Plant phenotyping methods.

HybOptic-CNN: A hybrid WOA-GWO-optimized convolutional neural network model for enhanced plant disease detection in the Nigerian environment →

Plant diseases threaten agricultural productivity, and automated image analysis can support early identification of visible disease symptoms. This study introduces HybOptic-CNN, a convolutional neural network (CNN) whose learning rate and batch size are selected using a hybrid Whale Optimization Algorithm--Grey Wolf Optimizer (WOA-GWO). Nine disease classes were selected from the 22-class CCMT field-image dataset, and 152 local farm leaf images were collected in Enugu State, Nigeria. Of the local images, 122 (80.3%) were added to the model-development data for training and validation, whereas 30 (19.7%) formed an independent Nigerian hold-out set excluded from augmentation, class balancing, early stopping, validation, and hyperparameter selection. Across 10 model-development runs, the optimized model achieved 96.8 ± 0.4% mean validation accuracy, 95.2 ± 0.5% macro-precision, 94.9 ± 0.6% macro-recall, and 95.0 ± 0.5% macro-F1, compared with 90.3 ± 0.9% validation accuracy and 86.3 ± 1.2% macro-F1 for the baseline. The optimized model improved mean validation accuracy by 6.5 percentage points and converged 14.6 epochs earlier. On the independent 30-image Nigerian hold-out, HybOptic-CNN achieved 93.3% accuracy and 93.1% macro-F1 across four represented disease classes. A web application integrating the trained classifier was also demonstrated. These results support improved model-development performance through hybrid hyperparameter selection and motivate broader multi-location field evaluation.

Why it matches plant phenotyping methods植物の可視病徴を画像から分類するCNN手法を開発・検証しており、病害状態のフェノタイピング手法が中心である。

abstractautomated image analysis can support early identification of visible disease symptoms

MAFH Based Maize Leaf Disease Detection and Classification →

Abstract Maize plant plays crucial role not only in the field of agriculture but also in global economy, since it is the third most cultivated crop across the globe. However, these plants are usually affected by various types of diseases such as blight, common rust, gray leaf spot, etc., protecting the plants from these disease is very important. This research proposes a new deep learning model for disease detection to perform well than other models. The hybrid model uses MobileNetV3 as the backbone architecture integrated with attention, fusion and head. The framework includes data preprocessing, feature extraction, classification and interpretation. We will compare the performance of this model with other models such as VGG16, ResNet50, DenseNet121, ALEXNET, etc. criteria for the final evaluation includes Accuracy, Precision, Recall, F1score, Specificity, Logloss, AUC-ROC curve. Through this proposed model we have achieved an accuracy of 98% which is high than the other models compared. The lightweight nature of MobileNetV3 enables us to implement the model in the mobile and IoT devices also. The present study contributes to the development of deep learning model in the field of agriculture, offering a efficient solution for early maize leaf disease detection.

Why it matches plant phenotyping methodsトウモロコシ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比較が中心であり、植物病害状態の画像ベース表現型計測に該当する。

abstractThis research proposes a new deep learning model for disease detection to perform well than other models.

Development and Evaluation of a Fine-Tuned EfficientNet-B0 Model for Maize Disease Detection →

Abstract Maize is the staple crop for millions of people in Sub-Saharan Africa, particularly for Zambia. Unfortunately, maize crops are exposed to several serious threats due to their susceptibility to foliar diseases like Maize Rust, Leaf Blight, Leaf Spot, Maize Streak Virus, and Maize Lethal Necrosis that may lead to great yield losses. Conventional methods of crop disease identification consist of field surveys that are not only subjective but also difficult to conduct for smallholder farmers. This paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios. The proposed model utilized the concept of transfer learning with ImageNet pre-trained weights and was trained on the Mendeley Maize Crop Disease (Leaf) Dataset which consists of 30,120 images in nine maize disease classes. The developed fine-tuned EfficientNet-B0 yielded 97.57% classification accuracy, macro precision of 97.61%, macro recall of 97.64%, and macro F1-score of 97.61%. From these results, it is evident that transfer learning and fine-tuning greatly boost maize disease classification accuracy while ensuring high computational efficiency. This study makes a significant contribution to precision agriculture as it offers an accurate and computationally efficient AI-based maize disease classification model, which could help smallholder farmers in early maize disease classification.

Why it matches plant phenotyping methodsトウモロコシ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、植物病害表現型の取得・分類手法が中心的な研究です。

abstractThis paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios.

A methodological framework for the standardised evaluation of olive genetic resources: GEN4OLIVE harmonized protocols →

Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.

Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。

abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.

A lightweight RPB-YOLO11-based detector improves mobile phenotyping of rice panicle blast →

Rice panicle blast detection is an important task in plant disease phenotyping. Field-based detection remains challenging because infected spike regions are often small, sparse, elongated, and affected by overlapping panicles, complex backgrounds, and variable illumination. In this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection. The model uses a Lightweight Ghost Backbone (LGB) to reduce redundant computation. It uses Anisotropic Axial Stripe Attention (A2SA) to represent elongated panicle structures. It also uses Focal Multi-Scale Attention (FMSA) for multi-scale feature refinement and Adaptive Geometric Shape IoU (AGS-IoU) for geometry-aware localization. The model was trained and evaluated on a rice panicle image dataset containing 1,055 training images, 69 validation images, and 169 test images. On the test set, RPB-YOLO11 achieved 76.09% mAP50, 45.44% mAP50-95, 73.98% precision, and 72.75% recall with 6.21 GFLOPs. Compared with the YOLO11n baseline, it improved mAP50, mAP50-95, precision, and recall by 2.73, 2.12, 1.64, and 2.11 percentage points, respectively. An Android-oriented inference application supports local image inference, detection visualization, class counting, and diseased-panicle incidence estimation. These results suggest that RPB-YOLO11 provides a practical approach for image-based rice panicle blast survey.

Why it matches plant phenotyping methodsイネ穂いもちの画像検出モデルを開発・比較検証し、罹病穂率を推定する実用アプリまで構築しており、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 study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection.

Multi-pathway neural network architecture with feedback-based validation learning for robust plant disease classification →

Automated plant disease classification from leaf images demands models that jointly achieve high accuracy and efficient training convergence. Standard deep learning approaches process images through a single feature pathway, limiting their ability to capture diverse visual manifestations such as color changes, texture patterns, and spatial distributions. This paper introduces DMCNNA-FBVL, a framework integrating two complementary innovations: (1) a Deep Multi-Component Neural Network Architecture (DMCNNA) employing three specialized pathways processing color-space statistics, texture descriptors, and raw image features before fusing them through SoftMax-weighted aggregation; and (2) Feedback-Based Validation Learning (FBVL), a training strategy that periodically blends validation-set gradients into weight updates to accelerate convergence. Experiments on the New Plant Diseases Dataset (87,000 images, 38 classes) show that DMCNNA-FBVL achieves 98.7% accuracy, 98.8% precision, 98.6% recall, and 98.7% F1-score, outperforming ResNet-50 by 3.2 percentage points ( 𝑝 < 0 . 0 0 1 ). The primary reported metrics are computed exclusively on an independent 10% hold-out test set, whereas the separate 10% validation partition is used during training for FBVL gradient blending and does not contribute to final test evaluation. Five-fold cross-validation confirms stability (98.7% ± 0.15%). Ablation experiments confirm additive gains, while FBVL reduces wall-clock training time by 15% through faster convergence.

Why it matches plant phenotyping methods葉画像から植物病害を分類するニューラルネットワークと学習戦略の開発・検証が研究の中心であり、植物の病害状態を直接推定している。

abstractAutomated plant disease classification from leaf images demands models that jointly achieve high accuracy and efficient training convergence.

HyperBird: A Hyperspectral Microscopic Imaging Robot for High-Throughput Plant Phenotyping →

The automation and standardization of hyperspectral imaging, particularly at high spatial resolution, are essential for advancing plant phenomics and supporting diverse plant science research. We developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments. System calibration established a spatial resolution of 24.3 μ m (full width at half maximum; FWHM), a spectral resolution of 1.78 nm (FWHM), and a depth of field of 4.53 mm, producing hyperspectral data cubes of 2195 × 2000 × 950 pixels across the 400–1000 nm spectral range. System-level and biological-sample consistency assessments confirmed stable spectral measurements during extended scanning sessions and across independently prepared trays. Thermal evaluation confirmed that the 150 W illumination design introduced minimal sample heating during scanning. HyperBird was first evaluated using grape downy and powdery mildews, where no consistent measurable effect of repeated imaging on pathosystem development was detected under the tested experimental conditions. Subsequently, HyperBird was used to characterize spatiotemporal spectral progression in grapevine leaves inoculated with Plasmopara viticola . An automated regional spectral tracing pipeline was developed to isolate spectra from retrospectively traced regions and compare them with whole-leaf averaged spectra across 0–9 days post-inoculation (DPI). Categorical mixed-effects modeling showed that these spatially resolved regional spectra exhibited significant disease-associated spectral change by 5 DPI, with a sharp transition at 6 DPI, whereas whole-leaf spectra showed delayed and weaker disease-aligned changes beginning at 7 DPI. These results demonstrate that HyperBird enables high-throughput, spatially resolved hyperspectral imaging for quantifying localized plant disease progression and provides a scalable platform for studying spectral biology across plant phenotyping applications.

Why it matches plant phenotyping methodsHyperBirdの高スループット・ハイパースペクトル画像取得ロボットを開発し、校正、再現性、加熱影響、病害進展の定量性能を検証しており、植物フェノタイピング手法が中心である。

abstractWe developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments.

Genetic dissection of southern corn leaf blight resistance in sweet corn through genome-wide association studies and genomic selection. →

Southern corn leaf blight (SCLB) is caused by the fungal pathogen Bipolaris maydis (syn. Cochliobolus heterostrophus Drechsler) and is a common disease of fall crops of sweet corn. Phenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait. As an alternative, we integrated computer vision (CV)-based phenotyping, genome-wide association studies (GWASs), and predictive breeding approaches to dissect the genetic basis of SCLB resistance. We utilized a sweet corn diversity panel with 693 genotypes, for which whole-genome resequencing produced a high-density single-nucleotide polymorphism (SNP) dataset. Broad-sense heritability for visual scoring ranged from 0.44 to 0.73, while CV-based phenotyping produced estimates ranging from 0.56 to 0.73 in multi-environment resistance trials conducted across 5 years and three locations. We performed GWAS using 16,755,210 SNPs and identified 41 associated SNPs. Genomic selection (GS) models on visual scoring phenotypes achieved moderate prediction accuracies under cross-validation of untested genotypes across characterized environments (0.22-0.47) and high prediction accuracies when predicting tested genotypes in uncharacterized environments (0.49-0.68). Using CV-based phenotypes for GS, we observed prediction accuracies of 0.45-0.47 under the untested genotypes in the characterized environments cross-validation scheme and 0.59-0.62 under the tested genotypes in the uncharacterized environments scheme. GS demonstrated reliability for ranking the individuals across a gradient of environments. These findings identify candidate loci and predictive breeding strategies to accelerate the development of resistant sweet corn cultivars.

Why it matches plant phenotyping methodsCVベースの病害抵抗性表現型測定を視覚評定と比較し、多環境・多年次試験で妥当性を評価しており、フェノタイピング手法が研究の中心である。

abstractPhenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait.

An explainable Deep Q-learning and convolutional neural network framework for rice leaf disease detection →

Rice leaf diseases pose a significant threat to global food security by reducing crop productivity and causing substantial economic losses. The traditional diagnosis method is manual method, which is low in efficiency, subjective and not suitable for large-scale agricultural monitoring. Despite the advances in automated disease detection using deep learning methods like CNNs, GANs, and transfer learning models, these techniques remain highly computational, not very flexible, and struggle to perform well in different imaging scenarios. Considering these drawbacks, this paper introduces an Explainable Deep Q-Learning based CNN framework which employs CNN-based feature extraction and Deep Q-learning-based adaptive policy optimization for rice leaf disease classification. The proposed model continuously refines the classification actions through reward-based learning, which makes the model more robust in various agricultural imaging environments, in contrast to traditional supervised CNN models that have static classification decisions. The proposed model achieved 98.5% accuracy, 98.52% precision, 98.50% recall, and a 98.51% F1-score, outperforming existing CNN, GAN, reinforcement learning, and transformer-based methods. It also offers a high computational efficiency of 14.2 GFLOPs, 248 MB memory consumption, ~ 48 min of training time, and 6.8 ms inference time per image suitable for resource constrained applications in agriculture. The results demonstrate the effectiveness, scalability, and practical applicability of the proposed framework. The proposed framework performs well on benchmark datasets but more research in the deployment of the edge-devices under different real-world agricultural settings will be investigated in future work.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法の開発・評価が研究の中心であり、植物の病害表現型を直接推定しているため。

abstractthis paper introduces an Explainable Deep Q-Learning based CNN framework which employs CNN-based feature extraction and Deep Q-learning-based adaptive policy optimization for rice leaf disease classification.

ADVANCING PLANT DISEASE DETECTION THROUGH STATE-OF-THE-ART DEEP LEARNING MODELS LEVER-AGING EFFICIENTNETV2, VISION TRANSFORMER, AND ENSEMBLE TECHNIQUES →

Plant diseases are still posing a challenge to the productivity, quality of crops, and food security, especially in locations where field diagnosis is based on manual visual inspec-tion. This paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets, such as the Kaggle New Plant Diseases Dataset (Augmented) and PlantVillage images. They investigated four archi-tectures: EfficientNetV2B0, ResNet152V2, DenseNet201, and one hybrid Vision Trans-former (ViT)-based model. The steps of the experiment involved loading the dataset, exploratory analysis, preprocessing, resizing, normalizing, augmentation, transfer learning, independent model training, and evaluation metrics such as accuracy, preci-sion, recall, F1-score, training curves, testing results, and confusion matrices. The hy-brid ViT-based model was reported to have the best accuracy of 99.5%. On the smaller seven class subset of PlantVillage, EfficientNetV2B0 scored 98.11%. On the 38-class dataset, DenseNet201 improved test accuracy (97.34) and validation classification ac-curacy (around 98). ResNet152V2 scored 97.01 on the 38-class test set. The results demonstrate that CNN and transformer-based models can help to recognize plant diseases accurately whereas hybrid attention-based structures provide a promising path to enhance fine-grained classification. Since the model notebooks had varying class settings and splits, the comparison is seen as a model-structured assessment as opposed to a precisely identical benchmark across all architectures.

Why it matches plant phenotyping methods植物葉画像から病害状態を分類する深層学習手法を複数モデルで比較・評価しており、病害表現型の取得・抽出と技術検証が研究の中心である。

abstractThis paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets

Cognitive UAV-driven agro-surveillance framework for predicting crop stress–induced yield loss using spatio-temporal learning and adaptive irrigation control →

Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.

Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。

abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data

A MATLAB-Based Image Processing Protocol for Quantitative Differentiation of Diseased and Healthy Plant Tissue From Digital Leaf Images. →

Accurate quantification of plant disease severity is essential for evaluating host-pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics. Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions. Key features • A reproducible MATLAB-based workflow for separating diseased and healthy plant tissue using color-space segmentation. • Applicable to plant diseases where symptomatic tissue contrasts clearly with healthy tissue (necrosis, blight lesions, rot patches). • Requires digital leaf images, MATLAB, and the MATLAB Image Processing Toolbox for image processing and disease quantification. • Enables rapid calculation of diseased leaf area and disease severity using automated pixel-based quantification.

Why it matches plant phenotyping methods植物病斑を画像から分割・定量し、感染面積と病害重症度を算出するMATLAB画像解析プロトコルが研究の中心であり、植物病害表現型の取得・抽出手法に該当する。

abstractHere, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images.

Quantifying Crop Disease Trait Dynamics through Longitudinal Imaging and Temporal Analytics →

Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ , enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust ( R 2 = 0.58) and strong for leaf rust ( R 2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.

Why it matches plant phenotyping methods画像時系列から植物病害の進展と重症度を抽出する半自動・自動解析パイプラインを開発し、専門家評価との比較で検証しており、表現型取得手法が研究の中心です。

abstractHere, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images.

AI driven multi modal deep learning system for wheat disease detection, yield prediction, and crop health monitoring →

Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.

Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。

abstractThis project develops a workable solution.

Optimized Multi-Class Rice Leaf Disease Classification Framework Using Rice Feature Selection (RiceFS) and Ensemble Machine Learning: Towards Sustainable Agriculture →

Abstract Sustainable agriculture has substantial share on improvement of food security and optimization of resources utilization particularly for high value crops like rice leaf. Rice varieties should be properly classified in order to benefit the harvest management, reduced loss after harvest and improved agriculture methods. The traditional classification method usually brings the low precision and the traditional classification method is also subjected to human error, which is difficult to bring about reliable output. This study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches. RiceFS is realized based on a feature selection mechanism based on Recursive Feature Elimination. Selected classifiers such as KNN, Random Forest, Gradient Boosting, Ensemble Learning and Optimized SVM are analyzed based on the extracted subset of features and the proposed system is used to classify the seven classes of rice leaf disease. The experimental results show that the Optimized SVM has the best classification results among the different classifiers with accuracy of 92.10%, Precision of 92.20%, balanced Recall and F1 Score, which shows that Optimized SVM is very effective in multi-class rice leaf disease classification. The performance can be improved by feature reduction, generalization capability and computational complexity reduction, which are realized with the help of RiceFS. The proposed framework is designed to provide an intelligent decision support system for timely intervention, loss minimization and sustainable agriculture. Results indicate that these algorithms are applicable for rice leaf disease classification since they are accurate, reliable and scalable.

Why it matches plant phenotyping methodsイネ葉の病害状態を観察データから分類する計算手法が研究の中心であり、RiceFSと複数の機械学習器を用いた分類フレームワークを開発・評価しているため。

abstractThis study introduces an optimized multi-class rice leaf disease classification system utilizing “Rice Feature Selection” (RiceFS) and ensemble machine learning approaches.

Backbone diversity beats text supervision: a systematic study of frozen multi-foundation model fusion for in-the-wild plant disease recognition. →

Automated plant disease recognition from field photographs remains challenging: models trained on laboratory datasets collapse on in-the-wild images, and the current state of the art on PlantWild-the largest open in-the-wild benchmark (18,542 images, 89 classes)-relies on text prototypes derived from a language model to reach 76.18% top 1 accuracy. We ask whether text supervision is truly necessary or whether the bottleneck is the diversity of the visual representation. Our central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering : across 118 experiments, a simple fixed-weight linear-prototype combination on top of three complementary frozen backbones yields larger and more reproducible gains than any head-level adaptive routing mechanism we test. Specifically, through a systematic study of six frozen vision backbones (three CLIP, two DINOv3, and one DINOv2), three classifier heads, and four fusion configurations, completed in a single day on one consumer GPU, we establish three findings. (i) A single self-supervised backbone (DINOv2 ViT-L/14) already surpasses text-augmented MVPDR (77.56% vs. 76.18%). (ii) Concatenating three complementary backbones (DINOv2 + DINOv3 + CLIP) reaches 80.23% ± 0.41% (five seeds), exceeding the published MVPDR accuracy by +4.05 points and our own reproduction of MVPDR under an identical evaluation protocol by +7.96 points, without any language supervision. (The difference between the two deltas reflects evaluation-protocol differences-our split, model-selection criterion, and training schedule-rather than any discrepancy in the reported numbers; see Section 4.6.6 for a full reconciliation.) (iii) Every form of learned routing we test-per-class gating, backbone gating, sample-wise gating-is inessential; the gain is entirely attributable to backbone diversity and a simple linear-prototype scoring combination. On the smaller PlantDoc benchmark, the same principle transfers but with substantially higher seed variance: the best configuration reaches 80.09% at a favourable seed but 76.97% ± 1.48% over five seeds-a suggestive rather than robust gain. Beyond the accuracy headline, we provide a pathology-aware per-class analysis showing that DINOv2/v3 dominate on fine-texture lesion classes (rusts, mildews, and leaf spots) whilst CLIP's narrow advantage concentrates on organ/species-level identification (rice leaf and potato late blight). All primary claims are validated over five seeds, and all code, feature caches, and result files are released for full reproducibility.

Why it matches plant phenotyping methods植物病害の画像認識を対象に、複数の視覚基盤モデル融合と分類器を体系比較・検証しており、病害状態の推定手法が研究の中心である。

abstractOur central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering

Integrated design of an efficient multi spectral imaging and federated learning framework for precision crop disease diagnosis in low-resource farming communities →

Abstract Crop diseases pose significant challenges to productivity in resource-constrained settings, often remaining undiagnosed when diagnostic tools and infrastructure are either non-existent or inadequate. Current crop disease diagnosis relies on manual inspection methods that are labor-intensive, prone to error, and incapable of delivering real-time or region-specific insights in the process. Such limitations call for developing advanced diagnostic systems that are scalable and efficient in resource-constrained settings. This research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities. Built within its core is the 3D Spectral-Spatial Convolutional Neural Network (3D SSCNN) that extracts high-resolution spectral-spatial features from hyperspectral image cubes. The accuracy achieved is around ~ 95% within 0.3 s per sample. Fed-DiagNet has provided support for distributed training that enables scalability and also data privacy to enhance the accuracy of regional models at approximately 92% as well as reduces training by almost 40%. Temporal disease progression modeling is enabled by Temporal Progression LSTM that provides dynamic trends with 90% accuracy up to a horizon of 10 days. This means that in addition to integrating disparate data sources-including hyperspectral imagery, environmental data, and pest observations-MTAN achieves an almost ~ 93% stress identification accuracy. Lastly, an RL-FO system tailors its treatment recommendations to local conditions so as to optimize for yield improvement and cost-effectiveness. With the proposed system, diagnostic precision increases to ~ 94%, and it is manifested in real-time efficiency while supporting scalability with actionable insights to empower farmers to mitigate crop losses and augment food security across several scenarios.

Why it matches plant phenotyping methods植物病害の状態をマルチスペクトル画像から推定する画像・機械学習フレームワークの開発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。

abstractThis research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities.

A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science. →

Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis. To address this, we present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making. It is compiled from 45 open-source datasets, including the widely used PlantVillage corpus, and comprises 765,186 high-quality question-answer (QA) pairs grounded over 150,841 images spanning 38 crop species and 89 disease conditions. Questions are organized into 3 levels of cognitive complexity and 9 distinct categories. Each was phrased following expert guidance and generated via an automated two-stage pipeline: template-based QA synthesis from image metadata, followed by multi-stage linguistic re-engineering. The dataset was iteratively reviewed by domain experts for scientific accuracy and relevance. We find that current frontier vision-language models, including recent open-source instruction-tuned multimodal LLMs, perform poorly on PlantExpertVQA. However, parameter-efficient fine-tuning of a compact 2B-parameter model on a small fraction of the dataset yields substantial improvements across all question categories, demonstrating its effectiveness for domain adaptation.

Why it matches plant phenotyping methods植物病害画像を対象とする大規模VQAデータセットの構築・ベンチマークが研究の中心であり、植物の病害状態を画像から評価する再利用可能なデータセットです。

abstractwe present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making.

A TinyMLOps Pipeline for Coarse-Grained Plant Disease Classification in Precision Agriculture →

Identifying plant health conditions is an emerging precision-agriculture and food-security challenge, intensified by deploying deep-learning models on memory- and power-constrained edge devices. We present a TinyMLOps pipeline spanning model design, optimization, quantization, and deployment across diverse edge devices, evaluated under controlled laboratory conditions. Using a dataset derived from the PlantVillage benchmark, 39 fine-grained classes are aggregated into three superclasses: healthy leaf, unhealthy leaf, and no leaves. The resulting system therefore performs plant health-status classification and background filtering rather than diagnosing specific diseases. We train a MobileNet-based convolutional neural network jointly optimized for classification accuracy and computational efficiency, adopting state-of-the-art hyperparameter optimization (HPO) tools. Five models are selected, four from the Pareto Front and one as the biggest evaluated model during HPO, converted to LiteRT and ONNX, and evaluated at float32 and post-training int8 precision on a Raspberry Pi Zero 2 W and an STM32H743ZI microcontroller. At float32, LiteRT is 1.87–2.65× faster than ONNX Runtime on the Raspberry Pi across all five models. Relative to their float32 LiteRT counterparts, the int8 LiteRT models are 2.83–3.67× smaller on disk and 21.3–31.2% faster on the same board, at a cost in F1-score of between 0.0010 and 0.0068. On the microcontroller, only the two smallest models deploy at both precisions; for these, the fully quantized int8 variants are 4.3× faster and 3.85× smaller in MCU flash footprint than the float32 counterparts. The mid-range model fits the 2 MB flash and 1 MB RAM budget only when quantized, while the two largest models exceed it in every configuration tested.

Why it matches plant phenotyping methods葉画像から植物の健康状態を推定する分類手法と、エッジデバイス向けの最適化・量子化・展開パイプラインが研究の中心であり、植物状態の画像ベースフェノタイピングに該当する。

abstractWe present a TinyMLOps pipeline spanning model design, optimization, quantization, and deployment across diverse edge devices, evaluated under controlled laboratory conditions.

A Dual Branch Fusion Network for Simultaneous Tea Leaf Disease Diagnosis and Age-Based Quality Grade Evaluation. →

The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.

Why it matches plant phenotyping methods茶葉の病害状態と葉齢品質を画像から推定する深層学習手法を開発し、データセット上でベースラインと比較評価しており、表現型取得・推定法が中心である。

abstractwe propose a novel dual branch fusion network

Sugarcane Plant Disease Classification Based on Leaf Image Using ConvNeXt V2 Deep Learning Model →

Sugarcane plant diseases pose a significant threat to agricultural productivity, yet early and accurate identification remains challenging for farmers due to the limitations of manual inspection. This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization (GRN) mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data. A dataset of 2,948 leaf images spanning five classes (Red Rot, Mosaic, Rust, Yellow Leaf, and Healthy) was used, with field-collected images held out as a fixed test set. The ensemble model achieved a mean validation accuracy of 98.49% ± 0.58% across six folds and a test accuracy of 98.39% on 427 unseen field images, with macro-average precision, recall, and F1-score each reaching 98%. ConvNeXt V2 Tiny substantially outperformed ResNet-50 (87.35%) and EfficientNetV2-S (83.37%) under identical experimental settings, demonstrating superior generalization across the domain gap between curated and field data. The primary contribution of this study is the first application of ConvNeXt V2 Tiny with ensemble K-Fold strategy for sugarcane disease classification, offering high accuracy with moderate computational complexity (28.6M parameters) and practical deployability, as demonstrated through the SugarScan web application.

Why it matches plant phenotyping methodsサトウキビ葉画像から病害状態を推定する画像ベースの表現型解析手法が研究の中心であり、モデル性能の検証・比較も実施しているため含める。

abstractThis study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny

AgriX-SENet: Squeeze-and-Excitation-based deep learning framework for explainable plant disease detection in sustainable agriculture →

Introduction Timely and accurate detection of plant diseases is essential for ensuring global food security and supporting sustainable agriculture. Conventional diagnostic approaches, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and impractical for large-scale or remote agricultural environments. Although deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved automated plant disease classification, they often lack interpretability and struggle to generalize under diverse field conditions. Methods This study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance. The SE blocks recalibrate channel-wise feature responses to emphasize disease-relevant information while suppressing background noise. To improve model transparency, Grad-CAM, SHAP, and LIME were incorporated to provide visual and feature-level explanations of the model’s predictions. The framework was trained and evaluated using the Plant Pathology 2020 dataset containing four classes: healthy, rust, scab, and multiple diseases. Results AgriX-SENet achieved a training accuracy of 97.47% and a validation accuracy of 95.07%, outperforming fourteen state-of-the-art deep learning models. The classification report demonstrated high precision and recall across most disease categories, although the scab class exhibited comparatively lower recall, indicating an opportunity for further improvement. The explainability analyses consistently showed that the model focused on pathologically relevant regions of leaf images, validating the reliability of its predictions. Discussion The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems. Its ability to provide accurate and explainable predictions makes it a promising solution for scalable agricultural diagnostics. Future work will focus on improving classification performance for challenging disease categories and optimizing the framework for deployment on mobile and edge computing devices to enable real-time field applications.

