Rice leaf diseases significantly reduce agricultural yield and pose a major challenge for sustainable food production, particularly owing to the limitations associated with manual and visual inspection methods that are subjective and often ineffective in early-stage detection. This investigation proposes a new Hybrid ResConvolutional Neural Network (HyResCN-Net) for effective classification and severity identification of rice plant leaf diseases. The proposed framework integrates an Internet of Things (IoT) -based data acquisition and routing simulation using CrowWhale Energy Trust Routing (CrowWhale-ETR) for efficient data handling. Initially, preprocessing is done by an averaging filter to reduce noise. Then, plant leaves are segmented using the Eff-UNet++ method. Augmentation techniques like rotation, scaling, and color change are applied to expand the dataset. Features, like entropy with Gradient Directional Pattern (GDP), Complete Local Binary Pattern (CLBP), and histogram features, are extracted to enhance feature representation. These features are then used within the proposed HyResCN-Net model, which integrates Parallel Convolutional Neural Network (PCNN) and ResNeXt to improve discriminative learning for disease classification and severity estimation. Experimental evaluation is conducted on the Rice Leaf Bacterial and Fungal Disease Dataset. Considering a k-value of 8, the HyResCN-Net gains an accuracy of 94.258%, a True Positive Rate (TPR) of 96.479%, a True Negative Rate (TNR) of 92.898%, a precision of 91.312% and an F1-score of 93.824% compared to existing methods. The HyResCN-Net efficiently enhances rice leaf disease identification and severity analysis, supporting its applicability in precision agriculture applications.
Why it matches plant phenotyping methodsイネ葉の病害分類と重症度という植物の状態を、画像取得・分割・特徴抽出・深層学習により推定する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractThis investigation proposes a new Hybrid ResConvolutional Neural Network (HyResCN-Net) for effective classification and severity identification of rice plant leaf diseases.
Abstract Deep learning models have achieved near-perfect accuracy in plant disease classification within controlled laboratory settings; however, their deployment in real-world agricultural environments is severely hindered by the “deployment gap”—a critical vulnerability to environmental corruptions such as sensor noise, motion blur, and occlusion. To bridge this gap, we propose the Recurrent Active Vision Agent (RAVA), formulating the disease detection task as a Partially Observable Markov Decision Process (POMDP). Unlike passive Convolutional Neural Networks (CNNs) that process images globally, RAVA mimics the active inspection behavior of human agronomists. Our architecture integrates a lightweight ResNet-18 backbone with a Recurrent Neural Network (RNN) and a Spatial Transformer Network (STN). Driven by Proximal Policy Optimization (PPO), the agent learns a sequential policy to intelligently navigate and zoom in on informative “glimpses,” effectively bypassing background clutter. To stabilize the reinforcement learning process and enforce noise-invariant feature representations, we introduce a hybrid objective incorporating Supervised Contrastive Learning (SupCon). Comprehensive experiments on a combined PlantVillage and PlantDoc dataset demonstrate RAVA’s overwhelming superiority under extreme conditions. In a “Severe Degradation” stress test, standard ResNet-50 accuracy collapses to 26.3%, whereas our active agent maintains a robust 77.0%. Under extreme noise and occlusion, RAVA preserves 54.6% accuracy compared to the baseline’s 17.2%. Notably, this resilience is achieved with merely ∼12M parameters—significantly fewer than large-scale Vision Transformers—proving that active visual attention, coupled with contrastive learning, offers a computationally efficient and highly robust pathway for field-ready precision agriculture.
Why it matches plant phenotyping methods植物病害状態を画像から診断するための能動視覚手法を開発し、環境ノイズ下で性能検証している。植物病害の画像ベース表現型推定が中心である。
abstractwe propose the Recurrent Active Vision Agent (RAVA), formulating the disease detection task as a Partially Observable Markov Decision Process (POMDP).
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.
