Plant leaf diseases must be detected and treated early to improve crop yield and reduce agricultural losses. However, pixel-level representations and the inability to be read limit the applicability of existing deep learning approaches to the agricultural sector. A graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease. The proposed framework models leaf pictures as a graph with nodes representing discriminative leaf areas and edges representing their spatial connection. Before creating the global context vector and classification, graph features are aggregated, and an attention weighting method is applied to refocus on disease-relevant nodes obscured by less informative background characteristics. Final disease prediction uses a multilayer perceptron classifier. A curated dataset of half-spinach and curry leaf pictures is used to assess the proposed method for fifteen illnesses and their healthy classifications. Grad-CAM-based explainable AI methods make the model predictions' most important areas clearer. The dataset and source code from this work are available on GitHub for reproducibility and openness. Experimental results reveal that the proposed AE-GNN outperforms convolutional neural networks and graph-based models in classification. Graph-structured learning, attention enhancement, and explainability create a robust and interpretable framework for multi-plant leaf disease diagnosis.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類・診断する画像解析手法を提案し、既存モデルとの比較評価と説明可能性解析を行っているため、植物フェノタイピング手法が中心である。
abstractA graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease.
Reproduction assets foundThe paper's Data availability section explicitly links a public GitHub repository containing the paper's spinach/curry leaf fungal disease image dataset used for the AE-GNN phenotyping/classification analysis.Dataset · publicData availability
The dataset is available at the link below. https://github.com/MeganathanE1990/FINAL-DISEASE-DATA-SET/tree/mainOpen asset ↗MeganathanE1990/FINAL-DISEASE-DATA-SETlines:413-463Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Ensuring the post-harvest quality and health of leafy vegetables is critical for minimizing economic loss, enhancing food security, and promoting sustainable agricultural practices. Spinach, a highly nutritious yet perishable crop, is particularly susceptible to rapid freshness degradation and foliar diseases. While computer vision and deep learning have shown promise for automated quality assessment, existing models often lack the robustness to handle the dual-task classification of both freshness and disease states across diverse local spinach varieties. To bridge this gap, this paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves. The proposed model synergistically integrates a powerful feature extraction backbone with a tailored attention and fusion mechanism, enhancing its ability to capture discriminative spatial and textural features critical for fine-grained classification. It was trained and validated on a curated dataset comprising high-resolution images of three prominent local varieties (Malabar, Water, and Red spinach) in both fresh and non-fresh conditions. The proposed hybrid model achieved a classification accuracy of 98.36%, significantly outperforming benchmark state-of-the-art models including DenseNet121, ResNet50, and EfficientNetB0. Furthermore, explainable AI (XAI) techniques visually validated the model’s decision-making process, confirming its focus on biologically relevant leaf regions. The results demonstrate that the proposed hybrid framework offers a highly accurate, reliable, and interpretable tool for non-destructive, real-time quality monitoring. This work provides a significant contribution towards intelligent post-harvest management systems, capable of reducing waste and supporting the value chain for local spinach cultivation.
Why it matches plant phenotyping methods葉画像から鮮度および視覚的な病徴を分類するCNN手法の開発・検証が中心であり、植物の状態を直接推定している。
abstractthis paper introduces a novel hybrid Convolutional Neural Network (CNN) architecture specifically designed for the multi-class detection of freshness and visual disease symptoms in local spinach leaves.