Why it matches plant phenotyping methods葉画像から植物病害状態を推定する説明可能な深層学習フレームワークを開発・評価しており、植物表現型取得・判定手法が中心である。

abstractThis study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance.

Stress phenotyping of wild desert legume Acacia senegal with machine learning application and phytochemical characterization of bipinnate leaves. →

Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.

Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。

abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves

AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring →

Early and accurate identification of plant diseases is essential for improving crop productivity and ensuring food security. Many existing deep learning-based plant disease classification methods rely solely on leaf images collected from a controlled environment, which limits their applicability in real-world agricultural conditions where symptoms may be visually unclear and influenced by environmental factors. To address these challenges, this study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification. The proposed architecture employs EfficientNet-B0 for visual feature extraction, BERT for semantic representation of symptom descriptions, and a lightweight multilayer perceptron for modeling environmental factors such as temperature, humidity, rainfall, and soil moisture. Features from all three modalities are fused into a unified representation to train the CNN model. The model is trained and tested upon the Context-Aware Multimodal Augmented PlantVillage dataset covering 38 plant diseases and healthy classes. Experimental results show that AgriFusionNet gives an overall accuracy of 98.94% on the dataset Context-Aware Multimodal Augmented PlantVillage, with competitive precision and recall and F1-score. The multimodal framework facilitates the co-learning of visual, semantic, and contextual environmental representations and the analyses of the confusion matrix and feature interactions give insights into cross-modal relationships. The proposed approach aims to explore context-aware multimodal representation learning for agricultural AI applications, with emphasis on integrating complementary visual, semantic, and contextual information.

Why it matches plant phenotyping methods葉画像を中心に、症状記述と環境情報を統合して植物病害状態を分類する手法を開発・評価しており、植物フェノタイピング手法が中心である。

abstractthis study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification.

Covered smut screening in barley: power analysis and effect on agronomic traits. →

Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.

Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。

abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.

Spatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy. →

The intelligent identification of tea diseases is crucial for ensuring tea quality and reducing economic losses in the tea industry. However, the deployment of deep learning models on edge devices remains challenging due to the conflict between detection accuracy and computational overhead. To address this, we propose CA-YOLOv8n, a lightweight object detection model tailored for tea disease diagnosis. Specifically, we introduce a Path-Decoupling strategy to streamline the network structure and integrate the Coordinate Attention (CA) mechanism to enhance the model's spatial awareness of subtle pathological features. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 98.89% while reducing the parameter count by 32.6% and FLOPs by 24.1% compared to the baseline YOLOv8n. The model was integrated into a diagnostic platform with an automated reporting interface, demonstrating that real-time tea disease identification is feasible on commodity CPU hardware in resource-constrained agricultural environments.

Why it matches plant phenotyping methods茶葉の病害を画像から検出する軽量深層学習モデルを開発・評価し、植物の病害状態を直接推定する方法が研究の中心である。

titleSpatial-aware lightweight network for real-time tea disease detection: A coordinate attention-enhanced YOLOv8n approach with path-decoupling strategy.

Multi-Crop Leaf Disease Detection using YOLOv12 with Class-Aware Multi-Scale Fusion and Adaptive Attention Modules →

- 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.

AI for Precision Fertilizer and Pesticide Application: An Integrated Real-Time Deep Learning and IoT-Driven Field Management System →

Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.

Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。

abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.

A Three-Dimensional Phenotyping Framework for Quantifying Soybean Resilience to Pest Stress in the Field →

Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.

Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。

abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure

A robust cross-crop disease detection framework based on SIS-YOLOv11 with climate-adaptive mechanisms →

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.

Multi-omics prediction for yellow rust in bread and durum wheat through conventional and Ai-based frameworks. →

Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.

Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。

abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)

Anisotropic boundary-aware detection for cotton leaf diseases with boundary-decoupled regression and lightweight feature adaptation. →

Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.

Why it matches plant phenotyping methods綿花葉の病斑・病害状態を画像から検出する手法を開発し、複数データセットとベースラインで性能検証しているため、植物表現型取得が中心である。

abstractWe present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages.

A high-performance detection model ISA-YOLO for eggplant pests and diseases. →

Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.

Why it matches plant phenotyping methodsナスの病害・害虫を画像から検出するISA-YOLOモデルの開発と性能評価が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

titleA high-performance detection model ISA-YOLO for eggplant pests and diseases.

RareAgriDetectAI a generative deep learning framework using RareSimGAN for early detection and simulation of rare crop diseases. →

Timely identification of crop diseases is imperative in precision agriculture to intervene at the right time and maximise yield sustainability. Despite achieving high accuracy, deep learning models are ineffective for rare plant disease classes, mainly due to severe data imbalance and insufficient training samples. Currently, most generative augmentation methods are designed to enhance either visual realism or data diversity, while ignoring methods that are sensitive to early-stage diseases or that control disease progression. In this paper, we propose RareAgriDetectAI, which comprises RareSimGAN, a generative deep learning framework for synthesising images of rare diseases, and a latent traversal mechanism that collaboratively visualises disease progression with increasing severity. We introduce a pipeline for synthesising realistic crop images for data augmentation. We augment the representation of rare classes using synthetic samples in a ResNet50-based classification pipeline. A strong experimental setup, relying on controlled baselines and synthetic-aided training scenarios, was employed. Evaluation on a real dataset shows significant improvement for the rare class ToLCNDV, with recall increasing from 0.42 in the baseline to 0.81 after synthetic augmentation. In contrast, the performance on other common disease classes remains stable. SSIM, Inception Score, and FID metrics were shown to validate generative quality. At the same time, an ablation study identified a suitable augmentation threshold at which sufficient performance is achieved without excessive synthetic data generation. The results further indicate improvements in feature diversity, which translate into earlier disease recognition (before full disease onset) and improved classification robustness with RareSimGAN. The post-framework combines generative modelling and latent space exploration to deliver a low-cost, scalable, and data-efficient solution for agricultural AI systems. RareAgriDetectAI utility can assist in the proactive monitoring of crop health and simulate rare disease scenarios to drive learning that can aid reliable, interpretable deep learning applications in precision agriculture.

Why it matches plant phenotyping methods希少作物病害の画像合成、病徴進行の可視化、早期病害認識を中心とする画像ベースの植物病害フェノタイピング手法であり、生成品質と分類性能も検証している。

abstractwe propose RareAgriDetectAI, which comprises RareSimGAN, a generative deep learning framework for synthesising images of rare diseases, and a latent traversal mechanism that collaboratively visualises disease progression with increasing severity.

A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning. →

Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two detection approaches have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning classifiers utilizing handcrafted features, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of maximum 0.90 on the Receiver Operating Characteristic in one of the classifiers, showing relatively high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect validation accuracy (AUC: 0.99-1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource as a proof-of-concept for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.

Why it matches plant phenotyping methodsトウモロコシの感染症状を画像から自動検出・スコア化する低コスト撮像プラットフォームと機械学習手法の開発が中心であり、植物病害表現型の取得・抽出方法に該当する。

abstractwe developed a rotating camera system that captures videos of plants under customized lighting and shutter settings.

Leaf-by-Leaf Diagnosis: A Custom CNN with Pyramidal Feature Extraction for Plant Disease Classification →

Plant diseases threaten global agriculture, causing 20–40% yield losses and food insecurity. Current diagnostic methods are costly and lack scalability. While deep learning advances plant disease detection, there remains a need for CNNs with simpler architectures, better generalizability, and lower computational cost. This study presents a novel CNN for multi-class classification of 38 diseases. Trained on a public dataset of over 87,000 RGB images, the architecture comprises five convolutional blocks (filters 32–512) with max pooling and dropout (0.25, 0.4), followed by a 1,500-unit dense layer and SoftMax output. Optimized with Adam (lr=0.0001) and categorical cross-entropy, the model achieved 98% training and 96% validation accuracy with approximately 28.7 million parameters significantly fewer than transfer learning architectures. These results demonstrate an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.

Why it matches plant phenotyping methods葉画像から植物病害を分類するCNNの開発と性能評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当します。

titleLeaf-by-Leaf Diagnosis: A Custom CNN with Pyramidal Feature Extraction for Plant Disease Classification

SPVD-field: a task-oriented multi-task visual dataset for sweet potato virus disease under real field conditions →

Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.

Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。

abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.

Text guidance is powerful but prompt-sensitive for weakly-supervised leaf symptom segmentation →

Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.

Why it matches plant phenotyping methods植物病害症状の画像セグメンテーション手法を開発・比較し、病斑の疑似マスク生成とセグメンテーション性能を評価しているため、植物フェノタイピング手法が中心です。

abstractAccurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping

Lightweight real-time detectors of apple-leaf diseases operating on embedded devices. →

Agricultural leaf disease detection is crucial for early intervention and yield protection in precision agriculture. Among representative economic crops, such as apples, leaf lesions are typically small and appear in complex backgrounds, making accurate detection performed on resource-constrained embedded devices challenging. To address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n. A novel Dual-Aspect Feature Complementary Mapping (DAFCM) module type is embedded in their backbone to recover lost semantic and spatial information, while the original YOLO11n's neck is replaced by an Efficient Enhanced Cross-Scale Feature Fusion (EE-CSFF) module, which incorporates Gated Differential Convolutional Fusion (GDCF) modules to strengthen cross-scale information flow and small-object representation. Experimental results obtained on the ALDSOD dataset show that, compared with the YOLO11n baseline, DCL-YOLO improves recall from 81.9% to 84.6%, mAP50 from 86.8% to 88.4%, and mAP50:95 from 47.0% to 47.8%, while also reducing the parameter count from 2.58 M to 1.91 M and Giga Floating-Point Operations (GFLOPs) from 6.3 to 5.5. After applying Layer-Adaptive Magnitude-based Pruning (LAMP), the parameter count and GFLOPs are further reduced to 0.75 M and 2.7, respectively, with mAP50 and mAP50:95 still exceeding the baseline by 1.2 and 0.5 percentage points, respectively. When deployed on an embedded device, the pruned model achieved 15.2 FPS and 139 msec per image, confirming its applicability in real-time scenarios. Furthermore, cross-domain validation, performed on the Global Wheat Head Detection (GWHD) dataset, indicates the stable generalization capabilities of the proposed models across environmental domain shifts. The DCL-YOLO's source code is publicly available at: https://github.com/q123-code/dcl-yolo.

Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する軽量モデルを開発し、データセットで性能比較・クロスドメイン検証・組込み機器での実装評価を行っており、植物の病害状態推定手法が研究の中心です。

abstractTo address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n.

A Novel Multi Class Real World Fruit and Leaf Disease Image Dataset for Crop Health Analysis →

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.

ReLeaf-SAM: reliability-guided detail compensation for SAM-based plant disease segmentation →

Accurate lesion segmentation is essential for automated plant disease analysis in precision agriculture. Although the Segment Anything Model (SAM) exhibits strong generalization ability, its direct application to plant disease images in natural field environments remains challenging due to cluttered backgrounds, dense leaf veins, uneven illumination, and frequent occlusions. In particular, SAM mainly relies on global structural cues and is often insufficiently sensitive to subtle lesion textures and weak local details, which can result in missed small or early-stage lesions and inaccurate boundary delineation. To address these limitations, we enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation. A ResNet-50based branch is employed to extract fine-grained local texture features that are difficult for SAM to capture. These fine-grained features are fused with SAM encoder representations and then injected into the SAM decoder, enabling more accurate lesion prediction while preserving SAM’s strong global modeling capability. More importantly, we propose a reliability-guided variational fusion framework to further improve the interaction between heterogeneous features. Specifically, instead of conventional similarity or addition-based fusion, we introduce an uncertainty-aware variational fusion strategy that explicitly quantifies the confidence of each feature stream. An uncertainty encoder models feature distributions probabilistically, and a variational fusion module dynamically assigns higher weights to more reliable features while suppressing uncertain or interfering responses. In addition, Kullback-Leibler divergence regularization is introduced to stabilize cross-feature alignment and improve fusion robustness. Extensive experiments on PlantSeg, PlantDoc-Seg, and ATLDSD demonstrate that the proposed method outperforms state-of-theart approaches, achieving DSC scores of 81.05%, 91.12%, and 88.27%, respectively. The proposed method addresses SAM’s weakness in fine-grained disease feature extraction, accurately identifies early and small lesions, and delivers reliable segmentation for field plant disease automatic diagnosis.

Why it matches plant phenotyping methods植物病斑を対象とする画像セグメンテーション手法を開発し、複数データセットで性能検証しているため、病害状態のフェノタイピング手法が中心である。

abstractwe enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation.

LeafLiteX mobile application for leaf disease detection using U-Net segmentation and lightweight deep learning. →

Agriculture is significant in world food production and global economic stability, but leaf disease and pest infection can cause a threat to crop quantity and quality. Thus, it became crucial to have timely and accurate identification of plant leaf disease to prevent loss in agriculture on a large scale and to have sustainable crop management. This paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases. This application uses U-Net segmentation to precisely find leaf regions and MobileNetV3-Large to quickly classify diseases with less computation on the computer. The application performs end-to-end processing, from image acquisition to segmentation and disease prediction on mobile devices. An experiment was performed on publicly available crop disease datasets containing various leaf images having different disease types. The model obtained an accuracy of 98.85% showing improved generalization with minimal latency. The design of the model was such that it was suitable for inference on-device while still being robust enough despite changes in lighting conditions, background noise, and camera resolution. LeafLiteX is a low-cost, easy to use, offline-capable, and in-the-moment decision-making supportive diagnostic application that supports farmers and agrarians who require early detection. This paper demonstrates the capabilities that can be achieved using edge-optimized machine learning and computer vision to support the development of smart agriculture technologies. While traditional methods rely solely on classification, this research focuses more on practical implementation by incorporating segmentation, lightweight classification, and explainability to develop a mobile-friendly model.

Why it matches plant phenotyping methods葉画像から病害状態をセグメンテーション・分類する手法とモバイルアプリ自体が研究の中心であり、植物病害の表現型推定に該当する。

abstractThis paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases.

ShuffleNetV2 SSM MLCA: a lightweight recognition network for wheat fungal diseases. →

Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production. Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation. Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment. Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.

Why it matches plant phenotyping methods小麦葉片の病斑特徴を画像から抽出し、植物の真菌病状態を分類する軽量深層学習手法を開発・検証しており、病害表現型の取得・推定が研究の中心である。

abstractThis paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification.

Automatic prediction of cotton leaf's diseases using deep learning techniques. →

Cotton leaf diseases present a major threat to global cotton production, significantly impacting both yield and fiber quality. Traditional diagnostic methods are labor-intensive, time-consuming, and demand highly skilled professionals, making them inefficient for large-scale agricultural applications. Although earlier deep learning -based approaches have shown promising results in identifying cotton leaf diseases such as Bacterial Blight, Fusarium Wilt, and Curl Virus Disease, their performance is often limited by complex preprocessing requirements and insufficient generalization to real-world field conditions. To address these challenges, this study proposes and optimized transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks. This study demonstrates the development of Cotton Leaf Disease Prediction Convolutional Neural Network (CLDP-CNN) automatically, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets. Two distinct datasets were used to train the model: the first consisted of field images from cotton farms, while the second was sourced from Kaggle. The main goal of this research examines how the model performs on real-world field datasets. The CLDP-CNN model has proven highly accurate by attaining 99.78% detection success rates for cotton leaf diseases when processing primary dataset which surpasses its secondary dataset accuracy rate of 99.62%. Both the primary dataset and secondary dataset resulted in high accuracy values for the VGG16 pre-trained model which achieved 99.56% accuracy on the primary dataset and 98.82% on the secondary dataset. A web-based application enhances the capabilities of the CLDP-CNN model by providing real-time updates on the health status of cotton plants. This technology empowers farmers with valuable information, enabling them to take timely protective actions to prevent potential severe yield losses in their cotton crops.

Why it matches plant phenotyping methods綿花葉の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、植物フェノタイピング手法が研究の中心です。

abstractThe main goal of this research examines how the model performs on real-world field datasets.

A deep learning optimized model for classification and detection of rice leaf diseases. →

Food productivity, quantity and quality are at stake when plant diseases such as rice diseases undermine the food security. Rice leaf disease treatment necessitates accurate and timely diagnosing. This study describes a deep learning model for categorizing and forecasting rice plant diseases. Using the remora optimization algorithm (ROA) on a rice leaf dataset demonstrates its potential for plant disease classification. The ROA-DM method detects rice leaf diseases using the ROA algorithm, a deep maxout network (DMN), and a deep autoencoder (DAE). ROA is applied to the learning parameters of deep model in order to achieve better convergence and avoiding local minima, which usually happens with conventional gradient-based optimizers. Experiments show that the suggested framework is accurate and precise across illness categories. The confusion matrices display the training and validation accuracy, losses of this model. The performance of our optimal learning method with respect to other methods indicated its potential for identifying leaf diseases. The accuracy of the ROA-DM method is 98.5%.

Why it matches plant phenotyping methodsイネ葉の観察画像から病害状態を分類・検出する深層学習手法が研究の中心であり、植物病害フェノタイピング手法に該当する。

abstractThis study describes a deep learning model for categorizing and forecasting rice plant diseases.

TopoLeaf: A Zero-Shot Visual Anomaly Detection Framework via Stable Feature Dimensionality and Local Persistent Homology for Self-Organizing Agricultural Cyber-Physical Systems →

Abstract Zero-shot visual anomaly detection in complex textured domains remains a fundamental challenge for building adaptive, self-organizing cyber-physical systems. Conventional deep learning approaches often rely on closed-set assumptions, require prohibitive pixel-level annotation costs, and suffer severe performance degradation under cross-domain shifts---limiting their deployability in real-world agricultural CPS where novel disease types and unseen crop species continuously emerge. To address these issues, we present TopoLeaf, a training-free and annotation-free anomaly detection framework. By leveraging the robust semantic representations of foundation models (specifically DINOv2), our method introduces two complementary scoring mechanisms: a geometric anomaly score based on local KNN distance in a stability-selected feature subspace, and a topological anomaly score derived from local persistent homology. The topological score effectively captures subtle structural deviations and micro-texture mutations that geometric distances often miss. Extensive experiments on cross-species plant disease benchmarks (3,100+ images across 40 source--target pairs) demonstrate that TopoLeaf achieves highly competitive and structurally robust zero-shot performance, providing a robust perception layer for closed-loop agricultural cyber-physical systems that must maintain diagnostic stability under previously unseen perturbations. Under well-aligned domains, our geometric score achieves near-perfect detection (e.g., 0.994 AUROC on Strawberry). The method exhibits informative failure modes on structurally isolated domains such as Corn (0.169 AUROC), revealing fundamental structural properties of the foundation model's feature manifold. Furthermore, the topological score demonstrates structural complementarity, achieving 0.542 AUROC on the challenging Corn-to-Apple pair where geometric scoring degenerates to 0.221. Module ablation studies confirm that stability-based dimensionality selection consistently improves cross-domain generalization.

Why it matches plant phenotyping methods植物病害の視覚的異常（植物の病徴・状態）を推定する新規画像解析手法を開発し、複数種の病害ベンチマークで検証しているため、植物フェノタイピング手法が中心である。

abstractwe present TopoLeaf, a training-free and annotation-free anomaly detection framework.

BanglaRiceLeaf: A benchmark dataset for automated rice leaf disease detection and health classification in Bangladesh. →

Rice leaf diseases pose a major challenge to crop health and agricultural productivity, particularly when timely and accurate diagnosis is required under natural field conditions. The development of automated disease recognition systems depends heavily on the availability of large, well-annotated image datasets. However, many existing rice leaf disease datasets are limited in terms of environmental variability, disease representation, and real-field imaging conditions. To address this gap, this paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors from the experimental fields of the Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh, between July 2023 and July 2024. The dataset contains 4152 images belonging to five classes: Bacterial Leaf Blight, Bacterial Leaf Streak, Sheath Blight, Leaf Blast, and Healthy Leaf. The images were acquired from two rice varieties, BR11 and BRRI dhan34, under natural field conditions across varying illumination environments in order to reflect practical disease recognition scenarios. All images were manually annotated by trained annotators under expert supervision. The dataset is systematically organized and publicly released to support reproducible research in rice disease classification. In addition to dataset presentation, benchmark experiments using Xception, NASNetMobile, and InceptionV3 are provided to demonstrate its applicability for deep learning-based disease recognition. BanglaRiceLeaf is expected to serve as a useful resource for plant disease analysis, comparative model evaluation, and future research in precision agriculture and agricultural computer vision.

Why it matches plant phenotyping methodsイネ葉の病徴・健全状態を画像で分類する公開ベンチマークデータセットであり、データ収集・注釈・ベンチマーク評価が中心です。

abstractthis paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors

Wild genes to the rescue: high-throughput genomics reveals the wild source of broomrape resistance in sunflower →

The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.

Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。

abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.

An Embedded AI System for Automated Crop irrigation and pest Monitoring →

Modern agriculture is rapidly adopting Artificial Intelligence (AI) and Internet of Things (IoT) technologies to improve crop monitoring and decision-making. Many existing systems focus either on water stress detection or pest detection separately. The proposed system integrates both functions into a single platform. It uses a camera module and environmental sensors connected to a Raspberry Pi (5/4) as the main controller. A Convolutional Neural Network (CNN) model processes leaf images captured by the AI camera, while a soil moisture sensor supports water stress analysis. The system classifies crops into three categories: healthy, water-stressed, and pest-infected. Based on the output, it provides real-time recommendations for irrigation and pesticide application. This reduces manual inspection, prevents unnecessary chemical usage, saves water, and improves crop productivity.

Why it matches plant phenotyping methods植物の葉画像と土壌水分センサーを用いて、健康・水ストレス・害虫感染という植物の状態を自動分類する統合センシング基盤を開発しており、表現型取得・判定が中心的です。

abstractThe proposed system integrates both functions into a single platform.

Ensemble-Based Plant Disease Detection with Mini TensorFlow on Risc Devices and Chatbot →

The research trains and evaluates multiple CNN architectures, including Basic CNN, AlexNet, VGG16, and EfficientNet B0, to enhance the accuracy of plant disease identification. Each model was tested using the New Plant Diseases Dataset from Kaggle, which includes various plant species and diseases, in order to assess performance, accuracy, and efficiency. The trained models were subsequently integrated into a Marathi language chatbot to facilitate real-time disease detection and provide agricultural guidance. This study provides valuable insights into the strengths and limitations of different models for precision agriculture, especially in applications that support regional languages to encourage accessible and sustainable farming practices. Additionally, a Marathi language chatbot is incorporated, enabling users to obtain plant disease information instantly through a user-friendly web application

Why it matches plant phenotyping methods植物病害状態を画像から識別するCNN群を訓練・評価し、リアルタイム検出システムへ統合しており、病害フェノタイプの取得・推定手法が中心である。

abstractThe research trains and evaluates multiple CNN architectures, including Basic CNN, AlexNet, VGG16, and EfficientNet B0, to enhance the accuracy of plant disease identification.

Paddy leaf disease detection and classification using improved Gorilla Troops optimized YOLO-V8 network. →

Paddy leaf disease (PLD) detection has grown more difficult, yet early detection might prevent significant losses due to decreased crop yield. However, existing models struggle to accurately classify diseases under difficult circumstances like intricate backgrounds, fluctuating lighting, and overlapping leaves. Additionally, existing models do not incorporate efficient optimization strategies, leading to suboptimal accuracy and poor generalization on unseen data. To address these challenges, a novel deep learning-based YOLO-LEAFNET method for PLD detection utilizing IGT-YOLO, integrating the YOLOv8 disease detection with the Improved Gorilla Troops (IGT) optimization. The input paddy leaf images are pre-processed using Bilateral Contrast Limited Adaptive Histogram Equalization (B-CLAHE) to enhance image quality and improve local contrast while preserving disease boundaries. YOLOv8 model is utilized to detect and classify paddy leaf diseases by accurately localizing affected regions with bounding boxes. Then, the IGT algorithm boosts the disease detection accuracy by optimizing YOLOv8 through effective hyperparameter tuning. The proposed YOLO-LEAFNET method effectiveness was evaluated using recall, F1 score, specificity, accuracy, and precision. B-CLAHE enhanced noise-free images improve contrast and detection accuracy, while the IGT-YOLO model ensures scalable, efficient early diagnosis of paddy leaf diseases with 99.07% accuracy. The YOLO-LEAFNET enhanced the total accuracy by 3.21%, 5.25%, and 1.98% related to CNN, DeepRice, and FasterR-CNN, respectively.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出・分類するYOLOベース手法を提案し、前処理・最適化・性能評価を中心に扱っているため、植物フェノタイピング手法として該当する。

abstractTo address these challenges, a novel deep learning-based YOLO-LEAFNET method for PLD detection utilizing IGT-YOLO, integrating the YOLOv8 disease detection with the Improved Gorilla Troops (IGT) optimization.

Evaluation of cold resistance in pear ( Pyrus L.) germplasms: integrating physiological and biochemical responses with anatomical traits under low temperature stress. →

Low temperature stress severely restricts the cultivation and distribution of pear ( Pyrus L.) germplasms, frequently resulting in frost injury and yield reduction. To accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress. In this study, 122 pear germplasms were classified into high (HR), medium (MR), and low (LR) cold-tolerance categories based on their semi-lethal temperature (LT 50 ). Further analysis of pear germplasms with different levels of cold resistance revealed that, with decreasing temperature, HR germplasms exhibited smaller increases in relative electrolyte conductivity (REC) and malondialdehyde (MDA) content and higher accumulation of proline (Pro), soluble proteins (SP), soluble sugars (SS), and peroxidase activity compared with LR germplasms. In addition, the peak values of these indicators generally occurred at lower temperatures in HR germplasms. A correlation analysis and principal component analysis indicated that physiological indices, including REC, bound water/free water ratio, SS, and MDA, as well as branch anatomical traits related to xylem and cortex proportions, were closely associated with variation in LT 50 . An integrated assessment using membership function analysis produced rankings consistent with LT 50 -based clustering, supporting the reliability of the multivariate evaluation framework. Overall, this study establishes an integrated, indicator-based approach for evaluating cold resistance in pear germplasm by integrating physiological, biochemical, and anatomical characteristics. These results provide a theoretical basis and methodological reference for screening cold resistance germplasms.

Why it matches plant phenotyping methods生理・生化学・解剖学的形質を統合し、LT50と多変量評価によってナシ遺伝資源の耐寒性を分類・スクリーニングする評価フレームワークが研究の中心である。

abstractTo accurately evaluate the cold resistance of pear germplasm resources, this study investigates the physiological and biochemical responses of one-year-old branches to different degrees of low-temperature stress, as well as differences in the tissue structure of these pear germplasms after low-temperature stress.

Implementation of a Plant Disease Identification System using the CNN Algorithm and the Web-Based Django Framework →

This study addresses the need for efficient and accessible plant disease identification systems in the era of Agriculture 4.0, where advances in artificial intelligence (AI) and machine learning (ML) support data-driven agricultural practices. The increasing popularity of home gardening highlights challenges faced by users in identifying plant diseases due to limited knowledge and diagnostic tools. Therefore, this research aims to develop a web plant disease detection system using the Django framework and convolutional neural networks (CNNs). The model was trained on a controlled dataset consisting of 57,320 leaf images collected from the PlantVillage and Turmeric Plant Disease datasets. Image preprocessing was applied, including resizing, normalization, and data augmentation such as image rotation, zooming, image inversion and brightness adjustmen. Class imbalance during training was handled using class weighting. The dataset is divided into a training set and a validation set for model development and evaluation. The CNN model achieved an accuracy of 92% on the labeled validation dataset, with a mean F1 score of 0.79 and a weighted mean F1 score of 0.92. For generalization testing, an uncontrolled (wild) dataset consisting of 223 images collected from online sources was used, resulting in an accuracy of 11%, indicating limited real-world generalization due to domain differences. Despite this limitation, the proposed system demonstrates the feasibility of CNN-based plant disease classification in a web application.