Reproduction assets foundThe paper trains its Deep Q-CNN rice leaf disease classifier on public Kaggle rice leaf image datasets, which are cited with explicit public URLs and qualify as paper-specific phenotyping image inputs. The authors' own derived data/analysis artifacts are only available upon request, so no authors' code or trained modelDataset · publicSoni Gautam. Rice Leaf Bacterial and Fungal Disease Dataset. Kaggle. Available:Open asset ↗Kagglepdf-page:24 lines:1-94Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
High-throughput phenotyping is key in modern breeding for rapidly and cost-effectively evaluating salt-adaptive traits. However, few studies have combined spectral reflectance indices (SRIs) with deep learning to assess field-grown wheat under salt stress. In this study, we developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY) in 32 recombinant inbred lines (RILs) and four cultivars under 150 mM NaCl field conditions. ANOVA revealed that genotype contributed the largest proportion of the treatment sum of squares across all traits (60–80%), followed by the genotype × year interaction (8–15%), whereas year contributed the smallest proportion (1–5%). Heatmap clustering of these traits clearly distinguished salt-tolerant from salt-sensitive genotypes. The study findings highlight using four key traits as screening criteria for salt tolerance in wheat. Our optimized 2D/3D spectral indices showed moderate to strong predictive power (R 2 = 0.25–0.75), outperforming earlier indices. Multi-season data improved accuracy by 15–25%, with best predictions for Chl a and TChl (R 2 = 0.34–0.75) versus Chl b and GY (R 2 = 0.25–0.64). Top models included ANN-3D-SRIs-8 for Chl a (R 2 = 0.735/0.644), ANN-3D-SRIs-3 for Chl b (R 2 = 0.611/0.549), ANN-2D-3D-SRIs-3 for TChl (R 2 = 0.713/0.619), and ANN-2D-SRIs-2 for GY (R 2 = 0.648/0.553). This framework combines optimized indices and machine learning for scalable, high-throughput phenotyping to advance precision breeding of salt-tolerant wheat.
Why it matches plant phenotyping methodsスペクトル指数とニューラルネットワークを開発・評価し、コムギのクロロフィルと収量を高スループット推定する方法が研究の中心である。
abstractwe developed optimized 2D and 3D SRIs integrated with artificial neural network (ANN) to assess chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (TChl), and grain yield (GY)
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.
Reproduction assets foundThe paper's Data Availability statement points to two public repositories containing the data analyzed: a Kaggle PlantVillage dataset and a GitHub hyperspectral datasets repository. No author code or models are explicitly deposited.Dataset · publicAll data analyzed during this study are available in the Kaggle and Github repository, in the links https://www.kaggle.com/datasets/rohithaaiswarya/plant-village and https://github.com/antmedellin/HyperspectralDatasets .Open asset ↗Kaggle · rohithaaiswarya/plant-villagelines:563-583Dataset · publicAll data analyzed during this study are available in the Kaggle and Github repository, in the links https://www.kaggle.com/datasets/rohithaaiswarya/plant-village and https://github.com/antmedellin/HyperspectralDatasets .Open asset ↗GitHub · antmedellin/HyperspectralDatasetslines:563-583Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The tomato plant is considered one of the most important crops in the world, yet it is vulnerable to various diseases that affect crop quality and agricultural productivity. These challenges have driven the need for an efficient and intelligent plant disease detection system. With the development of computer vision and artificial intelligence, this proposed methodology based on deep learning for tomato leaf diseases has been presented. Two public datasets: Taiwan DS with nine classes and Tomato Leaf Diseases Detection Computer Vision Dataset (TLDDCV DS) with seven classes have been used to test this system. This system begins with plant image processing, which includes gamma correction and bilateral filtering, to enhance image quality and clarity while preserving key disease features. Then, a genetic metaheuristic algorithm was used to automatically select the most significant hyperparameters, further optimizing both processing time and accuracy. After that, the tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model. The YOLOv11n backbone is edited through a Data-efficient Image Transformer (DeiT) to improve the system's capacity for learning global contextual information and long-range dependencies. Experimental results demonstrate that the proposed system outperforms existing methods. It achieved an average mAP@50 of 97.8%, mAP@50-95 of 93.4%, precision of 97.3%, recall of 93.8%, and F1-score of 95.5% on the Taiwan dataset. Additionally, it achieved an average mAP@50 of 87%, mAP@50-95 of 48%, precision of 83.9%, recall of 70.3%, and F1-score of 76.4% on the TLDDCV dataset. These results demonstrate the generalizability and effectiveness of the proposed system in real-world agricultural situations.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・分類する深層学習手法の開発と2データセットでの性能評価が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractthe tomato leaf disease detection applies the You Only Look Once version 11 Nano (YOLOv11n) model.
