中心に扱っており、植物病害状態の画像ベース表現型解析に該当する。
abstractwe present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition.
Reproduction assets foundThe paper introduces the TCP (Tomato-Chilli-Papaya) fruit and leaf disease image dataset and reports CNN experiments on it. The dataset is publicly deposited on Mendeley Data, and the authors state that analysis code is available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:27 lines:1-51Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:27 lines:1-51Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Why it matches plant phenotyping methods気孔・舗装細胞の形態形質を画像から自動抽出する深層学習プラットフォームを開発し、独立データで性能評価・比較検証しているため、植物フェノタイピング手法が中心である。
abstractwe developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomatoes are the most significant and widely consumed crops globally. Leaf diseases cause an important threat to crop production and quality. Further, various fungi, bacteria and viruses can influence the plant's various parts and gradually destroy the quality and production of tomatoes in an agricultural field, thus it impacts the surrounding cultivated plants to cause more economical loss to farmers. Therefore, various techniques are proposed recently to optimally recognize and categorize the epidemic pathogens. To enhance sustainable plant protection practices, accurate identification and classification of diseases is essential to enhance the production rates. Effective pathogen detection and monitoring of plant health are critical areas of research in agriculture. Understanding disease severity is a crucial role for management practices and preventing the spread of infections. Rapid assessment is crucial because early detection of disease can significantly enhance the crop yield and influence the management strategies implemented by farmers. In this proposed model, a deep learning approach is proposed to classify the severity of diseases in tomato plants. At first, the needed images are collected from publicly available resource. Further, the collected images are subjected to the Adaptive and Attention-based Mask Region Convolutional Neural Network (AA-MRCNN) for optimally segmenting the abnormal regions from the gathered image. Further, the hyperparameters, like epoch, steps per epoch, and hidden neuron count in the Adaptive and Attention-based Mask Region Convolutional Neural Network are tuned by the Fitness-based African Vultures Optimization (FAVO) algorithm. Also, the segmented images are passed into Multiscale Recurrent MobileNet (MRMNet) module for categorizing disease severity in the tomato plant epidemic. The assessment of the recommended severity detection approach of tomato plant disease is determined by conducting a simulation experiment. The proposed model attains better outcomes of 93% accuracy, 93% specificity, 93% precision, 7% False Negative Rate (FNR), 86% Matthews Correlation Coefficient (MCC), 93% Fowlkes mallow Index (FM), 86% Bookmaker Informedness (BM), and 86% Threat Score (TS) measures in the ReLu activation function, which is progressed than the conventional frameworks. The result defines that the suggested technique outperformed than other baseline models to ensure the dependability of the tomato plant epidemic pathogens detection performance.
Why it matches plant phenotyping methodsトマト葉画像から病変領域を分割し、植物病害の重症度を分類する画像ベースの表現型推定手法を開発・評価しており、方法が研究の中心である。
abstracta deep learning approach is proposed to classify the severity of diseases in tomato plants.
Reproduction assets foundThe paper uses a public Kaggle tomato leaf disease image dataset as its phenotyping input and states that the authors' source code is available in a public GitHub repository. Both are paper-specific, publicly accessible, and actionable.Code · publiche tomato disease classification performances were
carried out among the performance metrics like Prevalence Threshold
(PT), BM, Precision, FNR, Accuracy, FM, Specificity, MK (Markedness)
and TS to maximize the reliability of the designed approach. The source
code of the public repository on GitHub link is available on “GitHub-
https://github.com/pdeepika6078/Severity-Classification-of-Tomato-Plant-Epidemic-Pathogens-/tree/main”
In order to demonstrating the effectiveness of the designed approach,
several conventional segmentation, optimization, and classification
approaches are adopted to compare the overall process. The reason
behind selecting the traditional approaches to improve the clasOpen asset ↗pdeepika6078/Severity-Classification-of-Tomato-Plant-pdf-raw-page:55 lines:1-29Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and timely identification of plant diseases along with their severity is critical for effective crop management and minimizing agricultural losses. While recent advances in deep learning have demonstrated high performance in plant disease classification, limited attention has been given to quantifying disease severity, which is essential for informed agronomic decision-making. To address this gap, this study proposes TomatoMTL, a unified multi-task learning framework for simultaneous disease classification and severity estimation of tomato leaf diseases from a single image. The proposed architecture employs a shared ResNet50-based convolutional backbone augmented with CBAM-based feature refinement, followed by task-specific branches for disease classification and severity prediction. Furthermore, a cross-task attention mechanism is introduced to enable interaction between disease-specific and severity-related features, thereby enhancing the robustness of severity estimation. To effectively leverage partially labeled data, a masking strategy is incorporated during training. Experimental evaluation on a publicly available tomato leaf disease severity dataset demonstrates that the proposed model achieves 97.85% disease classification accuracy and 77.66% severity prediction accuracy, outperforming state-of-the-art single-task classifiers including EfficientNetV2-S, ViT-B/16, and ConvNeXt-Tiny as well as existing multi-task learning baselines including Cross-Stitch Networks and MTAN. Comprehensive ablation studies confirm the individual contributions of CBAM, MixUp and CutMix augmentation, and the cross-task attention mechanism. Statistical significance analysis across five independent runs yields p-values less than 0.001 and Cohen's d greater than 14, establishing the reliability of the reported improvements. Quantitative localization analysis reveals that the model achieves 89.4% Pointing Game accuracy, confirming that attention maps focus on biologically meaningful disease regions. The proposed framework represents a complete and effective approach for integrated plant disease analysis with strong potential for real-world precision agriculture applications.
Why it matches plant phenotyping methodsトマト葉画像から病害の重症度という植物状態を推定するマルチタスク画像解析手法を開発・評価しており、表現型取得・推定が研究の中心である。
abstractthis study proposes TomatoMTL, a unified multi-task learning framework for simultaneous disease classification and severity estimation of tomato leaf diseases from a single image.
Reproduction assets foundThe paper's Data availability and Code availability statements explicitly link a public Kaggle tomato leaf disease severity dataset (the phenotyping image data used) and the authors' public GitHub repository containing the TomatoMTL implementation scripts and documentation.Dataset · publicThe datasets analysed during the current study are publicly available in the kaggle Data repository at: https://www.kaggle.com/datasets/janiruwalisingha/tomato-leaf-disease-severity-dataset .Open asset ↗kaggle Data repository · tomato-leaf-disease-severity-datasetlines:354-386Code · publicThe implementation, along with relevant scripts and documentation, can be accessed through the following GitHub repository: https://github.com/Parnika798/tomato_leaf_disease .Open asset ↗GitHub repository · Parnika798/tomato_leaf_diseaselines:354-386Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop diseases play a significant role in food production globally; therefore, there is an urgent need to develop quick and accurate diagnostic techniques that are more effective than manual inspection methods. The proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection. This study also proposes a framework for pesticide recommendation and the treatment of plants. This study experiment on tomato and cotton crop leaf data for disease detection. Experimental results on a tomato crop disease detection dataset show that the proposed model shows high performance. EfficientNetB0 provides more stability and generalization capabilities in different scenarios compared to other models, such as YOLOv8, ResNet50, and a custom CNN model. The use of a knowledge-based decision support system provides sustainable pesticide recommendations based on environmental and symptom-specific parameters. Forecasting of pesticide prices through LSTM methods yields forecasts within 3.2% and 4.1% MAE, enabling improved decision-making by providing instant points of reference for potential price movements. Research uses SHAP and LIME to provide explainability to users, thus improving user buy-in through transparency. Overall, this modular system provides a data-driven decision-making model to improve the efficiency of managing crops.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像ベース手法を、複数モデルで比較評価しており、植物病害フェノタイピングがシステムの主要構成要素です。価格予測や農薬推薦も含みますが、病害検出の技術評価が明示されています。
abstractThe proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection.
Reproduction assets foundThe paper's disease-detection experiments use publicly available cotton and tomato leaf image datasets (Kaggle, IEEE DataPort, Roboflow), all cited with explicit public URLs in the references. No author analysis code or trained model checkpoints are stated as publicly available; the supplementary material is referencedDataset · publiccholar
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National College of Ireland ( 2025 ). “Cotton Disease Dataset.” Available online at: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset (Accessed May 19, 2025).
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Ngugi H. N. Ezugwu A. E. Akinyelu A. A. Abualigah L. ( 2024 ). Revolutionizing crop disease detection with computational deep learning: a comprehensive review . Environ. Monit. Assess. 196 : 302 . doi: 10.1007/s10661-024-12454-z
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Roboflow ( 2026a ). A Comprehensive Dataset of Cotton Plant Diseases for National Disease Identification and Treatment Guidance | IEEE DataPort. Available online at: https://ieee-dataport.org/documents/comprehensive-dataset-cotton-plant-diseases-national-disease-identification-and-treatment (Accessed March 29, 2026).
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Roboflow ( 2026b ). Cotton Plant Disease Prediction Object Detection Model by National College of Ireland . Available online at: https://universe.roboflow.com/national-colleOpen asset ↗IEEE DataPortlines:554-633Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Convolutional Neural Networks (CNNs) are widely used for plant disease detection, yet their performance is strongly influenced by hyperparameter selection. Traditional manual tuning or random search approaches are inefficient and may lead to suboptimal solutions. In this study, we propose and evaluate four hybrid metaheuristic strategies, Ant Lion Optimizer combined with Whale Optimization Algorithm (ALO-WOA), Ant Lion Optimizer with Dragonfly Algorithm (ALO-DA), Ant Lion Optimizer with Particle Swarm Optimization (ALO-PSO), and Particle Swarm Optimization with Whale Optimization Algorithm (PSO-WOA), for automatic hyperparameter tuning of CNNs. The methods were applied to a tomato leaf disease dataset comprising 21,421 training, 4,586 validation, and 4,602 test images across 10 classes. The CNN architecture was fixed with three convolutional blocks and a tunable dropout and learning rate. The experimental results show that ALO-DA achieved the highest test accuracy of 97.83%, closely followed by ALO-WOA (97.67%) and PSO-WOA (97.52%), while ALO-PSO achieved 95.26%. These findings demonstrate that hybrid metaheuristics can effectively improve CNN hyperparameter search compared to single optimizers, balancing exploration and exploitation more efficiently. Limitations and future research directions are discussed.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類するCNNについて、ハイブリッドメタヒューリスティックによるハイパーパラメータ最適化手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractIn this study, we propose and evaluate four hybrid metaheuristic strategies, Ant Lion Optimizer combined with Whale Optimization Algorithm (ALO-WOA), Ant Lion Optimizer with Dragonfly Algorithm (ALO-DA), Ant Lion Optimizer with Particle Swarm Optimization (ALO-PSO), and Particle Swarm Optimization with Whale Optimization Algorithm (PSO-WOA), for automatic hyperparameter tuning of CNNs.
Reproduction assets foundThe paper uses the public Kaggle Tomato Leaf Disease Dataset V2 as its phenotyping image data and publishes the authors' complete hybrid metaheuristic CNN optimization pipeline (training, optimization, evaluation scripts) in a public GitHub repository. Trained models/logs are only available on request.Code · publicThe source code, trained model configurations, and experimental scripts used in this study are publicly available at: https://github.com/simarkalsi24/Hybrid-of-Optimization-Algorithm-.git The repository includes the complete training and optimization pipeline, implementations of all hybrid algorithms (ALO–DA, ALO–PSO, ALO–WOA, and PSO–WOA), as well as experiment configurations, logs, evaluation scripts, and visualization outputs to ensure full reproducibility of the reported results.Open asset ↗GitHub · simarkalsi24/Hybrid-of-Optimization-Algorithm-lines:579-588Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
ToBRFV is a major threat to tomato and pepper crops because it spreads quickly and survives for a long time in the environment. Since there are few ways to control it after infection, early detection before symptoms are visible is crucial. Yet, only limited public datasets are available for this research. We present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection. In this study, two tomato cultivars and two pepper cultivars, all of which are commercially important and widely cultivated in greenhouses, were selected. Using these plants ensures that the dataset reflects real-world agricultural practices and captures variability across commercially grown types. Both healthy and ToBRFV-inoculated plants from each cultivar were included in the imaging process. All plants were cultivated under fully controlled greenhouse conditions in Adana Province, Türkiye. Healthy and infected tomato plants were grown in two separate greenhouses to prevent cross-contamination. Imaging was conducted over a 29-day period using Red-Green-Blue (RGB) and Visible Near Infrared (VNIR) cameras, including narrowband captures at 800 nm and 1000 nm, from multiple viewing angles. Infection status was confirmed via Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) analysis at multiple time points. The dataset is organized into four clean, labelled subsets and released under a CC BY 4.0 license. This resource provides unique opportunities for developing and benchmarking computer vision and machine learning approaches for pre-symptomatic plant disease detection, spectral feature analysis, and integration into precision agriculture systems. By combining controlled experimental design, spectral diversity, and open access, it establishes a robust foundation for cross-disciplinary research in plant pathology, agricultural engineering, and artificial intelligence.
Why it matches plant phenotyping methods植物病害状態を対象にした縦断マルチスペクトル画像データセットであり、公開データセットとして開発・ベンチマーク利用を目的とするため、表現型取得が中心です。
abstractWe present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection.
Reproduction assets foundThe article is a Data in Brief describing the authors' own openly released longitudinal multispectral plant image dataset (ToBRFV-LMID) for tomato and pepper disease detection, deposited on Zenodo under CC BY 4.0 with a direct DOI URL. This is a paper-specific, public, directly actionable phenotype/image asset. No codeDataset · publicData accessibility
Repository name: ZENODO
Data identification number: 10.5281/zenodo.17244968
Direct URL to data: https://doi.org/10.5281/zenodo.17244968Open asset ↗ZENODO · 10.5281/zenodo.17244968html-lines:98-126Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.
Why it matches plant phenotyping methods画像から花・果実などの植物形質を自動抽出するAIウェブアプリケーションの開発・提供が中心であり、植物フェノタイピング手法およびソフトウェアとして適格。
abstractWe present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images.
Reproduction assets foundThe article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code orDataset · publicDataset preparation
and the training process are detailed in Zhou et al. (2025a),
and a subset of the dataset has been publicly released on
Dryad (Zhou et al. 2025b).Open asset ↗Dryadpdf-raw-page:5 lines:1-55Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Why it matches plant phenotyping methods植物の3D形態表現型抽出を目的としたデータセットで、画像・点群・器官アノテーション・グラフ・形質値を提供し、再現可能なフェノタイピング手法の開発と評価を直接支援している。
abstractThis article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tomato diseases pose a significant threat to global agricultural production, often leading to substantial yield loss and major economic damage. Traditional disease detection methods rely on manual inspection, which is not only time-consuming and labor-intensive but also difficult to implement for real-time monitoring. While deep learning-based object detection techniques offer a potential alternative to manual inspection, existing models still face challenges in extracting subtle disease features, suppressing complex background interference, and in handling multi-scale disease representations in complex agricultural environments, limiting detection performance. To address these limitations, this paper proposes a novel TDD-YOLO model for precise tomato-disease detection (TDD) in complex agricultural settings. The proposed model is based on YOLOv11 with the following three main improvements: (1) a feature enhancement module is added to improve the backbone's ability to extract disease spot textures; (2) a joint attention mechanism is introduced to explicitly model cross-dimensional dependencies, effectively suppressing background interference; and (3) a feature fusion module is added to retain disease information across different scales while reducing computational costs. Experimental results, obtained on the Tomato-Village dataset (containing field-acquired images of tomato leaves with six diseases, collected in real agricultural environments, featuring complex backgrounds and varying illumination conditions) and Tomato-Disease dataset (emphasizing a greater diversity in tomato disease types along with healthy leaf samples), demonstrate that the proposed TDD-YOLO model outperforms the baseline in detection of tomato diseases (e.g., by improving mAP@50 and mAP@50:95, averaged across disease categories, by 4.1% and 6.0% on Tomato-Village and by 3.6% and 3.9% on Tomato-Disease, respectively) and state-of-the-art models (e.g., by improving the average mAP@50 and mAP@50:95, compared to the first runner-up, by 3.2% and 4.7% on Tomato-Village and by 2.4% and 2.1% on Tomato-Disease, respectively), while maintaining good parameter count and computational complexity, confirming its effectiveness and potential for practical usage in complex agricultural environments. The author-generated code and weight files are publicly available at https://github.com/LingShaQ/TDD-YOLOCode.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するYOLOモデルを開発し、複数データセットでベースラインおよび既存モデルと比較検証しており、植物病害フェノタイピング手法が中心である。
abstractExperimental results, obtained on the Tomato-Village dataset
Reproduction assets foundThe paper's tomato-disease detection experiments rely on two public image/annotation datasets (Tomato-Village on GitHub, Tomato-Disease on Zenodo), and the authors explicitly state their generated code and weight files are publicly available on GitHub. The Ultralytics YOLO repositories are generic third-party librariesCode · publicThe author-generated code and weight files are publicly available at https://github.com/LingShaQ/TDD-YOLOCode.Open asset ↗LingShaQ/TDD-YOLOCodehtml-lines:110-113Dataset · publicAll data used in this article are obtained from the publicly available Tomato-Village dataset (https://github.com/mamta-joshi-gehlot/Tomato-Village)Open asset ↗mamta-joshi-gehlot/Tomato-Villagehtml-lines:1159-1171Dataset · publicthe publicly available Tomato-Disease dataset (https://zenodo.org/records/15868289).Open asset ↗html-lines:1159-1171Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning. The approach solves the problem of superimposed patterns of disease especially Leaf Miner, Tomato Spotted Wilt Virus (TSWV), and nutrient deficiencies through the combination of multi-domain feature learning and inter-disease similarity modeling. In contrast to conventional metric learning or contrastive learning methods that function on pairwise or triplet sample associations, CDSAL develops a class-level Cross Disease Similarity Matrix that represents structured inter-disease proximity within the embedding space. Moreover, rather than employing episodic prototype construction typical of few-shot learning, the proposed system persistently updates centroid representations throughout supervised training and incorporates similarity-aware regularization directly into the loss function. This facilitates structural embedding reshaping specifically designed for visually overlapping illness categories, beyond traditional prototype-based learning methodologies. The input images are processed through HSV based green masking, morphological cleaning, extraction of leaf contours and resizing, and using a large amount of geometric and color-space augmentation to reduce the imbalance among the classes. DenseNet121 and EfficientNet-B0 are used to obtain feature representations and class-separated centroid of latent embedding's to form a Cross Disease Similarity Matrix, where similarity-aware optimization is possible during training. Grad-CAM on the target layers offers decipherable disease-specific activation signatures. The findings of the experiments show that classification accuracy at unseen samples is 99.77% with high resilience to visual confounding. The predictions, proximity of diseases that are similar and explainable features are provided by CDSAL, thereby facilitating reliable decision-making in agricultural diagnostics.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習手法を提案し、前処理・特徴抽出・類似度学習・説明可能性を技術的中心として評価しているため。
abstractThe research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning.
Reproduction assets foundThe paper's plant-phenotyping inputs are two publicly available Kaggle image datasets explicitly named in the Data Availability statement: PlantVillage (emmarex/plantdisease) used as the main dataset and TomatoVillage (mamtag/tomato-village) used for ablation/field-condition experiments. No author analysis code, modelsDataset · publicThe datasets analyzed during the current study are available in the Kaggle repository. [https://www.kaggle.com/datasets/emmarex/plantdisease]Open asset ↗Kaggle · emmarex/plantdiseasehtml-lines:605-624Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Today, the intelligent automation of agriculture has received much attention from researchers. One of the important factors for the success of this automation is the timely diagnosis of plant disease and making a decision appropriate to the existing conditions of the plant. Since the progress of the disease is a determining factor in the type of treatment method, the diagnosis of the severity of the disease is of particular importance. However, accurate diagnosis of plant disease progression depends on various factors, including the availability of appropriate and well-annotated training datasets for designing an efficient diagnostic system. On the other hand, the similarity of the complications of different diseases has made this work challenging. In this study, two tomato diseases, namely Bacterial Spot and Mosaic Virus, are investigated using images collected from the PlantVillage, Taiwan tomato leaves, Field-PlantVillage, and Syn-PlantVillage datasets. The disease severity levels are divided into six stages for Bacterial Spot and four stages for Mosaic Virus, and a specifically designed deep convolutional neural network is proposed for severity classification. Experimental results demonstrate that the proposed method achieves high accuracy under challenging field conditions and outperforms several state-of-the-art methods.
Why it matches plant phenotyping methodsトマト葉の病徴・病害重症度を画像から段階分類するCNN、背景除去、病斑セグメンテーションを開発しており、植物状態の取得・推定手法が中心である。
titleA parallel convolutional neural network with background removal and lesion segmentation for field plant disease severity classification
Reproduction assets foundThe paper's own severity-annotated datasets are explicitly restricted (available only on request), so no public paper-specific data asset qualifies. The authors do provide an explicit public code availability link for their proposed BaSPaC model. The Mendeley and Drive links are pre-existing external datasets cited as,Code · publicCode availability
https://github.com/m-hasheminejad/BaSPaC.Open asset ↗m-hasheminejad/BaSPaChtml-lines:878-908Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Under small-sample conditions, hyperspectral leaf chlorophyll estimation is affected by high-dimensional collinearity, measurement noise, and cross-source acquisition discrepancies. Existing studies often treat training-distribution expansion and model-error complementarity separately. This study proposed a physically constrained composite spectral augmentation-weighted ensemble framework for reproducible small-sample chlorophyll estimation. Methods Using 1,113 valid spectrum-label pairs from the leaf subset of the GreenHySpectra dataset in the 400-1000 nm range, spectra and chlorophyll reference values were matched by sample identifiers and divided into training and validation sets. Low-magnitude Gaussian noise and smooth wavelength warping were applied only to the training set. XGBoost, partial least squares regression, and ridge regression were optimized with Optuna using a CMA-ES sampler, and ensemble weights were calibrated by Bayesian optimization. An independent external set of 90 tomato leaf samples was used to evaluate transferability. Results Composite augmentation improved model stability and reduced validation error relative to the non-augmented baseline. The weighted ensemble model achieved the best internal performance, with R² = 0.6392 and RMSE = 8.8883. On the external samples, the model achieved R² = 0.498 and RMSE = 9.801. Discussion The proposed workflow integrates physically plausible augmentation, heterogeneous learner complementarity, and independent external validation. The external results indicate partial cross-source transferability while highlighting distributional and measurement-chain discrepancies that still limit absolute generalization.
Why it matches plant phenotyping methods葉のクロロフィル量という植物形質をハイパースペクトルから推定する手法を開発し、外部データで転移性を検証しており、表現型取得・推定が研究の中心である。
titleHyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning.
Reproduction assets foundThe paper's phenotyping analysis is built on the public GreenHySpectra hyperspectral dataset (leaf subset, 1,113 spectrum–chlorophyll pairs), which is a paper-specific, publicly available input with an authors' cited URL matching the allowed list. No author analysis code, trained models, or public deposit of the 90-solDataset · publicAvatarr05 ( 2023 ). GreenHySpectra/GreenHyperSpectra dataset (Hugging Face Datasets) [WWW document] . Available online at: https://huggingface.co/datasets/Avatarr05/GreenHySpectra (Accessed May 15, 2026).Open asset ↗Hugging Face Datasets · Avatarr05/GreenHySpectralines:749-785Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Plant disease detection using deep learning is essential for precision agriculture, enabling early and automated crop health monitoring. This study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset. The framework integrates data preprocessing, augmentation, and a VGG-16 backbone with a two-stage fine-tuning strategy. The proposed model is evaluated against CNN, DenseNet-121, Inception-V3, EfficientNetB0, and ResNet-50, achieving an accuracy of 0.93 with precision, recall, and F1-scores of 0.93, 0.90, and 0.92, respectively. These results demonstrate the effectiveness of transfer learning for fine-grained plant disease recognition. We further evaluate model robustness under adversarial cyber attacks to assess deployment reliability in agricultural systems. Under Fast Gradient Sign Method (FGSM) attacks ( ϵ = 0.01– 0.05), the model shows an accuracy drop of 1%–7.5%, while Projected Gradient Descent (PGD) attacks ( ϵ = 0.05, step size = 0.005, 10 iterations) produce similar degradation, highlighting the model’s vulnerability to adversarial perturbations. These findings highlight potential security and reliability risks in AI-based agricultural decision-making systems. Future work will focus on improving robustness and cyber-resilience and extending this framework to other crops for secure and context-aware deployment in resource-constrained environments.
Why it matches plant phenotyping methods植物葉の画像から病害・栄養欠乏状態を分類する深層学習パイプラインが研究の中心であり、複数モデルとの比較評価と敵対的攻撃下での頑健性検証も実施しているため。
abstractThis study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset.
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Tomato-Village Variant-a dataset (4,525 tomato leaf images across 8 disease/deficiency classes), which the authors explicitly cite with a public Kaggle URL. No author analysis code, trained model checkpoints, or supplementary data deposits are mentioned. TheDataset · publicThis study uses the publicly available Tomato-Village
dataset [18], which is designed for real-world tomato disease
detection in agricultural environments.Open asset ↗pdf-raw-page:3 lines:1-59Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP 50-95 , mAP 50 , and mAP 75 , respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP 50 over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するモデルを開発し、複数データセットで比較検証、エッジデバイス実装まで評価しており、植物病害表現型の取得手法が中心である。
abstractThis paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection.
Reproduction assets foundThe paper's M-TLD tomato leaf disease dataset (2212 images, 6581 annotations) and the GBR-DETR implementation/training code are explicitly stated to be publicly available at the authors' GitHub repository.Dataset · publicThe M-TLD dataset and all annotation files are publicly available at https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026) to facilitate reproducibility and future research.Open asset ↗zhuojiaxiong6/DETRlines:38-108Code · publicThe code and dataset used in this study are publicly available at the following GitHub repository: https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026). This repository contains the implementation of GBR-DETR, a Detection Transformer variant developed for detecting tomato leaf diseases and pests. All relevant training scripts, configuration files, and instructions for dataset usage are provided in the repository.Open asset ↗zhuojiaxiong6/DETRlines:673-675Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Plant disease is a serious threat to agricultural productivity and food security worldwide. Traditional diagnostic methods such as manual observation and laboratory testing are time-consuming, labor-intensive and error prone. The emergence of artificial intelligence (AI) and deep learning (DL) offer scalable solutions for precision agriculture in plant disease detection using advanced computational techniques to process large datasets. Hybrid deep learning architecture integrates Convolutional Neural Networks (CNNs) along with Artificial Neural Networks (ANNs) can leverage both visual and contextual data to improve detection performance. The hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics). The CNN module extracted spatial and textural features from plant images, while the ANN module processed environmental parameters. These outputs were fused into a unified feature vector for disease classification. A total of 15 plant species and their associated diseases were analyzed using 200-270 training samples and 150-190 testing samples for each disease across a total of 1000 images. The model was judged by metrics such as detection accuracy, AUC, sensitivity etc. Data augmentation, pre-trained architectures (e.g., ResNet50) and early stopping techniques were utilized to improvise model performance. The hybrid model saliently achieved detection accuracy consistently above 87% with majority of diseases surpassing 90%. Highperforming cases like Rice Blast (92.5%), Tomato Early Blight (93.8%), and Coffee Rust (93.0%), with AUC values of 0.93 or higher, sensitivity exceeding 94% and specifically above 90%. Diseases of Sugarcane Red Rot and Tea Blister Blight exhibited sensitivities of 92.4% and 92.1% and specificities of 91.1% and 90.5% respectively. Moderate accuracy for Coconut Bud Rot (87.5%) and Mustard Alternaria Blight (87.8%) was due to smaller training sample sizes.