Why it matches plant phenotyping methodsCNNによる葉画像からの植物病害識別システムを開発し、検証データと野外データで性能評価しているため、植物の病害状態を推定する画像ベースのフェノタイピング手法が中心である。

abstractthis research aims to develop a web plant disease detection system using the Django framework and convolutional neural networks (CNNs).

A novel Hybrid Vision Transformer with dense attention capsule network (HVT-DACapNet) model for cotton plant disease detection. →

Image processing plays a vital role in precision agriculture by enabling automated disease detection and crop health monitoring. This research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection. The proposed framework integrates Adaptive Wavelet Transform Filtering (AWTF) for noise removal while preserving disease-related features. A Hybrid Vision Transformer (HVT) is employed to extract both local spatial patterns and global contextual dependencies, and the Dense Attention Capsule Network (DACapNet) captures hierarchical spatial relationships with an attention mechanism that emphasizes infected regions. In addition, a hybrid optimization strategy combining Mayfly and Aquila Optimization (HMAO) is used to fine-tune model hyperparameters for improved convergence. The model is evaluated on a publicly available Kaggle cotton leaf disease dataset containing healthy leaves and multiple disease categories including Target Spot, Powdery Mildew, Bacterial Blight, Army Worm, and Aphids, using a 70:15:15 train-validation-test split under the simulation setup and hyperparameter configuration described in the manuscript. The proposed HVT-DACapNet achieves an F1-score of 99.68%, sensitivity of 99.68%, specificity of 98.89%, and an overall accuracy of 99.79%, outperforming existing models such as ConvLSTM-ZOA, GOA, SFO, Inception-V3, and VGG-16.

Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する深層学習手法を新規開発し、公開データセット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 research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection.

Adaptive attention and severity estimation framework for robust pearl millet leaf disease identification. →

Pearl millet is an important crop in arid regions, but its yield is reduced by foliar diseases like Downy Mildew and Rust. Traditional and deep learning methods struggle with accurate lesion detection, severity estimation, and robustness under complex field conditions, and often lack interpretability for practical agricultural deployment. To address these challenges, this study proposes the Adaptive Severity-Aware Swin Attention Network (ASA-SAN), an integrated framework designed for disease segmentation, classification, and severity estimation in pearl millet leaves. The proposed architecture combines a Swin Transformer encoder for hierarchical feature extraction with a ResUNet++ decoder for accurate lesion segmentation. This is further enhanced using Adaptive Channel Attention to improve feature discrimination and a dual-stream classification network to jointly capture local lesion characteristics and global contextual information. Additionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations. Experimental evaluations conducted on a pearl millet leaf dataset demonstrate that the proposed method achieves a Dice score of 97.8%, IoU of 95.6%, classification accuracy of 98.3%, and F1-score of 98.2%, outperforming several state-of-the-art methods. Furthermore, Grad-CAM visualizations enhance model interpretability by highlighting disease-relevant regions. Overall, the ASA-SAN framework provides a robust, interpretable, and severity-aware solution for automated pearl millet disease analysis, enabling early detection and supporting precision agriculture practices for improved crop protection and yield optimization.

Why it matches plant phenotyping methods真珠粟葉の病斑を画像から分割・分類し、病害重症度を定量推定する手法を中心に開発・評価しているため、植物表現型計測手法として含める。

abstractAdditionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations.

An efficient YOLO-based framework for multi-class plant disease detection →

Plant health plays a critical role in agriculture, climate balance, and economic stability. However, plant diseases caused by bacteria, fungi, and viruses can significantly reduce crop productivity if not detected early. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error, especially in large-scale farming. To address these challenges, this study proposes an automated and accurate plant disease detection system using deep learning-based object detection models for early disease diagnosis in agriculture. A publicly available dataset containing 38 different plant leaf diseases annotated in You Only Look Once (YOLO) format is used, along with a standardized preprocessing pipeline to ensure data quality and consistency. Three modern architectures: YOLOv8, YOLOv11, and YOLOv26 were trained and evaluated under identical conditions using the Ultralytics framework on Google Colab. Experimental results show that YOLOv11 achieves the highest accuracy in terms of precision, recall, and mean Average Precision (mAP), while YOLOv8 provides the fastest inference speed with lower computational complexity. Based on the results, the study concludes that YOLO-based models show great potential for plant disease detection, with YOLOv11 offering superior detection accuracy among the evaluated models. The practical implications of these findings lie in the potential for precision agriculture to monitor diseases in real-time, minimize crop losses, and aid in timely decision-making for farmers and agricultural stakeholders.

Why it matches plant phenotyping methods植物葉の病害状態を画像から検出するYOLOベース手法を提案し、複数モデルを同一条件で評価しているため、病害表現型の取得・抽出が中心である。

abstractthis study proposes an automated and accurate plant disease detection system using deep learning-based object detection models

GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management. →

Plant diseases cause 20-40% annual crop losses worldwide, yet conventional detection methods remain slow, subjective, and inaccessible to smallholder farmers. This work presents GreenAid, an end-to-end plant disease detection and management system that bridges the gap between laboratory-level deep learning performance and practical agricultural deployment. The system integrates a confidence-weighted ensemble of three CNN architectures (VGG16, ResNet50, InceptionV3), fused through per-class F1-score reliability weights, with a cross-platform mobile application supporting offline inference via TensorFlow Lite, a web-based analytics dashboard, and an NLP-powered chatbot. On the PlantVillage benchmark (87,000 images, 38 classes, 14 species), the ensemble achieves 98.74% accuracy and 98.48% F1-score. Systematic comparison of six fusion strategies confirms that per-class F1 weighting outperforms alternatives including majority voting, simple averaging, and stacking. The INT8-quantised deployment model (78 MB, 127 ms on a mid-range smartphone) retains 98.43% accuracy with per-class analysis confirming disproportionate impact on the five most challenging categories. All pairwise model comparisons are validated by McNemar's test ([Formula: see text]). The primary contribution is the complete, reproducible integration of competitive classification, edge deployment, and an end-to-end agricultural delivery pipeline (mobile application, web dashboard, and NLP chatbot) rather than the ensemble mechanism itself.

Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法の開発・比較検証と、モバイル実装が中心であり、植物病害フェノタイピング手法として適格。

abstractThis work presents GreenAid, an end-to-end plant disease detection and management system

A curated image dataset for sapodilla fruit (Manilkara zapota) disease and fruit quality analysis. →

Sapodilla (Manilkara zapota), or chickoo, is a key tropical fruit, very popular in India, Mexico, and Thailand, as it is nutritionally and economically valuable. Nonetheless, the production of sapodillas is often affected by several diseases, which reduce fruit quality and quantity. The dataset used in this paper is a sapodilla fruit image dataset, comprising 1,518 images, gathered in the field under the practicing conditions on 18 February 2025, 22 February 2025, in Rahu village, Pune district, Maharashtra, India, with the use of smartphone cameras. The data is sorted into four categories, namely: Anthracnose, Bacterial rot, Healthy, and Sap bleeding. The photographs were taken in different backgrounds and in different lighting conditions to represent real-life cultivation conditions. The data is expected to be useful in machine learning-based plant disease detection, classification, and analysis, and spur the creation of intelligent and sustainable agricultural systems.

Why it matches plant phenotyping methodsサポディラ果実の病害・健全状態を画像で記録したデータセット自体が中心で、植物病害の画像ベース表現型解析に利用できる。

titleA curated image dataset for sapodilla fruit (Manilkara zapota) disease and fruit quality analysis.

HybridViT for robust wheat leaf disease detection using CLAHE and attention-based feature fusion. →

Wheat (Triticum aestivum L.) is a staple crop of paramount importance to global food security; however, its productivity is significantly compromised by foliar diseases. Conventional diagnostic approaches, relying on manual observation or laboratory analyses, are often labor-intensive and susceptible to inaccuracies. While recent advancements in deep learning present promising avenues for automated disease detection, persistent challenges such as limited annotated datasets, environmental heterogeneity, and model generalization continue to hinder optimal performance. This study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases. While ConvNeXt ensures local feature extraction and ViT provides global contextual understanding, CBAM dynamically highlights the most discriminative features. Additionally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is employed to enhance the visibility of disease symptoms in low-contrast leaf images. Unlike conventional hybrid CNN-Transformer approaches that rely on static feature concatenation, the proposed model employs an adaptive gated fusion mechanism to dynamically balance local and global feature representations. The fused features are further refined using a lightweight CBAM module to enhance discriminative capability. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to improve feature visibility under varying illumination conditions. Evaluated on three different datasets obtained under both controlled and field conditions, HybridViT achieved 100% accuracy on balanced datasets and 99.10% accuracy on complex images captured in real-world conditions, surpassing existing methods. Furthermore, a 5-fold cross-validation strategy yielded an average accuracy of 99.04% ± 0.22, demonstrating the model's robustness and stability across different data splits. The results demonstrate the model's robustness against environmental noise, lighting variations, and class imbalance. This approach, which enables early and accurate disease diagnosis, supports sustainable agricultural practices, reduces pesticide use, and contributes to global food security.

Why it matches plant phenotyping methods小麦葉の病徴を画像から分類する深層学習手法を開発・検証しており、植物病害状態の取得・推定が研究の中心です。

abstractThis study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases.

Pre-symptomatic detection of wheat stem rust using hyperspectral imaging and deep learning. →

Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.

Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。

abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).

Tomato leaf disease and severity prediction using multi-task learning. →

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.

A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis. →

Accurate and efficient identification of cucumber leaf diseases is a critical step in preventing losses and facilitating timely intervention in agricultural activi-ties. However, most state-of-the-art plant disease recognition models, including those employing deep learning, often fail to identify spatial dependencies among symptomatic leaf feature regions, require high computational resources, and lack robustness in their predictions. To overcome these challenges, this paper pro-poses MobileGraph, a graph-aided deep learning model that jointly reasons local texture patterns and spatial dependencies among CNN-derived cucumber leaf feature regions using MobileNetV3 as a lightweight feature extractor. Experi-ments on a publicly available cucumber leaf disease dataset containing 5 classes and 4,000 images show that the proposed model achieves 99.75% accuracy, 99.75% macro F1-score, and 99.69% MCC, outperforming several state-of-the-art models including ResNet-152, EfficientNet-B7, DenseNet-201, ConvNeXt, and VGG16, while having a significantly lower computational cost of 0.465 GFLOPs. Explainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions. Furthermore, a proto-type mobile application illustrates the feasibility of real-time cucumber disease diagnosis for practical agricultural monitoring. These results indicate that Mobi-leGraph provides an efficient and interpretable solution for intelligent crop health surveillance.

Why it matches plant phenotyping methodsキュウリ葉の病斑という植物状態を画像から診断する深層学習手法を開発し、複数モデルとの性能比較・検証まで行っており、病害表現型の取得・推定が研究の中心である。

abstractExplainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions.

An Explainable Hybrid Deep Learning–Fuzzy Decision Framework for Human-Centered Plant Stress Severity Assessment →

Abstract Mild stress is often difficult to distinguish from non-stress signals that may even mask the detection of stress; therefore, early diagnosis and precision grading of plant/microbial stress severity are essential for sustainable precision agriculture toward achieving optimized yields. We propose an interpretable hybrid deep learning–fuzzy decision framework combining EfficientNet B7 and Inception-ResNet-v2 with multiscale feature aggregation integrating Sparse Pyramid Pool (SPP) and Atrous Spatial Pyramid Pooling (ASPP). A Gaussian-based fuzzy inference system is incorporated to derive severity reasoning in a linguistically interpretable manner to address uncertainty and overlapping stress stages. Unlike conventional approaches evaluated only on controlled datasets, the proposed framework is validated through stringent cross-dataset generalization between the PlantVillage and PlantDoc datasets. The model demonstrates robustness under environmental disturbances and passes statistical significance tests. On the PlantVillage benchmark, the framework achieves an exact-match accuracy of $97.82\%$, a macro F1-score of $97.60\%$, and an AUC of $0.979$. When evaluated across a different domain, the performance decreases by only $4.8\%$, indicating strong generalization capability. The integration of fuzzy logic reduces adjacent-class error by $3.4\%$ and improves probability calibration with an Expected Calibration Error (ECE) of $0.021$. Grad-CAM visualizations and saliency analyses further confirm that the model focuses on biologically relevant diseased regions. These results demonstrate that combining multiscale deep feature learning with structured fuzzy reasoning enhances robustness, interpretability, and decision stability, thereby supporting human-centered agricultural monitoring systems.

Why it matches plant phenotyping methods植物のストレス重症度を画像から推定する深層学習・ファジー推論手法を開発し、異なるデータセット間で検証しているため、植物フェノタイピング手法が中心である。

abstractWe propose an interpretable hybrid deep learning–fuzzy decision framework

Explainable CNN framework for accurate crop disease detection using plant leaf images →

Early and accurate disease detection is important for increasing the agricultural output, decreasing the financial costs, and ensuring food security. Traditional diagnostic procedures take much time and effort, involve the necessity of having deep expertise, and are not always suitable for large scale farming disease detection. For this purpose, the current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images. The model utilizes several innovative architectural solutions such as depth-wise separable convolution, SE blocks, skip connections, and guided attention-based feature learning that allow enhancing the effectiveness of features extraction and decreasing computation load. Moreover, Grad-CAM is used to visualize affected areas on a map and thus increase the interpretability of the model. The suggested solution was implemented and tested on the PlantVillage dataset containing 54,305 images for 38 crop diseases out of 14 crops. The results show that the training, validation, and testing accuracies equal 97.6%, 88.3%, and 97.63%, correspondingly, along with the Macro-F1 measure of 0.867 and Micro-ROC-AUC equal to 0.99. A comparative study reveals that the presented model performs comparably well in terms of classification with lightweight structure and built-in interpretability capabilities to be applied in the mobile and edge-enabled agriculture environment. The results show that the presented approach is capable of being used as an effective and interpretable tool for diagnosing plant diseases in real-time.

Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の開発・評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。

abstractthe current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images.

Hybrid multimodal learning framework for crop disease detection, adaptive treatment, and price forecasting →

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.

ZDAM: a new deep learning model for bean leaf disease diagnosis. →

Introduction Accurate disease diagnosis is crucial for enhancing agricultural productivity and reducing postharvest losses, directly impacting food quality and safety. Traditional detection methods often rely on extensive feature modeling and perform poorly in complex field environments. Methods This study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism. The classical ZFNet is first optimized to improve feature extraction efficiency. A combined channel and spatial attention mechanism is then incorporated to refine feature representation for disease identification in key crops. Finally, a residual module is added to boost accuracy. Results Evaluated on a dataset of 11,903 bean leaf images covering healthy leaves and four disease types, including leaf mould, rust, mosaic, and white spot, the model achieves an average recognition accuracy of 99.02%, outperforming MobileMamba, Vision Transformer, and Chest- OMD. Discussion This approach offers a scalable solution for automated disease monitoring, supporting postharvest quality preservation and sustainable crop production.

Why it matches plant phenotyping methods豆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し、複数モデルとの性能比較も行っており、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 deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism.

RubberFormer: a transformer-based detection benchmark for rubber tree powdery mildew. →

Introduction Rubber tree powdery mildew is a major foliar disease that threatens the yield and quality of natural rubber. Its lesions are typically small, irregular, and embedded in complex backgrounds, making accurate automated detection difficult. Methods To address this challenge, we propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios. RubberFormer adopts MobileNetV4 as a lightweight backbone, introduces the Hierarchical Attention with Local-global Optimization (HALO) module for multiscale local-global feature fusion, incorporates the Unified Cross-Attention Network (UCAN) to enhance multidimensional feature interaction, and applies Normalized Wasserstein Distance (NWD) Loss to improve small-object localization. Results Extensive experiments were conducted on PM-Dataset-Plus, which contains 9,765 images, and PD-40, a large-scale plant disease dataset containing 80,369 images across 40 disease categories and 8 crops. RubberFormer achieved superior detection accuracy and generalization performance compared with existing methods, while maintaining computational efficiency suitable for practical agricultural monitoring. Discussion These results demonstrate that RubberFormer is effective for detecting small and irregular rubber tree powdery mildew lesions under complex conditions. The framework has practical value for rubber tree disease monitoring and provides a transferable design strategy for agricultural vision tasks involving small objects and complex backgrounds.

Why it matches plant phenotyping methodsゴム樹の病斑という植物の病害状態を画像から検出するTransformer手法を開発し、複数データセットで性能検証しており、植物表現型取得が中心である。

abstractwe propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios.

Identification of candidate genes involved in root gall formation during early infection of Plasmodiophora brassicae in B.napus . →

Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.

Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。

abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.

Lightweight Visual Detection Framework for Complex Background Grape Leaf Disease Identification →

Abstract Accurate crop disease detection supports precision agriculture, but field-deployable identification remains hindered by complex backgrounds, varying illumination, and heavy deep learning models. This work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions. Built on the YOLO11n backbone, the method integrates three customized modules: C3k2-UltraLightBlock for efficient feature representation, LeafRepFusionStem for low-level feature enhancement, and RCSA-HSFPN for refined multi-scale fusion with residual channel-spatial attention. A dedicated dataset with complex backgrounds is constructed via augmentation and background replacement. Experiments show the model achieves 92.0% precision, 92.9% recall, and 93.0% mAP@0.5, with only 2.9 GFLOPs and 1.73 M parameters, representing 54.7% and 33.2% reductions over the baseline. Heatmap visualization confirms improved lesion focusing and background suppression, while cross-crop tests validate strong generalization. This framework provides an efficient solution for real-time, edge-deployable plant disease monitoring, balancing accuracy and computational efficiency for practical agricultural visual computing applications.The implementation code for this study is available at:https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-Complex-Background-Grape-Leaf-Disease-Identification.git

Why it matches plant phenotyping methodsブドウ葉の病斑・病害状態を画像から推定する軽量な視覚検出手法を開発・評価しており、植物病害表現型の取得が中心である。

abstractThis work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions.

A Lightweight Convolutional Neural Network with Neighbourhood Attention and a 100- Category Dataset for Plant Disease Detection →

Plant disease detection is vital for agricultural sustainability and food security. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have achieved high accuracy in this domain, CNNs often require millions of parameters and substantial computation. ViTs suffer from the quadratic time and space complexity of self-attention (SA), limiting their use on resource-constrained devices. Although SA is capable of modelling long-range dependencies when symptoms are dispersed, many plant diseases exhibit small, localized lesions or texture changes; therefore, Neighborhood Attention (NA) offers a more efficient and targeted alternative by focusing on nearby regions rather than the entire image. This work proposes a custom Localized NA block implemented in TensorFlow/Keras that operates directly on CNN feature maps, bypassing patch embedding and transformer modules. A lightweight CNN is then developed by combining depth-wise separable convolutions with the proposed localized NA block. In addition, a 100-category plant disease dataset covering 16 crops is presented. The dataset is curated, class-balanced, and made publicly available to support reproducibility and encourage further research. The proposed 9-layer CNN, with just 1.7M parameters and a size of 6.74 MB, achieved a favorable balance between accuracy, model size, and computational efficiency, compared with MobileNetV1, MobileNetV2, DenseNet121, InceptionV3, MobileViT-XXS, and EfficientViT-M0, achieving 98.97%± 0.33% accuracy on PlantVillage and 93.36%± 0.28% on the proposed dataset. The ablation study showed that the NA block improved test accuracy by approximately 2–3%, while Grad-CAM visualizations indicated more precise targeting of diseased areas in the leaf image.

Why it matches plant phenotyping methods植物葉画像から病徴を推定する軽量CNNと注意機構を開発し、複数データセットで比較評価・アブレーションを行い、さらに100カテゴリの公開データセットを提示しているため、植物フェノタイピング手法が中心である。

abstractThis work proposes a custom Localized NA block implemented in TensorFlow/Keras that operates directly on CNN feature maps

A knowledge-driven unified framework for plant disease classification and severity grading via domain adaptation. →

Plant leaf disease classification and severity grading are essential for precision agriculture, enabling timely intervention and optimized management. Existing models often fail to recognize previously unseen disease categories due to rigid label spaces and limited representation of plant phenotypes. To address these challenges, we propose a knowledge-driven unified framework for plant disease classification and severity grading. A Common Knowledge Learner consolidates fundamental features of plant species, disease categories, and severity levels from labeled data, forming a transferable representation space. It employs a multi-level contrastive learning strategy to capture both global semantic representations and fine-grained lesion patterns. Building on these representations, a Cross-Domain Adaptation module leverages a teacher-student framework with Low-Rank Adaptation (LoRA) bridges in-domain and out-of-domain feature spaces using large-scale unlabeled data. Meanwhile, a contrastive feature library enables similarity-based reasoning and supports flexible label space expansion during inference without retraining. We evaluate our approach on Leaf-CG, a large-scale dataset comprising 441,448 images from 59 plant species, 373 disease categories, and four severity levels. Experiments demonstrate that our framework outperforms existing baselines, achieving 94.9% disease classification accuracy and 90.6% severity grading accuracy in-domain. Under out-of-domain conditions, the method achieves 82.1% true positive rate (TPR) in open-set settings, highlighting its strong generalization ability and potential for practical plant disease management. Code and dataset are available at https://www.uniplantcg.samlab.cn.

Why it matches plant phenotyping methods植物画像から病害分類と病害重症度を推定する計算・画像ベース手法を開発し、大規模データセットで評価しており、フェノタイピング手法が中心である。

abstractwe propose a knowledge-driven unified framework for plant disease classification and severity grading.

Longitudinal multispectral image dataset for ToBRFV disease detection in tomato and pepper plants. →

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.

A curated dataset of 3,477 high-resolution Grapevine (Vitis vinifera) leaf images for automated detection of Black Rot, Esca, and Leaf Blight diseases. →

We introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology. Whereas the PlantVillage and Hermos datasets, for instance, contain mainly scanned or laboratory-acquired leaves, GVLiD features vineyard in situ images along with detailed metadata (GPS, lighting, weather, and device model) and expert-verified annotations. To measure the reliability of the annotation, label consistency was very high (κ = 0.86-0.92; 95% CI) as assessed by inter- and intra-rater agreement. Besides Indian viticulture, the dataset also aims to support the field of foliar disease detection in precision agriculture and ML benchmarking, which face significant challenges due to variable illumination and natural leaf backgrounds under field conditions. GVLiD is intended to enable worldwide, reproducible, real-world testing of AI systems for crop disease monitoring.

Why it matches plant phenotyping methodsブドウ葉の病徴を画像で注釈化したデータセットであり、植物病害状態の画像ベース表現型測定と再現可能なベンチマークが中心です。

abstractWe introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology.

An Attention-Enhanced MobileNetV2 with Squeeze-and-Excitation Architecture for Efficient Potato Leaf Disease Detection and Classification →

Abstract Potatoes are one of the major crops eaten in developing countries; however, their production is falling due to various diseases. Early identification and detection of potato leaf diseases play a vital role in improving potato quality and quantity. Existing methods are either computationally resource intensive or lack trust in their decision-making process, which makes them difficult to deploy for real-time potato disease classification and limits its accessibility. To mitigate these limitations, this study proposed an attention-enhanced MobileNetV2 with a squeeze-and-excitation architecture, which balances high accuracy with low computational resources. This method incorporates the strength of MobileNetV2 and Squeeze-and-excitation networks. A total of 2152 images of early blight, late blight, and healthy leafs were obtained from the Kaggle public repository, which are partitioned into 70% training, 20% validation, and 10% testing and were utilized to train, validate, and test the proposed model. The MobileNetV2 backbone is utilized for feature extraction, and then a squeeze-and-attention block is used to recalibrate the feature maps by focusing on important features and suppressing irrelevant ones. Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented to visualize the most relevant region of the leaf for decision-making, which increases model interpretability and user trust. The proposed model achieves a remarkable performance of 99% testing accuracy with 9.41 MB total parameters. The proposed model is suitable for real-time potato leaf disease detection and classification, which can be easily accessible to agricultural stakeholders, including farmers, and contributes to food security.

Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類する深層学習手法を提案・評価しており、植物表現型（病害状態）の取得・推定が中心である。

abstractthis study proposed an attention-enhanced MobileNetV2 with a squeeze-and-excitation architecture, which balances high accuracy with low computational resources.

Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine →

Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT–PCA–SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications.

Why it matches plant phenotyping methods葉画像から植物病害状態を推定するViT–PCA–SVM解析パイプラインが研究の中心であり、植物表現型（病斑・病害状態）の画像ベース推定手法に該当する。

titleTransformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine

Integrating image-based phenotyping and GWAS to map resistance to spittlebug nymphs in interspecific Urochloa grasses →

Urochloa grasses are among the most widely used forage grasses across the tropics. Spittlebugs (Hemiptera: Cercopidae) are major pests of tropical Urochloa (syn. Brachiaria) grass pastures, severely reducing forage productivity and quality. Understanding the genetic basis of host-plant resistance is essential for developing durable resistant cultivars. Here, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs in 339 interspecific F1 hybrids derived from crosses between resistant sexual and susceptible apomictic Urochloa parents. Digital image analysis using both unsupervised (DQU) and supervised (DTR) quantification pipelines enabled accurate estimation of plant damage, yielding moderate to high broad-sense heritability estimates (H2 = 0.49 to 0.66). In contrast, insect survival (NTS) exhibited low to moderate correlations with all damage traits and lower heritability estimates (H2 = 0.42). Using 57,051 high-quality SNPs aligned to the genome of the hybrid cultivar Basilisk, GWAS models identified 18 quantitative trait loci (QTLs) for plant damage traits, but none for insect survival (antibiosis). Six robust QTLs on chromosomes 1, 6, 7, 27, 29, and 36 were consistently detected across models and phenotyping methods, explaining up to 21.5% of phenotypic variance. Candidate gene analysis revealed proteins involved in hormone signaling, oxidative stress response, and cell wall modification, suggesting multifaceted plant-insect interaction mechanisms. These results provide a foundational set of molecular markers associated with spittlebug response in Urochloa grasses, useful for marker-assisted and genomic selection in the forage breeding program.

Why it matches plant phenotyping methods高スループット画像表現型解析と、植物損傷を推定する2つの画像解析パイプラインが研究の中心であり、異なる手法間の比較と形質推定性能も評価している。

abstractHere, we combined high-throughput image-based phenotyping and genome-wide association studies (GWAS) to dissect the genetic architecture of response to Aeneolamia varia nymphs

HAMNet: Hierarchical Multi-scale Attention Network for precise disease detection in pearl millet using spatial fusion. →

Pearl millet is a vital crop in arid and semi-arid regions, but its productivity is significantly impacted by fungal and bacterial diseases. Traditional disease detection methods, including manual inspection and conventional machine learning techniques, often suffer from inefficiencies due to subjectivity, time consumption, and limited feature extraction capabilities. Deep learning-based segmentation models such as U-Net, SegNet, DeepLabV3, and FCN have been employed to automate disease identification, but they exhibit limitations in accurately localizing disease-affected regions, handling complex spatial patterns, and maintaining high segmentation precision. To address these challenges, this study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection. The novel framework enhances disease localization by utilizing hierarchical multi-scale feature learning, where low, mid, and high-level features contribute to superior classification accuracy. The attention mechanism prioritizes critical disease regions, reducing false positives, while Mask R-CNN enables pixel-wise segmentation, refining disease boundary identification. The proposed model is implemented using Python and TensorFlow with high-resolution image datasets. Performance evaluation demonstrates HAMNet-Mask R-CNN achieving a 99.35% Dice score, 98.80% IoU, 99.75% Precision, 99.78% Recall, and 99.65% Accuracy, outperforming U-Net (95.03% Dice, 90.53% IoU), SegNet (94.58% Dice, 89.73% IoU), DeepLabV3 (98.31% Dice, 96.69% IoU) and FCN(98.47% Dice score, 97.00% IoU). The results confirm the model's superiority in disease segmentation and classification, offering a robust and scalable solution for real-time disease monitoring in smart agriculture. The integration of hierarchical attention and spectral-spatial fusion significantly enhances detection accuracy, ensuring reliable disease identification and contributing to improved agricultural productivity.