Reproduction assets foundThe paper uses two public Roboflow tomato leaf disease image datasets and states its source code is publicly available on Zenodo, all with explicit availability statements and URLs.Dataset · publicThe first dataset is the Taiwan dataset, which can be found at the following link: (https://universe.roboflow.com/bryan-b56jm/tomato-leaf-disease-ssoha).Open asset ↗tomato-leaf-disease-ssohalines:317-328Dataset · publicThe second dataset is the TLDDCV dataset, which can be found at the following link: (https://universe.roboflow.com/sylhet-agricultural-university/tomato-leaf-diseases-detect)Open asset ↗tomato-leaf-diseases-detectlines:317-328Code · publicThe source code of the proposed framework, including the implementation of the proposed methodology and experimental setup, is publicly available in the Zenodo repository: https://doi.org/10.5281/zenodo.20777853 .Open asset ↗Zenodo · 10.5281/zenodo.20777853lines:317-328Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotLeafClassificationDisease symptoms / severity
Early and reliable diagnosis of hibiscus leaf diseases is critical to protect horticultural yield. Yet, it remains challenging under real-time field conditions where uncontrolled lighting, clutter, and the non-contiguous nature of pathological symptoms blur diagnostic cues. To address these challenges, we introduce CNN-FusionViT-GNN. This explainable hybrid multi-branch framework synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual modeling of a Vision Transformer (ViT), and the relational reasoning of a Graph Neural Network (GNN). The model is trained and validated on 'Hibiscus,' a curated field dataset of 1165 images from Bangladesh, which is strategically augmented to 8000 samples for robust training following a strict train-validation-test split. The proposed framework achieves a state-of-the-art accuracy of 98.33% with a macro F1-score of 0.98. The framework's generalization is confirmed through high performance on external datasets: 98.78% accuracy on the 52-class Plant City dataset and 83.88% on the 10-class Tomato Leaf Disease dataset, while maintaining a rapid inference time of 10-45 ms. Furthermore, a multi-faceted Explainable AI (XAI) audit using LIME, Grad-CAM++, ViT Attention Maps, and Occlusion Sensitivity validates that the model's decisions are driven by biologically meaningful symptom patterns rather than background artifacts. This study establishes a computationally efficient, transparent, and robust pathway for automated disease diagnosis in precision agriculture.
Why it matches plant phenotyping methodsハイビスカス葉の病徴を画像から分類するCNN-ViT-GNN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、植物病害表現型の取得・推定が中心である。
abstractwe introduce CNN-FusionViT-GNN. This explainable hybrid multi-branch framework synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual modeling of a Vision Transformer (ViT), and the relational reasoning of a Graph Neural Network (GNN).
Reproduction assets foundThe paper's primary Hibiscus leaf disease image dataset is publicly deposited on Mendeley Data, and the external Tomato Leaf Disease dataset used for validation is also publicly available on Mendeley Data. No author analysis code or trained model checkpoints are reported.Dataset · publicThe primary dataset generated and analyzed during the current study,“Hibiscus Leaf Diseases Classification Dataset,”is publicly available in Mendeley Data 7 .Open asset ↗Mendeley Datalines:307-347Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.
Why it matches plant phenotyping methods植物葉画像から病斑領域を抽出し、病害状態を分類する深層学習手法が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractthis research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification.
Accurate identification of rice diseases from field images is critical for crop health monitoring and sustainable agriculture, particularly in low-resource environments. However, most deep learning approaches depend on large-scale labeled datasets and pretrained backbones, limiting their applicability to rare or emerging diseases. In this work, we formulate a domain-specific prototype-based few-shot framework that avoids pretrained visual backbones and treats rice disease recognition as structured matching over a pathogen-aware class graph. The individual components, including wavelet-scattering features, optimal transport, semantic prototype fusion, and transductive refinement, are established techniques; the contribution lies in their coupled use within a disease-taxonomy-guided few-shot matching process. This design combines fixed visual descriptors, root-to-leaf prototype matching, class symptom descriptors, and confidence-gated refinement to support rice disease recognition under limited labeled data. We evaluate the model on two publicly available rice disease datasets-the Philippines Rice Diseases and Roboflow Rice-under 1-shot and 5-shot classification settings. In in-domain experiments, our approach achieves up to 95.8% accuracy and 94.9% macro-F1 on the Philippines dataset, consistently outperforming a diverse set of baselines including CNN-from-scratch, ResNet-18-from-scratch, Matching Networks, MAML, ProtoNet, RelationNet, SimpleShot, FEAT, and a flat optimal-transport variant. In cross-domain evaluation, the model demonstrates strong generalization capability, attaining up to 91.7% accuracy and 90.6% macro-F1 when transferring across datasets. An ablation study further confirms the consistent contribution of hierarchical structure, semantic fusion, and transductive refinement to performance gains. These results demonstrate that the proposed framework delivers highly accurate, robust, and data-efficient disease recognition, making it well-suited for real-world agricultural deployment under limited supervision.