Why it matches plant phenotyping methods植物画像から病害状態を分類するハイブリッドCNN-ANN手法を開発し、精度・AUC・感度などで評価しており、病害フェノタイピング手法が研究の中心である。
abstractThe hybrid CNN-ANN model was developed to analyze visual data (plant images) and contextual data (environmental and soil metrics).
Reproduction assets foundThe paper's plant disease detection model was trained on public plant image datasets: the PlantSeg dataset (Zenodo record 13958858, DOI 10.5281/zenodo.13293891) and the UCI Machine Learning Repository Plants dataset. Both are cited in Materials and Methods as sources of the visual data used for the CNN module. No code,Dataset · publicebao (ZiranKexueBan)/Journal of
Huazhong University of Science and Technology
(Natural Science Edition). 2021;49(8).
37. Ma C, Mu X, Sha D. Multi-Layers Feature Fusion of
Convolutional Neural Network for Scene
Classification of Remote Sensing. IEEE Access.
2019;7.
38. Hämäläinen W.Plants Dataset[Internet]. 2024.
Available from:
https://archive.ics.uci.edu/dataset/180/plants
39. WeiT. PlantSeg: A Large-Scale In-the-wild Dataset
for Plant Disease Segmentation [Internet]. 2018.
Available from: https://zenodo.org/records/13958858Open asset ↗180pdf-raw-page:15 lines:1-48Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.
Why it matches plant phenotyping methodsトマトの電気生理シグナルから水ストレス状態を推定するセンシング・機械学習パイプラインを開発し、未学習データで性能検証しているため、植物フェノタイピング手法が中心である。
abstractDirect physiological sensing offers the potential to detect stress responses before visible symptoms appear.
Reproduction assets foundThe paper's electrophysiological time-series and soil moisture measurements from the water-stress tomato experiment are explicitly stated to be publicly available online via a Zenodo deposit (Buss et al. 2026a), referenced in both the Methods and Data availability sections.Dataset · publicAll recorded and
processed data are available online (Buss et al. 2026a).Open asset ↗pdf-page:5 lines:1-37Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract Current phytopathological diagnostic systems rely on manual inspections or laboratory analyses, which delay early detection and limit in-field responsiveness. Phytopathogenic fungal diseases pose a persistent threat to food security, directly affecting the productivity of essential crops such as potato ( Solanum tuberosum ) and tomato ( Solanum lycopersicum ) [1]–[5]. Among these diseases, Phytophthora infestans , the causal agent of late blight, is characterized by its high virulence and rapid spread, capable of generating significant losses in short periods when detection occurs too late [3], [4]. To address this issue, a computer vision and deep learning–based system for multistage detection of fungal infections in potato and tomato crops is proposed. The system comprises a convolutional neural network optimized for edge processing and a mobile robotic platform equipped with a manipulator arm for localized treatment application. The developed model was deployed on a Raspberry Pi 4 connected to a 12-MP Raspberry Pi Camera Module 3 NoIR, responsible for acquiring RGB images in the field. The proposed network was compared with reference architectures—ResNet-50, VGG16, MobileNetV2, and Inception-v3—within a four-stage detection pipeline: crop identification, health-state classification, infection diagnosis, and foliar severity estimation. A dataset of 18,200 images obtained from publicly accessible online sources, under diverse lighting and background conditions, was used, partitioned into 70% for training, 20% for validation, and 10% for testing. Preliminary results show an average accuracy in the range of 0.90–0.92, with inference latencies below 60 ms per image, ensuring smooth performance on the Raspberry Pi 4 without requiring cloud connectivity. Additionally, the network demonstrated higher sensitivity to visual variations compared to the baseline models.
Why it matches plant phenotyping methods植物病害の健康状態・感染・葉面重症度を画像から推定するコンピュータビジョン手法を開発・比較検証しており、植物表現型取得が中心である。
abstracta computer vision and deep learning–based system for multistage detection of fungal infections in potato and tomato crops is proposed
Reproduction assets foundThe paper's CNN training data are two publicly available third-party plant-disease image datasets (Mendeley Data and Kaggle Plant Village) with explicit URLs in the Data Availability Statement. The authors' field-test images and experimental records are only available on request, and no analysis code or trained model/сDataset · public10
Network, DOI: 10.17632/tywbtsjrjv.1, available at
https://data.mendeley.com/datasets/tywbtsjrjv/1, and the
Kaggle Plant Village dataset, available at
https://www.kaggle.com/datasets/emmarex/plantdisease.The field-test images and experimental records generated during
the current study during the real-world evaluation of the
embedded-vision system are available from the corresponding
author on reasonable request.
IX. REFERENCOpen asset ↗10.17632/tywbtsjrjv.1pdf-raw-page:11 lines:1-98Dataset · public10
Network, DOI: 10.17632/tywbtsjrjv.1, available at
https://data.mendeley.com/datasets/tywbtsjrjv/1, and the
Kaggle Plant Village dataset, available at
https://www.kaggle.com/datasets/emmarex/plantdisease.The field-test images and experimental records generated during
the current study during the real-world evaluation of the
embedded-vision system are available from the corresponding
author on reasonable request.
IX. REFERENCES
[1] P. W. Crous, A. Y. Rossman, M. C. Aime, W. C. Allen, T.
Burgess, J. Z. Groenewald y L. A. CastlebuOpen asset ↗Kagglepdf-raw-page:11 lines:1-98Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Rapid population growth and climate change have intensified the need for sustainable agricultural productivity. Plant leaf diseases significantly impact the crop yield, quality, and food safety, necessitating accurate and automated detection methods. Methods This study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases. The proposed framework is trained and evaluated over a large-scale datasets comprising 16,012 tomato leaf images and 6,410 soybean leaf images. Multiple convolutional neural network (CNN) models, including DenseNet121, MobileNetV2, and InceptionV3, are employed for classification. Object detection is performed using YOLOv12. To enhance interpretability, Gradient-Weighted Class Activation Mapping (Grad-CAM) is integrated. Furthermore, a novel Hybrid Attention-Based Stacking Ensemble Model is developed using ResNet152V2, VGG19, and EfficientNetB0, combined with Convolution Block Attention Module (CBAM) and spatial attention mechanisms. Results The CNN models achieved classification accuracies of 97% for DenseNet121, 98% for MobileNetV2, and 99.94% for InceptionV3. YOLOv12 attained a mean average precision (mAP) of 99.5%. The proposed hybrid ensemble model achieved an accuracy of 99.18%, demonstrating improved feature learning through combined channel and spatial attention. Grad-CAM visualizations confirmed that the model effectively identifies the disease-relevant regions. Discussion The results indicate that the proposed framework has attained a high accuracy, robustness, and interpretability for plant disease detection. The integration of attention mechanisms and explainable AI enhances model reliability and transparency. This framework shows a strong potential for the real-time agricultural monitoring, although further validation across diverse crops and real-world field conditions is required.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動検出・分類する深層学習フレームワークの開発であり、病害表現型の取得・推定が研究の中心。
abstractThis study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases.
Reproduction assets foundThe paper's leaf-disease classification/detection experiments are built on two public Kaggle image datasets cited by the authors as the study's data sources: a soybean leaf dataset (Patil 2024) and a tomato leaf disease dataset (Rex 2019). No authors' analysis code, trained model checkpoints, or paper-specific phenotypDataset · publicPatil A. ( 2024 ). Soyabean-Latest Dataset (
Kaggle ). Available online at: https://www.kaggle.com/datasets/adityapatil1205/soyabean-latestOpen asset ↗Kagglelines:1523-1646Dataset · publicRex E. ( 2019 ). Plant Disease Dataset (Tomato Leaf Diseases) (
Kaggle ). Available online at: https://www.kaggle.com/datasets/emmarex/plantdisease197Open asset ↗Kagglelines:1523-1646Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant diseases are a major problem for farmers around the world, reducing crop yields. The absence of expertise makes plant disease detection difficult and complicated. Plant disease detection is made easier by deep learning algorithms; however, they are computationally demanding and need huge training datasets. This research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy. Because of its architecture, the proposed network can identify leaf diseases with more accuracy by extracting the wider range of features from the images. The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories. The CNN model is trained using publicly accessible Kaggle dataset, utilising image augmentation techniques. It is evident from the simulation results that the proposed model outperforms several pre-trained, and other trending deep learning models. Proposed model achieves 99.18% classification accuracy with an average precision of 99.17% and area under the curve (AUC) of 1, making this model highly effective in leaf diseases detection. Additionally, Conv-7 DCNN achieved high FPS of 112.49, low inference time of 18.34 s, and low GFLOPS of 13.98, making it suitable for real-time applications in smart agriculture systems.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルを開発・比較しており、病害表現型の取得・分類手法が研究の中心である。
abstractThis research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy.
Reproduction assets foundThe paper's plant-phenotyping measurements (leaf disease classification of tomato, potato, and pepper-bell) are based on a publicly available Kaggle dataset explicitly named in the Data Availability statement. No author analysis code, trained model checkpoints, or other paper-specific assets are disclosed.Dataset · publicThe dataset is available online at https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseasehtml-lines:824-854Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Accurate detection of agricultural diseases and pests is essential for crop protection and food security worldwide. Real-world field applications face challenges including small objects, complex backgrounds, high class similarity, and limited computational resources. This work proposes a Random Rolling Transformer (RRT), which introduces random circular shifts along channel and sequence dimensions into multi-head self-attention to enrich feature interactions without increasing parameters or computation. Integrated into YOLOv12, the proposed RRT-YOLO is evaluated on the IP102 pest dataset and a tomato leaf disease dataset. Results show that RRT-YOLO improves mAP@50 by 2.5% and mAP@50–95 by 3.9% on IP102, and by 8.8% and 4.2% on the tomato disease dataset, while maintaining identical model size and complexity. This attention perturbation strategy offers an effective and efficient solution for lightweight agricultural vision detection and can be extended to other visual computing tasks. The code and detailed descriptions can be accessed via the following repository: \href{https://github.com/glorioustory/Random-Rolling-Attention-Augmentation.git}{https://github.com/glorioustory/Random-Rolling-Attention-Augmentation.git}.
Why it matches plant phenotyping methods植物葉の病害状態を画像から検出する計算手法を開発・評価しており、植物病害の表現型取得が技術的中心である。害虫検出も含むが、トマト葉病害データセットで手法性能を検証している。
abstractThis work proposes a Random Rolling Transformer (RRT), which introduces random circular shifts along channel and sequence dimensions into multi-head self-attention to enrich feature interactions without increasing parameters or computation.
Reproduction assets foundThe authors explicitly state their code and detailed descriptions are publicly available in a GitHub repository containing the RRT-YOLO implementation used for the paper's disease/pest detection experiments. The IP102 and tomato leaf disease datasets are third-party public resources, not paper-specific assets.Code · publicThe code and detailed descriptions can be accessed via the following repository:
https://github.com/glorioustory/Random-Rolling-Attention-Augmentation.git.Open asset ↗https://github.com/glorioustory/Random-Rolling-Attention-Augmentation.gitpdf-page:3 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Tomato leaf diseases significantly impact agricultural productivity, necessitating accurate and efficient diagnostic methods. Deep learning has emerged as a robust approach for plant disease detection, but challenges such as inefficient feature extraction, classification, model complexity and limited computational resources hinder its widespread adoption. This study introduces a PhytoNet (Mish-Optimized SqueezeNet) Framework to enhance tomato leaf disease prediction. The SqueezeNet architecture, known for its lightweight design, is optimized with the Mish activation function to improve feature extraction and classification capabilities while maintaining computational efficiency. The methodology involves training the SqueezeNet model on 10 classes of mendaley dataset of tomato leaf images, encompassing multiple disease classes and healthy samples. Data preprocessing techniques, including image augmentation and normalization, are employed to ensure model robustness. The integration of the Mish activation function in critical layers enhances non-linearity, aiding in better gradient flow and improved performance during training. Model evaluation is conducted using metrics such as accuracy, precision, recall and F1-score. Experimental results demonstrate that the PhytoNet outperforms traditional SqueezeNet and other lightweight architectures in terms of classification accuracy, achieving over 0.9957 accuracy on the dataset. Additionally, the model maintains low computational overhead, making it suitable for deployment on resource-constrained devices. Hence, the proposed framework effectively balances accuracy and efficiency, addressing critical limitations in existing plant disease detection models. This work underscores the potential of lightweight and activation-optimized deep learning frameworks for real-time agricultural applications, paving the way for scalable and sustainable solutions in precision farming.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する深層学習モデルを開発・評価しており、植物病害フェノタイピング手法が中心的な貢献である。
abstractThis study introduces a PhytoNet (Mish-Optimized SqueezeNet) Framework to enhance tomato leaf disease prediction.
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,Open asset ↗pdf-page:38 lines:1-41Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To achieve agricultural automation, deep learning applications for early and accurate disease detection in tomato plants have been extensively developed. However, there is a fundamental trade-off between computational efficiency and diagnostic accuracy in resource-constrained agricultural edge environments. This paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential. Through evaluations of explainability, computational efficiency, and diagnostic performance, seven lightweight architectures (ShuffleNetV2, MobileNetV3-Small, SqueezeNet, MobilePlantViT, DenseNet121, ResNet50, and VGG16) are thoroughly examined. Three significant findings are derived from experiments conducted on a subset of tomato diseases in the PlantVillage dataset. First, the MobilePlantViT architecture accurately strikes the ideal balance between efficiency and performance. Second, in order to quantitatively assess the explainability of XAI models (Grad-CAM, SHAP, and LIME) and identify the best option for edge devices, we propose the perturbation stability score (PSS) metric. Third, we test CPU inference measurements to better reflect the actual scenario and find that the hybrid design effectively leverages parallel computing. According to these findings, MobilePlantViT is the ideal architecture for applications that require operation on edge devices with limited resources and achieve high diagnosis accuracy (above 99.5%).
Why it matches plant phenotyping methodsトマト病害という植物状態を画像から分類する深層学習手法について、複数モデルの精度・計算効率・説明可能性を体系的に比較評価しており、病害フェノタイピング手法の技術評価が中心である。
abstractThis paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential.
Reproduction assets foundThe authors publicly deposited their paper-specific tomato phenotype image subsets (derived from PlantVillage and expert-curated PlantDoc) on Kaggle via explicit Data Availability links. No author analysis code or trained model checkpoints are shared; ONNX Runtime is a generic library, not a paper-specific asset.Dataset · publicThe datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-plantvillage-datasetshtml-lines:710-734Dataset · publicWe enhanced the quality of the PlantDoc dataset by collaborating with experts to identify and crop regions containing disease-specific symptoms, while eliminating irrelevant image content. For long-term preservation and ease of access, we have stored copies of the datasets in the published repository. The datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-onlyhtml-lines:710-734Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Diseases of tomato leaves are significant threats to the global food security and agricultural production. The old method of diagnosis is not reliable and is time consuming, and there is a demand to have effective and accurate automated systems. The paper uses transfer learning using Inception-V3 and Inception-ResNet-V2 network to detect tomato leaf diseases using an open dataset. To encourage generalizability, data augmentation and preprocessing techniques were used, whereas Grad-CAM was used to encourage visual interpretability. Experimentally, it has been demonstrated that Inception-ResNet-V2 and Inception-V3 performed with 92.33 and 89.33 accuracy, respectively, which is higher than the other existing methods. These results demonstrate the possibility of deep learning to improve precision agriculture and prepare further development of real-time and field-deployable systems of disease detection.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から深層学習で自動推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThe paper uses transfer learning using Inception-V3 and Inception-ResNet-V2 network to detect tomato leaf diseases using an open dataset.
Reproduction assets foundThe paper's Data Availability Statement explicitly identifies the public Kaggle tomato leaf image dataset used to train and evaluate the deep learning models, making it a paper-specific, publicly actionable asset. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe dataset used in this study is publicly available on Kaggle. It can be accessed at:
https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf
The dataset contains labeled images of healthy and diseased tomato leaves and was used for training
and evaluating the proposed deep learning model.Open asset ↗Kaggle · kaustubhb999/tomatoleafpdf-page:30 lines:1-9Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants are known to generate various types of electrical signals, which have been observed ever since Darwin's times. We studied the electrical signals acquired in tomato plants infected with the fungal pathogen Oidium neolycopersici (On) , the causative agent of powdery mildew, and applied statistical analyses to detect the differences in electrical responses between healthy and infected plants, as reported in [1].•The underlying mechanism in the generation and transmission of electrical signals is not fully understood, yet it's generally accepted that they can be classified according to functional properties. Action potentials (APs) and slow wave potentials, in particular, are elicited by biotic and abiotic stimuli, thus are interesting as a hallmark of plant health status.•To analyse the application of these potentials in plant disease detection, voltages from electrodes inserted in plants were acquired periodically by a scanning multimeter and recorded under control of a dedicated custom Python program running on a Raspberry Pi board.•Here we describe the design of the experiment and analyse in some detail the solutions adopted for specific issues found in the measurements, such as electrode's material and placement; immunity to electromagnetic noise; data logging over long periods of time with intermediate monitoring of results.
Why it matches plant phenotyping methodsトマトの感染状態を電気シグナルから検出する測定・解析法が中心で、電極配置、ノイズ対策、長期データ記録などの技術設計と適用を扱っている。
titleMethod for the detection of powdery mildew in tomato from electrical signalling.
Reproduction assets foundThe paper deposits its electrical signalling measurements from tomato plants (infected and healthy controls) in a public Mendeley Data repository, explicitly listed in the specifications table's resource availability.Dataset · publicRepository name: Mendeley DataOpen asset ↗Mendeley Datahtml-lines:1-106Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Plant Leaf Diseases represent a significant risk to global agricultural production. Crops ranging from Peppers and Tomatoes to Potatoes are affected by these diseases. Traditional methods of identifying leaf diseases based primarily on visual inspection have historically been slow and relatively inaccurate. A deep learning-based solution for the automated identification of plant leaf diseases utilizes Convolutional Neural Networks (CNNs) as the primary methodology. To identify leaf images into 15 disease categories, both pre-trained models such as VGG16, EfficientNetB3, Inception V3 and a custom CNN model were used. Using pre-trained models to allow for the use of Transfer Learning helps to mitigate some of the issues associated with computational resource limitations and data limitations in providing faster convergence rates and higher accuracy when compared to training a model from scratch. The Plant Village image collection which contains over 20,000 images of different plant leaf diseases was utilized for training and testing purposes. Each model's performance was evaluated based on its accuracy, loss and generalization capabilities. Additionally, each model was fine-tuned through hyperparameter optimization. As a result, the model that achieved the highest validation accuracy rate of 95% was the EfficientNetB3 model while the second highest accuracy rate was achieved by the Inception V3 model at 92%. This methodology provides an excellent answer to addressing early disease detection, enabling farmers to take the necessary actions quickly to reduce their losses and maximize their harvest.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法を比較・評価しており、病害フェノタイピングと手法検証が研究の中心です。
abstractA deep learning-based solution for the automated identification of plant leaf diseases utilizes Convolutional Neural Networks (CNNs) as the primary methodology.
Reproduction assets foundThe paper's plant-phenotyping measurements (CNN classification of 15 leaf disease classes) were performed on the public PlantVillage-derived Kaggle 'Plant Disease Dataset' by E. Marrex, which the authors cite as their training/testing image source. No author analysis code, trained model checkpoints, or paper-specific衍生Dataset · publicD. E. Popescu, M. K. Chowdary, and J. Hemanth, "Deep learning-based leaf
disease detection in crops using images for agricultural applications," Agronomy, vol. 12, no. 10,
p. 2395, 2022. doi: 10.3390/agronomy12102395. Available:
https://doi.org/10.3390/agronomy12102395.11. E. Marrex, "Plant Disease Dataset," Kaggle, Available:
https://www.kaggle.com/datasets/emmarex/ plantdisease. [Accessed: 03- Apr-2025].Open asset ↗Kagglepdf-raw-page:12 lines:1-8Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published15 Feb 2026Journal of Artificial Intelligence and Engineering Applications (JAIEA)Cited by 0 · OpenAlex ↗
Monitoring plant health is an important factor in maintaining agricultural productivity. Manual identification of leaf diseases requires expert knowledge and is prone to errors due to visual similarities among disease symptoms. This study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach. The dataset consists of plant leaf images representing three disease classes: Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot. Prior to model training, the images undergo preprocessing steps including image resizing and pixel normalization. The performance of the CNN model is evaluated using a testing dataset that is not involved in the training process, employing accuracy, confusion matrix, precision, recall, and F1-score as evaluation metrics. Experimental results show that the proposed model achieves a test accuracy of 95.56%, with balanced performance across all disease classes. In addition to quantitative evaluation, the trained model is implemented in a Streamlit-based application, allowing users to upload plant leaf images and obtain disease classification results interactively. The findings indicate that the CNN-based approach is effective for plant leaf disease classification and has potential application as an early decision-support system for plant health monitoring.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN分類法を開発し、独立テストデータで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach.
Reproduction assets foundThe paper's phenotyping input is a publicly available PlantVillage image dataset (900 leaf images across three disease classes) obtained from Kaggle, with an explicit authors' URL. No author analysis code or trained model is publicly deposited.Dataset · publicleaf disease images obtained from the PlantVillage Dataset, which is publicly available through the Kaggle platform [17]. The dataset is
organized using a folder-based class structure, where each folder represents a specific leaf disease category. In this study, three disease
classes are used—Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot—with 300 images per class, resulting in a total of
900 images.Open asset ↗Kagglepdf-page:3 lines:1-51Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early and precise diagnosis of diseases in tomato plants is critical in ensuring productivity in agriculture and reducing losses caused by diseases. Classical classification approaches, however, are often limited by different image quality, different lighting, low resolution, and imbalanced classes. In order to confront these issues, this paper suggests a hybrid ensemble model that integrates the advantages of deep learning, fuzzy logic, and a generative model to classify diseases successfully. The suggested approach combines three strong convolutional neural networks, ResNet-50, EfficientNet-B0, and DenseNet-121, into the adaptive ensemble system. Individual models are combined to form the final decision depending on the predictive accuracy and confidence level. The use of fuzzy logic to refine intelligently the decision-making process provides more flexibility than the decisions of the static ensemble methods. A Conditional Generative Adversarial Network (C-GAN) is used to alleviate the problem of class imbalance and overfitting through the production of multiple synthetic images of high quality. This, in turn, significantly enhances the generalization of models and even gives a balanced representation of the disease’s classes. The hybrid structure achieved a classification accuracy of 99.19% when tested on the PlantVillage dataset and outperformed the traditional ensemble and classical techniques. The results highlight the potential of the hybrid approach for real-world agricultural applications. This offers a scalable, accurate, and intelligent solution for automated plant disease diagnosis. This study contributes a novel, interpretable, and performance-driven model that can support sustainable agriculture through timely and precise disease management in tomato crops.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する深層学習・ファジー論理・生成モデルの統合手法を開発し、PlantVillageで性能評価しているため、植物病害フェノタイピング手法が中心です。
abstractthis paper suggests a hybrid ensemble model that integrates the advantages of deep learning, fuzzy logic, and a generative model to classify diseases successfully.
Reproduction assets foundThe paper uses four public tomato leaf image datasets (PlantVillage, Tomato Leaves, PlantDoc, Tomato-Village) plus two Mendeley-hosted datasets for robustness evaluation, and declares a public GitHub repository of C-GAN-generated tomato crop images under Code availability. No trained model checkpoints or analysis code/Dataset · publicTomato Leaves Dataset
https://www.kaggle.com/datasets/ashishmotwani/tomato?
Over 20,000 samples of tomato leaves with 10 diseases and 1 healthy class are availableOpen asset ↗html-lines:399-523Dataset · publicThe “Tomato-Village” dataset: The “Tomato-Village” dataset is designed to improve tomato disease detection in real-world agricultural conditions. It is available at the following link.: https://github.com/mamta-joshi-gehlot/Tomato-VillageOpen asset ↗Tomato-Villagehtml-lines:1009-1039Dataset · publicMendeley Data: This dataset contains images of tomato leaves afflicted with distinct diseases gathered under several conditions. This is available on link: https://data.mendeley.com/datasets/zfv4jj7855/1Open asset ↗html-lines:1009-1039Dataset · publicGTLD: The images in the dataset were taken with a DSLR and a quality mobile phone. Some images were taken in direct sunlight, while others were captured in shaded areas beneath the plants. This is available on link: https://data.mendeley.com/datasets/2bdfjb99k5/1Open asset ↗html-lines:1009-1039Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis in four economically vital agricultural crops: Bottle Gourd ( Lagenaria siceraria ), Zucchini ( Cucurbita pepo ), Papaya (Carica papaya), and Tomato ( Solanum lycopersicum ). Addressing the scarcity of region-specific agricultural data, a total of 5266 original images were acquired directly from diverse agricultural fields in Bangladesh using a SONY ALPHA 7 II full-frame camera under natural lighting conditions. The dataset encompasses 28 distinct classes, covering a wide spectrum of biotic stressors including viral (Mosaic Virus, Leaf Curl), fungal (Downy Mildew, Anthracnose, Alternaria Blight), bacterial (Bacterial Blight, Xanthomonas), and pest-induced damage (Insect Hole, White Spot), alongside Healthy samples. To ensure scientific reliability, each image underwent a rigorous two-stage validation process by senior agronomists. To tackle class imbalance and facilitate the training of data-intensive Deep Learning models, the dataset was expanded using a Python-based augmentation pipeline incorporating geometric transformations (rotation, flipping) and photometric adjustments (noise, brightness) resulting in a final repository of 28,000 images (5266 original and 22,734 augmented). All files are standardized to 512×512 pixels in JPG format. This expert-validated resource serves as a critical benchmark for developing robust computer vision algorithms (e.g., CNNs, Vision Transformers) for precision agriculture, enabling research into fine-grained classification, object detection, and cross-crop transfer learning in subtropical farming environments.