Why it matches plant phenotyping methods真珠粟の病変領域を画像から画素単位で抽出・分類する深層学習手法を開発し、既存モデルと性能比較しているため、植物病害状態のフェノタイピング手法が中心である。

abstractthis study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection.

Field-based and close-range multispectral imaging dataset for Huanglongbing (HLB) detection in orange trees: A resource for machine learning and digital agriculture. →

This article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB). HLB, also known as citrus greening disease, is a major pathology caused by the bacterial pathogen Candidatus Liberibacter asiaticus , particularly in species of the citrus genus. The dataset is constituted of terrestrial images acquired in a commercial sweet orange orchard of the variety Pera Rio ( Citrus sinensis (L.) Osbeck). The images describe large portions of canopy, with healthy leaves and sections infected by HLB as well as some confounding factors naturally present in orchards. Multispectral images were acquired with a multi-lens camera within the visible-near-infrared domain, resulting in 14 narrow spectral bands. The image acquisition was conducted during two field campaigns in 2023 and 2024. In total, the dataset contains 2,978 images divided into two classes HLB (1,681) and non-HLB (1,297). Originally, data are stored in TIFF format as 14 monochromatic images, organised by spectra band. Additionally, an HDF5-format version is provided, where images are stored as 3D arrays with spectral bands in ascending order. This format is compatible with various programming languages, enables efficient data handling, and is optimised for machine learning and image processing applications, supporting reproducible and portable analysis. This dataset is a valuable resource for the development and benchmarking of classification models, including deep learning approaches, aimed at the detection of HLB. Phytopathology imaging datasets are scarce yet essential for advancing digital agriculture and the development of robust tools for crop disease detection worldwide.

Why it matches plant phenotyping methods柑橘葉・樹冠のマルチスペクトル画像からHLB感染状態を推定するデータセットであり、植物病害状態の表現型取得と機械学習ベンチマークを中心とする。

abstractThis article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB).

An uncertainty-aware evaluation framework based on hierarchical vision transformers for robust cross-domain plant leaf disease classification. →

Plant leaf disease detection is a critical task in precision agriculture, where reliable diagnosis under real-world conditions is essential for reducing crop losses and supporting timely intervention. Although deep learning models have achieved high classification accuracy, their performance often degrades under domain shift between controlled laboratory datasets and real-field environments, while predictive uncertainty and confidence calibration remain largely unaddressed.This study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification. The framework integrates multi-scale feature learning with Monte Carlo Dropout-based predictive uncertainty estimation and temperature-based calibration to systematically analyze model behavior in terms of accuracy, reliability, and robustness. Experiments were conducted on two complementary datasets: the New Plant Diseases Dataset (controlled conditions) and the PlantDoc dataset (field conditions), enabling bidirectional cross-domain evaluation. Results demonstrate that the proposed framework achieves superior performance, attaining 97.8% accuracy on controlled data and 93.6% on field data, while significantly improving calibration with lower Expected Calibration Error (ECE = 0.032 / 0.041), reduced Negative Log-Likelihood, and lower Brier score compared to baseline CNN and transformer models. Furthermore, the framework exhibits improved robustness under domain shift, with reduced performance degradation and stable uncertainty behavior. Overall, this study highlights the importance of integrating uncertainty estimation and calibration within a hierarchical transformer-based framework, providing a more reliable and deployment-ready solution for real-world agricultural disease diagnosis.

Why it matches plant phenotyping methods植物葉の病害状態を直接推定する不確実性-aware分類フレームワークの開発・評価が中心であり、異なる条件のデータセット間で精度、校正、頑健性を検証している。

abstractThis study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification.

A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification. →

Cotton production is highly vulnerable to foliar diseases and pest-induced damage, which significantly reduce yield and compromise fiber quality. Rapid, reliable, and automated disease identification is therefore essential for supporting sustainable crop management. In this study, we propose a hybrid deep learning framework integrating a ResNet50 backbone with Squeeze-and-Excitation (SE) channel attention modules to enhance discriminative feature representation for cotton leaf disease classification. The model is trained on a publicly available disease dataset comprising six classes and optimized using Weighted CrossEntropyLoss, Adam optimization, ReduceLROnPlateau scheduling, and Early Stopping to ensure stable convergence and robust generalization. Experimental results demonstrate outstanding performance, achieving 99.72% training accuracy and 99.31% validation accuracy, with convergence at the 14th epoch. Visualization through Grad-CAM reveals that the model focuses on biologically relevant symptom regions, thereby enhancing interpretability and supporting expert validation. Comparative analysis with state-of-the-art methods shows that the proposed model surpasses existing CNN, transfer learning, and hybrid architectures in both accuracy and model transparency. These results indicate that the proposed SE-ResNet50 framework offers a highly accurate, interpretable, and computationally efficient solution suitable for real-world cotton disease monitoring and precision agriculture applications.Clinical trial registrationThis study is not a clinical trial; therefore, clinical trial registration is not applicable.

Why it matches plant phenotyping methods綿花葉の病徴を画像から分類する深層学習フレームワーク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、植物フェノタイピング手法として適格です。

titleA hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.

KLASIFIKASI PENYAKIT DAUN PADI BERBASIS ANDROID MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN TRANSFER LEARNING MOBILENETV3 →

Early identification of Rice leaf diseases remains a challenge in agricultural practices, as detection is commonly performed through manual visual observation that is time-consuming and prone to misclassification. Diseases such as blast, Bacterial Leaf Blight, tungro, and Brown Spot often exhibit similar visual characteristics, particularly at early stages. To address this problem, an Android-based application was developed to classify Rice leaf diseases using a Convolutional Neural Network (CNN) with a transfer learning approach based on the MobileNetV3 architecture. The model was trained using a labeled Rice leaf image Dataset obtained from Hugging Face, with preprocessing and data augmentation applied to improve generalization performance. The trained model was deployed through Hugging Face Space using an API-based architecture, allowing image classification to be performed without heavy computational requirements on mobile devices. Experimental results demonstrate that the proposed model achieved an accuracy of approximately 90% on the testing Dataset, exceeding the predefined minimum target accuracy of 85%, with precision and recall values above 80% across all disease classes based on confusion matrix evaluation. These results indicate that the MobileNetV3-based transfer learning approach provides reliable classification performance with good computational efficiency, making it suitable for mobile-based Rice leaf disease detection applications.

Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類するCNN手法を開発・評価し、モバイル実装まで行っているため、植物病害フェノタイピング手法が中心です。

abstractan Android-based application was developed to classify Rice leaf diseases using a Convolutional Neural Network (CNN) with a transfer learning approach based on the MobileNetV3 architecture

Geospatial multi-scale GNN for urban food security in climate-stressed environments. →

The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.

Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。

abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.

Quantifying the reliability gap in cross-domain plant disease classification: benchmarking the limited efficacy of standard mitigation techniques under controlled-to-field shift →

Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.

Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。

abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.

CNN MODEL OPTIMIZATION USING MULTI-STAGE DATA AUGMENTATION FOR LOCAL PLANT LEAF DISEASE CLASSIFICATION →

Plant leaf diseases are a major factor in reducing agricultural productivity, particularly for local commodities that often lack adequate artificial intelligence-based disease detection systems. This study aims to optimize the performance of a Convolutional Neural Network (CNN) model using the Inception V3 architecture through the application of multi-stage data augmentation to improve the classification accuracy of local plant leaf diseases. The dataset used is PlantifyDR from Kaggle, which has limited data volume and visual variation, requiring an effective augmentation strategy to improve the model's generalization ability. The proposed multi-stage augmentation approach consists of three stages—geometric, photometric, and texture-noise augmentation—that systematically enrich the diversity of training images. Evaluation results show that the proposed model provides significant performance improvements compared to the baseline model. The Inception V3 model with multi-stage augmentation achieved an accuracy of 0.762, an F1-score of 0.727, and a perfect AUC (1.00) across all classes, while the baseline model only achieved an accuracy of 0.595 and an average AUC of 0.877. Accuracy, loss, ROC curve, and confusion matrix analyses confirmed that multi-stage augmentation reduced overfitting and enhanced the model's ability to differentiate disease symptoms across leaf types. Therefore, this study concludes that multi-stage data augmentation is an effective approach for optimizing deep learning models on small and complex datasets, while also providing a significant contribution to the development of more accurate and reliable AI-based plant disease detection systems.

Why it matches plant phenotyping methods葉画像から植物病害症状を分類するCNNと、性能改善のための多段階データ拡張を開発・評価しており、植物の病害状態を推定する画像ベースのフェノタイピング手法が中心です。

abstractThis study aims to optimize the performance of a Convolutional Neural Network (CNN) model using the Inception V3 architecture through the application of multi-stage data augmentation to improve the classification accuracy of local plant leaf diseases.

Optimizing CNN-Based Transfer Learning through Fine-Tuning and Adaptive Augmentation for Chili Plant Disease Detection →

Chili peppers (Capsicum annuum L.) are a strategic horticultural commodity in Indonesia, but their productivity is often hampered by pathogen infections that cause leaf diseases such as anthracnose, leaf spot, and yellow virus. Early detection by farmers is still dominated by subjective visual observation and prone to misdiagnosis due to the similarity of symptoms between diseases. Although Deep Learning technology through Convolutional Neural Networks (CNN) offers an automated solution, implementation in real-world conditions still faces significant challenges such as lighting variations, complex backgrounds, and limited local datasets. This often leads to a drastic decrease in model performance compared to testing in a controlled environment. To address these issues, this study proposes an optimization of the transfer learning strategy on the MobileNetV2 architecture by integrating progressive layer-wise fine-tuning and adaptive data augmentation techniques. The fine-tuning method is carried out gradually on the pre-trained model layers, while adaptive augmentation dynamically manipulates images based on environmental characteristics to improve model robustness. The results of this study, which include multi-class classification on cross-location image data, are projected to be able to boost the accuracy and generalization ability of the model in heterogeneous field conditions. Practically, this research provides a framework for a more precise and robust disease detection system to accelerate the implementation of precision agriculture in the future.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するCNN手法の改良が研究の中心であり、病害状態の表現型推定に該当する。転移学習のファインチューニングと適応的画像拡張、異なる圃場条件での頑健性評価を扱っている。

abstractThe results of this study, which include multi-class classification on cross-location image data, are projected to be able to boost the accuracy and generalization ability of the model in heterogeneous field conditions.

Hybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2 →

Abstract Plant diseases are a serious danger to the world’s food security, because they lower agricultural output and increase economic losses. Due to subjectivity, fluctuating lighting, and environmental unpredictability, traditional visual examination techniques are frequently incorrect. The Excess Green (ExG) vegetation index and pseudo-thermal representations produced from RGB pictures are two synthetically developed complementary representations that are integrated with RGB imagery in this study’s lightweight multimodal deep learning system to address these issues. Histogram shifting and pseudo-infrared color mapping are used in a reproducible picture alteration pipeline to create the pseudo-thermal modality, which allows for extra visual signals without the need for specific thermal sensors. In order to classify plant diseases while preserving computational efficiency, the suggested framework uses MobileNetV3-Small backbones to extract modality-specific characteristics. This is followed by feature-level fusion. The publicly accessible Ginger Leaf Dataset, which includes RGB pictures of ginger leaves in four different conditions—Damage-Pest, Dehydrated, Healthy, and Leaf-blight—was used for the experiments. For training, validation, and testing, the dataset was split using a stratified 70:15:15 split. Python-based preprocessing procedures were used to create the extra modalities (ExG and pseudo-thermal representations) from the original RGB images. The experimental results show that the combination of the representations with RGB images can enhance the classification performance compared with the unimodal RGB-based models. Ablation experiments are also conducted to examine the contributions of different modalities to the overall categorization accuracy. The experimental results show that plant disease recognition can be improved with the help of efficient computing by combining lightweight convolutional neural networks with computationally generated visual representations.

Why it matches plant phenotyping methodsRGB画像からExG・疑似熱画像を生成し、植物葉の病害状態を分類するマルチモーダル手法が研究の中心であり、アブレーション評価も実施している。

titleHybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2

A hybrid deep learning model with adaptive feature fusion for automated rice leaf disease detection and classification. →

Many countries greatly rely on agriculture as a means of livelihood and economic growth. Even the most industrialized countries need food, medicine, clothing, and shelter produced by crops. Rice is one of the most significant and widely grown crops worldwide. Nonetheless, the severely impacted crops in rice production are those of bacteria, fungi, and viruses, which decrease yield and quality. Manual disease detection is hectic, challenging, and, in most cases, inaccurate. Recent advances in deep learning and computer vision have demonstrated significant potential to improve the detection and classification of diseases. This study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases. This method consists of five key stages: image preprocessing, segmentation, augmentation, multi-feature extraction via adaptive fusion, and classification. There are five rice leaf diseases to discuss and recognize: Blight, brown spot, sheath blight, tungro, and leaf blast. The first step is global contrast enhancement, which improves image quality. After that, the segmentation is performed using Otsu's Thresholding to extract the leaf area. Then, the modified VGG16 and modified ResNet50 networks are used in parallel to extract features using a transfer-learning approach. The adaptive fusion technique combines these features to obtain a dominant, proper feature representation. Lastly, the classification is done using an adaptive fusion score technique. Experimental results show excellent performance, with class-wise Precision in the range of 95.5-100%, class-wise recall in the range of 97.4-100%, and overall test accuracy of 98.5%.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動検出・分類する深層学習ワークフローが研究の中心であり、葉領域抽出、特徴抽出、分類性能まで評価しているため、植物病害フェノタイピング手法に該当する。

abstractThis study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases.

AI-driven grape crop risk evaluation with automated leaf disease segmentation triggered by environmental susceptibility conditions →

Early disease diagnosis plays a key role in grape production for minimizing crop risk and maximizing yield. Downy Mildew, Powdery Mildew, and Bacterial Leaf Spot are some of the major diseases that threaten productivity and require timely and accurate diagnosis. This research introduces a new multi-model framework that integrates AI-based image segmentation triggered by Environmental Susceptibility Conditions to inform precision grape farming. The proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy, by understanding environment data to evaluate the risk of disease propagation. Major contributions of the study are the understanding of environmental conditions for context-aware disease propagation, an efficient ensemble segmentation method for accurate leaf disease segmentation and severity analysis, performed on a self-collected dataset from a grape farm in Nashik, Maharashtra, India. The system enables early warning and decision support mechanisms to promote sustainable disease management in grape cultivation, with potential implications for reducing unnecessary pesticide usage. Experimental results show the efficacy of the proposed method, with segmentation accuracy of 96.81% and precision of 99.09%, with a Dice score of 0.95 and a mean Intersection over Union (mIoU) of 0.91, demonstrating excellent robustness under noise conditions. Unlike existing studies either image or sensor-approaches, this work introduces the integration of image data and knowledge of environmental insights offers a scalable, reliable, and real-time disease monitoring solution aligned with the goals of smart and sustainable farming.

Why it matches plant phenotyping methodsブドウ葉の病斑領域を画像分割し、病害の重症度を推定する手法を開発・評価しており、植物の病害状態の取得が研究の中心です。

abstractThe proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy

Integrating deep learning and field validation into a decision support system for Northern Corn Leaf Blight management in maize →

Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management ( https://maize-nclb.streamlit.app/ ), integrating advanced deep-learning for automated diagnosis and fungicide advisory. Among thirteen Machine-learning and deep-learning models evaluated for classification, the Visual Geometry Group 16-layer convolutional neural network (VGG16) outperformed all others, achieving 94.0% accuracy, with balanced precision, recall, and F1-score of 0.94, and an AUC-ROC of 0.93. Confusion matrix analysis revealed minimal misclassification, with only 12 errors out of 357 samples, confirming the model's high reliability in distinguishing healthy and infected plants, while Grad-CAM visualizations consistently highlighted biologically meaningful lesion regions, supporting the model's interpretability and alignment with plant pathological symptoms. Field validation of DSS-guided fungicide recommendations (Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC) demonstrated significant benefits, reducing disease incidence to 6.8% compared with 67.4% in controls, achieving 90% disease reduction, and enhancing grain yield by 35.4% (8.55 t/ha), with a favorable cost-benefit ratio of 1:2.49. Seasonal disease progression analysis further confirmed DSS effectiveness, with cumulative disease burden reduced by approximately 85% compared with untreated control. These results highlight the potential of integrating deep-learning with field-validated management strategies into a practical DSS, demonstrating its potential for precision disease management in maize.

Why it matches plant phenotyping methods葉の病斑を画像から分類・可視化する深層学習法を開発し、野外で検証した研究であり、植物病害状態のフェノタイピング手法が中心です。

abstractintegrating advanced deep-learning for automated diagnosis and fungicide advisory

Optimized CNN-based ensemble deep learning approach for potato leaf disease detection with data augmentation. →

This paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2. The dataset, sourced from Kaggle's Plant Village repository, includes 152 images of healthy potato leaves and 1000 images each of early and late blight. The methodology covers data preparation, model architecture design, training, evaluation, and deployment. During data preparation, the data set was split into training sets (80%) and testing sets (20%), with images resized to 128x128 pixels. The Deep Learning (DL) models built using CNN with 4 different optimizers (ADAM, SGD, RMSPROP, and ADAMAX) and trained using a sparse categorical cross-entropy loss function, include multiple convolutional and pooling layers for feature extraction, and fully connected layers for classification. Early stopping was used to prevent overfitting. Model performance was assessed using accuracy, loss curves, confusion matrix, ROC curve, precision recall curve, classification report, and F1 score. In addition, we have used data augmentation to balance the dataset by increasing healthy potato leaves 6 times and the use of Ensemble Deep Learning (EDL). EDL10 which contains DL1 (CNN + ADAM), DL2 (CNN + SGD), DL3 (CNN + RMSPROP) and DL4 (CNN + ADAMX) performs best with a accuracy score of 97.0%. This highlights the importance of data balancing and the use of the ensemble classification approach for the detection of blight in Potato Leaves.

Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類するCNN・アンサンブル手法の設計、評価、データ拡張が研究の中心であり、植物病害フェノタイピング手法に該当する。

abstractThis paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2.

LeafyVGG-16: Transfer Learning for Plant Disease Detection with Cyber Risk Analysis →

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.

Enhancing crop disease recognition framework via vision-language model with cross-attention and gated fusion. →

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate detection of such diseases is crucial for improving both crop yield and quality. While numerous deep learning approaches rely solely on image data for disease identification, they often overlook the complementary value of textual information in enhancing visual analysis. To address this limitation and effectively fuse features from different modalities, we propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition. Our approach utilizes the Zhipu.ai multi-modal model to generate comprehensive textual descriptions of diseased crop leaves, including global description, local lesion description, and color-texture description. These textual descriptions are then encoded into feature embeddings, while visual features are extracted using the ShuffleNet-v2 model as the image encoder. Subsequently, a cross-attention module aligns and fuses the two modalities, and a gated fusion module enables dynamic feature selection during the fusion process. Extensive evaluations on the Soybean Disease and PlantVillage datasets demonstrate that our method outperforms existing image-based models in terms of accuracy. Specifically, our model achieves recognition accuracies of 99.04% and 99.12% on the respective datasets, surpassing the ShuffleNet-V2 model by 1.09% and 2.53%, respectively. These results highlight the effectiveness of Cross-Model learning in integrating visual and textual cues for accurate and efficient disease recognition, offering a scalable solution for crop disease diagnosis.

Why it matches plant phenotyping methods植物葉の病徴を画像と言語情報から認識する融合フレームワークを開発し、複数データセットで既存手法と比較評価しているため、植物フェノタイピング手法が中心である。

abstractwe propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition.

Easy to use and low cost leaf disease quantification workflow using Ilastik →

Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.

Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。

abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously

Adaptive fuzzy deep learning with multimodal sensor fusion for enhanced plant disease detection →

Abstract Timely and accurate plant disease detection is important for enhancing agricultural productivity and promoting sustainability. The study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases. The proposed method combines fuzzy logic with multimodal data fusion to effectively address the complex interactions and uncertainties in agricultural datasets. The MAF-DNN employs a robust adaptive fuzzy framework with dynamic rule optimization and integrates Hyperspectral Imaging Data (HID) with RGB imaging data to acquire detailed spectral information and high-resolution visual cues for disease classification. The multimodal fusion enhances the model’s ability to capture intricate patterns that relate to plant health, improving the accuracy of disease classification. The experimental results showed that the MAF-DNN outperforms traditional models by achieving an accuracy of 97.8%, precision of 96.5%, recall of 98.2%, and F1-score of 97.3%. Additionally, the adaptive design reduces computational overhead, increases efficiency, and improves scalability for large-scale agricultural applications. The MAF-DNN represents a significant advancement in plant disease classification and provides a robust and efficient solution for precision agriculture.

Why it matches plant phenotyping methods植物病徴を画像から分類するマルチモーダル画像・深層学習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ため。

abstractThe study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases.

A curated dataset and lightweight deep learning framework for tea leaf disease classification. →

Tea (Camellia sinensis) is the world's second most consumed beverage, enjoyed daily by more than two billion people. In Bangladesh, it serves as a cornerstone agricultural export and a major sector of the domestic economy. However, commercial tea cultivation remains highly vulnerable to fungal and pest-related diseases such as Blight, Red Rust, and Helopeltis which severely reduce crop yield and compromise leaf quality. While early detection is critical to preventing widespread outbreaks, traditional manual inspection is slow, subjective, and highly error-prone. Deep learning provides a scalable alternative, yet single-branch networks often struggle to capture both minute disease lesions and broader structural degradation simultaneously. To address this, we propose a Hybrid Feature Fusion architecture that runs two highly efficient feature extractors in parallel: EfficientNetV2-Small to isolate fine-grained local textures, and MobileNetV3-Small to capture the global structural context of the leaf. The models were trained and evaluated on a real-world dataset of 2,000 annotated images, evenly distributed across the four target classes (Blight, Red Rust, Helopeltis, and Healthy). Before training, the images underwent a standardized preprocessing pipeline including resizing to 224 × 224 pixels and normalization, supplemented by a dynamic augmentation strategy featuring random rotations, horizontal flips, and brightness adjustments to improve model robustness. The proposed hybrid framework achieved an outstanding peak classification accuracy of 96.80% alongside a macro Area Under the Curve (AUC) of 0.9980. To rigorously validate its performance, the hybrid model was benchmarked against six diverse architectures: a Vision Transformer (ViT-B16 at 76.40%), a Custom CNN (89.60%), MobileNetV3 (94.40%), ResNet50 (95.60%), DenseNet121 (96.40%), and EfficientNetV2-B3 (97.60%). Although EfficientNetV2-B3 achieved a marginally higher raw accuracy, the proposed dual-branch framework delivered a superior precision-recall balance and faster convergence stability. These findings demonstrate that the proposed hybrid methodology is highly reliable and computationally balanced, making it an ideal candidate for integration into Internet of Things (IoT) edge devices for real-time disease monitoring in precision agriculture.

Why it matches plant phenotyping methods茶葉の病徴を画像から分類する深層学習手法の開発と、注釈付きデータセットおよび複数モデルとのベンチマーク検証が中心であり、植物病害状態の表現型推定に該当する。

abstractwe propose a Hybrid Feature Fusion architecture

ConvGeM-next: a deep learning framework for plant disease detection →

Introduction Plant diseases pose a major challenge to sustainable agriculture, particularly in regions that heavily depend on farming. Early and accurate identification of plant diseases is crucial for ensuring food production and minimizing crop losses. The rapid advancement of deep learning, particularly in convolutional neural networks (CNNs), has significantly enhanced plant disease classification performance. However, many models often struggle to generalize effectively in real-world scenarios due to challenges such as low-intensity visuals, low contrast between the background and foreground of the suspected sample, noise, and chrominance variation. Methods To address the challenges mentioned above, we introduce ConvGem-NeXt, an end-to-end deep learning architecture specifically designed for fine-grained plant disease classification, built on the ConvNeXt baseline model featuring enhanced generalization capabilities. More precisely, our method incorporates a learnable Generalized Mean pooling layer and ReLU activation in the ConvNeXt model to enhance spatial feature representation, and a custom classifier head that integrates batch normalization, ReLU activation, and dropout to mitigate overfitting and improve classification accuracy. Results We tested the presented model on two large-scale and diverse databases, PlantVillage and the PlantDoc. The model achieved 99.65% accuracy on the PlantVillage dataset and 94.69% accuracy on the real-world PlantDoc dataset, demonstrating the efficacy of our method for reliably classifying plant diseases. Discussion This work contributes to the rapidly growing field of agricultural automation by providing a reliable framework for timely disease diagnosis and supporting the enhancement of crop productivity.

Why it matches plant phenotyping methods植物病害を画像から分類する深層学習アーキテクチャを開発し、複数データセットで性能検証しており、植物の病害状態の取得・推定が研究の中心である。

abstractwe introduce ConvGem-NeXt, an end-to-end deep learning architecture specifically designed for fine-grained plant disease classification

GBR-DETR: A Real-Time Tomato Leaf Disease Detection Model for Edge Device Deployment. →

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.

High-throughput screening of heat stress response in Chinese cabbage (Brassica rapa L. ssp. pekinensis) seedlings using integrated 3D multispectral phenotyping and time-series analysis →

Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.

Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。

abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.

Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures. →

Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.

Why it matches plant phenotyping methodsイネ葉の病徴領域を画像から分類・局在化する深層学習ワークフローが研究の中心であり、GrabCut・輪郭検出と複数モデルの比較評価を含むため、植物病害状態の画像ベース表現型計測として採用。

abstractA deep learning approach is used as the core method to identify diseases in rice leaves.

RGB image-based drought stress classification of garden plants using SVM model. →

Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.

Why it matches plant phenotyping methodsRGB画像指標と機械学習により、植物の干ばつ応答パターンという生理状態を分類し、蛍光測定との再現性を評価しているため、表現型取得・抽出手法が中心です。

abstractThis study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns.

The Significance of Hybrid CNN and ANN Model in Design and Implementation of Deep Learning Model for Plant Disease Detection →

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).

Multi-FusNet-convolutional neural network with improved Huber loss function for plant leaf disease detection and classification. →

Background Recently, plant disease detection and classification have become major concerns in agriculture. Early detection of plant diseases supports farmers to take precautionary actions to prevent the spread of infections across different parts of the plant. However, detecting and classifying plant leaf diseases remain challenging tasks due to the overlapping characteristics of different diseases. Methods To mitigate these limitations, this research developed a Multi-FusNet–convolutional neural network (Multi-FusNet–CNN) with an improved Huber loss function to classify multiple classes of plant leaf diseases. Here, a multipath residual network (Multi-RG) with cross-filtering fusion is integrated, and the pixel shuffling fusion method is developed for fusing low-level to up-sampled features. An improved Huber loss function is incorporated into the Multi-FusNet–CNN to effectively handle outliers and enhance the model’s generalization capability during training. Results The developed Multi-FusNet–CNN with improved Huber loss function achieved 99.95% accuracy, 99.13% F1-score, 99.87% recall, 99.27% precision, and 99.93% specificity, thereby outperforming existing conventional techniques. Conclusion The proposed Multi-FusNet–CNN model improved the generalization capability of the method during the training process on plant leaf disease detection and classification.

Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類するCNN手法の開発と性能評価が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。

abstractthis research developed a Multi-FusNet–convolutional neural network (Multi-FusNet–CNN) with an improved Huber loss function to classify multiple classes of plant leaf diseases.

Research on Apple Surface Disease Detection Method Based on Improved YOLOv11s. →

Apple surface diseases are crucial factors affecting the quality and yield of apples. Traditional manual inspection methods suffer from low efficiency and poor real-time performance. To address these issues, this paper proposes an apple surface disease detection method based on an improved YOLOv11s. Firstly, three groups of GAM attention mechanisms are integrated into the neck structure of the YOLOv11s to enhance the efficiency of feature fusion and the capability of semantic information transmission. Secondly, the original convolutional downsampling in the backbone network is replaced with a Haar-based feature downsampling module, enabling the model to retain more high-frequency detail information during the downsampling process. In addition, the WFU module is introduced to realize the dynamic allocation of feature weights, enhancing the model's ability to recognize multi-scale defect features. Finally, the PIOUv2 loss function is adopted to optimize bounding box regression, improving the model's detection performance for tiny defect spots. In addition, various data augmentation methods for small datasets are employed to improve the model training performance and effectively avoid the problem of data overfitting. The experimental results demonstrate that the F1-score of the proposed model is increased by 4.2%, and the mAP@50:95 is boosted by 2.4%. The detection performance outperforms various comparative models, which verifies the effectiveness and superiority of the proposed method.