Why it matches plant phenotyping methodsイネの病害を圃場画像から認識する手法を開発・評価しており、植物の病害状態を画像から推定する方法が研究の中心である。
abstractwe formulate a domain-specific prototype-based few-shot framework
Reproduction assets foundThe paper evaluates its few-shot rice disease recognition framework on two publicly available rice disease image datasets, with explicit public URLs in the Data Availability statement and dataset description sections. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe datasets used in this study are publicly available: Philippines Rice Diseases dataset (https://www.kaggle.com/
datasets/shrupyag001/philippines-rice-diseases)Open asset ↗Kaggle · shrupyag001/philippines-rice-diseasespdf-page:19 lines:1-81Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant diseases significantly affect agricultural productivity and global food security, while accurate disease identification remains challenging because of uncertain and overlapping visual symptoms in leaf images. Existing deep learning approaches often require large annotated datasets and suffer from limited interpretability in practical agricultural environments. This study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset. The objective is to develop an interpretable and reliable classification model capable of handling uncertainty in plant disease patterns through feature-driven fuzzy similarity analysis. The methodology integrates image preprocessing, color and texture feature extraction, variance-based feature weighting, prototype generation using K-means clustering, and fuzzy similarity computation using Mahalanobis distance and Gaussian membership functions. RGB, HSV, and Gray-Level Co-occurrence Matrix (GLCM) features are extracted from standardized leaf images and evaluated within an improved fuzzy soft classification framework. Performance comparison is carried out using machine learning models including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) implemented in Python using Scikit-learn libraries. Experimental simulation results demonstrate that the proposed framework achieves competitive classification performance while preserving interpretability and robustness under uncertain feature distributions. Performance evaluation is conducted through accuracy analysis, ROC-AUC curves, confusion matrices, ablation studies, and Wilcoxon Signed-Rank statistical testing. The proposed Improved Fuzzy Soft model achieved an accuracy of 88.57% which is less than LDA (94.92%), Random Forest (97.78%) and SVM (97.94%) classifiers. However, in the cross data set validation, the proposed Improved Fuzzy Soft model achieved an accuracy of 67.35% which is greater than LDA (51.02%), Random Forest (51.02%) and SVM (55.10%) classifiers. Statistical validation using the Wilcoxon Signed-Rank Test produced a p-value of [Formula: see text], confirming that the performance difference between the Improved Fuzzy Soft framework and the Random Forest classifier is statistically significant under the current experimental setting.
Why it matches plant phenotyping methodsトマト葉画像から植物病害状態を推定する解釈可能な画像解析・分類フレームワークを開発し、複数モデル、交差データセット検証、アブレーション、統計検定で評価しており、病害表現型の取得・抽出手法が中心である。
abstractThis study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset.
Reproduction assets foundThe paper uses public tomato leaf image datasets (PlantVillage and PlantDoc from Kaggle) as phenotyping inputs and states the authors' Improved Fuzzy Soft Framework implementation is publicly available on Zenodo with source code and reproduction instructions.Dataset · publicThe dataset analyzed during the current study are available in the repository:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗kaggle.com/datasets/abdallahalidev/plantvillage-datasetpdf-page:24 lines:1-75Code · publicThe implementation of the proposed Improved Fuzzy Soft Framework is publicly available through the Zenodo repository:
https://doi.org/10.5281/zenodo.20570546
The repository contains the source code, documentation, and instructions required to reproduce the experiments reported in
this study.Open asset ↗zenodo · 10.5281/zenodo.20570546pdf-page:25 lines:1-74Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
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)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.
Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。
abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
In recent decades, global weather patterns have shifted dramatically, introducing greater unpredictability into agriculture. A major challenge in plant breeding is developing selection strategies that remain accurate under such uncertainty. Sparse testing is a well-established approach to increase the number of genotypes evaluated in field trials while keeping costs manageable. However, incorporating image-based data into sparse testing remains challenging. We developed a strategy to integrate high-throughput phenotyping data into sparse testing in potato breeding to improve predictive performance in multi-environment trials. Our approach involved constructing an environmental kernel derived from the covariance matrix of image-based data. We assessed the predictive performance of several regression models under sparse testing, including those based on genomic or phenomic data alone and in combination. Models using only the proposed environmental kernel achieved predictive accuracies comparable to, or exceeding, those of genomic prediction models in various sparse testing scenarios. The best results were observed for tuber yield, a key trait in potato breeding. These findings highlight the potential of image-based environmental kernels to improve the efficiency and accuracy of sparse testing. This approach is cost-effective and scalable, particularly useful for breeding programs with limited resources.
Why it matches plant phenotyping methods画像ベースの高スループット表現型データを環境カーネルとして構築し、ジャガイモ育種の疎試験に統合する方法が研究の中心であるため、表現型予測手法として収載する。
abstractWe developed a strategy to integrate high-throughput phenotyping data into sparse testing in potato breeding to improve predictive performance in multi-environment trials.
To address this challenge, we propose StyleGAN3-T, the translation-equivariant alias-free variant of StyleGAN3, as a generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. Accurate detection of plant leaf diseases is essential for sustainable agriculture and early intervention. However, deep learning models often struggle with small, imbalanced datasets that limit generalization and robustness. To address this challenge, we propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. The proposed approach ensures translation-equivariant, artifact-free image synthesis and enhanced feature diversity. A balanced dataset of 18,000 images was developed by combining real and StyleGAN3-T-generated samples. In pooled GAN benchmarking, StyleGAN2-ADA achieved the strongest generative-quality metrics, whereas StyleGAN3-T was selected as the preferred augmentation model because its alias-free synthesis and spatial consistency yielded superior downstream classification performance in the proposed pipeline.