Why it matches plant phenotyping methods植物病害症状を画像で分類するための専門家検証済みデータセットを構築し、再利用可能なベンチマークとして提供しているため、植物表現型取得法が中心です。
abstractThis article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis
Reproduction assets foundThe paper is a Data in Brief article describing the Agri-Vision Bangladesh multi-crop leaf disease image dataset, publicly deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/8t6k37ztxc.2). This is a paper-specific public asset containing the original and augmented plant images used in the study.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/8t6k37ztxc.2
Direct URL to data: https://data.mendeley.com/preview/8t6k37ztxc?a=a88a48f1-a9b0-4354-a081-cc8f1e936364Open asset ↗Mendeley Data · 10.17632/8t6k37ztxc.2html-lines:93-117Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Ensuring smarter and more sustainable farming practices is a critical challenge in modern agriculture. Agentic Artificial Intelligence (AAI), combined with Precision Agriculture (PA) and Federated Learning (FL), has the potential to enhance decision-making, optimize resource utilization, and reduce environmental impact. Methods This study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level. A practical deployment architecture is outlined, detailing inter-device communication and localized intelligence. The proposed model is evaluated across two distinct datasets tomato disease classification and weed detection. The model is designed to have DenseNet121, MobileNetV2, EfficientDet-D0, and YOLOv8 as local models within a federated learning environment. Results The federated global model achieved an accuracy of 96.4%, outperforming individual client models, with DenseNet121 and MobileNetV2 attaining accuracies of 95.0% and 93.9%, respectively. For weed species detection, EfficientDet-D0 demonstrated superior performance, achieving an mAP@0.5 of 0.978, average precision of 0.865, and an F1-score of 0.961, compared to YOLOv8 with an mAP@0.5 of 0.956 and an F1-score of 0.935. Discussion The results confirm the feasibility and effectiveness of integrating AAI with federated learning for intelligent precision agriculture. A SWOT analysis highlights the strengths of the proposed approach, along with deployment challenges and constraints. Overall, this study establishes a roadmap for future research, emphasizing sustainable intelligent farming systems.
Why it matches plant phenotyping methods植物病害分類を含む連合学習・エージェント型AI基盤を提案し、データセット上で性能評価しているため、植物の病害状態を推定する計算的フェノタイピング手法が中心である。雑草検出も含まれるが、病害分類の技術評価が明示されている。
abstractThis study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level.
Reproduction assets foundThe paper evaluates its federated learning phenotyping models on two publicly available Kaggle image datasets (tomato disease classification and weedcrop detection), explicitly linked in the data availability statement and references. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · ashishmotwani/tomatolines:838-838Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · vinayakshanawad/weedcrop-image-datasetlines:838-838Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Automatic and accurate segmentation of cherry tomato maturity in natural environment is the foundation for automatic picking. Lacking of significant differences in adjacent maturity and the problem of mutual occlusion between fruits usually affect the picking process. According to the changes in phenotypic characteristics of cherry tomato during its mature period and the Chinese national standard GH/T 1193-2021, a lightweight maturity instance segmentation method of cherry tomato with 5 levels, including green, turning, pink, light red and red was proposed based on improved YOLOv8n-Seg model, named as MobileViTv3-SK-WIoU-YOLOv8n-Seg (MSW-YOLOv8n-Seg). Methods In this model, MobileViTv3 was introduced into the original YOLOv8 model as backbone for feature extraction to reduce the parameters of the original model; selective kernel (SK) attention module was added to the neck part to improve the feature expression ability of the model; the complete intersection over union (CIoU) loss function in the original head part was replaced with wise intersection over union (WIoU), which can effectively filter low-quality samples and improve the stability and reliability of the model in complex scenes. The proposed model can better balance the relationship between segmentation speed, accuracy, and model computational complexity. Results The experimental results show that the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively, and the model size was 6.0 MB. Compared with YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN (Mask region-based convolutional neural network) and Mask2Former, the bounding box precision increased by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0%, the recall increased by 7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1%, and the mAP@0.5 increased by 10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4% respectively. In terms of inference speed, the MSW-YOLOv8n-Seg has the highest inference speed, with FPS of up to 52.9 f·s -1 and latency of only 18.2ms, which demonstrates its real-time processing capability. Discussion The results show that the improved MSW-YOLOv8n-Seg model is optimal, and it suitable for instance segmentation scenarios with high real-time performance and can provide effective exploration for automated cherry tomato fruit picking.
Why it matches plant phenotyping methodsチェリートマト果実の成熟度という植物状態を画像から推定するインスタンスセグメンテーション手法を開発し、精度・速度・モデルサイズを比較検証しており、表現型取得法が中心である。
abstracta lightweight maturity instance segmentation method of cherry tomato with 5 levels, including green, turning, pink, light red and red was proposed based on improved YOLOv8n-Seg model
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was merged into 5-levels based on the actual growth and peel color changes of cherry tomatoes.Open asset ↗lines:279-289Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding 87.35% accuracy for tomato maturity and R 2 = 0.878 for fruit firmness. In cross-software benchmarks, PlantSpecLab matched the accuracy of ENVI and code-based Python pipelines while reducing end-to-end workflow time by >90% (>80 min to ∼8 min). PlantSpecLab provides a transparent, efficient analytical environment that lowers the technical barrier to HSI analysis. This enables researchers to prioritize biological interpretation while minimizing computational overhead.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から表現型特徴を抽出・モデル化するオープンソース基盤を開発し、既存ソフトウェアとの性能・処理時間を比較検証しているため、フェノタイピング手法が中心である。
abstractwe developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface.
Reproduction assets foundThe authors explicitly state the PlantSpecLab source code (the platform used for all phenotyping analyses in the paper) is publicly available on GitHub under an MIT license, with a versioned release archived alongside the data.Code · publicsis. Jingye Liu: Data curation. Chu Zhang: Supervision, Writing—review & editing. Wei Xu: Supervision, Funding acquisition, Writing—review & editing.
Data and code availability
All data and code that support the findings of this study will be made publicly available upon publication. The PlantSpecLab source code is available at https://github.com/Another-Train/PlantSpecLab (MIT License), with a versioned release archived alongside the data.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62265015 and 32360750), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant No. 2023B02028-3), and the Finance Plan Project of the 8th Division of the Open asset ↗Another-Train/PlantSpecLablines:458-487Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoGreenhouseMultimodalRGB / grayscaleMultispectral / hyperspectralFruitClassificationSegmentationGrowth / development / phenology
Computer vision and multispectral imaging have increasingly become essential tools in modern precision agriculture. Accurate ripeness assessment is critical for yield optimization, reducing post-harvest losses, and enabling automated harvesting systems. However, traditional RGB-based approaches struggle to differentiate subtle maturity changes, and existing solutions often fail under varying lighting, occlusion, or cultivar-specific conditions. To address these challenges, this study focuses on the integration of complementary spectral cues for reliable tomato ripeness evaluation. The work utilizes a curated RGB-NIR tomato dataset comprising 224 hyperspectral samples, processed into aligned multimodal image pairs with balanced ripeness categories.The proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues. The novelty of the methodology lies in the dynamic cross-attention mechanism, which learns inter-modal dependencies between RGB and NIR signals for enhanced ripeness interpretation. Performance metrics including accuracy, precision, recall, F1-score, mIoU, and AUC were used to comprehensively evaluate the system. Experimental results demonstrate that TomatoRipen-MMT significantly outperforms all baseline RGB-only, NIR-only, and fusion methods, achieving 94.8% classification accuracy and 82.6% mIoU. These findings establish the effectiveness of multimodal Transformers for robust, high-precision fruit maturity assessment in controlled and greenhouse environments.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を、RGB・NIR画像融合とTransformerで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中心です。
abstractThe proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues.
Reproduction assets foundThe paper's phenotyping analysis is built on a publicly available USDA/NAL hyperspectral tomato dataset, explicitly linked in the Data Availability statement with an exact URL match. No author code or model checkpoints are disclosed.Dataset · publicThe dataset analyzed in this study is publicly available at the https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_b_Hyperspectral_Imaging_Analysis_for_Early_Detection_of_Tomato_Bacterial_Leaf_Spot_Disease_b_/26046328.Open asset ↗26046328html-lines:1038-1053Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tomato growth is highly susceptible to diseases, making accurate identification crucial for timely intervention. While deep learning models like the YOLO family have demonstrated success in detecting diseases in agricultural settings, they typically assume that training and testing data are independently and identically distributed (i.i.d.), which often doesn't hold in real-world scenarios. When pre-trained models are applied to new environments, performance can degrade due to domain shifts. To address this, we propose CTTA-DisDet, a continuous test-time domain adaptation framework for tomato disease detection that adapts models to evolving environments during testing, improving generalization in unseen domains. CTTA-DisDet utilizes a teacher-student architecture where both models share the same structure. Dynamic data augmentation is introduced, involving explicit and implicit augmentations. Explicit augmentation corrupts input images, while implicit augmentation uses large language models (LLMs) to generate new domain data. The teacher model learns generalized knowledge, and the student model mimics the teacher to distill domain-specific information. During testing, pseudo-labels generated by the teacher update the student model. To prevent catastrophic forgetting, a subset of neurons is randomly restored to their original weights during each test-time iteration. The teacher model is continuously updated via exponential moving average (EMA). Experimental results demonstrate that CTTA-DisDet achieves an impressive 67.9% performance in continuously changing cross-domain environments, significantly benefiting practical applications in non-stationary settings.
Why it matches plant phenotyping methodsトマトの病徴を画像から検出するためのドメイン適応手法を提案し、異なる環境で性能評価しており、植物病害状態の表現型取得が中心である。
abstractwe propose CTTA-DisDet, a continuous test-time domain adaptation framework for tomato disease detection that adapts models to evolving environments during testing, improving generalization in unseen domains.
Reproduction assets foundThe article's data availability statement says the required data was deposited in a public Zenodo repository with a DOI matching an allowed URL. The deposit is paper-specific (the data used for the tomato disease detection experiments), though the statement does not detail contents (e.g., whether code/models are also)Dataset · publicThe required data has been deposited in a public repository. It is available on Zenodo at the following https://doi.org/10.5281/zenodo.17659449.Open asset ↗Zenodo · 10.5281/zenodo.17659449html-lines:1047-1068Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
In the context of precision agriculture, high-throughput phenotyping (HTP) aims to rapidly and effectively identify factors that affect crop yield, enabling timely and appropriate interventions. However, interpreting data from HTP remains challenging. We performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN). We aimed to determine if RGB-based HTP is effectively able to: a) distinguish the effects of biotic from abiotic stress; b) differentiate resistant/tolerant from susceptible genotypes. Our HTP data analysis produced 12 morphometric and eight colorimetric indices. Principal Component Analysis (PCA; P < 0.0001; 83 % variation explained by three PCs) showed that factors such as shoot area solidity and certain color-based indices, including the senescence index and green area, effectively differentiated biotic from abiotic stress. Morphometric parameters, including plant height, projected shoot area, and convex hull area, proved to be applicable for identifying the stress status regardless of the type of stress. HTP effectively distinguished the genotype resistant to TSWV from the susceptible ones. This task was more challenging for below-ground stresses like CRR and RKN. Different profiles of HTP indices were observed among the genotypes assayed for drought tolerance, indicating variability in their ability to withstand drought conditions. In conclusion, our findings highlight the value of RGB-based HTP as a tool for precision farming of tomatoes, enabling the identification of both biotic and abiotic stressors.
Why it matches plant phenotyping methodsトマトのRGBベース高スループット表現型解析を用い、形態・色彩指標の抽出と、ストレス識別および遺伝子型判別への有効性を評価しており、フェノタイピング手法が研究の中心である。
abstractWe performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN).
Reproduction assets foundThe paper's HTP dataset (20 indices from five stress experiments) is stated to be available in the supplementary material hosted at the article DOI, which qualifies as a paper-specific public phenotype dataset. However, the analysis code has no public deposit: it is only available from the corresponding author upon 'a'Dataset · publicThe data collected and used in this study are available in the supplementary material. The code used for analysis is available from the corresponding author, GBu, upon reasonable request.Open asset ↗lines:400-518Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Varieties show their unique characteristics in morphology, growth, and fruits. Tomato maturity is related to multiple dimensional characteristics including color, texture, smell, etc. An effective classification method of tomato variety and maturity is crucial for evaluating its growth and yield. However, due to the complex growth environment, some problems such as leaf occlusion and fruit shaded by each other make it difficult to accurately and efficiently identify them. To solve these problems, this study innovatively proposes a simultaneous detection model on tomato variety and maturity based on improved YOLOv8n, with the combination of frequency-adaptive dilated convolution (FADC) feature extraction module and the high-level screening-feature path aggregation network (HSPAN) with the aim of local and global feature fusion by the channel attention module and feature selection fusion mechanism. In addition, we use the Powerful-IoU (PIoU) loss function to replace the original Complete IoU (CIoU) to enhance the accuracy of bounding boxes. We also introduce a dynamic detection head as the final output of the model, which can adaptively adjust the focus of feature extraction according to the color and size of tomato fruits, thereby improving the recognition accuracy. Experimental results show that our model with better global perception capability achieves the highest detection accuracy and lower computation complexity among the comparative models.
Why it matches plant phenotyping methodsトマト果実の成熟度という観察可能な植物状態を画像から推定する検出モデルを開発しており、特徴抽出・検出ヘッド・損失関数の改良と比較評価が研究の中心である。
abstractthis study innovatively proposes a simultaneous detection model on tomato variety and maturity based on improved YOLOv8n
Reproduction assets foundThe paper's tomato variety/maturity detection experiments are built on the public Laboro Tomato dataset, which is explicitly cited with a public GitHub URL. No author analysis code, trained models, or supplementary assets are reported as available.Dataset · publicThe constructed dataset in this study is based on the Laboro Tomato open-access dataset [ 24 ], which is an image dataset of tomatoes with different maturity collected in a greenhouse in winter (15 December 2019).Open asset ↗lines:33-42Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In this study, we propose a multi-scale feature fusion network based on an improved RT-DETR model for the efficient detection of tomato leaf disease. Our model combines the multi-scale extended residual module by capturing contextual information at various scales and the multi-scale feature pyramid network by integrating feature information from different levels, which improves feature extraction capability and reduces the interference of complex backgrounds on feature extraction, thereby improving information transmission efficiency and the accuracy of the model. In addition, the novel loss function called adaptive focal loss (AFL) was used, which is based on traditional focal loss with the introduction of attenuation factors to focus the model's attention to high-loss features to alleviate overfitting and of dynamic weight adjustment mechanisms to focus on the more important features during the training process to improve the overall learning performance. Another significant advantage of AFL is that it can more efficiently improve the detection accuracy on imbalanced datasets than on balanced datasets. These innovations optimized the learning strategy of the model, making AP@0.50 up to 97.9% on detecting the categories of tomato diseases. In addition, this model also achieves the high detection accuracy of 85.4% on other crop diseases. These results provide valuable references for agriculture applications.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出する深層学習手法を提案・改良しており、植物病害表現型の抽出法が研究の中心である。
abstractwe propose a multi-scale feature fusion network based on an improved RT-DETR model for the efficient detection of tomato leaf disease.
Reproduction assets foundThe paper constructs its LAB and ENV tomato leaf disease detection datasets by selecting and adjusting samples from two public Roboflow Universe datasets (cited as refs 30 and 31), which are public image inputs directly underlying this paper's phenotyping measurements. Both Roboflow URLs are given in the reference listDataset · public7-024-01188-1
38725014
PMC11080254
29.
Sun H.
Fu R.
Wang X.
Wu Y.
Al-Absi M.A.
Cheng Z.
Chen Q.
Sun Y.
Efficient Deep Learning-Based Tomato Leaf Disease Detection through Global and Local Feature Fusion
BMC Plant Biol. 2025 25 311
10.1186/s12870-025-06247-w
40069604
PMC11895386
30.
Sujansurya
Roboflow Universe
Available online: https://universe.roboflow.com/sujansurya/tomato_object (accessed on 15 August 2024)
31.
Roboflow Universe
Available online: https://universe.roboflow.com/classificationwithyolov8/tomato-leaf-diseases-4xa5i-3ajin-kyo9w (accessed on 15 August 2024)
32.
Singh D.
Jain N.
Jain P.
Kayal P.
Kumawat S.
Batra N.
PlantDoc: A Dataset for Visual Plant Disease Detection
ProceedingOpen asset ↗Roboflow Universelines:265-465Dataset · publicng-Based Tomato Leaf Disease Detection through Global and Local Feature Fusion
BMC Plant Biol. 2025 25 311
10.1186/s12870-025-06247-w
40069604
PMC11895386
30.
Sujansurya
Roboflow Universe
Available online: https://universe.roboflow.com/sujansurya/tomato_object (accessed on 15 August 2024)
31.
Roboflow Universe
Available online: https://universe.roboflow.com/classificationwithyolov8/tomato-leaf-diseases-4xa5i-3ajin-kyo9w (accessed on 15 August 2024)
32.
Singh D.
Jain N.
Jain P.
Kayal P.
Kumawat S.
Batra N.
PlantDoc: A Dataset for Visual Plant Disease Detection
Proceedings of the Proceedings of the 7th ACM IKDD CoDS and 25th COMAD Hyderabad, India 5–7 January 2020
ACM
New York, NY, USA
2020 24Open asset ↗Roboflow Universelines:265-465Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
TomatoMultimodalStereoFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traitsYield / yield components
Introduction The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. Methods To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. The system, implemented on a Jetson TX2 embedded platform, also incorporates ZED stereo vision for 3D localization and a PyQt6-based visualization platform. Results When implemented on Jetson TX2, the system achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, it establishes standardized assessment systems for tomato maturity and yield prediction, and offers integrated modules for disease diagnosis and agricultural large language model consultation. Discussion This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Why it matches plant phenotyping methodsトマトの成熟度・収量予測および病害診断を含む画像・3Dビジョン基盤を開発し、エッジ環境で性能評価しているため、植物表現型取得が中心的な研究である。
abstractan intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm
Reproduction assets foundThe paper's data availability statement explicitly states that the data and code supporting the study are publicly available on GitHub at the authors' repository (GAE-YOLO), which matches an allowed URL. This qualifies as a paper-specific public code asset for the tomato detection/phenotyping analysis.Code · publicThe data and code supporting this study are publicly available at GitHub under the following links: https://github.com/NSSCk/GAE-YOLO .Open asset ↗NSSCk/GAE-YOLOlines:756-834Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing.
Why it matches plant phenotyping methods植物の生体電気記録をセンサーで取得し、特徴抽出と機械学習によって植物の状態差を判別する方法が研究の中心であり、単なる生理測定ではない。
abstractWe introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions.
Reproduction assets foundThe paper's plant bioelectric recordings (wav sensor data from basil, salad, tomato with/without nearby human movement) are publicly deposited on figshare, with an explicit Data Availability Statement and URL matching an allowed URL.Dataset · publicThe datasets generated and analyzed during the current study are available from figshare https://figshare.com/articles/dataset/Machine_Learning_Detection_of_Plant_Bioelectric_Responses_to_Human_Eurythmic_Gestures_/30227083?file=58324288 (accessed 25 October 2025).Open asset ↗figshare · 30227083lines:172-191Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops.
Why it matches plant phenotyping methods深度カメラ、画像処理、深層学習を統合し、トマト果実の12形質を自動抽出・定量する装置とソフトウェアを開発しており、表現型取得法が研究の中心である。
abstractAn intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits some datasets, model weights, and code used in the study at a public GitHub repository, which is paper-specific and actionable. Full self-developed datasets require contacting the corresponding author.Code · publicSome datasets, model weights, and code used in the present study are available at https://github.com/Snail-code-wq/Plants_Tomato_2025 (accessed on 5 November 2025). All self-developed datasets can be obtained by contacting the corresponding author.Open asset ↗Snail-code-wq/Plants_Tomato_2025lines:466-479Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Common beanPotatoTomatoField / plotLeafObject detectionDisease symptoms / severity
In order to overcome the key challenges associated with detecting tomato leaf disease in complex agricultural environments, such as leaf occlusion, variation in lesion size and light interference, this study presents a lightweight detection model called ToMASD. This model integrates multi-scale feature decoupling and an adaptive alignment mechanism. The model innovatively comprises a dual-branch adaptive alignment module (TAAM) that achieves cross-scale lesion semantic alignment via a dynamic feature pyramid, a local context-aware gated unit (Faster-GLUDet) that uses a spatial attention mechanism to suppress background noise interference, and a multi-scale decoupling detection head (MDH) that balances the detection accuracy of small and diffuse lesions. On a dataset containing six types of disease under various weather conditions, ToMASD achieves an average precision of 84.3%,.by a margin of 4.7% to 12.1% over thirteen mainstream models. The computational load is compressed to 7.1 GFLOPs. Through the introduction of a transfer learning paradigm, the pre-trained weights of the tomato disease detection model can be transferred to common bean and potato detection tasks. Through domain adaptation layers and adversarial feature decoupling strategies, the domain shift problem is overcome, achieving an average precision of 92.7% on the target crop test set. False detection rates in foggy and strong light conditions are controlled at 6.3% and 9.8%, respectively. This study achieves dual breakthroughs in terms of both high-precision detection in complex scenarios and the cross-crop generalization ability of lightweight models. It provides a new paradigm for universal agricultural disease monitoring systems that can be deployed at the edge.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するモデルを開発し、複数モデル比較、悪条件評価、他作物への汎化検証を行っており、植物病害フェノタイピング手法が中心である。
abstractthis study presents a lightweight detection model called ToMASD.
Reproduction assets foundThe paper's data availability statement points to a public potato disease dataset hosted on GitCode, which was used in the study's cross-crop transfer experiments (potato disease detection). The tomato dataset is from Roboflow (third-party platform, no direct URL given), and no author analysis code or trained model is,Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://gitcode.com/open-source-toolkit/829ec .Open asset ↗gitcode.com/open-source-toolkit/829eclines:649-666Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物の画像ベース表現型を含むマルチオミクス統合と機械学習解析を自動化するツールを開発・適用しており、表現型解析ワークフローが中心的です。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Code / 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
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Value of the Data
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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 · checked 15 Sept 2026
Computer vision has emerged as a critical enabler of sustainable production in protected agriculture by offering efficient and non-invasive crop disease diagnosis. The development of accurate disease recognition models relies heavily on the availability of high-quality image datasets. This study introduces a tomato disease image dataset collected in 2024 from greenhouse facilities within a modern agricultural park in Sichuan Province, China. The dataset comprises 1026 high-resolution images, including 417 images of viral disease, 82 images of gray mold, and 527 images of bacterial wilt, totaling approximately 2.78 GB. Captured under real-world greenhouse conditions and from multiple angles and distances, the images effectively capture multi-scale phenotypic disease features. Manual annotation was conducted using the LabelImg tool under the guidance of plant pathology experts, with labeled regions covering leaves, fruits, and stems. Annotation files are stored in XML format, each corresponding to a specific image. This dataset is well-suited for research in disease classification, object detection, and phenotyping, and supports deep learning model training and cross-crop transfer learning applications.
Why it matches plant phenotyping methodsトマト病害の症状を画像で捉え、分類・検出モデル用に専門家アノテーションした再利用可能なデータセットであり、植物病害状態の表現型取得が中心である。
abstractThe development of accurate disease recognition models relies heavily on the availability of high-quality image datasets.
Reproduction assets foundThe paper is a Data in Brief article describing a public tomato disease image dataset (1026 annotated images) deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL exactly.Dataset · publicwas conducted at the Modern Agricultural Science and Technology Innovation Demonstration Park of the Sichuan Academy of Agricultural Sciences (30.7797° N, 104.2082° E), located in Sichuan Province, China.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c2×8rynybg.1
Direct URL to data: https://data.mendeley.com/datasets/c2×8rynybg/1
Related research article
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Value of the Data
The dataset contains 1026 annotated images of tomato plants exhibiting three major disease types, collected in 2024 from greenhouse environments in Sichuan’s Modern Agricultural Demonstration Park. Plant pathology specialists manually labeled all samples. Its technical sOpen asset ↗Mendeley Data · 10.17632/c2×8rynybg.1lines:1-52Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Global climate change and urbanization have posed challenges to sustainable food production and resource management in agriculture. Vertical farming, in particular, allows for high-density cultivation on limited land but requires precise control of crop height to suit vertical farming systems. Tomato, a globally significant vegetable crop, urgently requires mutant varieties that suppress indeterminate growth for effective cultivation in vertical farming systems. In this study, we utilized the CRISPR-Cas9 system to develop a new tomato cultivar optimized for vertical farming by editing the Gibberellin 20-oxidase ( SlGA20ox ) genes, which are well known for their roles in the "Green Revolution". Additionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence. The proposed model achieved over 84 % classification accuracy in distinguishing triple-determinate and slga20ox gene-edited plants, outperforming traditional machine learning methods and 1D-CNN approaches. Unlike previous studies that primarily relied on manual feature extraction from chlorophyll fluorescence data, this research introduced a deep learning framework capable of automating feature extraction in three dimensions while learning the temporal characteristics of chlorophyll fluorescence imaging data. The study demonstrated the potential to classify tomato plants customized for vertical farming, leveraging advanced phenotypic analysis methods. Our approach explores new analytical methods for chlorophyll fluorescence imaging data within AI-based phenotyping and can be extended to other crops and traits, accelerating breeding programs and enhancing the efficiency of genetic resource management.
Why it matches plant phenotyping methodsトマトのクロロフィル蛍光画像から遺伝子編集植物を識別する3次元深層学習モデルを提案し、既存手法と比較評価しており、表現型取得・抽出法が研究の中心である。
abstractAdditionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence.
Reproduction assets foundThe authors state that the dataset and source code used in this study are publicly available on GitHub, which qualifies as a paper-specific public code asset for the CF 3D-CNN phenotyping analysis.Code · publicThe dataset and source code used in this study are available at https://github.com/youzh-all/CF_3D-CNN .Open asset ↗https://github.com/youzh-all/CF_3D-CNN · CF_3D-CNNlines:358-392Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提示し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を直接支援するため、方法論が中心である。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
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 · publicIn addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUROpen asset ↗WUR-ABE/TomatoWURlines:1-45Code / dataset availability confirmedCrossref · checked 6 Sept 2026
The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics.
Why it matches plant phenotyping methodsトマト葉の画像検出を改善する深層学習アーキテクチャ自体が中心的な技術貢献であり、植物器官の画像ベース取得・推定手法に該当する。
abstractThis paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments
Reproduction assets foundThe authors explicitly state that the data (custom tomato leaf detection dataset with ground-truth annotations) and code used in this paper are publicly available in their GitHub repository andros1206/Leaf-Detection. This is a paper-specific, publicly actionable asset reproducing the paper's phenotyping images/labels (Code · publicData Availability Statement: We make the data and code used available at https://github.com/
andros1206/Leaf-DetectionOpen asset ↗andros1206/Leaf-Detectionpdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
With the increasing demand for precision agriculture, automatic detection of tomato leaf diseases has become a critical technological challenge in smart agriculture. Among various diseases, Tomato Yellow Virus Leaf, due to its unique pathological characteristics, presents a particularly challenging identification target. Traditional image recognition methods often fail to meet the high-precision detection requirements for this disease, leading to delayed responses in disease control by farmers, which severely impacts tomato yield and quality. To address this issue, this paper proposes an optimized YOLOv8n algorithm, incorporating a C2f-DynamicConv optimization module. By dynamically adjusting the weights of convolutional kernels, the model can adapt to the characteristics of different input data, thereby enhancing its ability to represent diverse features. Additionally, we introduce the SimAM attention mechanism, which enhances the model's focus on key areas by weighting the feature map, significantly improving the accuracy of disease detection while filtering out irrelevant features and enhancing sensitivity. During the upsampling process, we adopt the Dysample upsampling operator, optimizing the quality of feature map reconstruction and improving detection resolution through a refined upsampling strategy. To better address the bounding box regression problem in object detection, we incorporate the GIoU loss function. Compared to traditional loss functions, GIoU performs excellently in handling bounding box overlap and positional accuracy, further improving the model's detection performance. Experimental results show that the improved model achieves an average precision of 81.8%, precision of 77.1%, and recall of 77.4%. Compared to existing methods, our approach shows significant advantages in detection accuracy, localization precision, and model computational efficiency, achieving improved detection performance on the tomato leaf disease dataset.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出する改良YOLOv8n手法を開発し、精度・再現率・位置特定性能を評価しており、植物の病害状態の取得が研究の中心である。
abstractExperimental results show that the improved model achieves an average precision of 81.8%, precision of 77.1%, and recall of 77.4%.