Why it matches plant phenotyping methodsリンゴ表面の病斑・欠陥を画像から検出する改良YOLO手法の開発と比較検証が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

abstractthis paper proposes an apple surface disease detection method based on an improved YOLOv11s.

LDDHybridNet: an ROI-aware CNN-LSTM hybrid framework for accurate and early leaf disease detection in precision agriculture. →

Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food security. While much has been done recently, many deep learning-based approaches will still fail in real-world tests because of challenges such as background clutter, differences in illumination, occlusion, or the fact that visual symptoms for these diseases can be very subtle early on. Traditional CNN- and Transformer-based architectures generally lack accurate lesion localisation and interpretability, hindering their practical deployment in agricultural decision-support tools. To address these issues, we present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage. It then applies preprocessing steps guided by ROI, based on leaf segmentation from the U-Net, followed by a compact CNN-based spatial feature-extraction framework. We arrange spatial feature embeddings extracted from lesion regions into an ordered sequence and employ a Bi-LSTM with attention to model structured contextual dependencies, allowing progression-aware feature learning without requiring actual temporal image sequences. Lastly, Grad-CAM-based post-hoc explainability is employed to interpret model decisions, enabling transparent visualisation of disease-relevant regions. We conduct extensive experiments on the PlantVillage benchmark and the FieldPlant dataset and show that LDDHybridNet consistently outperforms representative CNN, transformer, and hybrid baselines across multiple evaluation metrics. Although the near-ceiling performance on PlantVillage reveals the dataset's artificial nature, the proposed framework achieves 95.37% accuracy under real-world field conditions and 92.84% on weak-lesion early-stage samples, demonstrating the method's robustness and early-stage detection potential. The performance boosts are statistically significant (P < 0.01). In general, LDDHybridNet is an interpretable and robust deep learning framework for leaf disease detection, which can support data-driven crop protection and precision agriculture applications.

Why it matches plant phenotyping methods葉の病害症状を画像から検出・局在化する深層学習手法の開発とベンチマーク評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。

abstractwe present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage.

Quantifying Growth and Lodging in Tef ( Eragrostis tef ) With Uncrewed Aerial Systems (UAS) →

ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.

Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。

abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.

AI-driven multi-agent framework for smart irrigation and crop health monitoring in Indian rice and sugarcane farming →

Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.

Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。

abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images

Comparative deep learning approaches for bean leaf disease recognition. →

Context Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification. Techniques Three deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes-angular leaf spot, bean rust, and healthy leaves-was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance. Outcomes ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis. In conclusion The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.

Why it matches plant phenotyping methods豆葉の病徴を画像から認識する深層学習手法を比較・ベンチマークしており、植物病害状態の取得手法が研究の中心である。

abstractThree deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification.

AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection. →

Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional methods of diagnosis are time-consuming, error-prone, and inefficient. With the rapid development of AI, deep learning (DL), and IoT, there is increasing demand for combined solutions that jointly address plant health monitoring and harvest optimization in a reproducible and deployment-oriented manner. This study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages to optimize yield quality and minimize agricultural losses. The work explicitly targets improved classification reliability, broader class evaluation, rigorous validation and generation of decision-ready outputs for precision agriculture. AgroDualNet comprises two modules. The crop-disease prediction module integrates ResNet50 with a Convolutional Block Attention Module (CBAM), and a Sequential Minimal Optimization (SMO)-based SVM classifier to enhance feature learning and classification performance Several different architectural designs are benchmarked and the resultant model is tested on both a dedicated 3-class subset and a large multi-class model of the PlantVillage dataset with leakage safe protocol(augmentation applied only on training data), cross-validation, statistical significance testing as well as ablation. The fruit-ripeness module employs YOLOv8 for real-time fruit localization and MobileNetV2 for lightweight ripeness classification suitable for edge deployment and a prototype decision-support layer maps predictions to actionable recommendations. That is able to run on the edge. Experiments show that the hybrid CBAM + ResNet50 + SMO model achieves 99.6% accuracy for crop disease classification on a three-class configuration of the PlantVillage dataset and maintains consistently higher accuracy than strong baseline in a 38-class setting, with statistically significant results confirmed by McNemar's test (p < 0.001) outperforming baseline and intermediate architectures in accuracy, precision, Recall and F1-Score The fruit ripeness pipeline achieves 98.88% classification accuracy across four ripeness stages (unripe, semi-ripe, ripe, over-ripe) on a combined Kaggle and real-field apple dataset with low inference time, confirming its suitability for near real-time deployment on edge devices. Cross-validation, Statistical significance tests and ablation studies collectively validate the robustness and significance of these gains and the decision-support layer demonstrates the feasibility of converting raw predictions into interpretable, recommendation-oriented outputs. AgroDualNet provides an efficient and unified system for monitoring plant diseases and evaluating fruit ripeness, with statically validated performance across both focused and full multi-class settings, addressing two critical challenges in precision agriculture with a single extensible framework. The dual-module design of AgroDualNet, which combines disease prediction with ripeness analysis and a preliminary decision-support prototype offers a more comprehensive and practically relevant AI-driven monitoring solution than conventional single-task models. By emphasizing multi-class validation on PlantVillage, leakage-aware experimentation, statistical verification, and system-level integration, this works supports real-time, precise and automated guidance to reduce crop losses, improve harvest timing, and enable smarter farm-level decision making.

Why it matches plant phenotyping methods植物病害状態と果実成熟度を画像から推定する深層学習パイプラインの開発・比較検証が中心であり、PlantVillageおよび実圃場データで交差検証、アブレーション、統計検定を実施しているため、植物フェノタイピング手法として含める。

abstractThis study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages

CADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification. →

Plant diseases threaten global agriculture, and deep learning-based disease recognition has become crucial for addressing this challenge. While DenseNet excels in plant disease classification due to its dense connectivity, its large size limits deployment on resource-constrained edge devices. This paper proposes Connection-Aware DenseNet Pruning (CADP), achieving efficient compression through three collaborative modules. First, the EdgePrune module explicitly models inter-channel feature flows via an edge weight network, using dual-channel importance scoring that fuses activation correlation and gradient information to remove redundant connections while preserving critical propagation paths. Second, connection-guided CP decomposition leverages EdgePrune's importance information, adaptively assigning differentiated ranks through the Connection Importance Index (CII) to balance preservation of critical layers with deep compression of secondary layers. Third, dual-stream knowledge distillation integrates throughout post-pruning and post-decomposition fine-tuning, combining output-level soft labels and intermediate spatial attention transfer to recover compression losses. CADP achieves 88% parameter reduction and 89% computational savings on DenseNet-121, maintaining 99.67% and 99.66% accuracy on PlantVillage and RiceLeaf datasets, achieving competitive accuracy with significantly fewer parameters. This provides a promising approach for resource-constrained deployment with potential generalizability and practical value.

Why it matches plant phenotyping methods植物画像から病害状態を推定する分類モデルの軽量化手法を開発し、PlantVillageおよびRiceLeafで性能を評価しているため、病害フェノタイピング手法が中心である。

titleCADP: Connection-Aware DenseNet Pruning for lightweight plant disease classification.

Rose leaf disease classification and severity estimation using an interpretable vision transformer-based multi-task framework. →

This study proposes RoViT-KAN, a multi-task deep learning framework for plant disease classification, severity estimation, and uncertainty quantification. The architecture integrates a DeiT-Tiny Vision Transformer backbone with task-specific heads for disease classification, ordinal severity prediction, and heteroscedastic uncertainty estimation. To enhance interpretability, a Kolmogorov–Arnold Network (KAN) module is introduced to model continuous disease severity through learnable spline-based transformations. A four-stage curriculum learning strategy is employed to stabilize multi-task optimization by progressively activating prediction objectives. The model is evaluated on a rose leaf disease dataset comprising 3,113 original images and 10,000 augmented samples across four classes: healthy leaf, leaf holes, black spot disease, and dry leaf condition. Experimental results demonstrate a classification accuracy of 99.70%, with calibrated uncertainty estimates (Brier score = 0.0914) and reliable severity prediction. Ablation studies validate the contribution of each architectural component. The model highlights the potential of combining transformer-based architectures with uncertainty-aware learning and interpretable neural representations for robust plant disease analysis.

Why it matches plant phenotyping methodsバラ葉の画像から病害分類と病害重症度を推定する手法を開発・評価しており、植物の病態を対象とした画像ベース表現型解析が研究の中心である。

abstractThis study proposes RoViT-KAN, a multi-task deep learning framework for plant disease classification, severity estimation, and uncertainty quantification.

Explainable deep learning-based comparative study for guava fruit and leaf disease classification: advancing agricultural diagnostics through AI. →

Introduction Early detection of plant diseases is essential for maintaining crop health and ensuring sustainable agricultural productivity. Guava fruit and leaf diseases, if not identified at an early stage, can lead to significant yield losses. Recent advances in deep learning offer promising solutions; however, challenges remain in achieving both high accuracy and model interpretability for practical agricultural deployment. Methods This study proposes an explainable deep learning-based framework for the classification of guava fruit and leaf diseases. A real-world dataset consisting of 527 annotated images across five classes-Disease Free, Phytophthora, Red Rust, Scab, and Styler and Root Rot-was utilized. Six hybrid model architectures were developed by integrating transfer learning backbones (VGG16, MobileNetV2, InceptionV3, and ResNet50) with custom convolutional neural network (CNN) classifiers. Model performance was evaluated using accuracy, precision, recall, F1-score, and class-wise metrics. To enhance transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize disease-relevant regions. Results Among all evaluated models, the proposed VGG16 + MobileNetV2 hybrid architecture achieved the best performance, attaining an accuracy of 96%, an F1-score of 0.96, and strong generalization across all disease classes. Comparative analyses using confusion matrices, ROC-AUC curves, precision-recall curves, and radar plots confirmed the superior and consistent performance of the proposed model over other hybrid configurations. Discussion The results demonstrate that combining deep feature extractors with lightweight architectures enhances both classification accuracy and computational efficiency. The integration of Grad-CAM provides meaningful visual explanations, increasing trust and interpretability in AI-assisted disease diagnosis. This framework shows strong potential for deployment in real-time smart farming systems and mobile-based diagnostic applications, particularly in resource-constrained agricultural environments.

Why it matches plant phenotyping methodsグアバの葉・果実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、複数モデルの比較評価とGrad-CAMによる説明可能性検証も行っているため、植物フェノタイピング方法論として採用する。

abstractThis study proposes an explainable deep learning-based framework for the classification of guava fruit and leaf diseases.

PlantaNet and PlantaNetLite: Efficient and explainable multi-crop plant disease classification via transformer benchmarking and custom lightweight CNNs. →

Plant disease diagnosis based on visual symptoms is crucial for preventing yield loss; however, deployment in practical settings remains challenging due to inter-class similarity, background noise, and limited computational resources. This study presents a plant disease classification framework evaluated on a curated multi-crop dataset aggregated from multiple publicly available repositories, comprising 51 disease and healthy classes. The dataset includes approximately 45,000 original images that were expanded through controlled augmentation during training to improve generalization. We benchmark eight ImageNet-pretrained tiny vision transformer architectures trained for up to 50 epochs. Among these, CAFormer-s18 achieved strong validation performance but with increased computational overhead. To enable efficient and computationally lightweight solutions, we design two fully customized convolutional neural networks: PlantaNetLite (1.28M parameters) and PlantaNet (2.58M parameters). After hyperparameter optimization and full 100-epoch training, PlantaNet achieved 99.37% validation accuracy and 99.66% test accuracy with a compact model size (9.85 MB) and moderate computational cost, while PlantaNetLite achieved a best validation accuracy of 99.22% under further parameter reduction. Qualitative Grad-CAM and Grad-CAM++ analyses provide insight into the regions influencing model predictions. Overall, the proposed models demonstrate competitive accuracy while maintaining computational efficiency, highlighting their potential suitability for resource-constrained deployment scenarios.

Why it matches plant phenotyping methods植物の視覚症状から病害状態を推定する画像ベースの表現型解析手法を開発・比較し、データセット上で性能評価しているため、方法が中心的である。

abstractThis study presents a plant disease classification framework evaluated on a curated multi-crop dataset aggregated from multiple publicly available repositories

AI-Powered Yield Prediction, Bacterial Blight and Crop Health Classification in Common Bean (Phaseolus vulgaris L.) Using Drone RGB and Multispectral Imaging →

Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.

Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。

abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.

Optimized convolutional neural network using bacterial colony optimization for plant leaf disease detection →

The currently growing effects that plant diseases have on global agriculture require the creation of highly intelligent and accurate detection systems. Convolutional Neural Networks (CNNs), as deep learning, have proved effective in the detection of plant diseases based on images. The CNN performance, however, is very sensitive to hyperparameter tuning, which usually requires manual, sub-optimal tuning. The study suggests a new method to detect plant diseases with an optimized CNN architecture optimized by the Bacterial Colony Optimization (BCO). The BCO algorithm replicates the adaptive foraging behavior of bacterial colonies to automatically determine optimal CNN hyperparameters, such as the number of filters, kernel size, pooling methods, learning rate, and dropout probability of the CNN. The experiment conducted on the PlantVillage dataset demonstrated that the proposed BCO-CNN achieved an accuracy of 96.68% with a false alarm rate (FAR) of 4.05% outperforming other heuristic-fitted models such as particle swarm optimization (PSO) - CNN, CNN with support vector machine (SVM), and CNN, VGG16 in accuracy, precision, recall, and F1-score. The given work offers an automated, scalable approach to the accurate, early detection of the diseases of the plant, contributing to better yields of crops and sustainable agriculture.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNNの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研究の中心である。

abstractThe study suggests a new method to detect plant diseases with an optimized CNN architecture optimized by the Bacterial Colony Optimization (BCO).

Corn or maize leaf disease classification based on CNN and vision mamba model. →

Accurate classification of corn leaf diseases is critical for timely detection and control of pests and diseases. By accurately recognizing different types of leaf diseases, farmers and agricultural experts can quickly take targeted control measures to reduce crop losses and safeguard corn yield and quality. Since these corn leaf disease images usually contain complex backgrounds, similar lesion features, and limited labeling data, it causes traditional convolutional neural networks (CNNs) to easily confuse the lesion region with the background, making it difficult to distinguish between different disease types. To address these limitations, we propose G-ResNet, a hybrid CNN-Vision Mamba network that enhances disease-relevant feature learning through a hierarchical feature attention module and a scale feature attention module. It was demonstrated experimentally that G-ResNet can better classify maize leaf disease images. The code is available at https://github.com/gustafmy/g_resnet.git.

Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から分類するCNN・Vision Mamba手法の開発と実験評価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。

titleCorn or maize leaf disease classification based on CNN and vision mamba model.

Digital morphological data can generate accurate pre-emergence herbicide dose-response curves in Chenopodium album L. →

Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.

Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。

abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters

Autonomous Embedded-Vision System for Multistage Detection of Phytopathogenic Fungi in Potato and Tomato Crops UsingConvolutional Neural Networks →

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

Deep learning techniques for early detection and classification of leaf diseases in crops. →

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.

Deep learning-based identification of visually similar foliar diseases in field-grown barley. →

Background Accurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping, as breeding programs rely on reliable severity estimates to identify genotypes with improved resistance. However, most deep learning approaches have been developed as pathogen-specific models, which limits scalability in field-grown barley where multiple diseases naturally co-occur and exhibit substantial visual similarity. Results We evaluated whether a multiclass segmentation model can simultaneously detect and distinguish two fungal diseases of barley, Puccinia hordei and Ramularia collo-cygni, and compared its performance with two disease-specific binary models. Using 336 high-resolution leaf scans collected in the field with naturally occurring co-infections, the multiclass model achieved higher Dice scores for brown rust (0.59 vs 0.40; +47.5% relative improvement) and ramularia (0.60 vs 0.53; +13.2% relative improvement). It also captured a greater proportion of individual lesions across both classes. At the genotype level, the model-predicted disease area percentages were highly consistent with those from ground truth annotations ([Formula: see text]). Conclusions A unified multiclass framework can more effectively segment visually similar foliar diseases than separate binary models, while simplifying the computational workflow. This provides a scalable basis for automated resistance assessment within breeding pipelines. Code and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.

Why it matches plant phenotyping methods圃場画像から葉面病害面積をセグメンテーションし、遺伝子型レベルの病害重症度を推定する手法を開発・比較・検証しており、植物フェノタイピングが中心です。

abstractAccurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping

Random Rolling Attention Augmentation for Efficient Agricultural Disease and Pest Detection →

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.

Frost damage segmentation in grapevine organs using YOLOv11s with ASPP and dynamic confidence thresholding. →

Climate change, particularly increasing frequency and intensity of spring frost events, poses a serious threat to viticulture by reducing yield and product quality. This study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP). A unique dataset called FGVL dataset from Sultana seedless grape vineyards in Manisa, Türkiye, following a severe frost event in April 2025. FGVL includes 418 frost-damaged grapes, 510 frost-damaged leaves, 395 healthy grapes, and 698 healthy leaves, all manually annotated by experts under natural field conditions. By integrating ASPP into YOLOv11s, proposed model improved multi-scale contextual feature extraction and achieved mAP@50 of 0.7686, demonstrating stronger performance in instance segmentation of small, overlapping, and visually similar grapevine organs. In addition, Dynamic Confidence Thresholding (DCT) strategy was introduced to improve prediction reliability in dense and visually complex vineyard scenes. Despite challenges such as background clutter, object overlap, and small target structures, model maintained stable performance with low computational demand, requiring only 6.45 GB of GPU memory. Proposed framework offers an accurate, efficient, and practically deployable early recognition system for frost damage assessment in viticulture.

Why it matches plant phenotyping methodsブドウの器官における霜害状態を画像からセグメンテーションする手法を開発・評価しており、植物の病害・障害状態の取得が研究の中心である。

abstractThis study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP).

ReMA: a residual gated multi-head attention module for MobileViT in sugarcane diseases and disease recognition. →

Purpose/significance Sugarcane is a vital global crop, critical for sugar and energy production. The accurate and timely identification of its leaf diseases is paramount for sustaining the health and stability of the sugarcane industry. While deep learning models offer promising solutions, their deployment on mobile or edge devices is often hindered by substantial model size and high computational demands. Conversely, existing lightweight models frequently compromise on feature extraction capabilities and recognition accuracy. To bridge this gap, this study develops an architecturally improved lightweight model designed to achieve both high accuracy and computational efficiency. Methods We propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module. This module ingeniously leverages a multi-head attention mechanism to capture richer contextual information from diverse subspaces, while its residual connection structure effectively mitigates network degradation and facilitates robust gradient flow. The proposed model underwent rigorous training and evaluation on a comprehensive Mendeley Data repository for classification tasks. Results Experimental evaluations demonstrate that the ReMA-MobileViT model achieves an outstanding classification accuracy of 99.02% on the sugarcane leaf disease dataset, substantially surpassing existing state-of-the-art methods. An ablation study confirms the module's efficacy, showing that the ReMA-MobileViT model, integrated with the ReMA module, improved accuracy, recall, and F1-Score by 1.58, 1.76, and 1.58 percentage points, respectively, over the baseline MobileViT. Comparative analyses further illustrate ReMA-MobileViT's superior overall performance; it exceeds classic lightweight MobileNetV2 by 15.77 percentage points and the mainstream Vision Transformer by 2.96 percentage points in accuracy. Critically, ReMA-MobileViT achieves this with significantly fewer model parameters and reduced computational complexity compared to Vision Transformer, establishing a superior balance between accuracy and efficiency. Conclusion The proposed ReMA-MobileViT model offers an effective and lightweight solution for improving sugarcane leaf disease recognition accuracy, particularly in challenging complex backgrounds. Its ability to balance high accuracy with computational efficiency presents a promising technical avenue and a deployable solution for high-precision crop disease diagnosis systems on resource-constrained mobile or edge platforms.

Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から認識する軽量深層学習モデルを開発し、精度・計算量・アブレーションを評価しており、植物表現型取得・判定手法が中心である。

abstractWe propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module.

AF-RT-DETR: Adaptive cross-scale feature interaction for real-time plant disease detection in complex field environments →

Introduction Accurate plant disease identification is of great importance for ensuring agricultural productivity and food security. However, complex illumination variations, leaf occlusion, and diverse disease spot scales throughout plant growth stages significantly increase the difficulty of real-time detection, leading to limited accuracy and robustness in existing approaches. Methods To address these challenges, we propose an improved RT-DETRv2-based plant disease detection model, termed AF-RT-DETR. A Bidirectional Cross Gate (BCG) module is introduced in the feature extraction stage to reduce channel redundancy and enhance discriminative feature representation through multi-level feature interactions. The original RepVGG structure is replaced with a Dynamic Channel Shift (DCS) module, effectively enlarging the receptive field and strengthening contextual feature fusion without additional computational overhead. Additionally, an improved Scale-aware Multi-level Loss (SML) emphasizes low-quality feature maps to improve detector robustness. Results The model achieves mAP50 and mAP50:95 of 93.6% and 67.2% on the Plant-Disease dataset, surpassing the baseline by 5.1% and 4.5%. Furthermore, the model was evaluated on multiple crops and growth stages under diverse field conditions, demonstrating robust performance and adaptability. Discussion These results indicate that AF-RT-DETR effectively enables real-time plant disease detection in complex field environments.

Why it matches plant phenotyping methods植物の病徴を画像から検出するモデルの開発と、複数作物・生育段階・圃場条件での性能評価が中心であり、植物病害状態の表現型計測手法に該当する。

abstractwe propose an improved RT-DETRv2-based plant disease detection model, termed AF-RT-DETR.

Country-wide, high-resolution monitoring of forest browning with Sentinel-2 →

Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.

Why it matches plant phenotyping methodsSentinel-2 NDVIを用いて森林キャノピーの季節変動から褐変・攪乱状態を推定する手法を開発し、適合度と独立参照データで検証しているため、単なる森林地図作成ではなく植物状態の取得・評価が中心である。

abstractwe present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2.

AdjLeafGNN: a hybrid deep learning and graph neural network framework for probabilistic modeling of adjacent leaf disease spread in precision agriculture. →

Proper detection and treatment of plant leaf diseases are essential factors for achieving good crop yields and ensuring food security. Convolutional Neural Networks (CNNs) have shown significant potential for classifying diseases from leaf images. Instead, most current work focuses on image-level prediction and ignores the relationship between infected leaves. This limitation somewhat constrains their use in modelling disease spread. Also, it makes them less efficient in typical field situations where disease is transmitted from plant to plant by physical contact. Moreover, existing CNN architectures do not access inter-lobar contextual information, an essential factor for early detection and control. To tackle this, we propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass. The method uses the enhanced CNN model (LDDNet), with Atrous Spatial Pyramid Pooling (ASPP) and a Channel-Spatial Attention Module (CSAM), to achieve a more precise representation across multiple scales. These embeddings are then used to construct a similarity graph, enabling a GNN to infer likely disease transmission paths among leaves. We evaluate the PlantVillage dataset on the proposed model, and the results show that it outperforms state-of-the-art CNN-based methods, achieving 98.88% classification accuracy and 98.71% F1 Score. Additionally, we were able to predict disease spread with a high AUC-ROC of 0.942 and an MCC of 0.884 using our framework. These results confirm that AdjLeafGNN can accurately model both local and relational patterns. The approach we propose is scalable and interpretable, facilitating real-time monitoring and control of diseases in precision agriculture.

Why it matches plant phenotyping methods葉画像から植物病害を分類し、隣接葉間の病害拡大を推定する深層学習・GNN手法を提案・評価しており、植物の病害状態の取得・推定が研究の中心である。

abstractwe propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass.

PlantCLR: contrastive self-supervised pretraining for generalizable plant disease detection. →

Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized. Here, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning. PlantCLR combines SimCLR-style contrastive pretraining with a lightweight convolutional classifier to balance representation quality and deployment efficiency. Experiments on PlantVillage and Cassava Leaf Disease show strong performance, achieving 99.10% accuracy and 99.04% F1-score on PlantVillage, and 96.83% accuracy and 96.70% F1-score on Cassava. Feature embedding visualization using t-SNE and explanation maps using Grad-CAM indicate improved class separability and attention to disease-relevant regions. These results suggest that contrastive SSL can improve representation transfer while maintaining computational efficiency, supporting scalable plant disease diagnostics in practical agricultural settings. Code is available at GitHub .

Why it matches plant phenotyping methods植物病害を画像から分類するPlantCLR手法を開発し、異なるデータセット間で性能評価・検証しているため、植物の病害状態を推定するフェノタイピング手法が中心である。

abstractHere, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning.

Enhanced corn leaf disease detection using sharpness-aware minimization optimized CNNs. →

Crop diseases significantly threaten global food security by directly affecting the crop yield and quality. The traditional diagnostic methods are labour intensive and human error prone. However, the existing deep learning solutions suffer with poor generalization due to the sharp loss landscapes. The proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM). This method minimizes both the training loss and loss landscape sharpness and enables the model to converge to a flatter-minima with improved generalization. The proposed work is evaluated on 60,000 corn leaf image samples for four classes with 15,000 balanced samples per class after augmentation. The optimized CNN model has achieved 99.66% test accuracy at 0.33% classification error rate and outperforms the conventional optimizers like Adam (98.44% accuracy) and the Stochastic Gradient Descent (SGD). The state-of-the-art analysis presents a 99% average precision rate along with 99.66% F1-score and 0.0013% mean squared error (MSE). The quantized model achieves an inference latency of 22.7 ms/image (≈44 FPS) on a Raspberry Pi 4 and reduces model overfitting and enhances feature discriminability. These results underscore the potential of SAM-based optimization in precision agriculture by driving a scalable automation of disease management. This work bridges the gap between theoretical advances in deep learning optimization and practical deployment in resource-constrained farming environments.

Why it matches plant phenotyping methodsトウモロコシ葉画像から病害を分類するCNNの最適化と性能評価が中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。

abstractThe proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM).

AI-IoT-Enabled Crop Monitoring Through Crop Stage and Leaf Disease Identification Using PECFIS and DGBESCNN →

The aspect of crop monitoring takes into consideration the timely detection of crop stages, leaf disorders, and deficiencies to enhance crop yield and decrease losses in agriculture. However, most of the current methods are limited to either disease detection or nutrient evaluation and do not examine the conditions of crops at various stages of growth, even though several AI -IoT-based solutions have been suggested to be applied to crop health monitoring. In addition, the estimation of the severity of the diseases is neglected, and this restricts decision-making in favor of the farmers. To address these constraints, the paper presents a Parametrized Elliptical Cauchy Fuzzy Inference System (PECFIS) combined with a Deep Glorot Bessel Elliott Softplus Convolutional Neural Network (DGBESCNN), proposed as an AI-based solution for crop monitoring and IoT support. The IoT devices in the form of drones are used to get real-time field images, and they are preprocessed in terms of noise reduction, contrast enhancement by LHM-CLAHE, conversion to HSV color space, and feature discrimination by vegetation indexing, as well as C3MEK-Means. PECFIS is used to determine eight key stages of rice growth and the severity of leaf diseases, whereas DGBESCNN provides proper classification of leaf diseases and nutrient deficiencies at each growth stage. The evaluation of the proposed framework was conducted using publicly available datasets on rice leaf disease and nutrient deficiency. The results of the experiments show that the system achieves high classification performance, with an accuracy of 98.82, a precision of 98.65, a recall of 98.73, an F1-score of 98.59, and low error rates (MSE = 0.0135, RMSE = 0.116). The findings show that the developed AI-IoT system is superior to available approaches and can serve as a dependable, real-time, and scalable solution in precision agriculture and intelligent crop monitoring.