Why it matches plant phenotyping methods植物病害画像を合成・認識する画像解析手法が研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractwe propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition.
Reproduction assets foundThe paper's grape leaf disease image inputs are two publicly available Kaggle datasets explicitly named in the Data Availability statement. No author code, models, or synthetic dataset deposit is provided; other processed data is request-only.Dataset · publictechnical guidance. Y.L. and A.W. supervised the study, provided critical revisions, and contributed to the interpretation of results. All authors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other daOpen asset ↗Kaggle · rm1000/grape-disease-dataset-originallines:549-576Dataset · publicthors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other data used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing inOpen asset ↗Kaggle · jawadulkarim117/grape-leaf-disease-4-classlines:549-576Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract This paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks. Unlike conventional antennas designed for free-space operation, the proposed antenna is explicitly engineered to operate in close proximity to a lossy, anisotropic, and dispersive cylindrical medium representing wood tissues. A physics-based electromagnetic model of the stem is incorporated into the design process, enabling accurate optimization under realistic dielectric loading conditions. The antenna consists of a compact quasi-planar dipole with blended arms integrated with a medium-adaptive dual-ring balun that ensures balanced current excitation and stable impedance matching under strong near-field loading. Both simulation and experimental measurements demonstrate wideband impedance matching to a 50 Ω source over the 2.0–3.0 GHz frequency range. Surface current distribution and specific absorption rate (SAR) analyses confirm efficient electromagnetic coupling into the stem tissues with minimal radiation leakage. To evaluate the sensing capability of the proposed design, a conceptual two-element antenna system is introduced as a feasibility study for cavity detection. The detection performance is assessed through a sensitivity-driven framework based on variations in both self- and mutual-scattering parameters. A comprehensive sensitivity analysis is conducted to quantify the response of the system to changes in cavity diameter, radial position, and angular location. The results demonstrate that while the reflection coefficient is primarily sensitive to near-surface inhomogeneities, the mutual coupling between antenna elements provides strong and reliable sensitivity to internal cavity characteristics. Based on the sensitivity analysis, an optimal operating frequency band centered at 2.76 GHz and an appropriate antenna clearance are identified to maximize detection performance. The proposed antenna and sensing methodology are further validated through experimental measurements, confirming the consistency with numerical results. Simulation results demonstrate cavity-detection sensitivity, while experimental measurements validate the antenna impedance matching and mutual-coupling characteristics. The compact geometry of the antenna enables scalable multi-element configurations, establishing a practical framework for non-destructive, microwave-based monitoring of internal tree degradation in agricultural and forestry applications.
Why it matches plant phenotyping methods植物茎内の空洞という状態をマイクロ波で非破壊検出するアンテナとセンシング手法を開発し、シミュレーションおよび実験で検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks.
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.
Reproduction assets foundThe paper's data availability statement lists the public plant-disease image datasets used (PlantVillage, AI Challenger mirror, PlantDoc, Tomato Leaf Disease), and the code availability statement provides an authors' GitHub repository containing the RareSimGAN implementation, preprocessing, classifier training, andGradDataset · publicThe datasets analysed during the current study are publicly available from the following sources:
•PlantVillage dataset:
https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗pdf-page:36 lines:1-66Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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.
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.
Reproduction assets foundThe paper's Data availability statement explicitly links the public PlantVillage (Mendeley) and PlantDoc (GitHub) leaf-image datasets used for its experiments, and provides the authors' LeafLiteX source code on GitHub.Dataset · publicThe dataset used in this study is publicly available from the repository:
https://data.mendeley.com/datasets/tywbtsjrjv/1Open asset ↗data.mendeley.com · tywbtsjrjv/1pdf-page:29 lines:1-74Code · publicThe source code is available on the following link: https://github.com/phdpawan/LeafLiteX.Open asset ↗github.com/phdpawan/LeafLiteXpdf-page:29 lines:1-74Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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.
Reproduction assets foundThe paper's cotton leaf disease image datasets are publicly available: the authors' primary field-collected dataset on the first author's GitHub repository, and the secondary Kaggle dataset used for comparison. No analysis code or trained model checkpoints are explicitly deposited.Dataset · publicbia.
Funding: This work was supported by Princess Nourah bint Abdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningOpen asset ↗https://github.com/mnaeem303/Cotton-Leaf_Disease-Datasetpdf-raw-page:29 lines:1-54Dataset · publicbdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningful ways. All the
authors have read and approved the final manuOpen asset ↗pdf-raw-page:29 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset collected from https://www.kaggle.com/datasets/emmarex/plantdisease (PlantVillage dataset) for algorithm testing in plant disease diagnosis 35 . The selected rice leaf disease samples from PlantVillage dataset consisting of 3050 colour leaf images across four classes.Open asset ↗Kaggle · emmarex/plantdiseaselines:85-97Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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.