Reproduction assets foundThe paper's Data Availability statement points to a public Mendeley Data deposit of the local spinach leaf image dataset used for the CNN freshness/disease classification, matching the paper's phenotyping inputs.Dataset · publicThe datasets analyzed during the current study are publicly available in the Mendeley Data repository at: [https://data.mendeley.com/datasets/skf6w2s2h2/2](https:/data.mendeley.com/datasets/skf6w2s2h2/2).Open asset ↗Mendeley Datahtml-lines:660-689Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification. A curated dataset of Malabar spinach images collected from Habiganj Agricultural University and supplemented with public samples was categorized into three classes: Alternaria, straw mite, and healthy leaves. A lightweight SpinachCNN established a strong baseline, while Spinach-ResSENet, enhanced with squeeze-and-excitation modules, improved channel-wise attention and feature discrimination. A customized Vision Transformer (SpinachViT) and SwinV2-Base were further investigated to assess the benefits of transformer-based architectures under limited data. To mitigate annotation scarcity, we employed SimSiam-based self-supervised pretraining on unlabeled images, followed by supervised fine-tuning with cross-entropy or a hybrid objective combining cross-entropy and supervised contrastive loss. The best-performing domain-optimized model, SimSiam-CBAM-ResNet-50, incorporated Convolutional Block Attention Modules and achieved 97.31% test accuracy, 0.9983 macro ROC-AUC, and low calibration error, while maintaining robustness to Gaussian and salt-and-pepper noise. Although a SwinV2-Base benchmark pretrained on ImageNet-22k reached slightly higher accuracy (97.98%, 98.99% with test-time augmentation), its 86.9M parameters and reliance on large-scale pretraining reduce feasibility for edge deployment. In contrast, the SimSiam-CBAM model offers a more parameter-efficient and deployment-friendly solution for real-world agricultural applications. Model decisions are interpretable via Grad-CAM, Grad-CAM++, and LayerCAM, which consistently highlight biologically relevant lesion regions. The spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.
Why it matches plant phenotyping methods葉画像から病害状態を分類する深層学習手法を開発・比較し、公開データセットと解釈可能性・頑健性も評価しており、植物表現型取得が中心である。
abstractThis work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification.
Reproduction assets foundThe paper's Malabar spinach leaf disease image dataset (the phenotyping input used for all measurements) is explicitly stated as publicly available on Hugging Face, with the URL given in the abstract and Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.Open asset ↗huggingface · saifullah03/malabar_spinach_leaf_disease_datasethtml-lines:585-614Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This research presents an AI-powered automated hydroponic system designed to enhance the efficiency and sustainability of modern agriculture. The system integrates real-time environmental monitoring, automated nutrient management, and AI-based disease detection to optimize plant growth and minimize manual intervention. An ESP32 microcontroller collects data from specialized sensors measuring Total Dissolved Solids (TDS), pH, temperature, and light intensity. Data is wirelessly transmitted via MQTT to an EMQX broker, subsequently processed by an ExpressJS backend, and stored in a Firebase Realtime Database. A NextJS web application provides a user-friendly dashboard for visualization, alerts, and remote control. Automation is achieved using relay-controlled peristaltic and water pumps that adjust nutrient dosing and circulation based on sensor readings. A camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention. This integrated system combines IoT, cloud data management, automation, and AI-based visual inspection to offer a comprehensive solution for precision hydroponic farming. Evaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings. The system, along with its full codebase, has been made publicly available to promote reproducibility.• Automated Precision Hydroponics: Combines real-time environmental monitoring, automated nutrient management, and AI-powered disease detection for optimized spinach cultivation. • Reproducible and Scalable Method: Provides a detailed, step-by-step protocol for constructing and operating the system, adaptable to various hydroponic setups and crop types. • Sustainable and Efficient Agriculture: Minimizes resource consumption, reduces manual labour, and promotes environmentally friendly practices.
Why it matches plant phenotyping methods水耕栽培の統合システム全体に加え、植物画像をCNNで解析してホウレンソウ病害を検出する、再利用可能な画像ベース表現型取得機能が明示されており、方法・プラットフォームの中心的構成要素である。
abstractA camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention.