Why it matches plant phenotyping methodsドローン画像からイネの生育段階と葉病害の重症度を推定・分類するAI-IoT手法が研究の中心であり、植物状態の取得・抽出方法を技術的に評価している。

abstractThe IoT devices in the form of drones are used to get real-time field images

Atlas-based spatiotemporal MRI phenotyping of 3D fungal spread in grapevine wood. →

In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.

Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。

abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.

A Comprehensive Image Dataset of Fruit and Leaf Diseases Across Six Horticultural Crops for Deep Learning Applications →

Abstract Abstract. Accurate and timely identi cation of plant diseases is essential for improving crop productivity and ensuring sustainable agricultural practices. This paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops: Apple, Banana, Citrus, Guava, Mango, and Papaya. The dataset comprises high-quality RGB images representing both healthy and diseased samples, with disease symptoms including spots, lesions, discoloration, blight, rot, and fungal and bacterial infections captured under diverse real-world conditions. Variations in illumination, background complexity, viewing angles, growth stages, and symptom severity are intentionally included to enhance the robustness and generalizability of learning models developed using this data. The dataset is structured in a class-wise manner and preprocessed to support direct integration with deep learning frameworks. It is extensively used to train, validate, and evaluate deep learning based plant disease classi cation models, enabling automatic feature learning from raw images without manual intervention. Experimental usage demonstrates that the dataset is well suited for convolutional neural networks and attentionbased architectures, facilitating e ective discrimination between multiple disease categories across di erent crops and plant organs. By providing a uni ed multi-crop, multi-disease benchmark, this dataset aims to accelerate research in automated crop disease diagnosis, precision agriculture, and intelligent decision-support systems for sustainable farming.

Why it matches plant phenotyping methods植物の葉・果実の病徴画像を収録したデータセット／ベンチマークであり、病害状態の画像ベース推定を中心的に扱うため。

abstractThis paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops

An ensemble of vision and swin transformers with LLM-based explanations for sugarcane leaf disease diagnosis. →

Sugarcane diseases significantly reduce crop yield and quality, posing persistent challenges to the agricultural sector. This study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection. By combining global self-attention with localized window-based attention mechanisms, the proposed model effectively captures multi-scale visual features associated with diverse disease symptoms. Experimental evaluation on a large, labeled sugarcane leaf dataset achieved a validation accuracy of 98.16% and a test accuracy of 97.06%, outperforming several convolutional neural network baselines. Additionally, a large language model (LLM) interface is employed as a post-prediction decision-support module, generating disease-specific descriptions and management suggestions based solely on the predicted disease class. This integrated framework indicates the potential effectiveness of transformer-based ensemble models combined with intelligent advisory support for practical decision-making in precision agriculture.

Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から分類するTransformerベースの手法開発と性能評価が中心であり、植物病害状態のフェノタイピングに該当する。

abstractThis study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection.

Precise leaf damage detection across diverse species and environments via a large-scale vision model. →

Precise detection of crop leaf damage is essential for real-time plant health monitoring and yield estimation. However, conventional deep learning models often exhibit poor generalization when deployed across varying species and complex, unstructured field environments. To address these limitations, we propose a new modeling paradigm that shifts from traditional task-specific training to foundation model adaptation. Specifically, we introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation. By incorporating a Spatial Prior Module (SPM) and a Projection Module, our approach effectively bridges the gap between general-purpose pre-training and domain-specific requirements. Experimental results on coffee and black gram datasets demonstrate that this paradigm consistently outperforms standard networks, including Unet, Unet++, and SwinUnet. On the coffee leaf dataset, the proposed model achieves an Intersection over Union (IoU) of 78.31% and a Pixel Accuracy of 88.00%, surpassing the baseline Unet by over 10.5% in IoU. Remarkably, the architecture reduces inference time by approximately 93.6% (from 63.41s to 4.07s), proving that high-parameter foundation models can be adapted for extreme computational efficiency in agricultural scenarios. To further validate scalability, we conduct additional experiments on a larger dataset, AMG HS . The proposed paradigm achieves the best overall detection performance while maintaining superior computational efficiency, confirming its robustness under increased data scale. Interpretability analysis reveals that the foundation model backbone effectively captures high-level semantic features of lesions, providing a clear explanation for its superior performance and cross-domain reliability. This research establishes a scalable, high-performance paradigm for intelligent crop protection, demonstrating that coupling customized encoders with foundation models is a superior strategy for cross-domain agricultural tasks.

Why it matches plant phenotyping methods植物葉の病変を画像からセグメンテーションし、葉の損傷状態を定量化する手法の開発・検証が研究の中心であるため。

abstractwe introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation.

MaizeFormerX: a lightweight vision transformer with cross-scale attention for explainable maize leaf disease diagnosis. →

Early detection of maize leaf diseases is essential to prevent yield losses. Existing vision-based models face challenges in real-world environments due to data imbalance, lighting variations, and interpretability. This study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings. MaizeFormerX employs multi-scale patch embeddings and a Cross-Scale Attention Fusion (CSAF) module to capture both detailed lesion textures and larger disease patterns. The CSAF output is processed through a transformer encoder stack using multi-head self-attention to model long-range dependencies. Robust preprocessing and dataset-specific augmentations were applied to improve feature extraction and address class imbalances in the Dataverse, Tanzania, and Plagues Maiz datasets. For interpretability, Grad-CAM was used for pixel-level saliency mapping in an efficient web application. When benchmarked against MobileViT, EfficientFormer, TinyViT, and Swin Transformer, MaizeFormerX achieved 97.8% accuracy on Dataverse, 97.5% on Tanzania, and 96.9% on Plagues Maiz, outperforming Swin Transformer V2 by 2–3%. Cross-domain testing yielded 88.9% accuracy when trained on Dataverse and tested on Tanzania, surpassing baseline performance by 3–6%. Class-wise analysis revealed F1 scores over 98% for Healthy and MLB classes with 6× augmentation, and over 97% for MSV. Ablation studies highlighted the significance of the cross-scale attention module for high MCC during domain shifts. This study introduces a precise, explainable, and efficient image-based method for classifying maize diseases, which could aid in more targeted crop management, reduce unnecessary agrochemical use, and promote sustainable maize production in future decision-support environments.

Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から分類する手法の開発・ベンチマーク・交差ドメイン検証が中心であり、植物病害状態の画像ベース表現型計測に該当する。

abstractThis study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings.

LeafFusionNet: a hybrid deep learning approach for robust plant disease detection →

Introduction Crop diseases have to be diagnosed early to save crop yields and food safety. Commonly used conventional practices are time-consuming and likely to involve human error. Automatic plant disease detection is a highly efficient technology that has been brought into existence by the development of deep learning and computer vision technology and can effectively detect the signs of diseases in plant species. Sustainable agriculture and early intervention depend on accurate and interpretable detection of plant diseases. Methods This study introduces a hybrid model based on deep learning techniques that effectively identifies and categorizes leaf diseases. The proposed model, LeafFusionNet, incorporates Convolutional Neural Network (CNN) and Vision Transformer (ViT) with an efficient attention module, LeafTAM (Leaf Texture Attention Module), to effectively capture both global and local information. This architecture is enhanced by the addition of a Gabor filter layer before the CNN-ViT fusion, hence augmenting the model’s ability to extract physiologically relevant texture features. Results and discussion The model was trained and validated on the Plant Village dataset. This model attained an accuracy of 99.33%, 99% precision, recall, and F1 score, demonstrating strong generalization on new data when compared with the state-of-the-art models. This proposed hybrid model can be utilized to develop a strong agricultural diagnostics system. These results point to the possibility of designing a powerful, interpretable system by utilizing transformer-based vision modules, Gabor filters, and explainability systems such as Grad-CAM.

Why it matches plant phenotyping methods植物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、PlantVillageデータセットで訓練・検証しており、植物病害状態の表現型取得が研究の中心です。

abstractThis study introduces a hybrid model based on deep learning techniques that effectively identifies and categorizes leaf diseases.

Multi-objective Big Bang Big Crunch framework for reliable rice disease and variety classification with conditional calibration. →

Deploying rice disease detectors in the field remains challenging because models that are accurate in the lab are often poorly calibrated and provide limited uncertainty estimates, raising the risk of costly misclassification. This paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria: classification error, calibration quality, uncertainty estimation, model size, inference latency, and energy consumption. The proposed framework presents conditional temperature scaling, an adaptive scheme that mitigates over-calibration and preserves reliability. The framework is implemented in Python on a lightweight two-headed classifier and evaluated on the Paddy Doctor dataset, MO-BBBC base framework achieves 90.6% disease accuracy and 97.9% variety accuracy; improves calibration to [Formula: see text] ([Formula: see text]% better than strong post-hoc baselines); achieves micro-AUC of 0.994/0.999 and micro-AP of 0.961/0.994 (disease/variety); delivers robust OOD detection (AUROC = 0.887/0.886); and supports real-time inference at [Formula: see text] ms and [Formula: see text] ms per 64-sample batch on CPU/GPU with Monte Carlo Dropout uncertainty. The resulting Pareto set enables practitioners to trade accuracy for efficiency and reliability, narrowing the gap between prototype validation and field deployment in precision agriculture.

Why it matches plant phenotyping methods植物病害状態を画像から検出する分類・校正・不確実性推定フレームワークが研究の中心であり、植物の病害表現型を対象とした手法開発と評価に該当する。

abstractThis paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria

Sun-induced fluorescence responses to structural and physiological effects caused by the Cercospora leaf spot in sugar beet →

Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.

Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。

abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.

An Efficient Hybrid Convolutional Vision Transformer Framework with Spatial Attention for Rice Leaf Disease Identification and Categorization →

Abstract Disease detection and categorization in rice leaf play a crucial role in mitigating crop damage and supporting sustainable agriculture. Traditional approaches, which often rely on manual inspection, are limited by labor intensity, variability and error susceptibility. This paper introduces a Hybrid Convolutional Vision Transformer (CVT) model with Spatial Attention (SA) to enhance the detection accuracy and classification reliability in rice leaves. The proposed CVT framework integrates a Convolutional Neural Network (CNN), which is the backbone for initial feature extraction with a Vision Transformer (ViT) in advanced feature representation. Convolutional Neural Network captures the essential textures and shapes, while the Vision Transformer applies attention across image patches, effectively learning the complex spatial dependencies necessary for identifying disease-specific characteristics within diverse field environments. Further, SA module refines the model by assigning greater weight to diseased regions, reducing interference from non-leafbackground areas. Experimental results on rice leafdataset demonstrate that the hybrid CVT with SA model achieves over 98.12% feature extraction accuracy, 98.56% classification accuracy in dataset 1 and 98.26% feature extraction accuracy, 98.67% classification accuracy in dataset 2 across multiple rice leaf categories, outperforming baseline CNN and ViT models. Spatial Attention heat maps highlight the most important locations during decision-making process, making the model more interpretable. This hybrid CVT model offers a scalable solution for rice leaf disease detection and categorization, with potential applications in precise agriculture systems, including drone-based or mobile implementations for field monitoring. The presented model exhibits maximum performancethan the othertraditional methods.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から推定する深層学習モデルの開発と比較評価が論文の中心であり、植物病害フェノタイピング手法に該当する。

abstractThis paper introduces a Hybrid Convolutional Vision Transformer (CVT) model with Spatial Attention (SA) to enhance the detection accuracy and classification reliability in rice leaves.

Identification of Diseases in Tea Crops using a Computational Convolutional Neural Network Model for Enhanced Crop Production →

Background: A major challenge to agricultural productivity in the tea industry is disease. that affects the quantity and quality of tea leaves produced. The extensive development of computational methods for treating diseases has been widely used due to fast and accurate detection. Methods: The proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups. By enhancing feature identification, this structure increases the criteria for accurate disease detection. The data having 5 diseased and one healthy category is obtained from the Kaggle database. After preprocessing the data, it is split into 80:20 ratios for training and testing steps. CNN is constructed using the Keras Sequential API in Jupiter notebook using Anaconda environment. Result: The total accuracy of the ML neural network training for classification was 98.52%. After 50 epochs of training, the model performed well, achieving high accuracy on training and validation datasets. The examination of the confusion matrix showed that several tea leaf diseases may be identified with high accuracy and few misclassifications. In general, the model demonstrated remarkable precision in differentiating between unhealthy and undamaged tea leaves.

Why it matches plant phenotyping methods茶葉画像から病害状態をCNNで分類する手法が研究の中心であり、植物の病徴・健全性という状態を直接推定しているため、植物フェノタイピング手法として含める。

abstractThe proposed method uses sequential Convolutional Neural Network (CNN) computations with many hidden layers to classify diseased and healthy tea leaves into multiple groups.

Smart Disease Detector in Crops: A Deep Learning-Based Approach for Automated Plant Disease Identification →

This paper presents a practical pipeline for image-based plant disease detection that operates without pre-trained weights or internet access. A compact Efficient Net-like convolutional network is trained from random initialisation on the Plant Village dataset (∼ 41,000 snapshots, 15 classes) at 224 × 224 resolution. Adam, Intensive Augmentation, Reduce LROn Plateau, Early Stopping, and the sampling of the optimal checkpoint to stabilise convergence and avoid overfitting are used to optimise the geometry. Despite early validation oscillations typical of de novo training, the model converges reliably and attains 98.4%–98.9% accuracy on held-out data, with a highly diagonal confusion matrix and uniformly strong per-class precision/recall. These results show that carefully tuned schedules and lightweight regularization can substitute for transfer learning when bandwidth or policy constraints prevent downloading external backbones, enabling classroom, extension, and field deployments. The paper details dataset preparation, architecture and training choices, learning curve behavior, and common failure modes, and concludes with limitations and a roadmap for higher-resolution, field-domain generalization and efficient on-device inference.

Why it matches plant phenotyping methods植物病害状態を画像から推定する深層学習パイプラインが研究の中心であり、モデル、データ準備、学習、検証性能が具体的に記述されているため。

abstractThis paper presents a practical pipeline for image-based plant disease detection

A comprehensive evaluation of lightweight deep learning models for tomato disease classification on edge computing environments. →

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.

High-Throughput Phenotyping for Revealing Key Morpho-Physiological Traits for Drought Tolerance in Pea (Pisum sativum and Wild Relatives). →

Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.

Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。

abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits

Design and implementation of a deep learning framework for automated crop classification and health diagnosis in precision agriculture. →

This paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification. This framework relies on a mathematical model based on neural networks that classifies and detects the condition of agriculture, removing the reliance on manual tasks and subjective diagnosis. This paper focuses on three main aspects of our framework: data acquisition, training and prediction. Data is collected using sensors like drones, cameras, and satellite imagery and is pre-processed to filter out noise and improve quality. The training part uses CNN to learn features from the data and become more meaningful. The prediction part of the task classifies, and diagnoses crop health through the trained model using the features. The framework accuracy for crops such as maize, potato, and wheat has been tested and yielded over 90% accuracy. The novelty of this work resides in the development of a multi-modal deep learning architecture that fuses macro-scale satellite imagery with micro-scale drone and IoT sensor data to improve diagnostic reliability. The framework was validated on a multi-source agricultural dataset using a 70% training, 15% validation, and 15% testing protocol. Experimental results demonstrate an accuracy exceeding 90% for staple crops. Using this framework can increase the visibility and quality of information maintained for crop health and improve the decision-making routine of farmers in real time. Additionally, automation of this process can significantly reduce labor costs and increase productivity per crop. Implementing this framework can contribute to precision agriculture and sustainable management practices.

Why it matches plant phenotyping methods作物の健康状態を植物の表現型・状態として推定するマルチモーダル画像・センサ基盤と深層学習手法を開発し、複数作物・データセットで検証しているため、方法が中心である。

abstractThis paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification.

Prediction of tomato leaf disease using deep learning approach →

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.

An explainable vision transformer model with transfer learning for accurate bean leaf disease classification. →

Early identification of bean leaf diseases, particularly Angular Leaf Spot and Bean Rust, is vital for ensuring crop productivity and global food security, especially within smallholder farming systems where disease outbreaks can rapidly escalate and cause severe yield losses. Conventional disease identification through visual inspection is labor-intensive, subjective, and highly dependent on expert knowledge, making it impractical for large-scale agricultural monitoring. Although recent deep learning-based approaches have demonstrated impressive accuracy in plant disease classification, their inherent “black-box” nature significantly limits real-world adoption, as farmers and agronomists often lack the ability to understand, trust, or act upon unexplained predictions. To address these challenges, this study proposes an automated and explainable disease diagnostic framework based on a Vision Transformer (ViT-B/16) architecture optimized through transfer learning from ImageNet. Unlike traditional convolutional neural networks that primarily focus on localized features, the Vision Transformer processes images as a sequence of flattened patches and leverages self-attention mechanisms to capture long-range dependencies and global contextual patterns across the entire leaf surface. This global representation enables the model to detect subtle and spatially distributed disease symptoms that are often overlooked by CNN-based approaches. To further enhance transparency and interpretability, GradCAM + + is integrated into the framework as an explainable artificial intelligence (XAI) mechanism. This method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions, thereby establishing a human-interpretable validation loop for farmers, agronomists, and domain experts. The proposed framework was evaluated on the publicly available I-Bean dataset, achieving a validation accuracy of 97.52% along with strong precision, recall, and F1-score performance. The generated GradCAM + + visualizations consistently demonstrate the model’s sensitivity to true diseased regions, reinforcing both the reliability and trustworthiness of its predictions. By combining high-capacity global feature learning with visual explainability, the proposed approach offers a scalable, transparent, and practical solution for real-world precision agriculture. This framework not only enhances diagnostic accuracy but also bridges the critical gap between model performance and user trust, enabling informed decision-making and timely disease management in modern farming environments.

Why it matches plant phenotyping methods画像から豆葉の病害症状を分類・可視化する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格。

abstractThis method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions

A Novel Phenotyping Approach for Reconciling Precision and Variance in Disease Severity Estimates from High-resolution Imaging →

1 Abstract Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.

Why it matches plant phenotyping methods高解像度画像とフォーカスブラケティングを用いて植物病害の重症度を定量化し、圃場プロット単位の推定精度と不確実性を評価する手法が研究の中心であるため。

abstractWe combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area.

Method for the detection of powdery mildew in tomato from electrical signalling. →

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.

Classification of rice plant diseases using efficient DenseNet121 →

Abstract Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.

Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類する深層学習手法が研究の中心であり、独立テストセットによる性能評価も実施しているため、植物病害フェノタイピング手法として収載する。

abstractThis study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification.

A Comparative Study of Deep Transfer Learning Architectures for Multi-Class Plant Leaf Disease Detection →

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.

A hybrid deep learning framework using convolutional and transformer models for robust plant disease classification →

Abstract Plant diseases continue to pose a significant threat to worldwide food security, resulting in notable yield reductions and economic consequences. Automated disease diagnosis through machine learning has arisen as a potential solution; nevertheless, current methods frequently have difficulty in capturing both detailed local attributes and overarching contextual patterns found in plant leaf images. This study presents a thorough comparative examination of conventional and deep learning methods—such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), YOLO, Support Vector Machines (SVMs), and Random Forests—for the classification of multi-class plant diseases. To overcome the constraints of individual CNN and transformer models, a new hybrid framework that integrates EfficientNet-B7 for strong spatial feature extraction with a Vision Transformer (ViT-B16) for comprehensive contextual modeling is suggested. The system is assessed on an extensive dataset consisting of 21,534 images covering 38 classes of plant diseases and healthy specimens. Experimental findings show that the suggested hybrid model reaches an accuracy of 98.13%, surpassing standalone CNN baselines and other rival models, while consistently achieving high precision, recall, and F1-scores for all classes. The results emphasize the success of combining convolutional and transformer-based models for scalable and precise plant disease detection, aiding the creation of smart decision-support systems for precision farming.

Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ハイブリッド画像解析手法を提案し、複数手法との比較評価と大規模データセットでの検証を行っており、フェノタイピング手法が中心である。

abstractAutomated disease diagnosis through machine learning has arisen as a potential solution

Swin-HViT for Accurate Early-Stage Crop Disease Diagnosis Using a Hybrid Transformer Model →

Abstract Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security. This study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of a vision transformer (ViT) and a Swin transformer to accurately predict crop diseases. While ViT captures global image features, the Swin Transformer excels at extracting fine-grained local details. Evaluated on two benchmark datasets, Corn and PlantDoc, our model achieved accuracies of 98.81% and 81.81%, respectively, surpassing recent works. Here, we demonstrate the effectiveness of combining complementary transformer architectures to improve disease identification in diverse agricultural settings. The code, data and the hybrid model are available at https://github.com/hema2107/Swin-HViT.

Why it matches plant phenotyping methods植物画像から病害状態を推定するハイブリッド画像解析モデルを開発し、2つのベンチマークデータセットで評価しており、病害フェノタイピング手法が中心である。

abstractThis study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of a vision transformer (ViT) and a Swin transformer to accurately predict crop diseases.

Noise-Tolerant Detection of Cucumber and Grape Leaf Diseases Using Median and Gaussian Filters with Advanced Machine Learning Classifiers →

The study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data, focusing on the ability to withstand salt-and-pepper and Gaussian noises. The image datasets used in agriculture are usually affected by noise because of changes in light, sensor defects, and environmental conditions, which may lead to lower diagnostic accuracy. In order to address this, the proposed system incorporates high noise reduction methods whereby a median filter and a Gaussian filter are used to restore the image quality without compromising on the important leaf texture information. After processing, colour, texture, and shape are used to extract features, which are effective in extracting disease-specific visual representations. These fine features are then trained on various optimized machine learning models, such as Light Gradient Boosted Machine (LGBM), Quantum Support Vector Machine (QSVM), a Modified Random Forest (MRF) with adaptive weighted features, and a Multi-SVM classifier with a custom kernel to map nonlinear features. Through experimental analyses, the proposed ensemble framework is shown to be highly accurate, robust, and noise-tolerant as opposed to the traditional frameworks. The hybrid method is effective in recognizing the significant cucumber diseases and grapes, including powdery mildew, downy mildew, and anthracnose, which will be utilized in the noisy agricultural conditions in the real world. In general, this system offers a noise-resistant, reliable, and computationally efficient system to detect early signs of plant diseases, which can be used in sustainable crop monitoring and precision farming.

Why it matches plant phenotyping methods植物葉の画像から病徴を推定する画像処理・特徴抽出・機械学習システムの設計開発が中心であり、植物病害状態のフェノタイピング手法に該当する。

abstractThe study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data

Lightweight scalable deep learning framework for real time detection of potato leaf diseases. →

Potato leaf diseases, if left undetected, threaten food security in agricultural economies and cause substantial crop losses. To address this critical challenge, we developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD), a variant of SSD that integrates multiscale feature fusion and Squeeze-and-Excitation attention to improve fine-grained lesion detection. The enhanced model processes high-resolution images (512×512 pixels) and analyzes leaves at ten magnification levels, enabling it to identify even minor signs of infection. The inclusion of Squeeze-and-Excitation filters allows the system to focus more effectively on characteristic disease patterns, increasing detection precision. After scanning the leaves, the system applies advanced image processing techniques to localize disease regions and assess their severity. We evaluated EF-SSD using 2,500 labeled potato leaf images representing healthy plants and cases of early and late blight. The proposed model achieved a mean Average Precision (mAP) of 97% at 0.5 IoU, an F1-score of 95%, and an Intersection over Union (IoU) of 89%, outperforming advanced detectors such as YOLOv5, YOLOv8, RetinaNet, and Faster R-CNN across all metrics. It also delivers real-time inference at 47 FPS, confirming its suitability for on-field deployment. An ablation study further demonstrates the effectiveness of SE blocks and extended feature hierarchies in enhancing detection accuracy. These outcomes highlight EF-SSD’s potential as a reliable, efficient, and scalable tool for smart agriculture and early crop disease management.

Why it matches plant phenotyping methodsジャガイモ葉の病斑を画像から検出・局在化し、病害の重症度を評価する深層学習手法を開発・検証しており、植物表現型取得が中心である。

abstractwe developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD)

A lightweight hybrid CNN and transformer model for medicinal leaf disease classification with explainable AI. →

Medicinal plants including Ocimum tenuiflorum L. (Tulsi), Azadirachta indica A. Juss. (Neem), and Kalanchoe pinnata (Lam.) Pers. (Patharkuchi) are essential sources of bioactive compounds, yet leaf diseases threaten their yield and phytochemical integrity. This study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks, achieving 99.72% accuracy, 1.00 macro F1-score, and AUC = 1.00 across 12 disease classes (1,000 images/class post-augmentation) using only 9.38 M parameters and 2.50 GFLOPs. Five-fold cross-validation yielded 99.74% ± 0.14% accuracy, with rapid convergence and no overfitting. Explainable Artificial Intelligence (XAI) via Gradient-weighted Class Activation Mapping (Grad-CAM) (mean intensity: 0.1664-0.2702), Local Interpretable Model-agnostic Explanations (LIME), and t-distributed Stochastic Neighbor Embedding (t-SNE) (silhouette score: 0.87) confirmed biologically meaningful attention on pathological regions. External validation on the independent BD-MediLeaves dataset (8 classes, 8,000 samples) achieved 99.42% accuracy and 0.99 macro F1. With 6.98 ms/image inference latency and 35.81 MB memory, LSeTNet enables real-time, edge-based deployment. It significantly outperforms DenseNet169 (95.56%), ViT-B16 (95.61%), and LW-CNN+SE (95.39%) ([Formula: see text], paired t-tests), establishing a transparent, efficient, and generalizable benchmark for precision phytopathology and sustainable medicinal plant cultivation.

Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するCNN・Transformer手法を開発し、交差検証、外部データセット、既存モデルとの比較で検証しており、植物フェノタイピング手法が中心である。

abstractThis study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks

A Large-Scale In-the-wild Dataset for Plant Disease Segmentation. →

Plant diseases pose significant threats to agriculture, making proper diagnosis and effective treatment crucial for protecting crop yields. In automatic diagnosis processing, image segmentation helps to identify and localize diseases. Developing robust image segmentation models for detecting plant diseases requires high-quality annotations. Unfortunately, existing datasets rarely include segmentation labels and are typically confined to controlled laboratory settings, which fail to capture the complexity of images taken in the wild. Motivated by these, we established a large-scale segmentation dataset for plant diseases, dubbed PlantSeg. In particular, PlantSeg is distinct from existing datasets in three key aspects: (1) Annotation types: PlantSeg includes detailed and high-quality disease area masks. (2) Image sources: PlantSeg primarily comprises in-the-wild plant disease images rather than laboratory images provided in existing datasets. (3) Scale: PlantSeg contains the largest number of in-the-wild plant disease images, including 7,774 diseased images with corresponding segmentation masks. This dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.

Why it matches plant phenotyping methods植物病害領域の画像セグメンテーション用データセットを構築し、病害領域マスクとベンチマーク基盤を提供することが中心で、植物の病害状態を直接推定する方法論的貢献である。

abstractThis dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.

A hybrid CNN model for multi-class freshness and disease detection in local spinach varieties. →

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.

Dual-encoder Variational Autoencoder for Detection and Classification of Plant Leaf Diseases →

Background: Plant disease detection remains a major challenge in agriculture, with direct implications for improving crop productivity and ensuring food security. Seasonal variation significantly influences plant characteristics, making the classification of plant leaves by season-specifically summer and winter-important for optimizing disease detection and management strategies. Methods: In this study, a plant leaf disease detection dataset was developed and categorized based on seasonal conditions. The dataset includes 47 classes representing summer crops and 16 classes for winter crops. To classify plant leaf diseases effectively, we propose a novel dual-encoder Variational Autoencoder (VAE) model that integrates ResNet and VGGNet as parallel encoders. These encoders extract complementary feature maps from the seasonal datasets, which are then concatenated to improve classification accuracy. Result: Experimental evaluation demonstrates the robustness and accuracy of the proposed approach. The dual-encoder VAE achieved a classification accuracy of 98.86% on the summer dataset and 97.53% on the winter dataset, highlighting the model’s ability to generalize effectively across seasonal variations in plant leaf disease detection.

Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する新規VAE手法を開発し、季節別データセットで精度評価しているため、病害状態の表現型取得が中心である。

abstractTo classify plant leaf diseases effectively, we propose a novel dual-encoder Variational Autoencoder (VAE) model that integrates ResNet and VGGNet as parallel encoders.

A wavelet-based frequency-domain approach for accurate multi-crop disease detection. →

Timely localization and diagnosis of crop lesions are critical for disease control and reducing pesticide use. However, in-field lesions often resemble leaf textures, vary widely in scale, and suffer from lighting and shadow interference-making simultaneous high accuracy and lightweight inference challenging. We propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition. Central to our design is Wavelet Channel Recalibration (WCR), a DWT-based downsampling module: discrete wavelet transform naturally provides multi-resolution, time-frequency localized representations that explicitly separate low-frequency approximations from high-frequency edge/texture details. WCR fuses high- and low-frequency components and enhances feature representation through their frequency-domain complementarity, thereby preserving semantic and fine texture information during resolution reduction with negligible extra cost. We also introduce PS-C2f, which integrates Pinwheel-shaped convolutions into C2f to better capture tiny lesion details via multi-directional, irregular kernels, and replace SPPF with Dynamic Group Attention Pooling (DGAP) for efficient multi-scale context aggregation. On our PlantDoc_boost dataset, WGA-YOLO improves over YOLOv8n by 3.02 and 2.85% points, while reducing parameters and FLOPs by ~ 0.18 M and ~ 0.3G, demonstrating improved inference efficiency and deployment friendliness while maintaining strong detection performance in field scenarios.

Why it matches plant phenotyping methods植物葉の病斑を画像から検出・診断するYOLO改良手法の開発が中心であり、病害状態の画像ベース表現型計測に該当する。

abstractWe propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition.

A hybrid deep learning and fuzzy logic framework for robust tomato disease detection and classification. →

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.

Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses. →

Advancements in phenotyping technologies, including object imaging, high-throughput monitoring, and soft computing, are pivotal for understanding plant responses to environmental stresses. These technologies enable detailed analyses of morphological, physiological, and structural adaptations under abiotic and biotic stresses, such as drought. Current work using multimodal and multi-perspective image processing methods can capture the essential processes that enhance plant resilience and counteract stress by identifying morphological and biochemical indicators. However, the dynamic and complex nature of plant responses poses multiple challenges for generating precise analytics and descriptors of evolving phenotypes. This work introduces analytics for concurrent imaging, adopting the underlying principle of cosegmentation to create taxonomies for new phenotypes. Here, unidimensional refers to the concurrent analysis of multiple images within a single phenotyping dimension: temporal, modal, or perspective, rather than combining information across dimensions. The proposed unidimensional phenotypes integrate concurrent images within individual temporal, modal, or perspective dimensions to capture dynamic morphological and physiological responses that are not observable with conventional single-image or cumulative metrics. Within a high-throughput imagery production system, these phenotypes enable more nuanced quantification of phenotypic changes, leveraging the strengths of simultaneous image analysis to enhance insight into plant adaptations. This workflow aligns with the investigation of plants’ adaptive strategies under abiotic stress and provides quantitative indicators of plant health under adverse environmental conditions.

Why it matches plant phenotyping methods植物の同時画像解析とコセグメンテーションに基づく新しい表現型抽出・定量化ワークフローを提案しており、植物フェノタイピング手法が中心である。

abstractThis work introduces analytics for concurrent imaging, adopting the underlying principle of cosegmentation to create taxonomies for new phenotypes.

LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification. →

The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX . This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.

Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別するCNN手法を開発し、複数データセット・交差検証・ベースライン比較で性能を評価しており、植物病害表現型の取得・推定が中心である。

abstractwe propose our solution approach LeafSightX

Agri-vision Bangladesh: A multi-crop augmented image dataset for automated disease diagnosis in Bottle Gourd, Zucchini, Papaya, and Tomato. →

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

LeafAI: Interpretable plant disease detection for edge computing →

In real-world agriculture, healthy plant leaves are significantly more common than diseased ones. This natural class imbalance presents challenges in automated plant disease detection, as analyzing each leaf with computationally intensive deep-learning models is problematic, leading to inefficiency and increased resource consumption. To tackle this challenge and promote sustainable AI solutions, this study presents an iterative, hybrid AI approach that boosts computational efficiency, interpretability, and scalability for real-time disease detection. This hybrid system operates in two stages: first, a lightweight traditional machine learning classifier performs binary classification to quickly separate and exclude healthy leaves, followed by a deep learning model (ResNet, DenseNet, MobileNet, and EfficientNet) that classifies the specific disease in the smaller group of diseased leaves. This two-stage method minimizes computational load while maintaining high classification accuracy. Additionally, this study uses Explainable AI (XAI) methods, particularly Gradient-weighted Class Activation Mapping (Grad-CAM), to generate heatmaps. These heatmaps highlight the image areas that most significantly influence the model’s predictions, thereby improving transparency and refining the feature extraction process. The proposed hybrid model, comprising Logistic Regression and Mobilenetv3, offers up to 77.6% faster inference than conventional deep learning models with only about 3% accuracy loss. For a large-scale test of 1,227 images on an entry-level laptop, the hybrid model reduced the total inference time from 4,548 seconds to just 1,010.13 seconds, with minimal CPU load. By addressing class imbalance, optimizing inference efficiency, and incorporating explainable AI, this work contributes a scalable, sustainable, and trustworthy solution for plant disease detection in precision agriculture.

Why it matches plant phenotyping methods葉画像から植物病害状態を推定する二段階AI手法とXAIを開発・評価しており、病害の表現型取得が研究の中心である。

abstractThis two-stage method minimizes computational load while maintaining high classification accuracy.

An Ensemble Convolutional Neural Network Framework for Automated Mango Leaf Disease Detection →

Abstract Mango diseases and pest infestations represent a major challenge to agricultural productivity, making early and accurate diagnosis crucial for reducing crop losses. This study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis. The proposed system is designed to work with images captured under real field conditions, ensuring its suitability for practical agricultural applications. The dataset includes mango leaf images affected by various diseases and pests such as Gall Midge, Powdery Mildew, Sooty Mould, Die Back, Cutting Weevil, and Anthracnose, each characterized by distinct visual symptoms including discoloration, necrotic spots, fungal growth, leaf deformation, and edge damage. Traditional manual diagnosis of these conditions is often time-consuming, labor-intensive, and susceptible to human error. To overcome these limitations, the proposed framework employs an ensemble of transfer-learning-based CNN models to extract meaningful features related to texture, color distribution, shape, and lesion patterns. A security-preserving learning mechanism is integrated to ensure the safe handling of agricultural image data, minimizing data exposure risks while maintaining high model performance. Additionally, data augmentation techniques are utilized to improve model robustness, reduce overfitting, and address class imbalance commonly found in agricultural datasets. The system is capable of multi-class classification, reflecting real-world scenarios where multiple diseases may exhibit visually similar characteristics. Experimental results indicate that the ensemble CNN framework achieves high classification accuracy and demonstrates strong generalization across varying lighting conditions and complex backgrounds. By effectively capturing disease-specific visual features, the proposed approach enhances detection reliability in real-world field environments. Overall, this system offers a scalable, non-invasive, and security-aware solution for early mango leaf disease detection, contributing to precision agriculture and informed decision-making. The findings highlight the potential of deep learning and computer vision technologies in developing intelligent, secure, and efficient plant health monitoring systems.

Why it matches plant phenotyping methodsマンゴー葉の病徴を画像から分類するCNNフレームワークの開発が研究の中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。

abstractThis study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis.

Chat Demeter: a multi-agent system for plant disease diagnosis integrating CNN-transformer models →

Plant diseases remain a significant challenge in global agricultural production. Achieving efficient and accurate disease detection is essential for reducing crop losses, controlling agricultural costs, and improving yields. As agriculture rapidly advances toward digitalization and intelligent transformation, the application of artificial intelligence technologies has become a key pathway to enhancing industrial competitiveness. In this study, Chat Demeter, a multi-agent system for plant disease diagnosis based on deep learning. The system captures real-time leaf images through camera devices. It employs a CNN-Transformer model to perform instance segmentation and object detection, thereby enabling automatic identification of diseased leaves and classification of disease types. To enhance interactivity and practical value, the system incorporates a natural language interface, allowing users to upload images and receive automated diagnostic results and treatment suggestions. Experimental results demonstrate that the system achieves an accuracy of 99.50% and an AUC o f 99.91% on the validation dataset, highlighting its superior performance. Overall, Chat Demeter provides an effective tool for crop health monitoring and disease intervention, while offering a feasible pathway and developmental direction for integrating and optimizing future agricultural multi-agent systems.

Why it matches plant phenotyping methods植物葉の画像から病葉をセグメンテーション・分類する診断システムが研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。

abstractChat Demeter, a multi-agent system for plant disease diagnosis based on deep learning.

Eff-swin-hgso: attention-driven and optimized plant leaf disease diagnosis using efficientNetV2B0 and swin transformer with HGSO feature selection →

Abstract Agriculture is essential to human civilization, providing food and raw materials. Plant diseases significantly threaten agricultural productivity, making early and accurate detection essential. Despite Recent advances of deep learning in making automatic plant leaf diseases diagnosis systems, some of them depend on simple features fusion methods and lack an efficient method to exploit complementary information. Therefore, this paper proposes a system for diagnosing plant leaf diseases by fusing two powerful deep learning models: EfficientNetV2B0 and Swin Transformer via attention-based feature fusion that adaptively weights each model’s contribution with features. These models extract complementary features: EfficientNetV2B0 extracts fine-grained local features, and the Swin Transformer extracts global contextual information, producing highly and complementary expressive fused features. The high-dimensional fused features demand High-Performance Computing (HPC) resources for efficient parallel processing and accelerated training. Moreover, the Henry Gases Solubility Optimization (HGSO) metaheuristic is applied to select the most discriminative and related features. Unlike previous methods that diagnose diseases affecting only one plant, the proposed approach handles multiple plant species simultaneously, further increasing computational demand. Finally, RBF-kernel SVM is applied for a classification step. The system was implemented on a GPU-based high-performance computing environment using CUDA acceleration to enhance computational efficiency. Experimental evaluation on the PlantVillage benchmark dataset with seven classes achieved a high classification accuracy of 99.2%, outperforming other state-of-the-art methods. These results enhance the model’s practical capability for application in real-world agricultural decision-support systems.

Why it matches plant phenotyping methods葉画像から植物病害を診断する深層学習システムの開発とベンチマーク評価が研究の中心であり、植物の病害状態を直接推定しているため。

abstractTherefore, this paper proposes a system for diagnosing plant leaf diseases by fusing two powerful deep learning models: EfficientNetV2B0 and Swin Transformer via attention-based feature fusion

Towards practical AI for agriculture: A self-supervised attention framework for Spinach leaf disease detection. →

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.

Web-Based Sustainable Detection and Treatment Recommendation System for Wheat Plant Diseases Using Convolutional Neural Networks. →

Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user-friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.

Why it matches plant phenotyping methods小麦植物画像から病害状態を分類するCNN手法を開発・評価しており、植物病害表現型の取得・推定が中心。治療推薦機能もあるが、画像ベース病害診断が主要な技術的貢献である。

abstractA web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases.

IoT-Integrated robotic system for automated plant disease detection and environmental monitoring →

Abstract Plant diseases pose a critical threat to global food security, agricultural sustainability, and farmer livelihoods, particularly in regions with limited access to advanced diagnostic technologies. Traditional methods of disease detection rely heavily on manual inspection, which is time-consuming, error-prone, and often results in delayed interventions. This paper presents a novel, solar-powered autonomous robotic system designed to detect plant diseases in real time using deep learning and IoT technologies. The proposed system integrates a high-resolution imaging unit, IoT-based environmental sensors, and an onboard processing module based on Raspberry Pi. Deep CNNs, trained on diverse datasets including PlantVillage, are used for accurate disease classification, while soil moisture and temperature sensors provide contextual environmental data to support diagnosis. The robot’s mobility, powered by solar energy, allows for continuous field monitoring with minimal human intervention. Experimental results demonstrate the system’s high classification performance, achieving 99.39% training accuracy, 97.47% validation accuracy, and 97.13% testing accuracy. Furthermore, the model achieved 99.63% overall accuracy, with a Precision of 99.40%, a Recall/Sensitivity of 99.56%, an F1-score of 99.46%, and a Specificity of 99.99% across multiple disease classes. These results highlight the robustness of the proposed approach in real-world agricultural conditions, enabling reliable disease detection and monitoring. The integration of cloud-based monitoring enables farmers to receive real-time alerts and insights, supporting timely and informed decision-making. This cost-effective, scalable, and environmentally sustainable solution has the potential to transform precision agriculture by enhancing early disease detection, reducing pesticide overuse, and improving crop yield and health.

Why it matches plant phenotyping methods植物病害状態を画像と深層学習で検出するロボット型フェノタイピング基盤が研究の中心であり、技術性能も評価している。

abstractThis paper presents a novel, solar-powered autonomous robotic system designed to detect plant diseases in real time using deep learning and IoT technologies.

Targeted expansion of a barley genebank core collection facilitates the discovery of disease resistance loci. →

Utilizing the diversity preserved in genebank collections is essential for accelerating crop improvement, yet information is often limited to selected core collections. Genome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation, with proven utility in practical pre-breeding contexts. In this study, we leveraged GWP to expand the German Federal ex situ barley core collection (core1000) with a focus on resistance to Puccinia hordei, Blumeria graminis hordei, and Rhynchosporium commune. Using the barley core1000 collection, which was originally selected to maximize molecular diversity, we trained genomic prediction models and imputed resistance scores for 20,458 genebank accessions based on sequence data encompassing 306,049 high-quality SNPs. To empirically validate prediction accuracy, we selected 300 spring and winter barley genotypes for field evaluation across four environments, resulting in moderate-to-strong correlations between predicted and observed resistance levels. Genome-wide association mapping in this set revealed five marker-trait associations that were not detected in the original core1000 collection. These results demonstrate that prediction-informed sampling can effectively expand trait-relevant genetic diversity and increase the frequency of resistance-associated alleles, thereby improving the power to detect loci that may be overlooked in conventional panels. Accordingly, GWP supports the targeted inclusion of accessions with trait-relevant variation and enhances the value of genebank resources for trait discovery and pre-breeding applications.

Why it matches plant phenotyping methodsゲノム情報から病害抵抗性という植物形質を大規模に推定・補完する方法を開発し、圃場評価で予測精度を検証しているため、方法論が中心である。

abstractGenome-wide prediction (GWP) offers a promising approach to large-scale phenotypic imputation

Wheat Rust Disease Detection and Classification using an improved Deep Learning Algorithm →

Abstract Wheat, the third most widely consumed cereal crop worldwide, faces substantial yield and quality losses as a result of rust disease, notably leaf rust, stem rust, and stripe rust. These rust disease, caused by Puccinia triticina , Puccinia graminis , and Puccinia striiformis , respectively, are capable of causing significant yield losses in wheat in the absence of timely detection. Conventional disease identification relies heavily on manual visual inspection, which is time consuming, labor intensive, and prone to error, especially in large scale agricultural systems. To address these limitations, this study proposes a deep learning-based framework for the early detection and classification of wheat rust diseases. A real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data. The dataset comprises images of healthy leaves and those affected with the three major rust diseases. A modified convolutional neural network (CNN) architecture was employed for extract features and disease classification. Experimental results demonstrate that the proposed approach achieves high classification accuracy, highlighting its effectiveness as a reliable tool for automated wheat rust detection in precision agriculture. By enabling rapid and accurate disease identification, the system supports timely decision-making, reduces potential yield losses, and improves crop management practices, thereby contributing to food security and sustainable agricultural production.

Why it matches plant phenotyping methods小麦葉の画像からさび病の有無・種類を推定する深層学習手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。

abstractA real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data.

Quantifying growth and lodging in Tef ( Eragrostis tef ) with Uncrewed Aerial Systems (UAS) →

Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.

Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。

abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.

Utilizing high‐throughput phenotyping to identify metribuzin tolerance in winter wheat →

Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.

Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。

abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.

Atlas-Based Spatio-temporal MRI Phenotyping of 3D Fungal Spread in Grapevine Wood →

Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.

Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。

abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.

Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery. →

Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.

Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。

abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.

Deep learning-based disease detection in potato and mango leaves: a comparative study of CNN, AlexNet, ResNet, and EfficientNet. →

Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly available datasets, the PlantVillage Potato Leaf Disease (2,152 images) dataset and the Kaggle Mango Leaf Disease dataset (4,000 images). Images were pre-processed, augmented, and split into training and testing datasets (80:20), to enable better model generalization. Four deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, Residual Networks (ResNet), and EfficientNet, were evaluated in the context of multi-class disease classification. The baseline CNN achieved a training accuracy of 93.67% and a testing accuracy of 92.61%, with balanced precision and recall (92.5%), thus providing a very strong feature extraction and classification capability. AlexNet showed moderate performance (91.3% training, 90.2% validation), and a very small overfitting was observed. ResNet had an efficient convergence, and attained 96.7% validation accuracy in just a few epochs, thus pointing out the advantage of residual connections in the context of deeper learning. EfficientNet surpassed all the other architectures, since it reached a training accuracy of 98.2% and a validation accuracy of 97.8%, with very small loss (≈ 0.015) and no overfitting, thus proving to have the best generalization ability. The models demonstrated stability and discriminative ability with the support of confusion matrices and accuracy and loss plots produced on an epoch-wise basis. Therefore, the findings indicate that DL models can be adapted for real-time and accurate plant disease diagnosis, establishing a pathway for early remediation, and supporting precision agriculture. The research establishes the opportunity for EfficientNet to be considered a promising solution for scalable smart farming.

Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法を開発・比較し、複数データセットで精度を検証しており、植物の病害状態の取得・推定が研究の中心である。

abstractdeep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants

Gradient-guided boundary-aware selective scanning with multi-scale context aggregation for plant lesion segmentation →

Introduction Plant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture. However, the task remains challenging due to large variations in lesion scale—ranging from minute incipient spots to coalesced regions—and ambiguous, low-contrast boundaries that blend into healthy tissue. Methods We present GARDEN, a Gradient-guided boundary-Aware Region-Driven Edge-refiNement network that unifies multi-scale context modeling with selective long-range boundary refinement. Our approach integrates a Multi-Scale Context Aggregation (MSCA) module to harvest contextual cues across diverse receptive fields, forming scale-consistent lesion priors to improve sensitivity to tiny lesions. Additionally, we introduce a Boundary-aware Selective Scanning (BASS) module conditioned on a Gradient-Guided Boundary Predictor (GGBP). This module produces an explicit boundary prior to steer a Mamba-based 2D selective scan, allocating long-range reasoning to boundary-uncertain pixels while relying on local evidence in confident interiors. Results Validated across two public plant disease datasets, GARDEN achieves state-of-the-art results on both overlap and boundary metrics. Specifically, the model demonstrates pronounced gains on small lesions and boundary-ambiguous cases. Qualitative results further show sharper contours and reduced spurious responses to illumination and viewpoint changes compared to existing methods. Discussion By coupling scale robustness with boundary precision in a single architecture, GARDEN delivers accurate and reliable plant lesion segmentation. This method effectively addresses key challenges in the field, offering a robust solution for automated disease analysis under challenging real-world conditions.

Why it matches plant phenotyping methods植物病斑を画像から分割し、病害状態・重症度を推定する新規手法を開発し、公開データセットで検証しているため、植物フェノタイピング手法が中心である。

abstractPlant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture.

Deep Learning Based Multiclass Detection of Corn Leaf Diseases Using a Convolutional Neural Network →

Abstract The research develop an accurate and efficient method for detecting multiple corn leaf diseases to support sustainable agricultural practices in Soppeng Regency, Indonesia. The goal is to design a Convolutional Neural Network (CNN) model capable of classifying corn leaf diseases, including rust, blight, and gray leaf spot, using high-resolution image data. The research employed a balanced dataset sourced from open-access repositories, followed by preprocessing, data augmentation, and CNN model optimization. The model’s performance was evaluated using accuracy, precision, recall, and F1-score to ensure comprehensive assessment. Experimental results show that the proposed CNN achieved high accuracy across all disease classes, with strong per-class metrics, indicating robust performance in distinguishing visually similar symptoms. The classification results with the Convolutional Neural Network algorithm have 95% training data accuracy and 93% test data accuracy in detecting leaf diseases in corn plants. The findings contribute to agricultural technology by offering a scalable and field-deployable disease detection system that can be integrated into mobile or edge-based platforms. Limitations include reliance on publicly available datasets, which may not fully capture the variability of local field conditions. The research concludes that the proposed CNN model can significantly enhance early disease detection, reduce dependency on manual inspections, and support precision agriculture. Future research should focus on expanding the dataset with locally captured images, incorporating real-time image acquisition, and optimizing the model for deployment in low-resource environments to improve adaptability and reliability.

Why it matches plant phenotyping methodsトウモロコシ葉の病害症状を画像から分類するCNN手法の開発・性能評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。

abstractThe goal is to design a Convolutional Neural Network (CNN) model capable of classifying corn leaf diseases, including rust, blight, and gray leaf spot, using high-resolution image data.

A Lightweight Edge-Deployable Framework for Intelligent Rice Disease Monitoring Based on Pruning and Distillation. →

Digital agriculture and smart farming require crop health monitoring methods that balance detection accuracy with computational cost. Rice leaf diseases threaten yield, while field images often contain small multi-scale lesions, variable illumination and cluttered backgrounds. This paper investigates SCD-YOLOv11n, a lightweight detector designed with these constraints in mind. The model replaces the YOLOv11n backbone with a StarNet backbone and integrates a C3k2-Star module to enhance fine-grained, multi-scale feature extraction. A Detail-Strengthened Cross-scale Detection (DSCD) head is further introduced to improve localization of small lesions. On this architecture, we design a DepGraph-based mixed group-normalization pruning rule and apply channel-wise feature distillation to recover performance after pruning. Experiments on a public rice leaf disease dataset show that the compressed model requires 1.9 MB of storage, achieves 97.4% mAP@50 and 76.2% mAP@50:95, and attains a measured speed of 184 FPS under the tested settings. These results provide a quantitative reference for designing lightweight object detectors for rice disease monitoring in digital agriculture scenarios.

Why it matches plant phenotyping methodsイネ葉の病斑を画像から検出・局在化する軽量モデルを開発し、精度・圧縮性能・速度を評価しており、植物病害状態の取得手法が研究の中心である。

abstractThis paper investigates SCD-YOLOv11n, a lightweight detector designed with these constraints in mind.

Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-Based Model and Direct Imaging Model. →

Rice leaf diseases significantly reduce productivity and cause economic losses, highlighting the need for early detection to enable effective management and improve yields. This study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases. Despite the prevailing approach of directly inputting images of rice leaves into ANNs, there is a noticeable absence of thorough comparative analysis between the Feature Analysis Detection Model (FADM) and the Direct Image-Centric Detection Model (DICDM), specifically when it comes to evaluating the effectiveness of Feature Extraction Algorithms (FEAs). Hence, this research presents initial experiments on the Feature Analysis Detection Model, utilizing various image Feature Extraction Algorithms, Dimensionality Reduction Algorithms (DRAs), Feature Selection Algorithms (FSAs), and Extreme Learning Machine (ELM). The experiments are carried out on datasets encompassing 3829 original rice leaf images across six classes (bacterial leaf blight, brown spot, leaf blast, leaf scald, sheath blight rot, and healthy leaf). A Direct Image-Centric Detection Model is established without the utilization of any FEA, and the evaluation of classification performance relies on different metrics. Ultimately, an exhaustive contrast is performed between the achievements of the Feature Analysis Detection Model and the Direct Image-Centric Detection Model in classifying rice leaf diseases. The results reveal that the highest performance is attained using the Feature Analysis Detection Model. We have also applied Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretability of the model's predictions. The adoption of the proposed Feature Analysis Detection Model for detecting rice leaf diseases holds excellent potential for improving crop health, minimizing yield losses, and enhancing the overall productivity and sustainability of rice farming.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する手法を中心に、特徴抽出モデルと直接画像モデルを比較・評価しており、植物病害フェノタイピング手法の開発・検証に該当する。

abstractThis study proposes Artificial Neural Network (ANN)-based image-processing techniques for timely classification and recognition of rice diseases.

Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust. →

Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.

Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能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ある。

abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection

Comprehensive AI framework for automated classification, detection, segmentation, and severity estimation of date palm diseases using vision-language models and generative AI. →

The date palm ( Phoenix dactylifera L.) is a vital crop in arid and semi-arid regions, contributing over $13 billion annually to the global economy. However, it faces significant yield losses due to pests, such as the red palm weevil, and diseases, including Bayoud and Black Scorch. Currently, expert visual inspection is the primary method of management, but it is time-consuming, subjective, and unsuitable for detecting large-scale or early-stage damage. Automated approaches based on classical machine learning offer limited improvements due to their lack of generalizability and environmental sensitivity. Recent deep learning methods, such as CNNs and Vision Transformers, have improved classification accuracy, but treat tasks like classification, detection, segmentation, and severity estimation as separate. This paper proposes an integrated Reveal-Aware Hybrid Vision-Language and Transformer-based AI framework that combines GAN-based augmentations for feature generation, CLIP for multimodal classification, PaliGemma2 for text-based detection, Grounding DINO + SAM 2.1 for zero-shot segmentation, and a Vision Transformer regression model for severity prediction. This end-to-end explainable diagnostic pipeline achieved 98% classification accuracy, 95.8% precision, 91.3% recall, and 94.2% F1-score across two datasets: nine classes of infected date palm leaves and three classes of date palm diseases. The proposed framework demonstrated detection accuracy of 94-98%, high-quality segmentations, and reliable severity estimates. This integrated approach highlights the potential of combining AI, vision-language models, and transformers for scalable, accurate, and sustainable plant disease management.

Why it matches plant phenotyping methods植物病害の分類・検出・セグメンテーション・重症度推定を統合した画像ベース手法の開発と評価が中心であり、感染植物の状態を直接推定するため収録対象。

abstractThis paper proposes an integrated Reveal-Aware Hybrid Vision-Language and Transformer-based AI framework

Optimised MobileNet for very lightweight and accurate plant leaf disease detection →

Abstract The development of accurate and efficient plant disease classification systems is vital for addressing the challenges of climate change and the growing global demand for food. This study presents $$\hbox {V}^2$$ PlantNet, a novel lightweight multi-class classification model based on a modified MobileNet architecture, designed to detect plant leaf diseases across a diverse range of crop types. $$\hbox {V}^2$$ PlantNet employs depthwise separable convolutions to significantly reduce model complexity without compromising accuracy. The architecture integrates Batch Normalization (BN) and Rectified Linear Unit (ReLU) activation after each convolutional layer, while a multi-stage design enhances feature extraction and overall performance. Despite its compact size, comprising only 389,286 parameters and requiring just 1.46 MB of memory, $$\hbox {V}^2$$ PlantNet achieved up to 99% training accuracy, with validation and test accuracies of 97% and 98%, respectively. Across most classes, precision, recall, and F1-scores ranged from 0.97 to 1.0, demonstrating consistent and robust generalization across diverse plant species. These architectural innovations enable $$\hbox {V}^2$$ PlantNet to outperform larger models such as ResNet-50 and Inception V3 in terms of computational efficiency, owing to its smaller model size (1.46 MB), reduced parameter count (389,286), and faster inference time (0.676 s), offering a scalable solution for real-time plant disease detection in precision agriculture.

Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する軽量な深層学習手法を開発し、精度・計算量・推論速度を比較評価しており、フェノタイピング手法が中心である。

abstractThis study presents $$\hbox {V}^2$$ PlantNet, a novel lightweight multi-class classification model based on a modified MobileNet architecture, designed to detect plant leaf diseases across a diverse range of crop types.