Reproduction assets foundThe paper's phenotyping input is the public UCI Rice Leaf Diseases dataset (paddy leaf images of bacterial leaf blight, leaf smut, brown spot), also mirrored on Kaggle. No author analysis code, trained model, or supplementary assets are disclosed.Dataset · publicThe dataset is publicly available
at: https://archive.ics.uci.edu/dataset/486/rice+leaf+diseases. The dataset is distributed
under the Creative Commons Attribution 4.0 (CC BY 4.0) license.Open asset ↗rice+leaf+diseasespdf-page:7 lines:1-33Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Plants face a range of biotic and abiotic stresses that reduce yield, and in agricultural countries like Bangladesh, manual detection of these stresses remains slow and error-prone. Convolutional Neural Networks (CNNs) classify plant leaves accurately, but they suffer from catastrophic forgetting when trained on tasks sequentially. This is a major obstacle in real agricultural settings, where new crops and stress conditions arrive over time. Existing continual learning (CL) studies in this domain rely on relatively heavy backbones, leaving open the question of whether lightweight CL pipelines can retain prior-task knowledge under realistic resource constraints. We investigate this question by integrating two established CL methods, Elastic Weight Consolidation (EWC) and Learning without Forgetting (LwF), into EfficientNet-B0, a backbone with only 5.3M parameters. The setup is evaluated on the Nutrispace cucurbit nutritional deficiency dataset, where three plant species (ash gourd, bitter gourd, and snake gourd) are treated as three sequential tasks, each with the same three classes: healthy, nitrogen deficiency, and potassium deficiency. Without continual learning, accuracy on the earliest task collapses to 30% by the end of training. EWC preserves over 61% accuracy on prior tasks while reaching 98% on the final task, and LwF reaches 98% on the final task with slightly lower retention on earlier ones. Pairwise Welch's t-tests confirm that both methods significantly outperform the baseline ([Formula: see text]) and that EWC retains prior-task knowledge significantly better than LwF ([Formula: see text]). These results show that lightweight CNNs paired with established CL techniques offer a workable path for plant stress classification in resource-constrained agricultural AI.
Why it matches plant phenotyping methods植物葉画像から栄養欠乏・健全状態を分類するCNNに、継続学習手法を組み込んで性能保持を評価しており、植物ストレス状態の取得・推定手法が研究の中心である。
abstractConvolutional Neural Networks (CNNs) classify plant leaves accurately, but they suffer from catastrophic forgetting when trained on tasks sequentially.
This paper presents a deep learning model to detect cotton plant pests and classify diseases, which must overcome limited datasets, class imbalance, and feature redundancy. At the preprocessing phase, the Gaussian blur filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to sharpen images by improving their clarity and contrast. To increase and diversify the data, Spa-GAN-based data augmentation is used to produce realistic synthetic samples. To obtain an accurate Region of Interest (RoI), an Attention-Guided Multi-Scale Residual U-Net (AGMS-U-Net) is considered to segment local and global structural information. The proposed framework makes three major contributions: (i) a new attention-based feature extractor, TriAttnNet, that incorporates spatial, channel, and contextual attention to represent diseases on a fine-grained level (ii) a new optimization strategy, Hybrid Mongoose Ray Chaotic Optimization (HMRCO), which includes chaotic strategies to better tune the parameters and explore the feature space and (iii) classification layer with focal loss for final decision. Experimental analyses prove that the suggested method is much more effective than the current state-of-the-art models, providing a powerful and understandable solution to precision agriculture and sustainable cotton crop health monitoring. Experimental results show that TriAttnNet achieves 98.66% accuracy, 98.71% recall, and 98.81% F1-score, which is better than the state-of-the-art algorithms, such as EfficientNetB1-CBAM (96.38%) and BERT-ResNet-PSO (95.69%). The proposed system is computationally feasible and interpretable, and it is interpretable to provide a practical solution to precision agriculture and sustainable monitoring of the health of cotton crops.