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 · publicData availability
The datasets can be downloaded in https://github.com/weilaibot/sanyau_02.git.Open asset ↗weilaibot/sanyau_02pdf-page:20 lines:1-65Code / dataset availability confirmedarXiv · checked 6 Sept 2026
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Why it matches plant phenotyping methods植物の多視点画像取得、アノテーション付きデータセット、深層学習による分類・検出・セグメンテーションを中心に開発・検証した、明確な植物フェノタイピング手法研究です。
abstractwe developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols.
Reproduction assets foundThe paper's TomatoMAP dataset (images, annotations) is publicly deposited in e!DAL at IPK with an explicit DOI URL given in the Data Records section.Dataset · publicDataset is deposited in e!DAL (electronic data archive library) of IPK (Leibniz Institute of Plant Genetics
and Crop Plant Research): https://doi.ipk-gatersleben.de/DOI/10bb9f14-ce90-4747-836f-cf61dfb5eea1/Open asset ↗e!DAL · 10bb9f14-ce90-4747-836f-cf61dfb5eea1pdf-page:7 lines:1-73Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5-70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R 2 ) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51-56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques' data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems.
Why it matches plant phenotyping methodsトマト果実の遮蔽点群を補完し、果実の長さ・幅・高さを推定する新規アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractThis study proposes an adaptive symmetry self-matching (ASSM) algorithm.
Reproduction assets foundThe paper's Data Availability Statement deposits the study's tomato/eggplant fruit point cloud data on ScienceDB, a public repository, making the paper-specific phenotyping data (3D point clouds of 150 tomato fruits used for completion and trait measurement) publicly actionable.Dataset · publicData Availability Statement
The data are available online at https://doi.org/10.57760/sciencedb.25084 .Open asset ↗sciencedb · 10.57760/sciencedb.25084lines:312-345Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring plant physiology. However, challenges remain in identifying direct physiological indicators of plant stresses, hindering the development of accurate diagnostic models for predicting symptom progression. Here, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes. MapS-Wear continuously tracks leaf surrounding temperature, humidity, and unique in-situ transmission spectra, which are critical stress-related indicators. The machine learning framework processes these multimodal data to predict gradual stress progression and diagnose nutrient deficiencies in plants over 10 days earlier than conventional computer vision methods. Moreover, MapS-Wears enables portable and large-scale screening of grafted tomato varieties in greenhouses, accelerating the identification of compatible grafting combinations. This demonstration highlights the potential for high-throughput plant phenotyping and yield improvement.
Why it matches plant phenotyping methods植物ストレスの生理状態を連続センシングし、機械学習で早期診断・進行予測するウェアラブル計測システムが研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractHere, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes.
Reproduction assets foundThe paper's Data and materials availability statement explicitly deposits the tomato leaf photos, transmission spectral data, and ML algorithms on Zenodo, matching an allowed URL.Dataset · publicThe photos of tomato leaves in different health statuses, the transmission spectral data of these leaves, and the ML algorithms are openly available on Zenodo ( https://zenodo.org/doi/10.5281/zenodo.15192884 ).Open asset ↗Zenodo · 10.5281/zenodo.15192884lines:129-274Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Tomato leaf disease poses a significant threat to global agricultural productivity, underscoring the need for accurate and automated segmentation techniques for early detection and intervention. In this study, we proposed DSGSU-Net, an enhanced U-Net-based architecture explicitly designed for the precise segmentation of tomato leaf diseases. The model incorporates depthwise separable convolutions for efficient feature extraction, dilated convolutions in deeper layers for multi-scale context aggregation, and Ghost Sampling in the decoder for improved upsampling. To further enhance segmentation performance, a hybrid loss function combining Dice Loss and Focal Loss is utilized to manage class imbalance and enhance the boundary delineation. Experiments conducted on the PlantVillage dataset (bacterial spot class) demonstrated that DSGSU-Net achieved an accuracy of 0.9572, an F1-score of 0.8276,precision of 0.7156,recall of 0.9885, IoU of 0.7102, and a Dice coefficient of 0.9822. The results show that DSGSU-Net outperforms conventional U-Net models in segmentation accuracy and computational efficiency, making it a strong contender for practical use in precision agriculture and disease surveillance.
Why it matches plant phenotyping methodsトマト葉の病徴を画像からセグメンテーションするモデルを開発・比較しており、植物病害状態の表現型抽出が研究の中心である。
abstractwe proposed DSGSU-Net, an enhanced U-Net-based architecture explicitly designed for the precise segmentation of tomato leaf diseases.
Reproduction assets foundThe paper's tomato leaf disease segmentation study uses the public PlantVillage tomato leaf dataset (bacterial spot class) from Kaggle, explicitly declared in the Data Availability section. No author analysis code, trained model checkpoints, or custom mask annotations are stated as publicly available.Dataset · publicThe datasets generated and analyzed during the current study are available in the Kaggle repository: https://www.kaggle.
com/datasets/charuchaudhry/plantvillage-tomato-leaf-datasetOpen asset ↗Kaggle · plantvillage-tomato-leaf-datasetpdf-page:20 lines:1-51Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeSugarcaneTomatoLiDAR / point cloudLeafStem / branchSegmentation
Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 % and 87.15 %, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 %, 85.60 %, 87.94 %, and 81.46 %, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.
Why it matches plant phenotyping methods植物器官の3D点群から茎・葉のインスタンスを抽出する手法を開発・比較検証しており、器官レベル表現型推定のための中心的なフェノタイピング手法である。
abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.
Reproduction assets foundThe authors state their dataset and code were uploaded to a public GitHub repository, and the paper's maize/tomato point cloud inputs come from the public Pheno4D dataset. Both are paper-specific, public, and actionable.Code · publicThe dataset and the code have been uploaded to Github: https://github.com/ice3664/3d-plant-organ-segmentation/tree/master.Open asset ↗https://github.com/ice3664/3d-plant-organ-segmentation/tree/masterhtml-lines:555-579Dataset · publicthe point clouds of maize and tomato were selected from the Pheno4D dataset [25] which can be accessed via https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗html-lines:109-124Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-of-the-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉画像から病害の有無・種類を推定する画像ベースの病害表現型計測法をYOLOモデルで開発・評価しており、手法が研究の中心です。
abstractThis study proposed a system designed to identify and monitor plant diseases using a transfer learning approach.
Reproduction assets foundThe paper's Data Availability statement provides a public Google Drive link to the study data and a public GitHub repository, and the study's input images come from the public Roboflow 'Detecting Diseases' dataset. These are paper-specific, publicly accessible assets supporting the plant disease detection experiments.Dataset · publicone who inspired our work.
Author contributions
BSM: conceptualization and drafting, HSN: data collection and analysis. MNW: Methodology and review. NOF: Drafting and interpretation. CCZ: Writing and method. HDA: writing and Data Collection; EMO: Drafting, Supervision and Editing.
Data availability
The data can be accessed via: https://drive.google.com/file/d/1kA_JWhHQhyzzuzlpzppK2nNTtbiR2N77/view . https://github.com/Sachinthana-Lokuyaddage/Plant_Disease_Detection_Using_Transfer_Learning_with_ResNet50 .
Declarations
Competing interests
The authors declare no competing interests.
Consent to participate
All authors consent to participate.
Footnotes
Publisher’s note
Springer Nature remains neuOpen asset ↗lines:209-247Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Agricultural productivity is a crucial determinant of economic stability. Within the agricultural sector, particularly in tomato production, the impact of plant diseases and pests poses a significant challenge. Detecting the severity of disorders in tomato plants is essential for addressing these challenges. Achieving accurate and rapid detection is imperative for developing early treatment strategies, ultimately minimizing economic losses. While various researchers have explored solutions using convolutional neural network (CNN) models to identify and classify disease severity in tomatoes, the limited availability of training data has led to overfitting issues and inter-class similarity, resulting in suboptimal performance measures. To address the overfitting problem arising from insufficient data, this research proposes a deep transfer-based framework. Three CNN models i.e., AlexNet, SqueezeNet, and InceptionV3 are employed to classify disease severity in tomato plants, specifically targeting tomato late blight, tomato early blight, tomato leaf mold, tomato bacteria spot, and healthy tomato leaves using the PlantVillage dataset. The study incorporates a weighted-cluster loss function to mitigate inter-class similarities. Computational accuracy serves as the performance metric. Following experimentation, InceptionV3 demonstrated the highest classification accuracy at 93.66%, surpassing AlexNet (83.03%) and SqueezeNet (80.09%). Consequently, the proposed system functions as a decision support tool for farmers, aiding in the identification of disorder severity in tomato plant leaves.
Why it matches plant phenotyping methodsトマト葉の病害重症度という植物状態をCNNで画像分類し、重み付きクラスタ損失と複数モデル比較を中心的に評価しているため、植物フェノタイピング手法として採用。
abstractthis research proposes a deep transfer-based framework
Reproduction assets foundThe paper's tomato disorder severity classification experiments were performed on the publicly available PlantVillage Tomato Leaf Disease dataset, which the authors state is maintained on Kaggle and originally sourced from the PlantVillage project. No author analysis code, trained model checkpoints, or other paper-phenDataset · publicThe dataset used in this study is publicly available and was obtained from the PlantVillage Tomato Leaf Disease
dataset. The dataset contains images of healthy and diseased tomato plant leaves, categorized into multiple
disease classes. It is maintained on Kaggle and originally sourced from the PlantVillage project.Open asset ↗Kagglepdf-page:15 lines:1-40Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract The identification of plant diseases has become increasingly challenging due to the interference of complex backgrounds in images, which often hinders the accuracy of classification models. Recent studies have employed various Deep Learning (DL) techniques to overcome this issue, utilizing both publicly available and custom datasets. However, achieving high accuracy while managing background complexity remains a significant hurdle. This paper aims to address this challenge by introducing a two-step DL approach for plant disease classification. The approach begins with an enhanced Convolutional Neural Network (CNN), developed through a comparative analysis of several CNN architectures, including customized and cascaded versions of prominent DL models, achieving an accuracy of 93.3%. To further enhance accuracy, segmentation techniques such as DeepLabV3+, UNet, Iterative UNet, and UNet with Atrous Spatial Pyramid Pooling (ASPP) are integrated before applying customized CNN architectures. These segmentation methods effectively isolate diseased portions of leaf images, improving classification performance. The proposed methodology introduces model pruning to optimize performance and computational efficiency by removing redundant parameters and less significant features. The UNet with ASPP architecture, in combination with pruning strategies, significantly reduces time complexity and feature redundancy, leading to an impressive accuracy of 99.8%. This approach outperforms other existing models in terms of accuracy and efficiency. The model is trained on the Plant Village dataset, which includes 10 different diseases across plant species such as tomato, corn, and potato, offering a comprehensive solution for plant disease identification.
Why it matches plant phenotyping methods植物病葉画像から病変部を分離し、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。
abstractThis paper aims to address this challenge by introducing a two-step DL approach for plant disease classification.
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 manuscript data set can be freely downloaded from
https://www.tensorflow.org/datasets/catalog/plant_villageOpen asset ↗plant_villagepdf-page:33 lines:1-34Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
Large-scale manual measurements of plant architectural traits in tomato growth are laborious and subjective, hindering deeper understanding of temporal variations in gene expression heterogeneity. This study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system. The SegFormer with fusion of multispectral and depth imaging modalities was employed to semantically segment plant organs from the registered RGB-D and multispectral images. Organ point clouds were then generated and clustered into instances. Finally, six key architectural traits, including fruit spacing (FS), inflorescence height (IH), stem thickness (ST), leaf spacing (LS), total leaf area (TLA), and leaf inclination angle (LIA) were extracted and the temporal broad-sense heritability folds were plotted. The root mean square errors (RMSEs) of the estimated FS, IH, ST, and LS were 0.014, 0.043, 0.003, and 0.015 m, respectively. The visualizations of the estimated TLA and LIA matched the actual growth trends. The broad-sense heritability of the extracted traits exhibited different trends across the growth stages: (i) ST, IH, and FS had a gradually increased broad-sense heritability over time, (ii) LS and LIA had a decreasing trend, and (iii) TLA showed fluctuations (i.e. an M-shaped pattern) of the broad-sense heritability throughout the growth period. The developed system and analytical approach are promising tools for accurate and rapid characterization of spatiotemporal changes of tomato plant architecture in controlled environments, laying the foundation for efficient crop breeding and precision production management in the future.
Why it matches plant phenotyping methods植物形態形質を取得するUGV型マルチモーダル画像フェノタイピングシステムと解析手法の開発・定量評価が研究の中心であり、誤差検証も行っているため。
abstractThis study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' trait-extraction pipeline code and example data on a public GitHub repository, matching the allowed URL.Code · publicThe pipeline code and example data related to this project are available as open source on GitHub ( https://github.com/DigBigPigForU/Tomato-architectural-trait-extraction ).Open asset ↗Tomato-architectural-trait-extractionlines:822-958Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Tomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection of these diseases is essential for enhancing agricultural productivity and mitigating economic losses. Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. Methods The multi-scale tomato leaf disease detection model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve the above problems. The model utilizes a multi-scale focus-diffusion network (MSFDNet) alongside an efficient parallel multi-scale convolutional module (EPMSC) to significantly enhance the extraction of multi-scale features. This combination particularly strengthens the model's capability to detect small targets amidst complex backgrounds. Results and discussion Experimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). Compared to the baseline YOLOv8 model, TomaFDNet achieves a 4.2% improvement in mAP, which is statistically significant (P < 0.01). These findings indicate that TomaFDNet offers a valid solution to the precise detection of tomato leaf diseases.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出する新規モデルを開発し、既存手法との性能比較で技術的に検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstractThe multi-scale tomato leaf disease detection model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve the above problems.
Reproduction assets foundThe paper's primary tomato leaf disease dataset (TDGA) is explicitly stated to be publicly available on GitHub, and a Kaggle tomato disease dataset is used for generalization tests. No author code or trained model release is mentioned.Dataset · publicn 4 summarizes the study and explores potential directions for future investigation.
2
Materials and methods
2.1
Tomato disease dataset
This study used the dataset called “Tomato Leaf Disease Detection with Global Attention” (TDGA), which comes from Wang et al. (2024c) . It includes tomato diseases and is publicly accessible at https://github.com/zafucslab/TDGA . This dataset encompasses three prevalent tomato leaf diseases: Early_Blight, Late_Blight, and Leaf_Mold, in addition to images of healthy tomato leaves. Typical examples of these various types of diseased leaves are depicted in
Figure 1
. To meet the requirements of this study, the dataset was organized and reclassified accorOpen asset ↗zafucslab/TDGAlines:61-108Dataset · publicthis study also acquired tomato leaf images from the Kaggle ( Tomato Disease Multiple Sources ) platform, encompassing diverse growing environments and shooting conditionsOpen asset ↗lines:61-108Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published22 Apr 2025Eastern-European Journal of Enterprise TechnologiesCited by 0 · OpenAlex ↗
This study focuses on tomato leaf disease classification using an optimized deep learning architecture. This study proposes an improved architecture called DenseNet-SEGR, which integrates a novel Squeeze-and-Excitation (SE) block with a customized growth rate of 48 to improve feature selection and classification accuracy. Unlike standard methods, this model replaces Global Average Pooling (GAP) with an integral-based squeeze method, thus enabling a more continuous and accurate feature representation. The use of SE blocks dynamically recalibrates the importance of features such as texture, color, and tissue patterns, thereby increasing sensitivity to disease symptoms. The model was trained using the PlantVillage dataset, which includes 12,246 images spanning 10 tomato leaf disease categories, such as bacterial spot, early blight, late blight, mosaic virus, and healthy leaves. Various augmentation techniques, including rotation, scaling, and contrast adjustment, were employed to strengthen generalization and improve robustness against environmental variations. Furthermore, batch normalization and adaptive learning rate scheduling were integrated to enhance model stability and prevent overfitting. As a result, the DenseNet-SEGR architecture is able to achieve a classification accuracy of 98.22 %, outperforming DenseNet-121, DenseNet-201, and MobileNetV2. This result is explained by the integration of adaptive attention mechanisms, sophisticated data augmentation strategies, and optimized architecture. The results can be effectively applied in real-world precision agriculture, especially in edge-based or mobile disease detection systems for early intervention and crop protection
Why it matches plant phenotyping methodsトマト葉の病徴を画像から分類する深層学習アーキテクチャを開発・比較しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractThis study focuses on tomato leaf disease classification using an optimized deep learning architecture.
Reproduction assets foundThe paper's tomato leaf disease classification uses a publicly available PlantVillage-derived Kaggle image dataset, explicitly linked by the authors. No author code or trained model is publicly deposited.Dataset · public. The tomato leaf dataset in this study
consists of 10.639 training samples, 1.607 validation samples
and 3211 samples. The dataset is evaluated using standard
deviation as a reference for dataset stability before model
testing. The dataset in this study uses 10 tomato leaf classes.
Dataset dataset can be accessed from website: https://www.kaggle.com/datasets/emmarex/plantdisease.The proposed classification framework is illustrated
in Fig. 1, which outlines the sequential steps from data pre-
processing to classification.
This flow illustrates the classification process using
the DenseNet-SEGR model. Starting with a dataset, it goes
through a preprocessing stage, which includes normalizationOpen asset ↗Kagglepdf-raw-page:3 lines:1-102Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Agriculture is a cornerstone of Bangladesh's economy, with tomatoes being one of the most widely cultivated vegetables, producing approximately 368,000 tons annually. However, tomato plants are vulnerable to various diseases and pest infestations that can significantly reduce crop yield, posing a threat to farmers' livelihoods. Early detection of these diseases, often visible through symptoms on the leaves, is critical for effective management. In this work, we present a dataset of 731 high-resolution images of tomato leaves affected by six common diseases, along with healthy samples, aimed at facilitating automated disease diagnosis using computer vision. The dataset is categorized into disease types such as Early Blight, Black Spot, Late Blight, Leaf Mold, Bacterial Spot, and Target Spot. This structured dataset offers a valuable resource for researchers developing machine learning models for disease classification and early detection. By making the dataset publicly available, we aim to accelerate research in precision agriculture and empower the development of AI-driven tools that can enhance tomato disease management, ultimately improving crop yields and supporting sustainable farming practices.
Why it matches plant phenotyping methodsトマト葉の病徴画像を収録した公開データセットであり、植物の病害状態を画像から分類するフェノタイピング用資源が中心です。
abstractIn this work, we present a dataset of 731 high-resolution images of tomato leaves affected by six common diseases, along with healthy samples, aimed at facilitating automated disease diagnosis using computer vision.
Reproduction assets foundThe paper's core asset is its own tomato leaf image dataset (731 raw images plus annotations), publicly deposited on Mendeley Data with an explicit direct URL and DOI.Dataset · publicr types.
Data source location
Tomato is one of the most commonly cultivated vegetables in Bangladesh.
We collected our data from these three locations:
1. Dinajpur
2. Thakurgaon
3. Kushtia
Data accessibility
The dataset is published in Mendeley Data.
• Data identification number(doi): 10.17632/bpfd9cns5g.2
• Direct URL to data: https://data.mendeley.com/datasets/bpfd9cns5g/2
1
Value of the Data
The dataset is highly valuable for agricultural research and machine learning applications, especially in tomato cultivation, providing valuable insights and potential impacts.
•
The Tomato Leaf Dataset [ 2 ] provides a comprehensive collection of tomato leaf, images, categorized by disease type, aidiOpen asset ↗Mendeley Data · 10.17632/bpfd9cns5g.2lines:1-62Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
MaizeTomatoGreenhouseLiDAR / point cloudLeafStem / branchSegmentation
Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. Furthermore, their heavy reliance on high-quality annotated data restricts their use in high-throughput settings. To address these limitations, we propose a novel multi-stage region-growing algorithm based on an octree structure for efficient stem-leaf segmentation in maize point cloud data. The method first extracts key geometric features through octree voxelization, significantly improving segmentation efficiency. In the region-growing phase, a preliminary structural segmentation strategy using fitted cylinder parameters is applied. A refinement strategy is then applied to improve segmentation accuracy in complex regions. Finally, stem segmentation consistency is enhanced through central axis fitting and distance-based filtering. In this study, we utilize the Pheno4D dataset, which comprises three-dimensional point cloud data of maize plants at different growth stages, collected from greenhouse environments. Experimental results show that the proposed algorithm achieves an average precision of 98.15% and an IoU of 84.81% on the Pheno4D dataset, demonstrating strong robustness across various growth stages. Segmentation time per instance is reduced to 4.8 s, offering over a fourfold improvement compared to PointNet while maintaining high accuracy and efficiency. Additionally, validation experiments on tomato point cloud data confirm the proposed method’s strong generalization capability. In this paper, we present an algorithm that addresses the shortcomings of traditional methods in complex agricultural environments. Specifically, our approach improves efficiency and accuracy while reducing dependency on high-quality annotated data. This solution not only delivers high precision and faster computational performance but also lays a strong technical foundation for high-throughput crop management and precision breeding.
Why it matches plant phenotyping methodsトウモロコシの3D点群から茎・葉を分割し、表現型パラメータ抽出を可能にするアルゴリズムを開発・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。
abstract3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction.
Reproduction assets foundThe paper's stem-leaf segmentation experiments are performed on the public Pheno4D maize/tomato point cloud dataset, which the authors explicitly state is publicly available at the IPB Bonn URL. No author analysis code or trained models are reported as publicly released.Dataset · publicThe dataset is available at https://www.ipb.uni-bonn.de/data/pheno4d/ (accessed on
20 January 2025).Open asset ↗Pheno4Dpdf-page:4 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. To address these challenges, this study proposes an efficient Tomato Disease Detection Network (E-TomatoDet), which enhances tomato leaf disease detection effectiveness by integrating and amplifying global and local feature perception capabilities. First, CSWinTransformer (CSWinT) is integrated into the backbone of the detection network, substantially improving tomato leaf diseases' global feature-capturing capacity. Second, a Comprehensive Multi-Kernel Module (CMKM) is designed to effectively incorporate large, medium, and small local capturing branches to learn multi-scale local features of tomato leaf diseases. Moreover, the Local Feature Enhance Pyramid (LFEP) neck network is developed based on the CMKM module, which integrates multi-scale features across different detection layers to acquire more comprehensive local features of tomato leaf diseases, thereby significantly improving the detection performance of tomato leaf disease targets at various scales under complex backgrounds. Finally, the proposed model's effectiveness was validated on two datasets. Notably, on the tomato leaf disease dataset, E-TomatoDet improved the mean Average Precision (mAP50) by 4.7% compared to the baseline model, reaching 97.2% and surpassing the advanced real-time detection network YOLOv10s. This research provides an effective solution for efficiently detecting vegetable pests and disease issues.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出する深層学習モデルを開発し、2つのデータセットで性能検証しており、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes an efficient Tomato Disease Detection Network (E-TomatoDet)
Reproduction assets foundThe paper's tomato leaf disease detection experiments use the public CCMT-derived tomato leaf disease dataset, which the authors state is publicly accessible via a Mendeley Data deposit (with the complete dataset available on request from the corresponding author). No author analysis code, trained model checkpoints, orDataset · publicThis study analyzed a combination of publicly available datasets and data collected by the authors. The publicly available datasets can be accessed at [ https://data.mendeley.com/datasets/bwh3zbpkpv/1 ] (accessed on 26 December 2024). If you want to request the complete dataset, please email the corresponding author.
Declarations
Ethics approval and consent to participate
This study did not involve human participants or animals, thus no ethics approval or consent to participate was required.
Consent for publication
All authoOpen asset ↗bwh3zbpkpvlines:1377-1456Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から認識するYOLOモデルを対象に、モデル比較とハイパーパラメータ最適化を行っており、植物の病害状態を推定する画像ベース手法が研究の中心です。
abstractThis study investigates the performance of YOLOv11 for tomato leaf disease recognition.
Reproduction assets foundThe paper's tomato leaf disease images derive from the public Kaggle 'Tomato Disease Multiple Sources' dataset, explicitly cited as openly available in the Data Availability Statement. The supplement (Data S1) contains the random-search performance configurations from the authors' hyperparameter optimization analysis.Dataset · publicand D.-H.A.; visualization, Y.-S.L.; supervision, Y.B.S.; project administration, D.-H.A. and G.-D.K.; funding acquisition, Y.-S.L. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The data presented in this study are openly available in Tomato Disease Multiple Sources at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources/data , accessed on 30 December 2023.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A1A0Open asset ↗Kaggle · Tomato Disease Multiple Sourceslines:2202-2219Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In natural environments, tomato leaf disease detection faces many challenges, such as variations in light conditions, overlapping disease symptoms, tiny size of lesion areas, and occlusion between leaves. Therefore, an improved tomato leaf disease detection method, DM-YOLO, based on the YOLOv9 algorithm, is proposed in this paper. Specifically, firstly, lightweight dynamic up-sampling DySample is incorporated into the feature fusion backbone network to enhance the ability to extract features of small lesions and suppress the interference from the background environment; secondly, the MPDIoU loss function is used to enhance the learning of the details of overlapping lesion margins in order to improve the accuracy of localizing overlapping lesion margins. The experimental results show that the precision (P) of this model increased by 2.2%, 1.7%, 2.3%, 2%, and 2.1%compared with those of multiple mainstream improved models, respectively. When evaluated based on the tomato leaf disease dataset, the precision (P) of the model was 92.5%, and the average precision (AP) and the mean average precision (mAP) were 95.1% and 86.4%, respectively, which were 3%, 1.7%, and 1.4% higher than the P, AP, and mAP of YOLOv9, the baseline model, respectively. The proposed detection method had good detection performance and detection potential, which will provide strong support for the development of smart agriculture and disease control.
Why it matches plant phenotyping methodsトマト葉の病斑を画像から検出するYOLO改良手法の開発と性能評価が中心であり、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。
abstractan improved tomato leaf disease detection method, DM-YOLO, based on the YOLOv9 algorithm, is proposed in this paper.