CitriBEiTNet: A Hybrid CNN-Transformer Architecture Combining MobileNetV2 with BEiT's Global Attention for Automated Citrus Leaf Disease Diagnosis →

Citrus farming plays an essential role in agriculture; however, diseases like canker, greening, black spot, and melanose significantly reduce yield and fruit quality. Efficient classification of citrus leaf diseases is important for crop health maintenance and optimal crop yield. Traditional methods for leaf disease detection are slow, labor-intensive, and often inaccurate, which highlights the need for automated solutions. This research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures. Using Bidirectional Encoder Representation from Image Transformers (BEIT) and MobileNetV2 as feature extractors, the proposed model captures distinctive features from images, which are then classified using Support Vector Machine (SVM). The dataset includes four different disease categories and a healthy class. Data augmentation techniques are applied to improve model robustness. The experimental findings demonstrate that CitriBEiTNet achieves a remarkable training accuracy of 99.82% and a testing accuracy of 99.57%, outperforming current leading techniques. This model provides an efficient, scalable, and economical approach for early disease identification, enabling farmers to take preventive measures and improve agricultural yields.

Why it matches plant phenotyping methods柑橘葉画像から病害状態を自動分類する深層学習手法の開発が研究の中心であり、植物の病害表現型を直接推定している。

abstractThis research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures.

B2-GraftingNet: A Hybrid Deep-Machine Learning Framework with Explainable AI for Automated Grape Leaf Disease Detection →

Owing to changing climatic and environmental conditions, plant diseases are becoming increasingly prevalent, posing a serious threat to global agriculture. Timely and accurate diagnosis remains challenging, especially where scouting still relies on manual inspection. We propose B2-GraftingNet, a deep learning framework for automated detection of grape leaf diseases. B2-GraftingNet is a streamlined variant of our earlier B4-GraftingNet, retaining its strengths while simplifying blocks for faster inference and deployment. The architecture combines a VGG16 backbone with Inception-style blocks inside a custom CNN to extract robust, multi-scale features based on color, size, and shape. To reduce redundancy and improve generalization, Binary Particle Swarm Optimization (BPSO) selects informative features prior to classification. We evaluate Support Vector Machines (SVM) and k-Nearest Neighbors (KNN); a cubic SVM attains 99.56% peak accuracy on the public Kaggle grape-leaf dataset. For context, we also benchmarked standard pretrained CNNs on the same data, observing validation accuracies of 34.04% (VGG16), 34.04% (VGG19), 97.95% (Xception), 94.91% (Darknet), and 98.44% (ResNet-50); B2-GraftingNet matches or exceeds these while remaining lighter and faster to train and deploy. To enhance transparency and actionability, we pair Grad-CAM, LIME, and occlusion-sensitivity visualizations with a local gpt-oss:20b assistant (served via Ollama) that converts evidence into plain, grower-focused guidance and supports interactive chat validated by horticulturists. Results are further checked against expert-annotated ground-truth labels, confirming high accuracy and computational efficiency. Overall, B2-GraftingNet offers a reliable, interpretable, and scalable solution for early grape-leaf disease detection. The complete setup (code, model, web platform, configuration, and assets) is available on Zenodo: https://doi.org/10.5281/zenodo.17353656.

Why it matches plant phenotyping methodsブドウ葉の病徴を画像から検出する深層学習手法を開発し、複数モデル・専門家アノテーションと比較検証しているため、植物フェノタイピング手法が中心である。

abstractWe propose B2-GraftingNet, a deep learning framework for automated detection of grape leaf diseases.

Explainable transformer framework for fast cotton leaf diagnostics and fabric defect detection. →

This study introduces a hybrid deep learning model that combines CNN-based hierarchical feature extraction with light-efficient vision transformer self-attention to classify multiple types of cotton leaf diseases and fabric defects. Using Explainable AI (XAI) techniques, the framework enhances interpretability, allowing domain experts to better understand the model's decisions. Evaluated on four benchmark datasets, the proposed XCottL-FebViT achieved consistent improvements in accuracy, MCC, and F1 Score compared with leading transformer-based models, while maintaining computational efficiency through hyperparameter optimization. For CottonLeafNet and SAR-CLD, it attained training accuracies of 99.97% and 99.95%, with validation accuracies of 99.93% and 99.91%, respectively. In fabric defect classification, the model achieved 99.97% training accuracy on CottonFabricImageBD and FabricSpotDefect, with validation accuracies of 99.93% and 99.95%, respectively. A lightweight web-based application enables practical deployment for remote disease and defect detection. This work highlights the integration of interpretability, efficiency, and high performance in AI-driven agricultural and textile quality assessment.

Why it matches plant phenotyping methods綿花葉の病害を画像から分類する深層学習手法の開発・比較評価が中心であり、植物の病害状態を直接推定するため、フェノタイピング方法論として採用する。

abstractThis study introduces a hybrid deep learning model that combines CNN-based hierarchical feature extraction with light-efficient vision transformer self-attention to classify multiple types of cotton leaf diseases and fabric defects.

Genome-wide association mapping and candidate genes analysis of high-throughput image descriptors for wheat frost tolerance. →

Repeated occurrences of extreme weather events, such as low temperatures, due to global warming present a serious risk to the safety of wheat production. Quantitative assessment of frost damage can facilitate the analysis of key genetic factors related to wheat tolerance to abiotic stress. We collected 491 wheat accessions and selected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage. Image descriptors can complement the visual estimation of frost damage. Combined with genome-wide association study (GWAS), a total of 107 quantitative trait loci (QTL) (r 2 ranging from 0.75% to 9.48%) were identified, including the well-known frost-resistant locus Frost Resistance (FR)-A1/ Vernalization (VRN)-A1. Additionally, through quantitative gene expression data and mutation experience verification experiments, we identified two other frost tolerance candidate genes TraesCS2A03G1077800 and TraesCS5B03G1008500. Furthermore, when combined with genomic selection (GS), image-based descriptors can predict frost damage with high accuracy (r ≤ 0.84). In conclusion, our research confirms the accuracy of image-based high-throughput acquisition of frost damage, thereby supplementing the exploration of the genetic structure of frost tolerance in wheat within complex field environments.

Why it matches plant phenotyping methods小麦の霜害を画像記述子で定量評価し、その精度を検証しているため、画像ベース植物フェノタイピングが研究の中心です。

abstractselected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage.

ORANGE PLANT LEAF DISEASE DETECTION AND CLASSIFICATION WITH IMAGE PROCESSING USING A DEEP CONVOLUTIONAL NEURAL NETWORK →

The farming of citrus is a crucial component of Pakistan’s fruit-based agricultural economy. But, the foliar diseases citrus canker, black spot, and greening have been posing a constant threat on citrus’s productivity. An optimal solution is an early and accurate detection of these diseases to improve the productivity. Therefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs). The proposed solution has five stages: image acquisition (dataset), preprocessing, data augmentation, deep feature extraction and optimization, and disease classification. Firstly, the images are obtained from a public dataset downloaded from Kaggle. Secondly, preprocessing techniques are used to improve the image quality and shape, thirdly the data augmentation techniques are used to enhance the model generalization, fourthly pre-trained models DenseNet-121, MobileNet, and InceptionV3 with transfer learning technique to extract deep features, and finally Adam optimizer and categorical cross-entropy loss function are used to fine tune the pre-trained models for classifications. The proposed model is evaluated on accuracy, precision, recall, and F1-score metrics. All the models demonstrated robust performance while DenseNet-121 achieved the best performance. The evaluation results assured the robustness of the use of transfer learning-based DCNN in citrus leaf disease detection.

Why it matches plant phenotyping methods柑橘葉の病害状態を画像から直接検出・分類する深層学習ワークフロー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。

abstractTherefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs).

Machine learning-enabled UAV hyperspectral identification of tomato spotted wilt virus in tobacco. →

Problems Tomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control. Methodology A UAV-mounted hyperspectral camera (400-1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms-support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting -with the derived feature variables. Results This study demonstrates that while all integrated models combining Relief- and Correlation- selected feature bands with three machine learning algorithms delivered excellent performance, the SVM-Relief model achieved the most outstanding results (OA = 97.3%, AUC = 0.994, Kappa=0.947). Based on the SVM-Relief combination, a proposed method called RPR -which integrates PCA with recursive feature elimination- was further employed to reduce the number of feature indicators from 15 to 4 (775.6/772.9/781.1/756.4 nm). The resulting SVM-RPR combination model achieved performance (OA = 97.3%, AUC = 0.990, Kappa=0.947) comparable to that of the SVM-Relief model. Contribution This indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習により、タバコ個体のウイルス感染状態を圃場規模で推定する手法の開発・評価が研究の中心であるため。

abstractThis study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control.

AI-Enabled Intelligent System for Automatic Detection and Classification of Plant Diseases Towards Precision Agriculture →

Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture.

Why it matches plant phenotyping methods植物葉の病徴領域を画像から検出・分類する深層学習手法の開発が中心であり、植物の病害状態を直接推定するため、方法論文として採用する。

abstractwe used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases.

Sustainable Plant Disease Management with Real-Time Crop Optimization →

Plant diseases significantly threaten global food security, often leading to severe yield losses and unsustainable reliance on chemical usage and pesticides. This paper presents an integrated, real-time system for sustainable plant disease management using Internet of Things (IoT) sensors, deep learning models, and cloud-edge computing. The proposed framework enables early disease detection and adaptive crop optimization by fusing environmental telemetry with AI-driven image diagnostics. Using the PlantVillage dataset and real-world sensor data, the system achieves 99.1% disease detection accuracy, a 27% reduction in pesticide usage, and a 22% improvement in crop yield, a critical metric in assessing the broader effectiveness of plant disease management strategies compared to leading benchmarks. Field trials confirm its efficacy in enhancing farm productivity while minimizing environmental impact. This work demonstrates a practical, scalable solution for precision agriculture that aligns with the principles of sustainability, resilience, and data-driven decision-making.

Why it matches plant phenotyping methods植物画像から病害状態を推定するAI診断とセンサー統合基盤が研究の中心であり、植物病害フェノタイプの実質的な取得・評価を行っている。

abstractThis paper presents an integrated, real-time system for sustainable plant disease management using Internet of Things (IoT) sensors, deep learning models, and cloud-edge computing.

Edge-enhanced dual branch CNN with adaptive attention for robust apple leaf disease detection. →

Accurate detection of apple leaf diseases remains a critical challenge in precision agriculture, where complex field conditions and subtle symptom variations often degrade model performance. This paper introduces a novel hybrid architecture combining enhanced spatial attention with edge-aware feature extraction to improve disease classification robustness. The proposed model integrates a multi-scale feature fusion module to capture both local lesion patterns and global contextual cues, while a lightweight attention mechanism dynamically prioritizes disease-relevant regions. Experiments on a curated dataset of 12,350 apple leaf images demonstrate the effectiveness of proposed approach, achieving 96.7% classification accuracy across six disease categories - a significant improvement over baseline models like EfficientNet-B4 (94.1%) and ResNet-50 (93.8%). The system particularly excels in detecting early-stage infections, showing 15% higher precision for subtle scab lesions compared to existing methods. With only 3.2 million parameters, the model maintains practical deployment potential for edge devices in orchard environments. These advances address key limitations in current vision-based plant disease detection systems while balancing accuracy and computational efficiency for real-world agricultural applications.

Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を推定するCNN手法の開発と評価が研究の中心であり、植物病害表現型の画像ベース計測に該当する。

abstractThis paper introduces a novel hybrid architecture combining enhanced spatial attention with edge-aware feature extraction to improve disease classification robustness.

Detection techniques for tomato diseases under non-stationary climatic conditions. →

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.

Image-based cotton leaf disease diagnosis using YOLO and faster R-CNN techniques. →

Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts producing slow processes and human errors. Automated detection methods establish low accuracies, limited scalability and real time applications. To tackle the research issue, this study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms into a single action to achieve ultimately real time detection of accurate diseases the combination helps identify both the high detection speed of YOLOv5 along with Faster-RCNN regional proposal accuracy. The new method is that the compilation of these two modern object detection methods has been compiled and designed specifically for detecting leaf disease across varying environmental conditions. Notable contributions to this method include increases in classification accuracy, processing speed, real time detection making these methods suitable for farmers agronomists and sensor deployment. CLD-Net integrates YOLOv5 and Faster R-CNN, combining real-time detection capability with precise classification, to deliver robust cotton leaf disease identification. Experimental validation on a curated dataset of cotton leaf images demonstrates the superiority of CLD-Net, achieving an accuracy of 96.7%, which surpasses that of traditional models. These results confirm the potential of the proposed approach to revolutionize crop disease detection, leading to timely intervention and increased yield.

Why it matches plant phenotyping methods綿花葉の病害状態を画像から推定する深層学習手法を開発し、画像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す。

abstractthis study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms

High-throughput plant phenotyping identifies and discriminates biotic and abiotic stresses in tomato →

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).

Deep learning-based phenotyping of lettuce diseases using Efficient-FBM-FRMNet for precision agriculture. →

Lettuce ( Lactuca sativa ), a widely cultivated leafy vegetable, is highly susceptible to bacterial and fungal infections that severely reduce yield and quality. Rapid and accurate disease identification is therefore essential for precision agriculture and sustainable crop management. This study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection. The model integrates EfficientNetB4 with dilated convolutions, a Feature Bottleneck Module (FBM) for redundancy reduction, a Reasoning Engine for higher-order semantic inference, and a Feature Refinement Module (FRM) for enhanced generalization. The framework was trained and validated on a publicly available dataset of 2,813 lettuce leaf images (bacterial, fungal, and healthy classes) using stratified 5-fold cross-validation. The proposed Efficient-FBM-FRMNet achieved an overall accuracy of 97.5%, outperforming baseline CNNs such as EfficientNetB4, ResNet50, and DenseNet121. It demonstrated superior precision (96.0%), recall (96.6%), and F1-score (97.0%), confirming its robustness and consistency across multiple folds. Statistical significance analysis (p

Why it matches plant phenotyping methodsレタス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し、公開データセット上で交差検証・ベースライン比較により性能を評価しており、植物表現型取得手法が中心である。

abstractThis study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection.

Improved multiscale attention based deep learning approach for automated sugarcane leaf disease detection using BSRI data. →

Early and accurate detection of sugarcane leaf diseases is critical for improving crop productivity and reducing economic losses in the agricultural sector. Timely interventions enable sustainable crop management and better resource use. In this study, we propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN). The MADRN model integrates dense residual learning and multi-scale attention mechanisms to effectively capture fine-grained, disease-specific features and address challenges related to domain variability and complex data patterns. Two datasets are used to evaluate the model: a Kaggle dataset and a blended dataset created by combining Kaggle images with those from the Bangladesh Sugarcrop Research Institute (BSRI), simulating real-world conditions. All images undergo preprocessing steps, including resizing, normalization, and data augmentation, before training. Additionally, several baseline models (CNN, VGG16, MobileNetV2, and XceptionNet) are fine-tuned and compared with the MADRN model. Experimental results demonstrate that MADRN consistently outperforms baseline models in accuracy, precision, recall, and F1-score across both datasets, achieving up to 94.78% accuracy on the Kaggle dataset and 92.25% on the blended dataset. These findings highlight MADRN's superior ability to learn discriminative features and generalize effectively across diverse data sources, making it a promising tool for precision agriculture and disease management. To facilitate practical implementation, a web-based application is developed, enabling real-time and user-friendly disease detection. This research lays a strong foundation for the development of accurate, scalable, and practical disease classification tools that can support sustainable agricultural practices.

Why it matches plant phenotyping methodsサトウキビ葉の画像から病害状態を推定する深層学習手法を開発し、複数データセットとベースラインで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す。

abstractwe propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN).

Predicting plant stress using SAM-L: novel self-adaptive-meta learner with XAI based on soil moisture and chlorophyll analysis. →

Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model's architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.

Why it matches plant phenotyping methods植物ストレス状態を土壌水分とクロロフィルから推定するSAM-L・XAI・LSTM統合手法が研究の中心であり、植物の生理状態を対象とした計算的フェノタイピング手法に該当する。

abstractIn this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content.

Ensemble Learning Framework for Image-Based Crop Disease Detection Using CNN Models →

Crop diseases pose a significant threat to global food security, causing substantial yield losses estimated at 10-40% annually. Traditional methods of disease identification, reliant on visual inspection by farmers or experts, are often subjective, time-consuming, and limited by the availability of specialists. This study proposes an ensemble learning framework for robust image-based crop disease detection, specifically designed to address the challenges of heterogeneous, non-Independent and Identically Distributed (non-IID) agricultural datasets in decentralized environments. Utilizing the Plant Village dataset, we implement a stacking ensemble model integrating diverse Convolutional Neural Networks (CNNs) such as VGG (Visual Geometry Group), ResNet, and Inception as base learners, with a meta-learner to optimize prediction fusion. The system employs comprehensive data preprocessing, including resizing, normalization, noise removal, segmentation, and augmentation, to enhance robustness against real-world variability. Transfer learning with ResNet50 was adopted as a baseline model. The baseline ResNet50 achieved 59% test accuracy across seven grape and potato disease classes. The ensemble model improved performance, attaining 63% accuracy with average precision, recall, and F1-scores of 56%, 52%, and 52% respectively. Class imbalance remained a limiting factor for certain categories. The ensemble learning approach outperformed individual models, demonstrating improved generalization across diverse datasets. Although computational demands and imbalance challenges persist, the system provides a promising AI-driven pipeline for accurate crop disease diagnosis, supporting sustainable agricultural practices.

Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNアンサンブル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手法に該当する。

titleEnsemble Learning Framework for Image-Based Crop Disease Detection Using CNN Models

Attention guided convolutional neural network with explainable AI for papaya leaf disease detection in edge and drone agricultural systems. →

Existing disease discovery in papaya leaves is most significant in achieving yield and profitability stability in the tropics but has proven difficult in the presence of deficiencies in manual exploration and tailored crop models in crop-AI systems. Therefore, this study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves. For real-world deployment in scarce-resource farming contexts, PapayaNet adopts batch norm and hierarchical attention steps in five convolution stages and accelerates both computational celerity and discriminability. Trained on 6618 manually annotated orchard images sourced from orchards in Bangladesh at a very high resolution, it has a 98.79% classification accuracy, all of which was realized using 483,926 parameters and an average infer time of 0.01 s, which is significantly better when evaluated using EfficientNetB6, DenseNet121, and VGG16. XAI methods, including Grad-CAM and LIME, showed model decisions towards the biologically informative parts of the leaf, thus boosting interpretability and user confidence. Systematic ablation analysis also confirmed the importance of distributed attention in ensuring robust generalization towards visually similar disease classes. An in-browser diagnostic portal deployed using Gradio provides intra-browser predictive deployment and interpretability overlay in real time, thus inviting field practicability. Given its low-latency inference and minimal computational footprint, PapayaNet is well-suited for integration into edge devices and drone platforms, offering a scalable solution for real-time in-situ crop health monitoring. This study advances the field of precision agriculture by delivering a crop-specialized, explainable, and deployable AI system for sustainable management of papaya diseases.

Why it matches plant phenotyping methodsパパイヤ葉の病害・健全状態を画像から分類するCNN手法を開発し、データセット、比較評価、アブレーション、実運用ポータルまで中心的に扱っているため、植物病害フェノタイピング手法に該当する。

abstractthis study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves.

Plant disease detection using a hybrid dilated CNN with attention mechanisms and optimized mask RCNN segmentation. →

In accordance with human life, agriculture has main role in it, and in addition to that most people are involved in some kind of agricultural activity either in a direct or indirect manner. Moreover, the agricultural sectors acquired a major role in supplying better quality food and thus made the greatest attribution to the growth of populations and economics. But, the disease over the crop has influenced the growth of the corresponding species and thus requires an earlier diagnosis of plant disease by utilizing the most adequate and automatic detection approach for improving the quality of the production of food as well as to reduce the loss in economic. But, there are no techniques in the conventional system for identifying the disease in diverse crops in the agricultural environment. In modern times, deep learning approaches have acquired tremendous enhancement in the identification of image categorization as well as the object detection system. For precise detection of plant disease, an improved classification model is developed. Initially, from the standard publicly available database, the images of the plants are aggregated. The gathered images are segmented using Dilated, Adaptive, and Attention-based Mask Recurrent Convolutional Neural Networks (DAA-MRCNN). Then, it is fed into a hybrid classification phase, where the new model namely Dilated, Adaptive, and Attention-based Multiscale DenseNet termed as (DAA-MDeNet) for classification. The classifier performance is improved by optimizing the parameter in Mask RCNN and Multiscale DenseNet using the hybrid optimization algorithm named African Vulture and Lemur Optimizer (AVLO). When compared with the other model, a superior performance is shown in the proposed model.

Why it matches plant phenotyping methods植物画像から病害をセグメンテーション・分類する深層学習手法を開発しており、罹病状態の推定が中心的な方法論的貢献である。

abstractFor precise detection of plant disease, an improved classification model is developed.

CottonNet-MHA: a multi-head attention-based deep learning framework for cotton disease detection. →

India is an agro-based country. The major goal of agriculture is to produce disease-free healthy crops. For Indian agronomists, cotton is a profitable commercial and fibre crop, it is the world's second-biggest export crop after China. Cotton production is also affected in a negative way by high use of water, authority of soil erosion and the practice of using dangerous fertilizers and pesticides. The two greatest threats to the rapid growth of the crop are the sucking bugs and cotton diseases. Prompt detection and accurate identification of diseases is vital to ensure healthy crop growth and achieve better yields. The primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops. Deep learning is used because of its exceptional results in classification and image processing tasks. To address this issue, we developed CottonNet-MHA a novel deep learning framework to identify pathological symptoms in cotton leaves. The model employs multi-head attention mechanisms to strengthen feature learning and highlight the diseased-affected regions. To evaluate the performance of the proposed model, five pretrained transfer learning architectures-VGG16, VGG19, InceptionV3, Xception, and MobileNet were used as benchmark models. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was applied to enhance the trustworthiness and interpretability of the model. A web-based application was developed to deploy the trained model for real-world applicability. The performance analysis is carried out on the developed model based on the conventional models and the results indicate that CottonNet-MHA dominates the conventional models with respect to its accuracy as well as efficiency in the detection of diseases. The use of attention mechanisms approach strengthens the model's diagnostic accuracy and overall reliability. Grad-CAM results further demonstrated that the model effectively targets diseased areas, enhancing interpretability and reliability. Discussion: The study shows that CottonNet-MHA not only automates disease detection but also enhances interpretability through Grad-CAM analysis. The developed web platform allows the model to be applied in real-world environments, supporting live disease monitoring. The proposed framework not only improves the accuracy of cotton disease diagnosis but also offers potential for extension to other crop disease detection systems.

Why it matches plant phenotyping methods綿花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、既存モデルとの比較検証とGrad-CAMによる病変領域の解釈を行っており、植物病害状態の表現型取得が中心である。

abstractThe primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops.

Pomegranate disease detection and classification dataset for deep learning applications: A case study from Halabja city. →

Timely and accurate detection of pomegranate fruit diseases is critical for minimizing crop losses, preserving fruit quality, and supporting sustainable agricultural practices. This study introduces the Halabja Pomegranate Fruit Disease Image Dataset, a systematically compiled collection of images from orchards in one of Iraq's major pomegranate-producing regions. The dataset comprises 2178 original images and 28,314 augmented images, categorized into four specific classes: ectomyelois ceratoniae, colletotrichum spp., sunburn, and healthy fruit samples. To create an ecological setting and ensure significant class variation, images were captured in natural outdoor environments. A standard preprocessing step was applied, which involved resizing all images to 512×512 pixels and using several image augmentation techniques to improve the flexibility and robustness of machine learning models. The unique characteristics of this dataset make it highly suitable for developing machine learning and deep learning models aimed at plant disease detection and other computer vision tasks in precision agriculture. Its contextual relevance and content diversity make it valuable for building an effective diagnostic tool capable of functioning in real field conditions.

Why it matches plant phenotyping methods植物病害状態を画像で分類するデータセットの構築が中心で、再利用可能な植物表現型データとして適格です。

abstractThis study introduces the Halabja Pomegranate Fruit Disease Image Dataset

Unlocking plant health survey data: An approach to quantify the sensitivity and specificity of visual inspections. →

Invasive plant pests and pathogens cause substantial environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified. As knowing sensitivity and specificity of visual inspection is critical for effective contingency planning and outbreak management, we address this deficiency using empirical data and statistical analyses. Twenty-three citizen scientist surveyors assessed up to 175 labelled oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these individual trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the 'gold-standard' (i.e., assuming perfect sensitivity and specificity). The utility of an approach using Bayesian modelling to estimate the sensitivity and specificity of visual inspection in the absence of a rarely available 'gold-standard' dataset was then examined with simulated plant health survey datasets. There was large variation in sensitivity and specificity between surveyors and between different symptoms, although the sensitivity of detecting a symptom was positively related to the frequency of the symptom on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with reliable prior knowledge of sites with a higher (~0.6) and lower (~0.3) true disease prevalence we show that sensitivity and specificity can be estimated without 'gold-standard' data using Bayesian modelling. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management.

Why it matches plant phenotyping methods植物の病徴を対象とする目視検査の感度・特異度を定量化し、ゴールドスタンダードなしで推定するベイズモデルを検討しており、植物病害状態の取得・評価法が研究の中心である。

abstractVisual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified.

VitiForge: a new procedural pipeline approach for grapevine disease identification under data scarcity. →

Early identification of grapevine diseases is critical for reducing yield losses and ensuring sustainable viticulture. CNNs trained on benchmark datasets such as PlantVillage often achieve near-perfect accuracy, yet this performance fails to translate to real-world field conditions where lighting, backgrounds, and lesion appearance vary widely. To address challenges of data scarcity and imbalance, this study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions. VitiForge is systematically evaluated against GAN-based augmentation through a data ablation study on PlantVillage and FieldVitis, a curated field dataset, using MobileNetV2, InceptionV3, and ResNet50V2 classifiers. Results show that VitiForge significantly improves performance in low-data regimes, enabling model training even without real samples, whereas GAN augmentation proves more effective once sufficient real data is available. On field imagery, VitiForge often matched or surpassed GAN-based methods, particularly when paired with MobileNetV2. These findings highlight the complementary roles of procedural and GAN-based synthetic data: VitiForge offers flexibility and scalability under cross-domain and data-scarce conditions, while GANs enhance realism and variability when ample data exists. Together, they support the development of robust and generalizable models for automated grape disease detection in precision agriculture.

Why it matches plant phenotyping methodsブドウ葉の病徴を画像から識別するための合成画像生成パイプラインを開発し、GAN augmentationと比較評価しているため、植物病害状態のフェノタイピング手法が中心である。

abstractthis study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions.

A hybrid vision transformer and ResNet18 based model for biotic rice leaf disease detection. →

Introduction Agriculture is crucial to human survival. The growing of biotic rice plants is very helpful for feeding a lot of people around the world, especially in places where rice is a main food. The detection of rice leaf disease is critical to increasing crop productivity. Methods To improve the accuracy of rice leaf disease prediction, this paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18). In general, the input images apply to the pre-trained ViT and ResNet18 models independently. The output features of these two models are combined and fed into the final Fully Connected (FC) layer, followed by a Softmax layer for final classification. Results The output of rice leaf diseases from the FC layer of the proposed hybrid ViT with ResNet18 model achieved 94.4% accuracy, a precision of 0.948, a recall of 0.944, an F1-Score of 0.942, and an Area Under Curve (AUC) of 0.985. Discussion The proposed hybrid model ViT-ResNet18 shows a 5%, 1%, and 1% improvement in accuracy compared to VGG16 with Neural Network, Inception V3 with Neural Network, and SqueezeNet with Neural Network classifier, respectively.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法の提案・比較評価が研究の中心であり、植物病害フェノタイピング手法に該当する。

abstractthis paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18).

Hyperspectral image analysis for classification of multiple infections in wheat. →

Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.

Why it matches plant phenotyping methods小麦葉の感染状態をハイパースペクトル画像と深層学習で分類する手法が研究の中心であり、植物病害状態の表現型推定に該当する。