Why it matches plant phenotyping methods綿花植物の画像から病害を分類する深層学習手法を開発・評価しており、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。
abstractThis paper presents a deep learning model to detect cotton plant pests and classify diseases
Reproduction assets foundThe paper's plant-phenotyping input is the public Kaggle Cotton Plant Disease Dataset (Dhamodharan R), explicitly cited as the study's data source with a matching public URL. The authors' model/code is not publicly deposited (available only upon request), so no qualifying code asset exists.Dataset · publicThe dataset of this study is taken from the publicly available Kaggle repository, Cotton Plant Disease Dataset 43Open asset ↗Kagglelines:48-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Tomatoes are the most significant and widely consumed crops globally. Leaf diseases cause an important threat to crop production and quality. Further, various fungi, bacteria and viruses can influence the plant's various parts and gradually destroy the quality and production of tomatoes in an agricultural field, thus it impacts the surrounding cultivated plants to cause more economical loss to farmers. Therefore, various techniques are proposed recently to optimally recognize and categorize the epidemic pathogens. To enhance sustainable plant protection practices, accurate identification and classification of diseases is essential to enhance the production rates. Effective pathogen detection and monitoring of plant health are critical areas of research in agriculture. Understanding disease severity is a crucial role for management practices and preventing the spread of infections. Rapid assessment is crucial because early detection of disease can significantly enhance the crop yield and influence the management strategies implemented by farmers. In this proposed model, a deep learning approach is proposed to classify the severity of diseases in tomato plants. At first, the needed images are collected from publicly available resource. Further, the collected images are subjected to the Adaptive and Attention-based Mask Region Convolutional Neural Network (AA-MRCNN) for optimally segmenting the abnormal regions from the gathered image. Further, the hyperparameters, like epoch, steps per epoch, and hidden neuron count in the Adaptive and Attention-based Mask Region Convolutional Neural Network are tuned by the Fitness-based African Vultures Optimization (FAVO) algorithm. Also, the segmented images are passed into Multiscale Recurrent MobileNet (MRMNet) module for categorizing disease severity in the tomato plant epidemic. The assessment of the recommended severity detection approach of tomato plant disease is determined by conducting a simulation experiment. The proposed model attains better outcomes of 93% accuracy, 93% specificity, 93% precision, 7% False Negative Rate (FNR), 86% Matthews Correlation Coefficient (MCC), 93% Fowlkes mallow Index (FM), 86% Bookmaker Informedness (BM), and 86% Threat Score (TS) measures in the ReLu activation function, which is progressed than the conventional frameworks. The result defines that the suggested technique outperformed than other baseline models to ensure the dependability of the tomato plant epidemic pathogens detection performance.
Why it matches plant phenotyping methodsトマト葉画像から病変領域を分割し、植物病害の重症度を分類する画像ベースの表現型推定手法を開発・評価しており、方法が研究の中心である。
abstracta deep learning approach is proposed to classify the severity of diseases in tomato plants.
Reproduction assets foundThe paper uses a public Kaggle tomato leaf disease image dataset as its phenotyping input and states that the authors' source code is available in a public GitHub repository. Both are paper-specific, publicly accessible, and actionable.Code · publiche tomato disease classification performances were
carried out among the performance metrics like Prevalence Threshold
(PT), BM, Precision, FNR, Accuracy, FM, Specificity, MK (Markedness)
and TS to maximize the reliability of the designed approach. The source
code of the public repository on GitHub link is available on “GitHub-
https://github.com/pdeepika6078/Severity-Classification-of-Tomato-Plant-Epidemic-Pathogens-/tree/main”
In order to demonstrating the effectiveness of the designed approach,
several conventional segmentation, optimization, and classification
approaches are adopted to compare the overall process. The reason
behind selecting the traditional approaches to improve the clasOpen asset ↗pdeepika6078/Severity-Classification-of-Tomato-Plant-pdf-raw-page:55 lines:1-29Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Global food security is largely based on the accurate and timely diagnosis of crop diseases, where paddy rice is an extremely essential staple of more than half of the world population. The conventional disease identification techniques tend to be laborious, time consuming and demand a great deal of domain knowledge, which becomes a bottleneck in the efficient management of the farms. Although deep learning [and especially Convolutional Neural Networks (CNNs)] have demonstrated a spectacular performance in automated classification of diseases based on leaf images, they tend to overlook important contextual features that are implicitly processed by agronomic experts. The visual defects of a disease might be unclear and this can greatly differ depending on factors like the genetic variety of the plant and the stage of development. We overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement. The proposed method is the only one that combines high-level visual information obtained using leaf images and related tabular information, namely the paddy type and number of days. The MFBP model uses Factorized Bilinear Pooling (FBP) rather than the simple feature concatenation which commonly loses the complex relationship between different data types. This systematic method efficiently encodes all the complex interactions between all components of the visual and tabular features vectors in such a way that helps the model to pick up subtle, context-specific patterns. As an example, it will only be possible to educate the model that a specific visual blemish is predictive of a given disease through a specific species at a specific age. We test our model on the Paddy Doctor: Paddy Disease Classification dataset, which is a detailed public dataset comprising of more than 10,000 labeled images and containing relevant metadata, and thus it forms a perfect testing bed to conduct multi-modal research. Through our detailed experiments, we have shown that the proposed MFBP model is much better than a baseline model based on concatenation fusion, which proves that deep, multiplicative interactions can be best modeled in this task. The findings highlight the massive possibilities of multi-modes AI in the development of more robust, more accurate, and more context-aware diagnostic instruments and precision agriculture to enable more sustainable and productive agricultural activities.