Reproduction assets foundThe paper's tomato leaf disease detection experiments use a public Roboflow dataset explicitly cited by the authors as the dataset used in this study. The ultralytics YOLOv5 repository is a generic third-party library, not a paper-specific asset, and no author analysis code or trained model checkpoint is reported as a.Dataset · publicThe dataset used in this paper is a tomato leaf disease dataset “Tomato Diseases Detection available on Roboflow platform ( Bryan 2023 )Open asset ↗lines:86-136Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The advancement of digital technologies has significantly impacted plant pest and disease management, yet gaps remain, especially in developing regions. This paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops. The dataset encompasses 25,844 raw images and over 12,000 labeled images, categorized into 30 classes (healthy crop, infested crop, and pest) across the three cropping systems. Acquired through meticulous fieldwork in Burkina Faso using high-resolution cameras, the dataset includes diverse environmental conditions and crop stages, ensuring a robust resource for AI model training and validation. The dataset is segmented into three categories: processed images (Category A), selected images with augmentation (Category B), and an online repository with over 25,000 raw images (Category C). Category A and B features images of crops affected by 21 distinct pests and diseases. This dataset addresses critical gaps in existing collections by offering extensive coverage and high-resolution imagery that can be used to developed AI models for automatic identification and classification of pests and diseases that affects crops. TOM2024's versatility extends to research, educational purposes, and the practical application of digital tools in agriculture thereby contributes to the advancement of precision agriculture, sustainable agricultural practices, and food security globally.
Why it matches plant phenotyping methods植物の健全・感染状態を含む画像データセットを構築し、病害・害虫状態の自動分類モデル開発用リソースとして提供することが中心であり、再利用可能な画像ベース表現型データセットに該当する。
abstractThis paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops.
Reproduction assets foundThe paper is a Data in Brief article describing the TOM2024 dataset of tomato, onion, and maize pest/disease images, publicly deposited on Mendeley Data with an explicit direct URL and DOI. This is a paper-specific public image dataset (phenotyping-style plant image asset) directly produced by this paper.Dataset · publicrce location
West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)
6 BP 9507
Ouagadougou, Burkina Faso
Tel: +226 25375423
Email: secretariat_cc@wascal.org
Website: www.wascal.org .
Data accessibility
Repository name: TOM2024
Data identification number: doi: 10.17632/3d4yg89rtr.1
Direct URL to data: https://data.mendeley.com/datasets/3d4yg89rtr/1
Related research articleOpen asset ↗10.17632/3d4yg89rtr.1lines:1-43Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published1 Feb 2025International Journal of Electrical and Computer Engineering (IJECE)Cited by 7 · OpenAlex ↗
Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する深層学習フレームワークを開発・評価しており、植物病害表現型の取得・抽出が研究の中心です。
abstracta deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy.
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 data employed
in this study is sourced from rural farm fields and is accessible atOpen asset ↗pdf-page:5 lines:1-52Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Timely and accurate recognition of tomato diseases is crucial for improving tomato yield. While large deep learning models can achieve high-precision disease recognition, these models often have a large number of parameters, making them difficult to deploy on edge devices. To address this issue, this study proposes an ensemble self-distillation method and applies it to the lightweight model ShuffleNetV2. Methods Specifically, based on the architecture of ShuffleNetV2, multiple shallow models at different depths are constructed to establish a distillation framework. Based on the fused feature map that integrates the intermediate feature maps of ShuffleNetV2 and shallow models, a depthwise separable convolution layer is introduced to further extract more effective feature information. This method ensures that the intermediate features from each model are fully preserved to the ensemble model, thereby improving the overall performance of the ensemble model. The ensemble model, acting as the teacher, dynamically transfers knowledge to ShuffleNetV2 and the shallow models during training, significantly enhancing the performance of ShuffleNetV2 without changing the original structure. Results Experimental results show that the optimized ShuffleNetV2 achieves an accuracy of 95.08%, precision of 94.58%, recall of 94.55%, and an F1 score of 94.54% on the test set, surpassing large models such as VGG16 and ResNet18. Among lightweight models, it has the smallest parameter count and the highest recognition accuracy. Discussion The results demonstrate that the optimized ShuffleNetV2 is more suitable for deployment on edge devices for real-time tomato disease detection. Additionally, multiple shallow models achieve varying degrees of compression for ShuffleNetV2, providing flexibility for model deployment.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から認識する深層学習モデルを開発・評価しており、植物病害状態の取得・推定手法が研究の中心である。
abstractthis study proposes an ensemble self-distillation method and applies it to the lightweight model ShuffleNetV2.
Reproduction assets foundThe paper's tomato leaf disease image datasets are publicly available: PlantVillage (GitHub), AI Challenger 2018 (GitHub), a Mendeley Data tomato leaf dataset (DOI), and PlantDoc (ACM DOI). All are cited in the data availability statement and used directly as phenotyping inputs. No authors' analysis code or trained模型s/Dataset · publicThe names of the repository/repositories and accession number(s) can be found below: https://github.com/AIChallenger/AI_Challenger_2018 ; https://github.com/spMohanty/PlantVillage-Dataset ; https://doi.org/10.17632/ngdgg79rzb.1 ; https://doi.org/10.1145/3371158.3371196 .Open asset ↗spMohanty/PlantVillage-Datasetlines:860-880Dataset · publicThe names of the repository/repositories and accession number(s) can be found below: https://github.com/AIChallenger/AI_Challenger_2018 ; https://github.com/spMohanty/PlantVillage-Dataset ; https://doi.org/10.17632/ngdgg79rzb.1 ; https://doi.org/10.1145/3371158.3371196 .Open asset ↗AIChallenger/AI_Challenger_2018lines:860-880Dataset · publicThe names of the repository/repositories and accession number(s) can be found below: https://github.com/AIChallenger/AI_Challenger_2018 ; https://github.com/spMohanty/PlantVillage-Dataset ; https://doi.org/10.17632/ngdgg79rzb.1 ; https://doi.org/10.1145/3371158.3371196 .Open asset ↗10.17632/ngdgg79rzb.1lines:860-880Dataset · publicThe names of the repository/repositories and accession number(s) can be found below: https://github.com/AIChallenger/AI_Challenger_2018 ; https://github.com/spMohanty/PlantVillage-Dataset ; https://doi.org/10.17632/ngdgg79rzb.1 ; https://doi.org/10.1145/3371158.3371196 .Open asset ↗10.1145/3371158.3371196lines:860-880Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tomatoes are considered one of the most valuable vegetables around the world due to their usage and minimal harvesting period. However, effective harvesting still remains a major issue because tomatoes are easily susceptible to weather conditions and other types of attacks. Thus, numerous research studies have been introduced based on deep learning models for the efficient classification of tomato leaf disease. However, the usage of a single architecture does not provide the best results due to the limited computational ability and classification complexity. Thus, this research used Transductive Long Short-Term Memory (T-LSTM) with an attention mechanism. The attention mechanism introduced in T-LSTM has the ability to focus on various parts of the image sequence. Transductive learning exploits the specific characteristics of the training instances to make accurate predictions. This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. The data was gathered from the PlantVillage dataset and the pre-processing was conducted based on image resizing, color enhancement, and data augmentation. These outputs were then processed in the segmentation stage where the U-Net architecture was applied. After segmentation, VGG-16 architecture was used for feature extraction and the classification was done through the proposed T-LSTM with an attention mechanism. The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する分類・セグメンテーション手法を提案し、前処理、U-Net、特徴抽出、T-LSTM分類、比較評価までが中心であるため、植物病害フェノタイピング手法として含める。
abstractThese outputs were then processed in the segmentation stage where the U-Net architecture was applied.
Reproduction assets foundThe paper uses three public Kaggle tomato leaf image datasets: the PlantVillage tomato leaf dataset as the primary training/evaluation data, and two additional Kaggle image datasets for the independent real-time analysis. No author analysis code or trained model checkpoint is reported as publicly available.Dataset · publicDataset . Available online at: https://www.kaggle.com/datasets/charuchaudhry/plantvillage-tomato-leaf-dataset (Accessed July 10, 2024 ).Open asset ↗Kaggle · charuchaudhry/plantvillage-tomato-leaf-datasetlines:720-807Dataset · publicFigures 11 , 12 show the collected real-time images 1 ( https://www.kaggle.com/datasets/ashishmotwani/tomato/data ) and 2 ( https://www.kaggle.com/datasets/farukalam/tomato-leaf-diseases-detection-computer-vision ) for the independent analysis.Open asset ↗Kaggle · ashishmotwani/tomatolines:526-638Dataset · publicFigures 11 , 12 show the collected real-time images 1 ( https://www.kaggle.com/datasets/ashishmotwani/tomato/data ) and 2 ( https://www.kaggle.com/datasets/farukalam/tomato-leaf-diseases-detection-computer-vision ) for the independent analysis.Open asset ↗Kaggle · farukalam/tomato-leaf-diseases-detection-computer-visionlines:526-638Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The article considers modern methods based on deep learning for solving the problem of plant disease recognition. A comparative analysis of some existing methods is carried out. A modified neural network model is created that allows to surpass existing methods in recognition accuracy and memory costs. Using the VGG16 architecture, a modified convolutional model is developed using Keras. The dataset used were images of plants—tomato leaves, both healthy and diseased. A computational experiment was carried out in comparison with such architectures as VGG16, ResNet-50, and EfficientNet-85. The proposed model allows to detect plant diseases with the best results in computations and accuracy.
Why it matches plant phenotyping methods植物葉の病変領域を画像から認識する深層学習モデルの開発・比較が中心で、病害状態という植物表現型を直接推定しているため。
titleDeep learning model for plant disease detection based on visual analysis of leaf infestation area
Reproduction assets foundThe paper's Data availability statement points to the public PlantVillage plant image dataset (arXiv 1511.08060) used as the paper's phenotyping input. No author analysis code or trained model is publicly deposited.Dataset · publicData availability A dataset of plant images, both diseased and healthy, is available at https://doi.org/10.48550/arXiv.
1511.08060.Open asset ↗arXiv · 1511.08060pdf-page:7 lines:1-53Code / 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 confirmedCrossref · checked 6 Sept 2026
Abstract As the importance of precision agriculture grows, scalable and efficient methods for real-time data collection and analysis have become essential. In this study, we developed a system to collect real-time crop images, focusing on physiological disorders in tomatoes. This system systematically collects crop images and related data, with the potential to evolve into a valuable tool for researchers and agricultural practitioners. A total of 58 479 images were produced under stress conditions, including bacterial wilt (BW), Tomato Yellow Leaf Curl Virus (TYLCV), Tomato Spotted Wilt Virus (TSWV), drought, and salinity, across seven tomato varieties. The images include front views at 0 degrees, 120 degrees, 240 degrees, and top views and petiole images. Of these, 43 894 images were suitable for labeling. Based on this, 24 000 images were used for AI model training, and 13 037 images for model testing. By training a deep learning model, we achieved a mean Average Precision (mAP) of 0.46 and a recall rate of 0.60. Additionally, we discussed data augmentation and hyperparameter tuning strategies to improve AI model performance and explored the potential for generalizing the system across various agricultural environments. The database constructed in this study will serve as a crucial resource for the future development of agricultural AI. Database URL: https://crops.phyzen.com/
Why it matches plant phenotyping methodsトマトの生理障害・病害を対象に画像収集データベースと深層学習解析モデルを開発しており、植物状態の取得・推定手法が研究の中心である。
titleCPDMS: a database system for crop physiological disorder management
Reproduction assets foundThe paper's tomato physiological-disorder image dataset (58,479 images, annotations, and AI training data) is publicly available via the authors' CPDMS database. LabelImg and YOLOv5 are generic third-party tools, not paper-specific assets.Dataset · publicAll data used in this study are publicly available at https://crops.phyzen.com/ and https://crops.phyzen.com/appOpen asset ↗crops.phyzen.comlines:141-251Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( Solanum lycopersicum var. cerasiforme ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Why it matches plant phenotyping methods植物の生育・ストレス・健全性評価を目的とした、放射補正済みマルチスペクトル画像とオルソモザイクを含む再利用可能なデータセットであり、植物表現型取得基盤が中心です。
abstractThe dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels.
Reproduction assets foundThe paper is itself a data descriptor for a public UAV multispectral cherry tomato phenotyping dataset (calibrated aerial images, manual plant images, orthomosaics, binary masks) deposited in Dryad, with an explicit DOI and direct URL matching an allowed URL.Dataset · publicRepository name: tomatodb
Data identification number: 10.5061/dryad.63xsj3vbd
Direct URL to data: https://datadryad.org/stash/share/Wq_X7QUyGryJ-ZnmgfwRn4MtOCr4VBm_MSnhF40sv_8#readmeOpen asset ↗Dryad · 10.5061/dryad.63xsj3vbdlines:1-42Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop’s growth, reproduction, and overall yield potential. The objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves. This model proposes to address the drawbacks of conventional CNNs, which are resource-intensive and time-consuming, by using optimization methods that reduce processing complexity and enhance classification accuracy. Traditional plant disease classification methods predominantly utilize CNN based deep learning techniques, originally developed for fundamental image classification tasks. It relies on computationally intensive CNNs, hindering real-time application due to long training times. To address this, a lighter CNN framework is proposed to enhance with two key components. Firstly, an Elephant Herding Optimization (EHO) algorithm selects pertinent features for classification tasks. The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen’s kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model’s size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. The proposed classifier performance was compared to existing deep learning models, the experimental results show that the pruned DenseNet achieves an accuracy of 86.64% with a model size of 10.6 MB, while GhostNet reaches an accuracy of 92.15% at 10.9 MB. CACPNET demonstrates an accuracy of 92.4% with a model size of 18.0 MB. In contrast, the proposed approach significantly outperforms these models in terms of accuracy and processing time.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する軽量CNNと特徴選択・枝刈り手法を開発し、精度や計算性能を比較検証しており、植物表現型取得・推定手法が中心である。
abstractThe objective of this work is to build deep learning based lightweight convolutional neural network (CNN) architecture for the real-time classification of biotic stress in tomato plant leaves.
Reproduction assets foundThe paper's Data Availability statement links an authors' GitHub repository containing the tomato leaf disease dataset used in this study, plus the public Kaggle PlantVillage image dataset used for the classification experiments. No analysis code or trained model is explicitly deposited.Dataset · publicyes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability GitHub link: https://github.com/MAmudha/Tomato-leaf-disease-dataset.git Kaggle dataset link: https://www.kaggle.com/datasets/emmarex/plantdisease
Data AvailabilityOpen asset ↗MAmudha/Tomato-leaf-disease-dataset · MAmudha/Tomato-leaf-disease-datasetlines:1-32Dataset · publicrop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability GitHub link: https://github.com/MAmudha/Tomato-leaf-disease-dataset.git Kaggle dataset link: https://www.kaggle.com/datasets/emmarex/plantdisease
Data AvailabilityOpen asset ↗emmarex/plantdisease · emmarex/plantdiseaselines:1-32Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する深層学習手法を開発・比較しており、植物病害表現型の取得・推定が研究の中心である。
abstractWe have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease.
Reproduction assets foundThe paper's tomato leaf disease classification study uses the public PlantVillage-derived Mendeley dataset, provides its restructured training/validation data on Kaggle, and releases its T-Net analysis source code on GitHub and Zenodo, all with explicit availability statements and public URLs.Dataset · publicThe dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .Open asset ↗Mendeley Data · 10.17632/tywbtsjrjv.1lines:498-522Dataset · publicThe dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .
The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeDOpen asset ↗Kagglelines:498-522Code · publicon of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .
The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–Open asset ↗GitHublines:498-522Code · publicing and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–301. doi: 10.1016/j.procs.2020.03.225.
Ahmad, Saraswat & El Gamal (2023) Ahmad A, Saraswat D, El Gamal A. A survey on using deep learnOpen asset ↗Zenodo · 10.5281/zenodo.14020689lines:498-522Code / dataset availability confirmedbioRxiv · Europe PMC · checked 7 Sept 2026
Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG
Why it matches plant phenotyping methods植物の花粉管画像から発芽時間、伸長速度、生存性などの表現型を抽出するソフトウェア手法を開発し、複数トマト品種で精度検証しているため。
abstractHere we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival.
Reproduction assets foundThe paper's authors publicly released TubeTracker, the Python software used to perform all automated pollen germination, elongation, and survival phenotyping measurements in this study, on GitHub with explicit availability language and a video sample for training.Code · publicWe further encourage users to independently
improve upon our tool and have provided the complete python code at
https://github.com/souonkap/TubeTracker, along with installation instructions and a video
sample for training purposes.Open asset ↗souonkap/TubeTrackerpdf-page:22 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400 nm) and plant defense hormone-mediated responses (750 nm) rather than changes in leaf pigments or internal structure (800-900 nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.
Why it matches plant phenotyping methodsトマト葉の病徴状態をハイパースペクトル画像と機械学習で推定し、病害進行段階、前症状検出、異常葉斑との識別を検証することが中心である。
abstractHyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves.
Reproduction assets foundThe paper's raw hyperspectral image data (tomato leaf HSI used for phenotyping/analysis) are publicly deposited on Ag Data Commons. No author analysis code is publicly shared; evaluation metrics are only available upon request.Dataset · publicSpecies at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.
Author contributions
X.Z. designed and conducted the experiment. X.Z. analyzed the data. X.Z., B.V, and S.L. wrote the manuscript.
Data availability
The raw hyperspectral image data have been uploaded to Ag Data Commons: https://data.nal.usda.gov/dataset/early-detection-bacterial-spot-disease-tomato-hyperspectral-imaging . Full evaluation metrics for all models are available upon request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and instOpen asset ↗Ag Data Commonslines:100-122Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.
Why it matches plant phenotyping methods葉分光法とスペクトルアンミキシングにより、植物のFQEや熱散逸に関連する吸収変化を非破壊・定量的に取得し、ストレス診断への有効性を評価しているため、植物生理フェノタイピング手法の応用・評価が中心です。
abstractUsing leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the paper's raw and processed phenotyping/spectroscopy measurements open access on Zenodo (doi: 10.5281/zenodo.12800064). This is a paper-specific, public, actionable dataset. However, the Zenodo URL is not among the allowed_urls, so no asset URL is providedDataset · publicData Availability Statement
Raw and processed data are available open access through the Zenodo repository (doi: 10.5281/zenodo.12800064 ).Zenodo · 10.5281/zenodo.12800064lines:539-574Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.
Why it matches plant phenotyping methodsトマト葉の病斑を画像から直接検出・識別する手法を開発し、複雑背景下での精度を比較評価しており、植物病害状態の表現型推定が中心である。
abstractthis article proposes a visual leaf disease identification method based on an enhanced attention mechanism.
Reproduction assets foundThe authors publicly deposited the paper's code and processed data on Figshare, and the study's plant image input (PlantDoc dataset) is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publiced drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The code and processed data are available on Figshare: Zhang, Xu (2024). code of “Automatic visual recognition for leaf disease based on enhanced attention mechanism”. figshare. Software. https://doi.org/10.6084/m9.figshare.27210138.v1 .
The original PlantDoc dataset is available at GitHub: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master .
References
Al Bashish, Braik & Bani-Ahmad (2011) Al Bashish D, Braik M, Bani-Ahmad S. Detection and classification of leaf diseases using k-means-based segmentation and. Information TechnOpen asset ↗figshare · 10.6084/m9.figshare.27210138.v1lines:336-366Dataset · publicsupplied regarding data availability:
The code and processed data are available on Figshare: Zhang, Xu (2024). code of “Automatic visual recognition for leaf disease based on enhanced attention mechanism”. figshare. Software. https://doi.org/10.6084/m9.figshare.27210138.v1 .
The original PlantDoc dataset is available at GitHub: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master .
References
Al Bashish, Braik & Bani-Ahmad (2011) Al Bashish D, Braik M, Bani-Ahmad S. Detection and classification of leaf diseases using k-means-based segmentation and. Information Technology Journal. 2011;10(2):267–275. doi: 10.3923/itj.2011.267.275.
Al-Hiary et al. (2011) Al-Hiary H, BanOpen asset ↗GitHublines:336-366Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process of diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. Conversely, incorrect diagnoses can render interventions ineffective, leading to agricultural crop deterioration and compounding economic hardships for both farmers and their respective nations. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as "Jordan 22" was meticulously curated. Jordan 22 was painstakingly compiled through the collection of images featuring both diseased and healthy plants, captured within the confines of Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists, well-versed in plant disease identification and prevention. The Jordan 22 dataset comprises a substantial size, amounting to 3210 images. Following the compilation of this dataset, a series of preprocessing steps were executed. These encompassed the standardization of image backgrounds and the uniformization of image dimensions. Furthermore, image augmentation techniques were applied to the dataset to expand its diversity. Subsequently, a deep learning model, the Convolutional Neural Network (CNN), was meticulously trained on the augmented dataset. The results yielded by the CNN were nothing short of remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256x256 dimensions, and max pooling was employed in lieu of average pooling within the pooling layer. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan 22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNとデータセットを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis research endeavors to diagnose the prevalent crops in Jordan
Reproduction assets foundThe paper's Jordan22 plant disease image dataset (2310 RGB leaf images of cucumber, tomato, cabbage, and lettuce collected in Jordan and expert-classified) is explicitly stated as openly available on the authors' public GitHub repository. No separate analysis code or trained model checkpoint is explicitly deposited.Dataset · publicThe data that support the findings of this study are openly available in [Jordan22_Dataset] at
[https://github.com/shahd1995913/Jordan22_Dataset], reference number [17].Open asset ↗Jordan22_Datasetpdf-page:25 lines:1-40Code / dataset availability confirmedbioRxiv · Crossref · checked 13 Sept 2026
Deep learning models have shown significant potential for plant pest and disease (PPD) diagnosis; however, their real-world effectiveness is often limited by variability between datasets, where models trained on one dataset perform poorly on others collected under different conditions. In this study, I evaluated the cross-dataset generalization of widely used deep learning architectures, including ResNet, EfficientNet, Inception, and MobileNet, across multiple tomato pest and disease datasets. As expected, models trained and tested on the same dataset achieved high performance. However, substantial performance degradation occurred when these models were tested on different datasets, highlighting the challenges posed by dataset variability. This trend was consistent across all evaluated architectures, indicating that changing the model architecture alone is insufficient to address these issues. The findings emphasize the need for more diverse and representative datasets to better capture variability in agricultural data and enhance the practical deployment of deep learning models for PPD diagnosis.
Why it matches plant phenotyping methodsトマトの病害・害虫状態を対象に、複数データセット間で深層学習診断モデルの汎化性能を比較評価しており、植物状態の推定手法の技術検証が中心である。
abstractIn this study, I evaluated the cross-dataset generalization of widely used deep learning architectures, including ResNet, EfficientNet, Inception, and MobileNet, across multiple tomato pest and disease datasets.
Reproduction assets foundThe paper's cross-dataset evaluation uses three public tomato pest/disease image datasets (PlantVillage, Tomato-Village, Tomato Leaf Disease), each with an explicit public URL in the data availability statement. No author analysis code or trained models are deposited.Dataset · publicThe PlantVillage dataset is available at https://github.com/spMohanty/PlantVillage-Dataset.Open asset ↗spMohanty/PlantVillage-Datasetpdf-page:8 lines:1-31Dataset · publicThe
Tomato-Village dataset is accessible at https://github.com/mamta-joshi-gehlot/Tomato-Village.Open asset ↗mamta-joshi-gehlot/Tomato-Villagepdf-page:8 lines:1-31Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Bangladesh's agricultural landscape is significantly influenced by vegetable cultivation, which substantially enhances nutrition, the economy, and food security in the nation. Millions of people rely on vegetable production for their daily sustenance, generating considerable income for numerous farmers. However, leaf diseases frequently compromise the yield and quality of vegetable crops. Plant diseases are a common impediment to global agricultural productivity, adversely affecting crop quality and yield, leading to substantial economic losses for farmers. Early detection of plant leaf diseases is crucial for improving cultivation and vegetable production. Common diseases such as Bacterial Spot, Mosaic Virus, and Downy Mildew often reduce vegetable plant cultivation and severely impact vegetable production and the food economy. Consequently, many farmers in Bangladesh struggle to identify the specific diseases, incurring significant losses. This dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones. The dataset includes images of vegetable leaves such as Bitter Gourd (2223 images), Bottle Gourd (1803 images), Eggplants (2944 images), Cauliflowers (1598 images), Cucumbers (1626 images), and Tomatoes (2449 images). Each vegetable class encompasses several common diseases that affect cultivation. By identifying early leaf diseases, this dataset will be invaluable for farmers and agricultural researchers alike.
Why it matches plant phenotyping methods植物葉の画像から健康状態と病徴を識別するデータセットを提供しており、植物の病害状態を観測する再利用可能な画像ベースのフェノタイピング資源が中心です。
abstractThis dataset contains 12,643 images of widely grown crops in Bangladesh, facilitating the identification of unhealthy leaves compared to healthy ones.
Reproduction assets foundThe paper is a Data in Brief article describing a public smartphone image dataset of vegetable leaf diseases hosted on Mendeley Data, with an explicit direct URL matching an allowed URL.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/n67gctmjyj.3
Direct URL to data: https://data.mendeley.com/datasets/n67gctmjyj/3Open asset ↗Mendeley Data · 10.17632/n67gctmjyj.3lines:1-48Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法の開発・比較が中心であり、植物表現型計測法として採用する。
abstractthis study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification.
Reproduction assets foundThe paper's Data Availability Statement explicitly lists three public Kaggle leaf-image datasets (potato, tomato, mango) that constitute the phenotyping image inputs used for the study's disease-detection experiments. No author analysis code or trained model checkpoints are reported.Dataset · publicAgronomy 2024, 14, 2230 32 of 33
Data Availability Statement: These data were derived from the following resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts ofOpen asset ↗Kagglepdf-raw-page:32 lines:1-53Dataset · publicAgronomy 2024, 14, 2230 32 of 33
Data Availability Statement: These data were derived from the following resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this study.
References
1. Mohanty, S.P.; Hughes,Open asset ↗Kagglepdf-raw-page:32 lines:1-53Dataset · publicng resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this study.
References
1. Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci. 2016,
Open asset ↗Kagglepdf-raw-page:32 lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).
Why it matches plant phenotyping methodsトマト病害を画像から分類する深層学習モデルを開発し、Fog/FPGA向けに最適化・精度検証しており、植物の病害状態推定が中心的な方法貢献である。
abstractThe goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing.
Reproduction assets foundThe paper's smart-agriculture image classification analysis (tomato disease, pest, and weed classification, with FPGA deployment) is built on three public Kaggle image datasets cited by the authors as the data sources. No authors' analysis code or trained model repository is disclosed; the other URLs are generic tools/Dataset · public7–19 December 2021
SPIE
Bellingham, WA, USA
2022 Volume 12174 194 201
57.
Maurício J.
Domingues I.
Bernardino J.
Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review
Appl. Sci. 2023 13 5521
10.3390/app13095521
58.
Tomato Leaf Disease Image Classification
Available online: https://kaggle.com/code/rohanpatnaik/tomato-leaf-disease-image-classification (accessed on 7 June 2024)
59.
Pest Dataset
Available online: https://www.kaggle.com/datasets/simranvolunesia/pest-dataset (accessed on 7 June 2024)
60.
Weed-Classification
Available online: https://www.kaggle.com/datasets/aminelaatam/weed-classification (accessed on 7 June 2024)
61.
Pang B.
Open asset ↗kagglelines:611-800Dataset · publicers and Convolutional Neural Networks for Image Classification: A Literature Review
Appl. Sci. 2023 13 5521
10.3390/app13095521
58.
Tomato Leaf Disease Image Classification
Available online: https://kaggle.com/code/rohanpatnaik/tomato-leaf-disease-image-classification (accessed on 7 June 2024)
59.