Reproduction assets foundThe paper publicly releases its full codebase (ESP32 firmware, backend/frontend servers, AI model server) and uses a public Mendeley spinach disease image dataset as the phenotyping input for its CNN disease-detection analysis. All four assets are paper-specific, public, and actionable via author-provided URLs.Dataset · publicThe spinach disease dataset was obtained from the publicly available Mendeley Data repository: https://data.mendeley.com/datasets/n56pn9fncw/2.Open asset ↗Mendeley Data · n56pn9fncw/2html-lines:154-183Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The thylakoid membrane is the site of the light-dependent reactions of photosynthesis. It is a continuous membrane, folded into grana stacks and the interconnecting stroma lamellae. The CURVATURE THYLAKOID1 (CURT1) protein family is involved in the folding of the membrane into the grana stacks. The thylakoid membrane remodels its architecture in response to light conditions, but its 3D organisation and dynamics remain incompletely understood. To resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner. Recently, we have used expansion microscopy, a technique that meets these criteria, to visualise the thylakoid membrane isolated from spinach. Here, we show that this protocol can also be used to visualise enveloped spinach chloroplasts. Additionally, we present an improved protocol for resolving the thylakoid structure of Arabidopsis thaliana. Using this protocol, we show the changes in thylakoid architecture in response to long-term far-red light acclimation and due to knocking out CURT1A. We show that far-red light acclimation results in higher grana stacks that are packed closer together. In addition, the distance between stroma lamellae, which are wrapped around the grana, decreases. In the curt1a mutant, grana have an increased diameter and height, and the distance between grana is increased. Interestingly, in this mutant, the stroma lamellae occasionally approach the grana stacks from the top. These observations show the potential of expansion microscopy to study the thylakoid membrane architecture.
Why it matches plant phenotyping methods植物のチラコイド膜構造を高解像度3D画像で取得する拡大顕微鏡法の改良・適用が中心であり、膜構造という植物形態形質を測定しているため。
abstractTo resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner.
Reproduction assets foundThe article states that the data underlying the publication (expansion microscopy imaging/measurements of thylakoid architecture) are publicly available in the 4TU Research Data repository via the DOI 10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885. This is a paper-specific, publicly accessible data deposit with an authorDataset · publicUte Armbruster for providing the
seeds of the Ler0 curt1a-1 mutant. This work was supported by the Dutch
Organisation for Scientific Research (NWO) via a Vidi grant no. VI.Vidi
192.042 (E.W.) and by Wageningen Graduates Schools through a PhD
grant (J.B.).
Data availability
The data underlying this publication can be accessed at https://doi.org/10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885.References
[1] R.E. Blankenship, Molecular Mechanisms of Photosynthesis, John Wiley & Sons,
2021, https://doi.org/10.1002/9780470758472.
[2] H. Kirchhoff, Chloroplast ultrastructure in plants, New Phytol. 223 (2) (2019)
565–574, https://doi.org/10.1111/nph.15730.
[3] H. Kirchhoff, C. Hall, M. Wood, M. HerbstOpen asset ↗10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885pdf-raw-page:9 lines:1-68Code / 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 14 Sept 2026
Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.
Why it matches plant phenotyping methods葉画像から窒素状態を推定するコンピュータビジョン手法の開発・評価が中心であり、植物生理状態の表現型取得法に該当する。
abstractIn this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops.
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 · publicA software release of the proposed
vision-based framework for N-nutrient estimation in crops is made publicly available on
the project website: http://faculty.pucit.edu.pk/~farid/Research/GCV.html, accessed on
8 June 2021.Open asset ↗pdf-page:16 lines:1-55Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.
Why it matches plant phenotyping methods葉の病斑・損傷面積を染色と自動画像解析で定量する手法自体が中心的に開発・実証されており、植物病害・損傷状態の表現型計測に該当する。
abstractWe stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale.
Reproduction assets foundThe paper's LiMu image analysis pipeline (used for lesion quantification) is publicly available on PyPI. The leaf image datasets are only available from the corresponding author on request, and no public URL for the image data or supplements is present in the allowed list.Code · publicThe original LiMu code is made freely available in the Python Package Index (PyPI), and can be downloaded from https://pypi.org/project/limu/ .Open asset ↗limulines:184-227