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. →