Pest Dataset
Available online: https://www.kaggle.com/datasets/simranvolunesia/pest-dataset (accessed on 7 June 2024)
60.
Weed-Classification
Available online: https://www.kaggle.com/datasets/aminelaatam/weed-classification (accessed on 7 June 2024)
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Pang B.
Nijkamp E.
Wu Y.N.
Deep Learning with TensorFlow: A Review
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62.
TensorFlow
AvOpen asset ↗kagglelines:611-800Dataset · public8.
Tomato Leaf Disease Image Classification
Available online: https://kaggle.com/code/rohanpatnaik/tomato-leaf-disease-image-classification (accessed on 7 June 2024)
59.
Pest Dataset
Available online: https://www.kaggle.com/datasets/simranvolunesia/pest-dataset (accessed on 7 June 2024)
60.
Weed-Classification
Available online: https://www.kaggle.com/datasets/aminelaatam/weed-classification (accessed on 7 June 2024)
61.
Pang B.
Nijkamp E.
Wu Y.N.
Deep Learning with TensorFlow: A Review
J. Educ. Behav. Stat. 2020 45 227 248
10.3102/1076998619872761
62.
TensorFlow
Available online: https://www.tensorflow.org/ (accessed on 10 June 2024)
63.
TensorFlow Lite | ML for Mobile and Edge Devices
AvailOpen asset ↗kagglelines:611-800Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Grape disease image recognition is a crucial part of agricultural disease detection, and accurate identification of grape leaves plays a vital role in agricultural production. This study proposes a deep learning-based method for grape disease classification and recognition to address issues such as the complexity of grape disease features, uneven distribution of disease features, and data imbalance. First, the adversarial generative network FastGAN is used to generate grape disease images to enrich the sample information for different categories in the dataset and address the data imbalance problem. Then, a novel Transformer structure called LVT Block and a CNN structure called MARI Block are proposed to process the global and local information of images, respectively. Dense connections between these structures result in the DLVT Block, which leads to the lightweight neural network model DLVTNet. Additionally, a lightweight self-attention mechanism combined with CNN called CLSHSA is introduced , which maintains high recognition performance while reducing the model size. Moreover, a multi-scale attention mechanism (MELA) is proposed, which combines positional and multi-scale information to obtain attention weights. Experimental results show that this method achieves an average recognition accuracy of 98.48% in grape leaf disease detection, outperforming mainstream CNN and Transformer models, and effectively focuses on disease areas in leaf images. The method also demonstrates high recognition accuracy in tomato disease detection, indicating good generalization ability and suitability for detecting and recognizing various leaf diseases. The proposed method provides an effective solution for detecting and recognizing grape and other plant leaf diseases, offering a new research approach that combines CNN and Transformer structures.
Why it matches plant phenotyping methods植物葉の病害領域・病害状態を画像から認識する深層学習法の開発が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning-based method for grape disease classification and recognition
Reproduction assets foundThe paper's grape (and tomato) leaf disease image inputs come directly from the public New Plant Diseases Dataset on Kaggle, which is the image dataset used for the paper's phenotyping measurements. No author code, trained models, or generated FastGAN dataset is publicly deposited; the data availability statement only'Dataset · publicformation to obtain attention weights in images, aiding
the model in effectively extracting diseased areas.
2. Materials and Methods
2.1. Image datasets and preprocessing
The grape leaf disease dataset used in this study comes from the publicly available plant disease classification dataset New
Plant Diseases Dataset on Kaggle (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset). Grape leaf images
from this dataset were selected as the image dataset for this study. The dataset includes images of three types of grape leaf diseases
as well as healthy leaves, totaling 7,222 images, divided into four categories. The images have been resized to 256×256 pixels and
processed usingOpen asset ↗Kaggle · new-plant-diseases-datasetpdf-raw-page:4 lines:1-36Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precise disease detection is crucial in modern precision agriculture, especially in ensuring the health of tomato crops and enhancing agricultural productivity and product quality. Although most existing disease detection methods have helped growers identify tomato leaf diseases to some extent, these methods typically target fixed categories. When faced with new diseases, extensive and costly manual annotation is required to retrain the dataset. To overcome these limitations, this study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology within the VLDet framework, which can accurately identify new tomato leaf diseases without manual annotation by using only image-text pairs. First, we developed a progressive visual transformer-convolutional pyramid module (PVT-C) that effectively extracts tomato leaf disease features and optimizes anchor box positioning using the self-supervised learning algorithm DINO, suppressing interference from irrelevant backgrounds. Then, a context feature guided module (CFG) was adopted to address the low adaptability and recognition accuracy of the model in data-scarce environments. To validate the model's effectiveness, we constructed a tomato leaf disease image dataset containing 4 base classes and 2 new categories. Experimental results show that the PDC-VLD model achieved 61.2% on the main evaluation metric mAPnovel50 , and 56.4% on mAPnovel75 , 87.7% on mAPbase50 , 81.0% on mAPall50 , and 45.5% on average recall, outperforming existing OVD models. Our research provides an innovative solution for efficiently and accurately detecting new diseases, substantially reducing the need for manual annotation, and offering critical technical support and practical reference for agricultural workers.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出するマルチモーダルモデルを開発し、データセット構築と性能評価まで行っており、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology
Reproduction assets foundThe paper's Data Availability statement says all datasets used were uploaded to the authors' public GitHub repository (PDC-VLD), and the paper also uses the public Kaggle PlantVillage dataset as a plant image source. Bespoke portions of the dataset require contacting the corresponding author.Dataset · publicAll datasets that were used and analyzed in this study have been uploaded to the website https://github.com/ZhouGuoXiong/PDC-VLD . Furthermore, for access to all bespoke datasets used in this study (comprising a total of 6,923 images and 13,864 texts), please contact the corresponding author.Open asset ↗ZhouGuoXiong/PDC-VLDlines:797-797Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomato disease image recognition plays a crucial role in agricultural production. Today, while machine vision methods based on deep learning have achieved some success in disease recognition, they still face several challenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanism called DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust to noisy labels, aimed at reducing the impact of noisy labels on the LDAMNet model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から認識・領域抽出する深層学習手法を開発し、複数データセットで性能評価しており、植物フェノタイピング手法が中心である。
abstractthis paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision.
Reproduction assets foundThe paper's phenotyping inputs are two public Kaggle image datasets used directly in its experiments: the Plant Disease Classification Merged Dataset (tomato leaf disease images) and the Rice Leaf Disease Images (cross-dataset generalization tests). Both are publicly downloadable with explicit URLs in the text. No codeDataset · publich five different experiments. Finally, the fourth part mainly summarizes the work and experimental conclusions of this paper.
2
Materials and methods
2.1
Image preprocessing
2.1.1
Sample
The tomato image dataset used in this study is derived from the Plant Disease Classification Merged Dataset published on the Kaggle platform ( https://www.kaggle.com/datasets/alinedobrovsky/plant-disease-classification-merged-dataset ).Open asset ↗Kaggle · alinedobrovsky/plant-disease-classification-merged-datasetlines:40-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.
Why it matches plant phenotyping methods植物花粉のSEM観察用試料調製法そのものを開発し、複数植物で適用・比較検証しているため、形態計測に関する中心的な方法論研究である。
abstractThe article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM).
Reproduction assets foundThe paper's quantitative pollen shrinkage measurements (Table S1) and light microscopy images (Figures S3–S4) are contained in the publicly downloadable MDPI Supplementary Materials, which directly reproduce this paper's phenotyping measurements. No author analysis code or trained models are mentioned.Supplement · publicoly Bogdanov—at the department of electron microscopy, Lomonosov Moscow State University.
Abbreviations
The following abbreviations are used in this manuscript:
SEM
Scanning Electron Microscopy
HMDS
Hexamethyldisilazane
SA
Short axis
LA
Long axis
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13152140/s1 , Figure S1: The order of steps for pollen preparation according to the developed protocol; Figure S2: Scheme of measured pollen grain diameters; Figure S3: Light microscopy of pollen grains of insect-pollinated species; Figure S4: Light microscopy of pollen grains of wind-pollinated species; Table S1: Comparison Open asset ↗lines:98-127Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Tomatoes, widely cherished for their high nutritional value, necessitate precise ripeness identification and selective harvesting of mature fruits to significantly enhance the efficiency and economic benefits of tomato harvesting management. Previous studies on intelligent harvesting often focused solely on identifying tomatoes as the target, lacking fine-grained detection of tomato ripeness. This deficiency leads to the inadvertent harvesting of immature and rotten fruits, resulting in economic losses. Moreover, in natural settings, uneven illumination, occlusion by leaves, and fruit overlap hinder the precise assessment of tomato ripeness by robotic systems. Simultaneously, the demand for high accuracy and rapid response in tomato ripeness detection is compounded by the need for making the model lightweight to mitigate hardware costs. This study proposes a lightweight model named PDSI-RTDETR to address these challenges. Initially, the PConv_Block module, integrating partial convolution with residual blocks, replaces the Basic_Block structure in the legacy backbone to alleviate computing load and enhance feature extraction efficiency. Subsequently, a deformable attention module is amalgamated with intra-scale feature interaction structure, bolstering the capability to extract detailed features for fine-grained classification. Additionally, the proposed slimneck-SSFF feature fusion structure, merging the Scale Sequence Feature Fusion framework with a slim-neck design utilizing GSConv and VoVGSCSP modules, aims to reduce volume of computation and inference latency. Lastly, by amalgamating Inner-IoU with EIoU to formulate Inner-EIoU, replacing the original GIoU to expedite convergence while utilizing auxiliary frames enhances small object detection capabilities. Comprehensive assessments validate that the PDSI-RTDETR model achieves an average precision mAP50 of 86.8%, marking a 3.9% enhancement over the original RT-DETR model, and a 38.7% increase in FPS. Furthermore, the GFLOPs of PDSI-RTDETR have been diminished by 17.6%. Surpassing the baseline RT-DETR and other prevalent methods regarding precision and speed, it unveils its considerable potential for detecting tomato ripeness. When applied to intelligent harvesting robots in the future, this approach can improve the quality of tomato harvesting by reducing the collection of immature and spoiled fruits.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物状態を画像から推定する軽量検出モデルを開発し、精度・速度・計算量を評価しており、表現型取得手法が中心である。
abstractThis study proposes a lightweight model named PDSI-RTDETR to address these challenges.
Reproduction assets foundThe paper's tomato ripeness detection model was trained on a composite dataset: 112 tomato images drawn from the public Kaggle Fruits and Vegetables Image Recognition Dataset (augmented alongside authors' own field images). The Kaggle dataset is a public, paper-specific image input asset with an actionable URL. The 112Dataset · publicThe second batch of images was sourced from 112 tomato images in the publicly available Fruits and Vegetables Image Recognition Dataset ( Seth, 2020 ) on Kaggle.Open asset ↗Kagglelines:40-61Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This paper proposes a study conducted on various techniques that can be employed for the early detection of plant diseases. With exponential growth in the global population, there is a dire need for the detection and prevention of various types of plant diseases such as Mosaic virus in Solanum Lycopersicon (tomato), bacterial spot in Fragaria Ananassa (strawberry), late and early blight in Solanum Tuberosum (potato), huanglongbing in Citrus sinensis (orange), and Isariopsis leaf spot in Vitis vinifera (grapes). These diseases generally lead to lower yields and hence less profit. In the last two decades, there has been rapid development in the fields of image processing and deep learning. Various models of deep learning can be used for plant disease detection. The main objective is that as soon as plant leaf disease appears, there should be one device to monitor the symptoms and detect them over a large field with as much accuracy as possible. This study compares the deep learning models Resnet, MobileNet, and inceptionV3 that are implemented on a large dataset taken from the Kaggle repository. We implemented the models using Google Colaboratory tools, which provide us with Python’s Jupyter notebook that runs on the Google cloud server. The GPU “Tesla T4” and CPU “Intel Xenon” were used during training, validation, and testing respectively. The training and validation accuracy of the InceptionV3 model was 98.78% and 93.94%, respectively. MobileNet classified various plant diseases with training and validation accuracies of 99.57% and 97.31. Similarly, for ResNet, the training accuracy was found to be around 99.62% and the validation accuracy was 97.16%. We hope that this work will provide a helpful resource for other researchers working in the field of agriculture to detect various types of crop diseases. Future work and some challenges still faced are also discussed in this study.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を比較評価しており、病害状態の表現型推定と手法検証が研究の中心である。
abstractThis paper proposes a study conducted on various techniques that can be employed for the early detection of plant diseases.
Reproduction assets foundThe paper's plant-disease classification experiments are built entirely on two public leaf-image datasets: the augmented New Plant Diseases Dataset from Kaggle (87.9k RGB leaf images, 38 classes) and the original PlantVillage-Dataset on GitHub. Both are explicitly cited with public URLs and directly constitute the phenDataset · publicWe used the New Plant Disease Dataset (augmented) [18],
which can be found in the Kaggle repository.Open asset ↗Kagglepdf-raw-page:3 lines:1-117Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.
Why it matches plant phenotyping methodsトマトの成熟度という植物器官の状態を深層学習で推定する画像ベース手法を、農業ローバーの主要機能として開発しているため。
abstractthe tomato ripeness detection system using deep learning models
Reproduction assets foundThe paper's Data Availability statement points to a public Figshare repository containing the authors' tomato image dataset (500 field images with ripe/unripe annotations) used to train the YOLOv5 ripeness-detection model, which is a paper-specific, publicly actionable asset. The other allowed URLs are generic externalDataset · publics and researchers seeking to optimize farming operations through advanced technologies.
Supporting information
S1 File
See Supplement 1 for supporting content.
(DOCX)
Acknowledgments
The authors would like to thank Brac University for their research support.
Data Availability
Data could be available at the following repository: https://figshare.com/articles/dataset/Tomato_Dataset_YOLOV5/25249051 .
Funding Statement
The authors received no specific funding for this work.
References
1. Mahmud M. S. A., Abidin M. S. Z., Emmanuel A. A., and Hasan H. S., “Robotics and automation in agriculture: Present and future applications,” Applications of Modelling and Simulation, vol. 4, no. 0, pp.30–140, 2Open asset ↗figshare · Tomato_Dataset_YOLOV5/25249051lines:239-267Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR's self-attention mechanism to craft a backbone feature extraction network, enhancing the model's capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network's ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.
Why it matches plant phenotyping methodsトマト葉の病斑・病害を画像から検出する新規深層学習手法を開発し、データセット上で精度と速度を評価しており、植物病害状態の表現型取得が中心である。
abstractThis study focuses on tomato disease images within intricate settings
Reproduction assets foundThe paper's Data availability statement explicitly deposits part of the self-gathered tomato disease image dataset (used for TomatoDet training/evaluation) on the authors' GitHub repository, with the complete dataset and code available only by emailing the corresponding author. The public partial dataset qualifies as aDataset · publicThe data utilized in this paper is obtained through self-gathering and is made publicly available (a part of it) to make the study reproducible. It can be accessed at https://github.com/tyuiouio/plant-disease-detection-in-real-field . If you want to request the complete dataset and code, please email the corresponding author.Open asset ↗tyuiouio/plant-disease-detection-in-real-fieldlines:250-271Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting. In this study, an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting. We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses. Further, we improved the deep learning network YOLO-TGI by incorporating Ghost and CBAM modules, which was trained and tested in conjunction with premier lightweight detection models like YOLOX and NanoDet in evaluating leaf health conditions. For the cascading with various base detectors, we integrated state-of-the-art trackers such as Byte-Track, Motpy, and FairMot to enable fruit counting in video streams. Experimental results indicated that the combination of YOLO-TGI and Byte-Track achieved the most robust performance. Particularly, YOLO-TGI-N emerged as the model with the least computational demands, registering the lowest FLOPs at 2.05 G and checkpoint weights at 3.7 M, while still maintaining a mAP of 0.72 for leaf disease detection. Regarding the fruit counting, the combination of YOLO-TGI-S and Byte-Track achieved the best R 2 of 0.93 and the lowest RMSE of 9.17, boasting an inference speed that doubles that of the YOLOX series, and is 2.5 times faster than the NanoDet series. The developed network framework is a potential solution for researchers facilitating the deployment of similar surveillance models for a broad spectrum of fruit and vegetable crops.
Why it matches plant phenotyping methodsトマト葉の病害状態と果実数という植物形質を、ロボット撮影画像から検出・計数する深層学習および追跡フレームワークを開発・評価しており、表現型取得手法が中心である。
abstractan integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting.
Reproduction assets foundThe paper's greenhouse tomato leaf/fruit image dataset is publicly hosted on Roboflow, and the authors' analysis code (YOLO-TGI detection/tracking framework) is publicly available on GitHub. NanoDet is a cited third-party library, not a paper-specific asset.Code · publicssisted in the creation and programming of the deep learning networks. R.K. was responsible for drafting the manuscript and conducting all programming tasks, under the supervision of N.R. and S.S.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Dataset and code can be reached at https://github.com/RuiKangnj/TGI/tree/main .
References
1. Dorais M, Ehret DL, Papadopoulos AP.Open asset ↗github.com/RuiKangnj/TGIlines:272-285Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
When detecting tomato leaf diseases in natural environments, factors such as changes in lighting, occlusion, and the small size of leaf lesions pose challenges to detection accuracy. Therefore, this study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion. Firstly, the Convolutional Block Attention Module (CBAM) is introduced into the backbone feature extraction network to enhance the ability to extract lesion features and suppress the effects of environmental interference. Secondly, shallow feature maps are introduced into the re-parameterized generalized feature pyramid network (RepGFPN), constructing a new multi-scale re-parameterized generalized feature fusion module (BiRepGFPN) to enhance feature fusion expression and improve the localization ability for small lesion features. Finally, the BiRepGFPN replaces the Path Aggregation Feature Pyramid Network (PAFPN) in the YOLOv6 model to achieve effective fusion of deep semantic and shallow spatial information. Experimental results indicate that, when evaluated on the publicly available PlantDoc dataset, the model's mean average precision (mAP) showed improvements of 7.7%, 11.8%, 3.4%, 5.7%, 4.3%, and 2.6% compared to YOLOX, YOLOv5, YOLOv6, YOLOv6-s, YOLOv7, and YOLOv8, respectively. When evaluated on the tomato leaf disease dataset, the model demonstrated a precision of 92.9%, a recall rate of 95.2%, an F1 score of 94.0%, and a mean average precision (mAP) of 93.8%, showing improvements of 2.3%, 4.0%, 3.1%, and 2.7% respectively compared to the baseline model. These results indicate that the proposed detection method possesses significant detection performance and generalization capabilities.
Why it matches plant phenotyping methodsトマト葉の病変を画像から検出・局在化する新規深層学習手法を開発し、複数データセットと既存モデルで性能評価しており、植物病害状態の表現型取得が中心である。
abstractthis study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion.
Reproduction assets foundThe paper's tomato leaf disease dataset is assembled from three publicly available Roboflow Universe datasets explicitly cited by the authors (Bryan 2023, SREC 2023, projectdesign 2023), which are the image/annotation inputs used for the study's detection experiments. No author analysis code, trained model checkpoints,Dataset · publicBryan . ( 2023 ). Tomato leaf disease dataset [Open source dataset] . Roboflow Universe . Available at: https://universe.roboflow.com/bryan-b56jm/tomato-leaf-disease-ssoha .Open asset ↗Roboflow Universe · tomato-leaf-disease-ssohalines:563-606Dataset · publicSREC . ( 2023 ). Early- dataset [Open source dataset] . Roboflow Universe . Available at: https://universe.roboflow.com/srec/early .Open asset ↗Roboflow Universe · earlylines:695-785Dataset · publicprojectdesign . ( 2023 ). Tomato biotic stress classification dataset [Open source dataset] . Roboflow Universe . Available at: https://universe.roboflow.com/projectdesign-rw5fo/tomato-biotic-stress-classification .Open asset ↗Roboflow Universe · tomato-biotic-stress-classificationlines:607-694Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Tomato plants are defenseless to different illnesses, including bacterial, contagious, and viral contaminations, which can fundamentally lessen crop yield and quality on the off chance that not identified and treated early. Farmers may experience increased crop damage and financial losses as a result of this detection delay. The goal is to foster a robotized framework utilizing IoT (Internet of Things) gadgets, for example, cameras conveyed in the field, joined with profound learning strategies, to precisely and quickly distinguish illnesses in tomato plants. This framework intends to give ranchers an early admonition framework that can recognize and order infections quickly, empowering convenient intercession and designated treatment, accordingly further developing harvest wellbeing and yield. Profound learning has essentially expanded the precision of picture classification and article identification frameworks' acknowledgment as of late. The exploration zeroed in on computerizing the early location of tomato leaf sicknesses utilizing IoT innovation and a changed ResNet50 profound learning model. At first, IoT gadgets, including sensors and cameras, were conveyed in tomato fields to gather plant-related information and pictures. We focused on calibrating the hyper boundaries of pre-prepared models, including GoogLeNet, SquezeNet and ResNet-50. The notable Tomato leaf disease detection dataset, which incorporates 3,890 picture tests of different sickness and healthy leaves, was utilized for the tests. Using comparable cutting-edge research, a comparative analysis was also conducted. The tests showed that ResNet-50 outflanked cutting edge models with a 99.87% more prominent characterization exactness. The framework demonstrated commendable capability in identifying whether tomato plant leaves were affected by disease in their early stages. This capability enabled farmers to receive timely alerts through mobile application, allowing for more effective management of the issue.
Why it matches plant phenotyping methodsトマト葉の画像から病害状態を推定するIoT・深層学習システムが研究の中心であり、植物病害表現型の取得・分類手法を評価している。
abstractThe goal is to foster a robotized framework utilizing IoT (Internet of Things) gadgets, for example, cameras conveyed in the field, joined with profound learning strategies, to precisely and quickly distinguish illnesses in tomato plants.
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 · publiccan be accessed at the following URL: https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf?Open asset ↗kaustubhb999/tomatoleafpdf-page:11 lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements, the machine learning models are trained with spectral features to predict individual element concentrations; this type of single-target prediction is known as single-target regression. Although this method can achieve reliable accuracy for some elements, there are others that remain less accurate. We aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values. To evaluate the multi-target method, the concentrations of 17 elements in tomato leaves were predicted and compared with the single-target regression results. We trained 5 machine learning models with hyperspectral data and predicted element concentration values and found a significant improvement in the prediction accuracy for 10 elements (Mg, P, S, Mn, Fe, Co, Cu, Sr, Mo, and Cd). Furthermore, our multi-target regression method outperformed single-target predictions by increasing the coefficient of determination ( R 2 ) for elements such as Mn, Cu, Co, Fe, and Mg by 12.5%, 10.3%, 11%, 10%, and 8.4%, respectively. Hence, our multi-target method can improve the accuracy of predicting 10-element concentrations compared to single-target regression.
Why it matches plant phenotyping methodsトマト葉の元素濃度という植物状態を、ハイパースペクトル画像とマルチターゲット回帰で推定する手法を開発・比較評価しており、フェノタイピング手法が中心である。
abstractWe aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values.
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 · publicAll codes for data cleaning and analysis associated with the current submission are available at https://github.com/anaguilarar/MT_elements .Open asset ↗anaguilarar/MT_elementslines:428-472Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.
Why it matches plant phenotyping methodsトマト葉の病害症状と重症度を、画像・疑似ハイパースペクトル・深層学習で直接推定する方法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractthis study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity
Reproduction assets foundThe paper uses two publicly available tomato leaf disease image datasets (Kaggle tomatoleaf; Google Drive dataset) as phenotyping inputs and provides the authors' analysis code (RGB-to-SHSI conversion, VGG-16 feature extraction, GMM/K-means severity pipeline) via a public Colab notebook listed in the Data Availability.Dataset · publicards in the field. In summary,
the compilation of our diverse dataset and the incorporation of benchmark datasets form the
foundation of this research endeavor, ensuring a thorough and principled evaluation of our proposed
approaches in the context of plant disease assessment [8].
2.1.1. Dataset 1:
This data was collected from "https://www.kaggle.com/datasets/kaus-tubhb999/tomatoleaf:
Access Date: 2023-10-25." This dataset includes diseases for tomato leaves such as "Septoria leaf spot,
tomato healthy, Spider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photosOpen asset ↗kaggle · kaus-tubhb999/tomatoleafpdf-raw-page:7 lines:1-31Dataset · publicider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photos in total.
2.1.2. Dataset 2:
Dataset-2 is also a publicly available one which can be downloaded and utilized from the drive
link provided. "https://drive.google.com/file/d/1DVy0LyUUfJciyo7BUFm1sHKSRdTVJgjF/view:
Access Date: 2023-10-25." This dataset is divided into seven classes: yellow curving, tomato mosaic,
Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 January 2024 doi:10.20944/preprints202401.1973.v1Open asset ↗pdf-raw-page:7 lines:1-31Code · public.B.; writing— S.K., M.M., B.B., Y.S., and A.B.; writing—review
and editing, M.M, B.B.; supervision, S.K., M.M., B.B. All authors have read and agreed to the published version
of the manuscript.
Funding: This research was partly funded by Zayed University, grant number 12091.
Data Availability Statement: Our code is available at
https://colab.research.google.com/drive/1wMvqsuZNY_lB2INmyWWqSZYm87wVckv0?usp=sharing
Acknowledgments: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the resultsOpen asset ↗pdf-raw-page:19 lines:1-52Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
MaizePepper / chilliTomatoRootMorphology / geometry measurementRoot system architecture
Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination ( R 2 ), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.
Why it matches plant phenotyping methods画像から根長という植物形質を推定するCNN手法を開発し、複数データセットで精度・頑健性を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation.
Reproduction assets foundThe authors explicitly deposited the 4,015 minirhizotron root images with Rootfly TRL and point-coordinate annotations used to train and test their CNN models in a public Zenodo repository, making it a directly qualifying paper-specific public dataset.Dataset · publicThe datasets generated and analyzed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7482146 .Open asset ↗Zenodo · 10.5281/zenodo.7482146lines:373-471Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
CucumberPepper / chilliTomatoThermalLeafPhysiological trait estimationPlant / canopy temperature
Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.
Why it matches plant phenotyping methods熱画像と葉エネルギーバランスモデルにより、植物キャノピー内の吸収光量を迅速に推定・可視化するフェノタイピング手法を開発し、点測定で検証しているため。
abstractWe developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature.
Reproduction assets foundThe paper's authors explicitly state that all analysis code (R/STAN energy-balance fitting and Julia absorbed-irradiance mapping) is publicly available on their GitHub repository, which directly reproduces the paper's phenotyping computations. No separate phenotype dataset or image deposit is stated in the supplied.Code · publicData analysis
Calculations to solve Eqns 2 and 3 were run in R (R project,
v.4.2.0). The absorbed irradiance map was calculated in JULIA
(v.1.40.1; https://julialang.org/). All codes are available on
GitHub (https://github.com/jiayu0903/leaf-absorbed-irradiance.git). Statistical analysis was performed using a Student’s t-test for
paired samples to determine significant differences (P < 0.05)
between means.
Results
The temperature of sweet pepper (Fig. 2a), cucumber (Fig. S6a),
and tomato leaves (Fig. S7) showed a near-linear increase when
exposed to a brief 30 s period of irradiance fromOpen asset ↗https://github.com/jiayu0903/leaf-absorbed-irradiance.gitpdf-raw-page:7 lines:1-87Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、ダウンサンプリングと深層学習による意味・個体セグメンテーション手法を開発しており、表現型抽出が中心的な技術貢献である。
abstractsegmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters.
Reproduction assets foundThe paper's plant-phenotyping measurements are based entirely on the public Pheno4D dataset of maize and tomato point clouds, which the authors explicitly state is openly available at the IPB Bonn URL. No author analysis code or trained model is disclosed.Dataset · publicervision, X.P.; project administration, X.P.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are openly available in https://www.ipb.uni-bonn.de/data/pheno4d/ (accessed on 15 November 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded by the Key Research and Development Program of Shaanxi (Grant No. 2019ZDLNY07-06-01).
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publicatioOpen asset ↗Pheno4Dlines:423-444Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code. RESULTS: We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content. CONCLUSION: A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.
Why it matches plant phenotyping methods植物画像からサイズ・色を抽出し、果実リコピンや葉クロロフィルを推定するオープンソース手法と撮像系を開発・検証しており、表現型取得が研究の中心である。
abstractWe provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart.
Reproduction assets foundThe authors provide public, paper-specific assets: the PlantSizeClr Python scripts on GitHub, a snapshot of all scripts and data on OSF, and all data generated for the manuscript on the University of Sheffield data repository.Code · publicThe example lightbox contains LED lighting; this could be further improved by using bulbs that are closer to standard illuminants (D65 for sRGB).
An object of known size (coins work well).
Software: Python 3.8.
Python packages: List of packages and their versions used available in Additional file 1 : S0.
Custom Python Scripts: https://github.com/HarryCWright/PlantSizeClr
Snapshot of all scripts and data is available on Open Science Framework: www.doi.org/10.17605/OSF.IO/QAYMU
Optional for extraction of lycopene: acetone, high purity ethanol, hexane deionised water and a UV/vis spectrophotometerOpen asset ↗HarryCWright/PlantSizeClrlines:34-50Code · publicSnapshot of all scripts and data is available on Open Science Framework: www.doi.org/10.17605/OSF.IO/QAYMUOpen asset ↗OSF.IO/QAYMU · 10.17605/OSF.IO/QAYMUlines:34-50Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Introduction The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. Methods In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Results Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). Discussion This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.
Why it matches plant phenotyping methods植物病害画像を対象に拡散モデルとGANを比較し、病害画像データ拡張の性能をFID・KIDで検証する研究であり、植物病害状態の画像ベース評価を支える方法が中心である。
titleHarnessing the power of diffusion models for plant disease image augmentation
Reproduction assets foundThe paper's experiments use a subset of the public PlantVillage image dataset, which the authors explicitly link in the data availability statement. No author analysis code, trained models, or generated-image deposits are mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage .Open asset ↗Kaggle · mohitsingh1804/plantvillagelines:841-873Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.
Why it matches plant phenotyping methodsトマト葉の画像から病害状態を推定する深層学習・スマートフォンシステムの開発と検証が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractA complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper.
Reproduction assets foundThe authors publicly deposited their main tomato leaf image dataset and smartphone application (.apk) on Zenodo, and deployed a live web application for tomato leaf disease detection on Streamlit. Both are paper-specific, public, and actionable. The Kaggle and Mendeley datasets are third-party source datasets cited as输Dataset · publicmatoleaf (accessed on 30 June 2023). This research also used the plant village dataset for comparison and collected only tomato leaves. The dataset was collected from https://data.mendeley.com/datasets/tywbtsjrjv/1 (accessed on 30 June 2023). The main dataset and smartphone application (.apk file) of this study are available at https://zenodo.org/record/8311631 (accessed on 2 September 2023).
Conflicts of Interest
The authors declare that they have no conflict of interest.
Funding Statement
The researchers would like to acknowledge the deanship of Scientific Research, Taif University, for funding this project.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data coOpen asset ↗zenodo · 8311631lines:464-502Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomato, a fruiting plant species within the Solanaceae family, is a widely used ingredient in culinary dishes due to its sweet and acidic flavor profile, as well as its rich nutritional content. Recognized for its potential health benefits, including reducing the risk of coronary artery disease and specific types of cancer, tomatoes have become a staple in global cuisine. Traditional methods for tomato maturity assessment, harvesting, quality grading, and packaging are often labor-intensive and economically inefficient. This paper introduces an extensive dataset of high-resolution tomato images collected over an eight-month period from the demonstration fields of Sher-E-Bangla Agricultural University in Dhaka, Bangladesh, in collaboration with plant breeding experts of the same university. The dataset was meticulously curated to ensure precision and consistency, encompassing various stages of tomato maturity, including images of both fresh and defective tomatoes. This dataset is a valuable resource for researchers, stakeholders, and individuals interested in tomato production in Bangladesh, providing a robust foundation for leveraging computer vision and deep learning techniques in the agriculture sector. The dataset's potential applications extend to automating tasks such as robotic harvesting, quality assessment, and packaging systems, ultimately enhancing the efficiency of tomato production processes.
Why it matches plant phenotyping methodsトマト果実の成熟段階と欠陥を対象とする大規模画像データセットを構築しており、植物状態の画像ベース評価が研究の中心です。
abstractThis paper introduces an extensive dataset of high-resolution tomato images
Reproduction assets foundThis Data in Brief article describes its own public tomato image dataset (maturity detection and quality grading) deposited on Mendeley Data, with explicit direct URL and DOI. The dataset is the paper's plant-phenotyping image asset and is publicly actionable. No separate analysis code repository is provided.Dataset · publict this dataset is entirely new, and no prior research has been conducted using it.
Data source location
Location: Sher-E-Bangla Agricultural University
Zone: Sher-E-Bangla Nagar, Dhaka-1207
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/s42kpg8h37.1
Direct URL to data: https://data.mendeley.com/datasets/s42kpg8h37/1
Instructions for accessing these data: Adhering to the appropriate citation guidelines is crucial when utilizing these datasets.
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Value of the Data
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Robotic harvesting represents an advanced agricultural technology that offers the potential for substantial enhancements in both quality and productivity, while concurrentOpen asset ↗Mendeley Data · 10.17632/s42kpg8h37.1lines:1-51Code / 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 confirmedCrossref · checked 14 Sept 2026
Published3 Oct 2023European Journal of Information Technologies and Computer ScienceCited by 4 · OpenAlex ↗
Tomato plant diseases pose a big problem as they drastically reduce the quantity of a farm’s yield and also result in poor tomato quality, which may affect users. Detecting and identifying leaf diseases in tomato plants is a big challenge for farmers and agricultural officers due to the lack of necessary knowledge and diagnosis tools. This study developed a diagnostic tool accessible through a mobile phone application that can easily be used in the field. The tool uses image recognition technology to classify tomato disease from affected plants. The methodology used to develop the image recognition model was a deep learning technique using Convolutional Neural Networks (CNN) architecture, trained and evaluated using four different models for detecting bacterial spots, late blight, early blight, and healthy tomato leaf. Those models were ResNet18, ResNet50, InceptionV3, and EfficientNet. Since the existing dataset was limited, the learning approach was used to transfer knowledge (weight and bias) of selected models and use it to train on the existing data of tomato. The dataset contains 1000 images for each class, but for unknown images only contains 100 images used in training, 50 images for each class used in validation (val), and 50 for each class used in the test. The four classes of common tomato leaf diseases, early blight, late blight, bacterial spots, healthy tomato leaf, and unknown images, were used for training, validation, and testing. The EfficientNet model achieved an F-score accuracy of 0.91%, Resnet50 achieved an F-score accuracy of 0.99%, Resnet18 achieved an F-score accuracy of 0.99%, and InceptionV3 achieved an F-score accuracy of 0.84%. The model evaluation results for all classes were efficient since the confusion matrix gave correct precision, recall, and F-score values for both test and validation datasets. The research picked the resnet18 model for integration with mobile applications because it only uses less memory, and it has given high prediction in the classification of tomato diseases compared to other models. The developed system can detect tomato plant leaf diseases and give farmers procedures on how to control and prevent the disease; also, the system has the benefit of supporting smallholder. Farmers and extension officers detect tomato plant leaf diseases, thus helping to detect diseases at an early stage and helping to increase the quality of tomatoes.
Why it matches plant phenotyping methodsトマト葉の画像から病害状態を推定するCNNモデルとモバイル診断ツールを開発・評価しており、植物病害表現型の取得・分類手法が中心である。
abstractThis study developed a diagnostic tool accessible through a mobile phone application that can easily be used in the field.
Reproduction assets foundThe paper's phenotyping analysis (tomato leaf disease classification with ResNet18/50, InceptionV3, EfficientNet) is built directly on a public Kaggle tomato leaf image dataset, explicitly cited with URL. No author code, models, or other paper-specific assets are reported.Dataset · publicficiency to reduce environmental pollution, minimal
resources usage, fewer labor expenses, and time-consuming
[16].
VI. TOMATO SUB-SYSTEM (MODEL) DEVELOPMENT
A. Dataset
The dataset used to train, validate, and test the tomato sub-
system was obtained from the Kaggle data science company;
the dataset was obtained through the URL
https://www.kaggle.com/kaustubhb999/tomatoleaf. The
dataset contains 1000 images for each class in train and 50
images for each class in validation (val) and 50 images for
each class in the test. The five classes of common tomato leaf
diseases, early blight, late blight, bacterial spots, unknown
image, and healthy tomato leaf were used for training,
validation, and teOpen asset ↗Kaggle · kaustubhb999/tomatoleafpdf-raw-page:4 lines:1-95Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background One of the key elements in maintaining the consistent marketing of tomato fruit is tomato quality. Since ripeness is the most important factor for tomato quality in the viewpoint of consumers, determining the stages of tomato ripeness is a fundamental industrial concern with regard to tomato production to obtain a high quality product. Since tomatoes are one of the most important crops in the world, automatic ripeness evaluation of tomatoes is a significant study topic as it may prove beneficial in ensuring an optimal production of high-quality product, increasing profitability. This article explores and categorises the various maturity/ripeness phases to propose an automated multi-class classification approach for tomato ripeness testing and evaluation. Methods Object detection is the critical component in a wide variety of computer vision problems and applications such as manufacturing, agriculture, medicine, and autonomous driving. Due to the tomato fruits' complex identification background, texture disruption, and partial occlusion, the classic deep learning object detection approach (YOLO) has a poor rate of success in detecting tomato fruits. To figure out these issues, this article proposes an improved YOLOv5 tomato detection algorithm. The proposed algorithm CAM-YOLO uses YOLOv5 for feature extraction, target identification and Convolutional Block Attention Module (CBAM). The CBAM is added to the CAM-YOLO to focus the model on improving accuracy. Finally, non-maximum suppression and distance intersection over union (DIoU) are applied to enhance the identification of overlapping objects in the image. Results Several images from the dataset were chosen for testing to assess the model's performance, and the detection performance of the CAM-YOLO and standard YOLOv5 models under various conditions was compared. The experimental results affirms that CAM-YOLO algorithm is efficient in detecting the overlapped and small tomatoes with an average precision of 88.1%.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を画像から自動推定する改良YOLO手法を開発し、標準手法と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ある。
abstractThis article explores and categorises the various maturity/ripeness phases to propose an automated multi-class classification approach for tomato ripeness testing and evaluation.
Reproduction assets foundThe paper's tomato detection study uses the public Laboro Tomato dataset, and the authors publicly release their analysis notebook on GitHub and their Tomatoes dataset on Zenodo via explicit Data Availability statements.Dataset · publicThe images utilised in this study are collected from the Laboro Tomato dataset ( LaboroAI, 2020 ), which is a tomato dataset consisting of tomatoes collected at various stages of their ripening developed for instance segmentation and object detection tasks.Open asset ↗lines:31-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.
Why it matches plant phenotyping methodsトマト果皮画像の色からリコペン含量を非破壊推定する低コストセンサーと予測モデルの開発が中心であり、植物器官の形質測定法に該当する。
abstractthis work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp.
Reproduction assets foundThe paper's Data Availability Statement links to a public Google Drive folder containing the data supporting the reported lycopene measurement results (tomato RGB/L*a*b* image-derived measurements and HPLC-calibrated model data). No separate code deposit is described; the models were built in MATLAB toolboxes without aDataset · publicl analysis, M.-G.B.-S.; investigation, J.-A.P.-M.; writing—original draft preparation, J.P.-O. and M.-J.V.-A.; writing—review and editing, A.-I.B.-G.; supervision, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data supporting reported results can be found at: https://drive.google.com/drive/folders/1d1Q_RtEWmo2lbpipMCNG4x53s09-pB-C?usp=sharing .
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Listed below are the 18 inference rules and weights for each of the two fuzzy systems red, green, and blue:
If (L is Low_L) and (a is Low_a) and (b is Low_b) then (Lycopene is Lycopenemf1)
If (L is Low_L) Open asset ↗lines:75-128Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits.
Why it matches plant phenotyping methods植物上のトマト果実を画像からセグメンテーションする手法を開発・比較し、RGB-D画像と画素アノテーションのデータセットも提供しており、植物器官の状態・位置推定に関わる方法が中心である。
abstractwe propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes
Reproduction assets foundThe paper introduces TomatoDIFF and the Tomatopia dataset, with explicit public availability of source code and dataset at the authors' GitHub repository. It also trains/evaluates on the public Kaggle 'Tomato dataset' (andrewmvd/tomato-detection), which is a paper-specific public image dataset used directly in the phenCode · publicThe source code of TomatoDIFF and Tomatopia are available at https://github.com/MIvanovska/TomatoDIFF .Open asset ↗MIvanovska/TomatoDIFFlines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others. In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test sets. The latter consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.
Why it matches plant phenotyping methods植物の病害状態を画像で扱う再利用可能なデータセットの構築が主題であり、植物病害フェノタイピング用データセットとして中心的な方法貢献がある。
titleCCMT: Dataset for crop pest and disease detection.
Reproduction assets foundThe paper is a data descriptor for the CCMT crop pest/disease image dataset, with the authors' own images publicly deposited on Mendeley Data (DOI 10.17632/bwh3zbpkpv.1), explicitly stated as freely available.Dataset · publicw.uenr.edu.gh
African Technology Policy Society Network
8 th Floor – The Chancery – Valley Road - Nairobi
P.O. Box 10081-00100, Nairobi, Kenya
Website: http://www.atpsnet.org
Data accessibility
Repository name: Dataset for Crop Pest and Disease Detection
Data identification number(doi): 10.17632/bwh3zbpkpv.1
Direct URL to data: https://data.mendeley.com/datasets/bwh3zbpkpv
Value of the Data
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The dataset is comprehensive and consists of 102,976 high-quality images of four crops with 22 different classes, respectively cashew (5 classes), cassava (5 classes), maize (7 classes), and tomato (5 classes).
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The dataset consists of plant leaves, pests, fruits and images of sick parts of cashew, casOpen asset ↗10.17632/bwh3zbpkpv.1lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.
Why it matches plant phenotyping methodsトマト葉の病斑を画像から定量的にセグメンテーションする新規深層学習手法を開発し、データセット上で比較検証しているため、植物病害状態の表現型取得が中心である。
abstractwe propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet).
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 · publicSome of the datasets that were used and analyzed in this study have been uploaded to the website https://github.com/ZhouGuoXiong/MC-UNet . In addition, all the homemade datasets in this study (6,372 sheets in total) can be obtained by contacting the corresponding author.Open asset ↗ZhouGuoXiong/MC-UNetlines:414-421Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease's defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network's feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出するPLPNetを開発し、データセット上で性能評価しているため、植物病害状態の画像ベース表現型取得が中心である。
abstractIn this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet.
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 · publicSome of the datasets that were used and analyzed in this study have been uploaded to the website https://github.com/ZhouGuoXiong/PLPNet , and all the homemade datasets in this study (13,597 sheets in total) can be obtained by contacting the corresponding author.Open asset ↗ZhouGuoXiong/PLPNetlines:613-717Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
Why it matches plant phenotyping methodsトマト感染株の病害状態をハイパースペクトル測定と機械学習で非破壊・早期推定する方法を開発しており、表現型取得・判定手法が中心である。
abstractan approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented.
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' hyperspectral tomato disease measurements (the paper-specific phenotyping dataset) at a public link.Dataset · publicData availability
The hyperspectral measurements presented in this study are available at https://bit.ly/3W7VroF .Open asset ↗lines:175-230Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から識別する軽量CNNを開発し、精度・計算量を比較評価しており、植物病害状態の取得・推定手法が中心である。
abstracta lightweight tomato leaf disease identification model called LightMixer was designed in this study.
Reproduction assets foundThe paper's phenotyping inputs are public tomato leaf disease image datasets: the PlantVillage dataset (18,835 tomato leaf images, 10 classes) used for all LightMixer training/evaluation, obtained via Kaggle and the original Mendeley Data deposit (Arun Pandian and Gopal, 2019). No author analysis code, trained model,或 Dataset · publicrmers accurately identify and detect tomato leaf diseases. Further exploration of the generalizability of the proposed model to detect and identify a variety of other plant diseases will be part of our future plans.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset .
Author contributions
MT contributed to conception and design of the study. YZ designed and performed the experiment, managed the algorithms and result analysis and wrote the manuscript. ZT participated in the experiments and manuscript revision. All authors contributed to the article and approved thOpen asset ↗Kaggle · plantvillage-datasetlines:452-510Dataset · public2022 ).
Tomato leaf disease classification by exploiting transfer learning and feature concatenation
. IET Image Process.
16 , 913 – 925 . doi:
10.1049/ipr2.12397
Arun Pandian J. Gopal G.
( 2019 ). Data from: identification of plant leaf diseases using a 9-layer deep convolutional neural network (
Mendeley Data
). Available at: https://data.mendeley.com/datasets/tywbtsjrjv/1 .
Barman U. Choudhury R. D. Sahu D. Barman G. G.
( 2020 ).
Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease
. Comput. Electron. Agric.
177 , 105661 . doi:
10.1016/j.compag.2020.105661
Bhagat M. Kumar D. Haque I. Munda H. S. Bhagat R.
( 2020 ). “
Plant leOpen asset ↗Mendeley Data · tywbtsjrjv/1lines:452-510Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousands of points, which fails to take full advantage of the high-resolution of advanced scanning devices. To address this issue, we propose a feature-fusion-based method called FF-Net, which consists of two branches, namely the voxel-branch and the point-branch. In particular, the voxel-branch partitions a point cloud into voxels and then employs sparse 3D convolution to learn the context features, and the point-branch learns the point features within a voxel to preserve the detailed point information. Finally, an attention-based module was designed to fuse the two branch features to produce the final segmentation. We conducted extensive experiments on two large plant point clouds (maize and tomato), and the results showed that our method outperformed three commonly used models on both datasets and achieved the best mIoU of 80.95% on the maize dataset and 86.65% on the tomato dataset. Extensive cross-validation experiments were performed to evaluate the generalization ability of the models, and our method achieved promising segmentation results. In addition, the drawbacks of the proposed method were analyzed, and the directions for future works are given.
Why it matches plant phenotyping methods植物の3D点群を対象としたセマンティックセグメンテーション手法を開発し、トウモロコシとトマトのデータセットで比較・交差検証しているため、フェノタイピング用の画像解析手法が中心です。
abstractSemantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years.
Reproduction assets foundThe authors explicitly state that their analysis code and the relabeled Pheno4D-derived dataset used in this paper are publicly available on GitHub.Code · publicOur code and relabeled dataset are available at: https://github.com/daojianqingchou/FF-Net , accessed on 8 April 2023.Open asset ↗daojianqingchou/FF-Netlines:56-66Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
As human population continue to increase, our food production system is challenged. With tomatoes as the main indoor produced fruit, the selection of adapter varieties to each specific condition and higher yields is an imperative task if we wish to supply the growing demand of coming years. To help farmers and researchers in the task of phenotyping, we here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions. We prove that using the ASPEN pipeline it is possible to obtain real time in situ yield estimation not only in a commercial-like greenhouse level but also within growing line. To discuss our results, we analyse the two main steps of the pipeline in a desktop computer: object detection and tracking, and yield prediction. Thanks to the use of YOLOv5, we reach a mean average precision for all categories of 0.85 at interception over union 0.5 with an inference time of 8 ms, who together with the best multiple object tracking (MOT) tested allows to reach a 0.97 correlation value compared with the real harvest number of tomatoes and a 0.91 correlation when considering yield thanks to the usage of a SLAM algorithm. Moreover, the ASPEN pipeline demonstrated to predict also the sub following harvests. Confidently, our results demonstrate in situ size and quality estimation per fruit, which could be beneficial for multiple users. To increase accessibility and usage of new technologies, we make publicly available the required hardware material and software to reproduce this pipeline, which include a dataset of more than 850 relabelled images for the task of tomato object detection and the trained YOLOv5 model[1] [1]https://github.com/camilochiang/aspen
Why it matches plant phenotyping methodsASPENはトマト果実の検出・追跡から収量、果実サイズ、品質を推定する画像ベースの表現型解析パイプラインであり、手法の評価と再現可能なソフトウェア・データセット提供が中心です。
abstractwe here present a study case of the A gro s cope ph en otyping tool (ASPEN) in tomato under indoor conditions.
Reproduction assets foundThe authors explicitly make publicly available the ASPEN pipeline software/hardware materials, a dataset of 850+ relabelled tomato images, and the trained YOLOv5 model via their GitHub repository.Dataset · publicThe dataset supporting the conclusions of this article is available in the github repository (https://github.com/camilochiang/aspen).Open asset ↗github.com/camilochiang/aspenlines:119-149Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Tomato leaf diseases can incur significant financial damage by having adverse impacts on crops and, consequently, they are a major concern for tomato growers all over the world. The diseases may come in a variety of forms, caused by environmental stress and various pathogens. An automated approach to detect leaf disease from images would assist farmers to take effective control measures quickly and affordably. Therefore, the proposed study aims to analyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data. Four state-of-the-art transformer-based models, namely, External Attention Transformer (EANet), Multi-Axis Vision Transformer (MaxViT), Compact Convolutional Transformers (CCT), and Pyramid Vision Transformer (PVT), are trained and tested on a multiclass tomato disease dataset. The result analysis showcases that MaxViT comfortably outperforms the other three transformer models with 97% overall accuracy, as opposed to the 89% accuracy achieved by EANet, 91% by CCT, and 93% by PVT. MaxViT also achieves a smoother learning curve compared to the other transformers. Afterwards, we further verified the legitimacy of the results on another relatively smaller dataset. Overall, the exhaustive empirical analysis presented in the paper proves that the MaxViT architecture is the most effective transformer model to classify tomato leaf disease, providing the availability of powerful hardware to incorporate the model.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する画像解析手法を複数のTransformerで比較・検証しており、植物の病害表現型の取得が研究の中心である。
abstractanalyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data
Reproduction assets foundThe paper used two public plant-image datasets for tomato leaf disease classification, both explicitly linked in the Data Availability Statement: a Kaggle tomato disease dataset (20,000 images, 11 classes) and a Mendeley/PlantVillage-derived dataset (4,972 images, 6 classes). No author analysis code or trained model isDataset · publicsupervision, A.C. and Y.J.J.; funding acquisition, A.C. and Y.J.J. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
This dataset was collected from Kaggle, this can be found at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources (accessed on 2 January 2023). This dataset was collected from Mendeley, this can be found at https://data.mendeley.com/datasets/ngdgg79rzb/1 (accessed on 17 March 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded in part by theOpen asset ↗Kaggle · cookiefinder/tomato-disease-multiple-sourceslines:335-374Dataset · publicd Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
This dataset was collected from Kaggle, this can be found at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources (accessed on 2 January 2023). This dataset was collected from Mendeley, this can be found at https://data.mendeley.com/datasets/ngdgg79rzb/1 (accessed on 17 March 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded in part by the National Research Foundation of Korea (grant no. NRF-2020R1A2C1008753).
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publicOpen asset ↗Mendeley · ngdgg79rzblines:335-374Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many studies proposed the use of stable carbon isotope ratio (δ 13 C) as a predictor of abiotic stresses in plants, considering only drought and nitrogen deficiency without further investigating the impact of other nutrient deficiencies, that is, phosphorus (P) and/or iron (Fe) deficiencies. To fill this knowledge gap, we assessed the δ 13 C of barley ( Hordeum vulgare L.), cucumber ( Cucumis sativus L.), maize ( Zea mays L.), and tomato ( Solanum lycopersicon L.) plants suffering from P, Fe, and combined P/Fe deficiencies during a two-week period using an isotope-ratio mass spectrometer. Simultaneously, plant physiological status was monitored with an infra-red gas analyzer. Results show clear contrasting time-, treatment-, species-, and tissue-specific variations. Furthermore, physiological parameters showed limited correlation with δ 13 C shifts, highlighting that the plants' δ 13 C, does not depend solely on photosynthetic carbon isotope fractionation/discrimination (Δ). Hence, the use of δ 13 C as a predictor is highly discouraged due to its inability to detect and discern different nutrient stresses, especially when combined stresses are present.
Why it matches plant phenotyping methodsδ13Cを用いた栄養ストレス予測法の有効性を複数作物で評価・検証しており、植物状態の推定手法の技術的妥当性が中心である。
titleδ 13 C as a tool for iron and phosphorus deficiency prediction in crops.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the raw δ13C/physiology data and the analysis scripts used to generate figures, which is a paper-specific, publicly actionable asset.Code · publick Dr. Christian Ceccon for providing support for the isotope analysis.
DATA AVAILABILITY STATEMENT
The following information was supplied regarding data and code availability: the raw data, the version of the individual packages and scripts used to analyze the data and generate the figures of this study are available at GitHub: https://github.com/Fabio-Trevisan/13C-Experiment.git .
REFERENCES
Andaluz , S.
,
López‐Millán , A. F.
,
Peleato , M. L.
,
Abadía , J.
, &
Abadía , A.
( 2002 ).
Increases in phosphoenolpyruvate carboxylase activity in iron‐deficient sugar beet roots: Analysis of spatial localization and post‐translational modification
. Plant and Soil , 241 ( 1 ), 43 – 48 .
10.1023/A:1Open asset ↗Fabio-Trevisan/13C-Experiment · 13C-Experimentlines:309-505Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.
Why it matches plant phenotyping methodsトマト葉の画像から感染性疾患を分類する深層学習手法が研究の中心であり、精度検証と説明可能性評価も行っているため、植物病害状態の画像ベース・フェノタイピングに該当する。
abstractWe implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to the public Kaggle tomato leaf disease dataset used as the phenotyping image input for this study. No author analysis code or trained model checkpoints are deposited.Dataset · publicoriginal draft, M.B.; Writing—review and editing, M.B., T.B.S., A.N. and K.B.W. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Publicly available tomato leaf diseases dataset, https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf (Accessed on 3 July 2022).
Conflicts of Interest
The authors declare no conflict of interest.
References
1.
Bock C.
Parker P.
Cook A.
Gottwald T.
Visual rating and the use of image analysis for assessing different symptoms of citrus canker on grapefruit leaves
Plant Dis. 2008 92 530 541
10.1094/PDIS-92-4-0530
307Open asset ↗Kaggle · kaustubhb999/tomatoleaflines:98-278Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Digitalization in agriculture requires critical research into applications of artificial intelligence to various specialization domains. This work aimed at investigating the application of image synthesis technology to the mitigation of the data volume constraint to digital plant disease phenotyping accuracy. We designed an experiment involving the use of a deep convolutional generative adversarial network (DC-GAN) to synthesize photorealistic data for healthy and bacterial spot disease-infected tomato leaves. The training dataset contained 1,272 instances per class. We further employed a 3-block visual geometry group (VGG) convolutional neural network (CNN) model with dropout regularization and 1 epoch to compare classification accuracies of the original dataset and various synthetic datasets. Our results showed that the third DC-GAN synthesized training dataset containing 3,816 synthetic examples of both healthy and bacterial spot infected tomato leaf classes outperformed the original training dataset containing 1,272 real examples of both tomato leaf classes (77.088% accuracy with the former dataset on a 3-block VGG CNN model with dropout regularization and 1 epoch, as compared to 76.447% accuracy with the latter dataset on the same classifier).
Why it matches plant phenotyping methodsトマト葉の健全・細菌斑点病状態を画像から分類するため、DC-GANによる合成データ生成とCNN分類精度を比較検証しており、植物病害フェノタイピング手法が中心である。
abstractThis work aimed at investigating the application of image synthesis technology to the mitigation of the data volume constraint to digital plant disease phenotyping accuracy.
Reproduction assets foundThe authors explicitly deposited their analysis scripts and research dataset link in a public GitHub repository, and the paper's phenotyping input images (tomato healthy/bacterial spot classes) come from the public PlantVillage dataset. Both are paper-specific, public, and actionable.Code · publicAfter the
resultswereobtained,boththescriptsandtheresearchdatasetlink
were pushed to a GitHub repository (GitHub-Enowtakang, n.d.).Open asset ↗pdf-raw-page:2 lines:1-86Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Abstract Background Object detection, size determination, and colour detection of optical images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code. Results We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content. Conclusion A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.
Why it matches plant phenotyping methods植物画像からサイズ・色を抽出し、リコペンおよびクロロフィル含量を推定するオープンソース手法と画像取得系を開発・実証しており、植物フェノタイピング手法が中心である。
abstractWe provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart.
Reproduction assets foundThe paper provides authors' public Python scripts for plant image colour/size phenotyping on GitHub, plus a public data deposit (University of Sheffield repository DOI) and an OSF snapshot containing all scripts and data.Code · publicCustom Python Scripts: https://github.com/HarryCWright/PlantSizeClrOpen asset ↗HarryCWright/PlantSizeClrlines:62-83Code · publicSnapshot of all scripts and data is available on Open Science Framework: DOI: 10.17605/OSF.IO/QAYMUOpen asset ↗10.17605/OSF.IO/QAYMUlines:62-83Code / dataset availability confirmedCrossref · checked 14 Sept 2026
In Indonesia, tomato is one of the horticultural products with the highest economic value. To maintain enhanced tomato plant production, it is necessary to monitor the growth of tomato plants, particularly the leaves. The quality and quantity of tomato plant production can be preserved with the aid of computer technology. It can identify diseases in tomato plant leaves. An algorithm for deep learning with a DenseNet architecture was implemented in this study. Multiple hyperparameter tests were conducted to determine the optimal model. Using two hidden layers, a DenseNet trainable layer on dense block 5, and a dropout rate of 0.4, the optimal model was constructed. The 10-fold cross-validation evaluation of the model yielded an accuracy value of 95.7 percent and an F1-score of 95.4 percent. To recognize tomato plant leaves, the model with the best assessment results was implemented in a mobile application.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習モデルを開発・検証し、モバイルアプリへ実装しており、植物表現型取得が中心的です。
abstractIt can identify diseases in tomato plant leaves.
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 data in this study was image data of diseases on tomato plant leaves taken from
the Kaggle website The data were obtained by downloading it from
https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf, accessed on 3 November
2022.Open asset ↗Kaggle · kaustubhb999/tomatoleafpdf-page:4 lines:1-44Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
Why it matches plant phenotyping methods画像解析パイプラインを開発し、植物形態から病害進展を定量化する方法が研究の中心であるため含める。
abstractExpanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
Reproduction assets foundThe paper's data availability statement points to a public Purdue-hosted repository containing the raw plant images and genotype data used for the image-based disease phenotyping and QTL analysis. The analysis code, however, is only available upon request from an author, so it is not a public asset.Dataset · publicrd (1755401) to BPD, and the endowment of the Charles
William Harrison Distinguished Professorship at Purdue University
to EJD.
CONFLICT OF INTEREST
Authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Raw images of RILs and parents for each replicate and
each time point as well as genotype data are available at
https://skynet.ecn.purdue.edu/~sbairedd/downloads/Rs_ril_data/. Code is available from Dr. Edward Delp.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. Design of our low-cost phenotyping platform including
automatic turntable, backdrop, lightning, and RGB camera.
Figure S2. Raw RGB pictures showOpen asset ↗skynet.ecn.purdue.edupdf-raw-page:15 lines:1-93Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.
Why it matches plant phenotyping methodsトマト葉の顕微鏡画像から病徴を分類する深層学習モデルを開発・比較し、外部検証まで実施しており、植物病害状態の表現型取得が中心である。
abstractUpon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%.
Reproduction assets foundThe paper's data availability statement explicitly deposits the microscopic tomato disease image dataset used for training the NCDL models in a public GitHub repository, which is a paper-specific, publicly actionable asset.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://github.com/manoj044/Tomato_microscopic_images.git .Open asset ↗https://github.com/manoj044/Tomato_microscopic_images.gitlines:993-1025Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).
Why it matches plant phenotyping methods植物3D点群から器官を自動分割・識別するグラフベース手法を開発し、合成および実データで評価しており、植物表現型取得の技術が中心である。
abstractAccurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping.
Reproduction assets foundThe paper's Chenopodium 3D point cloud dataset (with ground truth annotations) is publicly deposited on Zenodo, and the reconstruction pipeline code is open source on GitHub (romi/plant-3d-vision).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://zenodo.org/record/6962994#.YuvYkS8itqs .Open asset ↗zenodo · 6962994lines:641-715Code · publicThe entire code is open source and available online ( https://github.com/romi/plant-3d-vision ).Open asset ↗github · romi/plant-3d-visionlines:428-438Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract Plant disease detection has a huge impact on plant farming. Early diagnosis of plant illness can help control disease spread and reduce loss. It is a soil-borne disease that affects leaves of plants. In the current research, the emphasis is on the early diagnosis and prevention of plant leaf disease. In this paper, strawberry, tomato, pepper bell, and potato disease detection network (STPP- DDN) based on Faster R-CNN and multi-task learning. STPP-ddn is developed which leverages attention mechanisms in feature extraction. STPP-DDN detects disease based on plant symptoms. Unlike other approaches for diagnosing disease from the total plant look, the STPP-DDN automatically classifies the petioles and young leaves. A large dataset with number of photos divided into various groups is constructed to serve as a basis for analyzing and testing our proposed technique. Each image also includes a label that indicates whether or not the plants has been affected. With the proposed STPP-DDN, we achieved a mAP of 77:54% on object detection of 4 categories and 99:95% accuracy for strawberry verticillium wilt detection.
Why it matches plant phenotyping methods植物病徴を画像から検出・分類する深層学習手法を開発し、データセットと精度評価を提示しており、植物の疾病状態の表現型取得が中心です。撤回表示はあるものの、内容はスクリーニング対象に該当します。
abstractSTPP-DDN detects disease based on plant symptoms.
Reproduction assets foundThe withdrawn preprint states that the plant leaf image datasets used for its disease-detection experiments (potato, tomato, pepper bell, and strawberry) are freely available on open-source platforms, with explicit public URLs given in footnotes: a Kaggle plant disease dataset and a GitHub strawberry verticillium wilt.Dataset · publicthese datasets are freely available on open source plat- forms; potato, tomato, pepper bell 1 strawberry 2 .Open asset ↗lines:103-113Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する深層学習手法を開発・評価しており、植物の病徴認識が研究の中心です。
abstractan intelligent method based on deep learning is presented to recognize nine common tomato diseases.
Reproduction assets foundThe paper's phenotyping inputs are public leaf image datasets: the tomato leaf disease images from PlantVillage and the Flavia leaf dataset, both explicitly cited with public download URLs in the Data Availability Statement and Methods. No author code, models, or checkpoints are reported.Dataset · publicThe PlantVillage dataset is publicly available online at https://github.com/spMohanty/PlantVillage-Dataset (accessed on 13 November 2021).Open asset ↗PlantVillage-Datasetlines:818-820Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.
Why it matches plant phenotyping methods植物病害の症状を画像から検出する深層学習手法を開発し、データセット、モデル比較、外部検証、交差検証まで行っており、植物状態の取得・推定が中心である。
abstractThis paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification.
Reproduction assets foundThe paper's NZDLPlantDisease-v2 plant disease image dataset is explicitly stated as publicly available on the authors' GitHub repository.Dataset · publicThe dataset presented in this study is made publicly available in a GitHub repository: https://github.com/kmarif/NZDLPlantDisease-v2 .Open asset ↗kmarif/NZDLPlantDisease-v2 · NZDLPlantDisease-v2lines:981-1043Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.
Why it matches plant phenotyping methodsトマト葉画像から病斑領域をセグメンテーションし、植物病害を分類する深層学習手法の開発・評価が研究の中心であるため、植物表現型計測手法として含める。
abstractIn this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data.
Reproduction assets foundThe paper trains its tomato leaf segmentation/classification models on the public PlantVillage dataset, which the authors cite with an explicit public GitHub URL. No authors' analysis code or trained model checkpoints are reported as publicly available; the data availability statement only offers inquiries to the对应ing.Dataset · publicSpMohanty ( 2018 ) PlantVillage-dataset . Available at: https://github.com/spMohanty/PlantVillage-Dataset (Accessed 21 January 2021 ).Open asset ↗PlantVillage-Datasetlines:991-1067Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Why it matches plant phenotyping methodsDNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
Reproduction assets foundThe paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.Dataset · publichways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a
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second pathway would be wild gourd marrow straightneck zucchini cocozelle
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(Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the
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‘contours.png’ file, based in (Paris 1989) and available in GitHub
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(https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using
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OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were
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obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a
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generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al.
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2022Open asset ↗https://github.com/miguelperezenciso/dna2image · contours.pngpdf-raw-page:5 lines:1-76Code · publicy, we have shown that very simple networks can be successfully trained in small
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datasets to accurately predict fruit images. Although much work remains to be done, this
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research opens new possibilities in the area of prediction of complex traits.
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Data availability statement
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All data and code are available at https://github.com/miguelperezenciso/dna2image.327
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 22, 2022.
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https://doi.org/10.1101/2022.09.19.Open asset ↗https://github.com/miguelperezenciso/dna2image.327 · dna2image.327pdf-raw-page:10 lines:1-73Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Yield estimation (YE) of the crop is one of the main tasks in fruit management and marketing. Based on the results of YE, the farmers can make a better decision on the harvesting period, prevention strategies for crop disease, subsequent follow-up for cultivation practice, etc. In the current scenario, crop YE is performed manually, which has many limitations such as the requirement of experts for the bigger fields, subjective decisions and a more time-consuming process. To overcome these issues, an intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19 (a deep learning-based semantic segmentation architecture). The dataset of 672 images was given as an input to the SegNet with VGG19 architecture for training. It extracts features corresponding to the tomato in each layer and detection was performed based on the feature score. The results were compared against the other semantic segmentation architectures such as U-Net and SegNet with VGG16. The proposed method performed better and unveiled reasonable results. For testing the trained model, a case study was conducted in the real tomato field at Manapparai village, Trichy, India. The proposed method portrayed the test precision, recall and F1-score values of 89.7%, 72.55% and 80.22%, respectively along with reasonable localization capability for tomatoes.
Why it matches plant phenotyping methodsトマト果実の検出・局在化・計数による収量推定手法を開発し、複数モデルと比較・実地検証しており、植物フェノタイピング手法が中心である。
abstractan intelligent YE system was proposed which detects, localizes and counts the number of tomatoes in the field using SegNet with VGG19
Reproduction assets foundThe paper's phenotyping input images come from a publicly available annotated tomato image dataset (Rob2Pheno, 123 RGB images) hosted on 4TU under CC BY 4.0, which the authors explicitly used for training their SegNet-VGG19 yield estimation model. No author analysis code or trained model is reported as publicly shared.Dataset · publicThe dataset of 123 RGB images 18 used for this work is acquired from the publicly available dataset under a creative common license. ( https://data.4tu.nl/articles/dataset/Rob2Pheno_Annotated_Tomato_Image_Dataset/13173422 ), ( https://creativecommons.org/licenses/by/4.0/ ).Open asset ↗data.4tu.nl · Rob2Pheno_Annotated_Tomato_Image_Dataset/13173422lines:74-89Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TomatoGreenhouseLeafPhysiological trait estimationGrowth / development / phenology
While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R 2 value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.
Why it matches plant phenotyping methodsトマトの開花率という植物形質をメタボロームデータから予測するLASSOモデルを構築し、別データセットで検証しており、形質推定手法が中心である。
abstractmetabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes.
Reproduction assets foundThe paper's tomato leaf metabolome dataset (DM0041) used for the LASSO anthesis-rate modeling is publicly deposited in DROP Met at PRIMe, with an explicit Data Availability Statement and URL. No author analysis code or trained model checkpoint is stated as publicly available; the supplementary material link is generic.Dataset · publicss spectrometer (LC-QqQ-MS) (UPLC coupled with Xevo TQ-S, Waters, Milford, MA, United States) ( Sawada et al., 2009 ; Sawada et al., 2019 ). The analytical conditions are described in detail in Supplementary Tables S1–S3 . The metabolome data were deposited in the DROP Met in PRIMe (the Platform for RIKEN Metabolomics) (DM0041, http://prime.psc.riken.jp/archives/data/DropMet/059/ ).
2.3.3 Measurement of Relative Metabolite Contents
For the Tsukuba data (TK01), the peak areas of 501 target metabolites (including two internal standards) were processed as follows. Values below the detection limit were set to zero. The peak area of each metabolite in a leaf sample was divided by the mean peak arOpen asset ↗DROP Met · DM0041lines:87-98Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Why it matches plant phenotyping methods野菜の長さ・直径という植物器官形質を、ステレオカメラ、深度画像、キーポイント検出で非接触推定する手法が研究の中心であるため。
abstractThis work focuses on the problem of non-contact measurement for vegetables in agricultural automation.
Reproduction assets foundThe authors publicly released both the vegetable keypoint dataset (1600 COCO-format images with ROI boxes and six keypoints) and the implementation code for their size estimation method on GitHub. Labelme is a generic third-party annotation tool and is excluded.Code · publicThe implementation code of our size estimation method can be accessed on https://github.com/BourneZ130/VegetableDetection , accessed on 15 February 2022.Open asset ↗BourneZ130/VegetableDetectionlines:76-141Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Published1 Feb 2022Proceedings of the National Academy of Sciences of the United States of AmericaCited by 220 · OpenAlex ↗
Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
Why it matches plant phenotyping methods果実の風味という植物器官形質を、メタボロームから予測する統計・機械学習モデルを開発し、交差検証で性能評価している。育種に利用可能な風味表現型推定法が中心であり、単なる代謝測定ではない。
abstractwe leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor.
Reproduction assets foundThe paper provides public author analysis scripts on GitHub and paper-specific phenotype data (sensory panel ratings, metabolite concentrations, model accuracies) in Datasets S1–S7 within the PNAS supporting information. The caret R package is a generic library and excluded.Code · publicRelevant scripts are provided in the GitHub repository at https://github.com/Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavor .Open asset ↗Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavorlines:126-357Dataset · publicSensory panel ratings and metabolite concentrations are provided in Datasets S1 and S2 . Underlying data for Fig. 3 are provided in Dataset S3 . Model accuracies in Fig. 4 are provided in Datasets S4–S7 .Open asset ↗lines:126-357Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類するCNN手法が研究の中心であり、植物の病徴を直接推定する画像ベース表現型解析に該当する。
titleEarly Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.
Reproduction assets foundThe paper's tomato leaf disease classification experiments were performed on a publicly available Kaggle dataset (PlantVillage), explicitly cited by the authors with a public URL. No author analysis code, trained models, or supplementary data deposits are reported; the Data Availability Statement says 'Not applicable.'Dataset · publicence (CCDC); Hefei, China. 22–24 August 2020; pp. 1446–1450.
30. Agarwal M., Gupta S.K., Biswas K.K. Development of Efficient CNN model for Tomato crop disease identification. Sustain. Comput. Inform. Syst. 2020;28:100407–100421. doi: 10.1016/j.suscom.2020.100407.
31. PlantVillege. [(accessed on 3 July 2021)]. Available online: https://www.kaggle.com/emmarex/plantdisease .
32. Kaur P., Gautam V. Research patterns and trends in classification of biotic and abiotic stress in plant leaf. Mater. Today Proc. 2021;45:4377–4382. doi: 10.1016/j.matpr.2020.11.198.
33. Kaur P., Gautam V. Plant Biotic Disease Identification and Classification Based on Leaf Image: A Review; Proceedings of the 3rd InternOpen asset ↗Kaggle · emmarex/plantdiseaselines:257-279Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.
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・比較・検証が研究の中心であり、植物病害という表現型状態を直接推定しているため採用。
abstractIn this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases.
Reproduction assets foundThe paper's corn disease classification experiments directly use the public Kaggle 'Corn or Maize Plant Leaf Diseases' dataset (4188 images), cited with an explicit public URL. The PlantVillage subset and authors' field images have no public deposit (data availability is on-request only), and no author analysis code orDataset · public39. Corn or Maize Plant Leaf Diseases. Available online: https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on 15 January 2021).
40. Nachtigall, L.G.; Araujo, R.M.; Nachtigall, G.R. Classification of apple tree disorders using convolutional neural networks.
In Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA,
6–8 November 2016; pp. 472–476.
41. Wang,Open asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetpdf-raw-page:19 lines:53-58Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
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Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
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thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
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experimental resources.
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Data Availability
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All the phenotypic data and images can be viewed and downloaded via the link
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(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
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References
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Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
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Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
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of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
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An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
Why it matches plant phenotyping methods植物の3D点群から分枝構造を骨格化し、分枝角度を推定するアルゴリズムを開発・比較評価しており、植物表現型の抽出手法が研究の中心です。
abstractHere, we studied the skeletonization of plant architectures from 3D point clouds
Reproduction assets foundThe paper's Data Availability Statement explicitly states that data and code executable are publicly available at the authors' GitHub repository iziamtso/P3D, which is an allowed URL. This covers the paper-specific plant point cloud data and skeletonization analysis code.Code · publicData Availability Statement: Data and code executable are available at: https://github.com/iziamtso/P3D.Open asset ↗iziamtso/P3Dpdf-page:14 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
Why it matches plant phenotyping methods植物フェノタイピング用の時系列3D点群データセットを構築し、手動アノテーションと複数の解析ベースラインを提供することが中心であり、再利用可能なデータ基盤として適格です。
abstractwe present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks
Reproduction assets foundThe paper's core contribution is the Pheno4D dataset of labeled maize and tomato plant point clouds, explicitly stated to be freely and publicly available at the authors' Bonn repository. A companion public data-loader API (Python/C++) is also provided on GitHub. Both are paper-specific, public, and directly actionableDataset · publicThe dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗https://www.ipb.uni-bonn.de/data/pheno4d/pdf-page:1 lines:1-65Code · publicWe provide the
code and the examples for loading the data at https://github.com/AIS-Bonn/data_loaders.Open asset ↗https://github.com/AIS-Bonn/data_loaderspdf-page:10 lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TomatoRootClassificationMorphology / geometry measurementRoot system architectureStress response / tolerance
The root architecture of wild tomato, Solanum pimpinellifolium , can be viewed as a network connecting the main root to various lateral roots. Several constraints have been proposed on the structure of such biological networks, including minimizing the total amount of wire necessary for constructing the root architecture (wiring cost), and minimizing the distances (and by extension, resource transport time) between the base of the main root and the lateral roots (conduction delay). For a given set of lateral root tip locations, these two objectives compete with each other — optimizing one results in poorer performance on the other — raising the question how well S. pimpinellifolium root architectures balance this network design trade-off in a distributed manner. Here, we describe how well S. pimpinellifolium roots resolve this trade-off using the theory of Pareto optimality. We describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture. We demonstrate that S. pimpinellifolium arbors construct architectures that are more optimal than would be expected by chance. Finally, we use this framework to quantify structural differences between arbors grown in the presence of salt stress, classify arbors into four distinct architectural ideotypes, and test for heritability of variation in root architecture structure.
Why it matches plant phenotyping methods根系アーキテクチャをネットワークとして定量化・分類する数学的解析フレームワークが研究の中心であり、植物表現型の構造差とイデオタイプを抽出しているため。
abstractWe describe a mathematical model for characterizing the network structure and design trade-offs governing the structure of S. pimpinellifolium root architecture.
Reproduction assets foundThe paper's root-architecture analysis code is publicly available on GitHub. The phenotype/root-image data itself is only available upon request, so it is listed as a request-only asset.Code · publicpeer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY-ND 4.0 International license.
222 Data availability
223 We will make data available upon request. Our code for analyzing arbors and performing statistical
224 analysis can be found here https://github.com/arjunc12/Plant-Architecture.
7Open asset ↗arjunc12/Plant-Architecturepdf-layout-page:7 lines:1-15Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.
Why it matches plant phenotyping methods植物の生理形質を取得・解析するFPP-FM統合フレームワークを開発し、トマト集団の蒸散測定で実証しており、表現型取得と解析手法が中心である。
abstractHere, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM).
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 · publicWe have coded all statistical algorithms that build our framework into a user-friendly R package for public use ( https://github.com/FFP-FM/Version1 ).Open asset ↗FFP-FM/Version1lines:232-250Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO 2 assimilation rate ( A L , µmol CO 2 m -2 s -1 ) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between A L and light-quantum yield of photosystem II (Φ 2 ), we defined three objectives: (1) to explore the relationship between climate variables and A L , as well as Φ 2 ; (2) create a simple and reliable method for real-time prediction of A L with continuously Φ 2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in A L modelling. Due to practical obstacles in measuring CO 2 gas-exchange in commercial production, we explored a method to predict A L by measuring Φ 2 of leaves in a commercial hydroponic greenhouse tomato crop ("Pureza"). We calculated A L with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict A L with large uncertainty (R 2 0.64; RMSE 2.21), while using Φ 2 as input to Model II could be used to improve the prediction accuracy of A L (R 2 0.71; RMSE 1.98). Our results suggests that (1) Φ 2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate ( J ) and A L . The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.
Why it matches plant phenotyping methods葉のΦ2測定とモデル化により光合成速度・電子伝達速度をリアルタイム推定する監視システムを開発・評価しており、植物生理形質の取得手法が研究の中心である。
abstractcreate a simple and reliable method for real-time prediction of A L with continuously Φ 2 measurements
Reproduction assets foundThe paper's measured phenotyping data (leaf CO2 assimilation, chlorophyll fluorescence Φ2, and greenhouse environmental variables from the tomato crop) are stated to be included in the article's Supplementary Material (Supplementary Material A–B), publicly accessible at the Frontiers supplementary-material URL. No codeDataset · publicconstrued as a potential conflict of interest.
Acknowledgments
The authors thank the financial support from the program of China Scholarships Council for the first author and Wolfgang Pfeiffer for the technique support with the BERMONIS.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.01038/full#supplementary-material
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References
Aggarwal C., Yu S. (2005). An effective and efficient algorithm for high-dimensional outlier detection. VLDB J.
14, 211–221.
Allen J. F. (2003). Cyclic, pseudocOpen asset ↗lines:575-613Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
BACKGROUND: Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods. RESULTS: The SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points. CONCLUSIONS: A SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.
Why it matches plant phenotyping methods植物の非破壊ハイスループット表現型データから成長形質を抽出するSET法を開発し、従来の縦断解析と比較・検証しているため、表現型取得・解析手法が研究の中心である。
abstractWe provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods.
Reproduction assets foundThe paper's availability statement describes R scripts and the tomato phenotype dataset (tomato.dat.csv) used for the SET and longitudinal analyses, noting the data is also distributed with the authors' growthPheno R package, which is publicly available on CRAN.Dataset · publicR scripts and data for preparing the tomato data and carrying out the reported analyses. The data is provided in the file tomato.dat.csv , but in R is also available with the growthPheno package.Open asset ↗growthPheno · tomato.dat.csvlines:437-510Code / dataset availability confirmedbioRxiv · checked 9 Sept 2026
The aerial epidermis of plants plays a major role in their environment interactions, and the development of its cellular components -trichomes, stomata and pavement cells- is still not fully understood. We have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques. Quantification of the trichome and stomatal densities in the ILs revealed 18 genomic regions with a low trichome density and 4 ILs with a high stomatal density. We also found ILs with abnormal proportions of different trichome types and aberrant trichome morphologies. This work has led to the identification of new, unexplored genomic regions with roles in trichome and stomatal formation and provides an important dataset for further studies on tomato epidermal development that is publically available to the research community.
Why it matches plant phenotyping methods走査電子顕微鏡を用いた葉表皮の画像取得と、毛状突起・気孔密度および形態の定量が研究の中心であり、再利用可能な表現型データセットも提供しているため。
abstractWe have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques.
Reproduction assets foundThe paper states that all SEM micrographs used for the trichome/stomatal phenotyping screen are publicly available in the BioStudies database under accession S-BSST262. This is a paper-specific public asset (the SEM images underlying the phenotyping measurements). No author analysis code was deposited.Dataset · public321 study are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) (McEntyre et al.,Open asset ↗BioStudiespdf-page:10 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology. Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.
Why it matches plant phenotyping methods植物の生理状態・ストレスをクロロフィル蛍光とマルチスペクトル画像から非破壊・連続的に推定する高スループット監視システムと手法が研究の中心であり、植物フェノタイピング手法に該当する。
abstractWe present a high throughput crop physiology condition monitoring system and corresponding monitoring method.
Reproduction assets foundThe paper's supplementary material publicly hosts the paper-specific phenotyping images: pseudo-color ΦPSII, Fv/Fm, and 550/510 parameter images and photos of tomato plants under drought, nitrogen deficiency, and Botrytis cinerea stress, plus a photo of the monitoring system. These are the plant images/phenotyping dataDataset · publicinterest.
Funding. This work was supported by the National High Technology Research, Development Program of China (863 Program) (Grant No. 2012AA10A503).
1
http://www.walz.com/
2
http://www.psi.cz/
3
http://www.hansatech-instruments.com/
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.00407/full#supplementary-material
FIGURE S1
The physiology monitoring system with chlorophyll fluorescence module and multispectral module (A,B) and the scene when the system is working (C) .
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FIGURE S2
Φ PSII , F v / F m , and 550/510 pseudo color images and photos of tomatoes uOpen asset ↗lines:96-124