Why it matches plant phenotyping methods葉画像とメタデータを統合してイネの健康状態・病害を推定する新規深層学習手法を提案し、ベースライン比較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 overcome this shortcoming by proposing a new multi-modal deep learning framework, Multi-Modal Factorized Bilinear Pooling (MFBP) model which is capable of a more holistic and precise paddy health measurement.
Reproduction assets foundThe paper's phenotyping inputs are the public Kaggle 'Paddy Doctor: Paddy Disease Classification' dataset (10,407 leaf images with tabular metadata for variety and age), explicitly named in the Data Availability statement with a persistent public URL. No author analysis code, trained models, or checkpoints are reportedDataset · publicThe datasets used and/or analysed during the current study are publicly available in the
“Paddy-doctor: paddy disease classification” repository at the following persistent
URL: https://www.kaggle.com/datasets/vbookshelf/paddy-disease-classification.Open asset ↗Kaggle · vbookshelf/paddy-disease-classificationpdf-page:20 lines:1-74Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
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.
Reproduction assets foundThe paper's only qualifying asset is the public Kaggle Cotton Plant Disease Dataset used as the input image dataset for all experiments. The authors' code is explicitly not publicly available (institutional restrictions), with only a supplementary algorithm document and on-request implementation details.Dataset · publicthodological workflow of the proposed model. Additional
implementation details may be made available from the corresponding
author upon reasonable request for academic and non-commercial
research purposes.
Data Availability-The datasets generated and/or analysed during the
current study are available in the Kaggle repository,
https://www.kaggle.com/datasets/dhamur/cotton-plant-disease
Author’s contribution – G.Neelavathi– Research proposal –
construction of the workflow and model – Final Drafting– Survey of
Existing works – Improvisation of the proposed model;
Dr.K.Venkatasalam – Initial Drafting of the paper – Collection of
datasets and choice of their suitability – Formulation of pseudOpen asset ↗Kaggle · dhamur/cotton-plant-diseasepdf-raw-page:39 lines:1-42Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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.
Reproduction assets foundThe paper's phenotyping inputs are drawn from a public, open-access image dataset: the Pearl Millet Leaf Disease dataset (Version 2) hosted on Roboflow Universe, containing annotated images of Downy Mildew, Rust, and healthy pearl millet leaves. This is a paper-specific, publicly available asset directly used for the作者Dataset · publicThe dataset used in this research was taken from the publicly available open-access Pearl
Millet Leaf Disease dataset hosted on Roboflow Universe, which has images of Downy
Mildew, Rust and healthy pearl millet leaves annotated publicly available [26]. To ensure
experimental consistency and reproducibility, all experiments were conducted with Version
2 of the open access dataset.Open asset ↗Roboflow Universepdf-raw-page:10 lines:1-28Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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
Reproduction assets foundThe paper's plant-disease phenotyping analysis is built on the public PlantVillage dataset (87,000 leaf images, 38 classes), which the authors explicitly state is publicly accessible via Kaggle. No authors' analysis code, trained models, or checkpoints are released with an explicit public URL in the supplied blocks.Dataset · publicThe dataset used in this study is the publicly available PlantVillage dataset, accessible via Kaggle at:Open asset ↗Kagglelines:270-340Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat (Triticum aestivum L.) is a staple crop of paramount importance to global food security; however, its productivity is significantly compromised by foliar diseases. Conventional diagnostic approaches, relying on manual observation or laboratory analyses, are often labor-intensive and susceptible to inaccuracies. While recent advancements in deep learning present promising avenues for automated disease detection, persistent challenges such as limited annotated datasets, environmental heterogeneity, and model generalization continue to hinder optimal performance. This study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases. While ConvNeXt ensures local feature extraction and ViT provides global contextual understanding, CBAM dynamically highlights the most discriminative features. Additionally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is employed to enhance the visibility of disease symptoms in low-contrast leaf images. Unlike conventional hybrid CNN-Transformer approaches that rely on static feature concatenation, the proposed model employs an adaptive gated fusion mechanism to dynamically balance local and global feature representations. The fused features are further refined using a lightweight CBAM module to enhance discriminative capability. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to improve feature visibility under varying illumination conditions. Evaluated on three different datasets obtained under both controlled and field conditions, HybridViT achieved 100% accuracy on balanced datasets and 99.10% accuracy on complex images captured in real-world conditions, surpassing existing methods. Furthermore, a 5-fold cross-validation strategy yielded an average accuracy of 99.04% ± 0.22, demonstrating the model's robustness and stability across different data splits. The results demonstrate the model's robustness against environmental noise, lighting variations, and class imbalance. This approach, which enables early and accurate disease diagnosis, supports sustainable agricultural practices, reduces pesticide use, and contributes to global food security.
Why it matches plant phenotyping methods小麦葉の病徴を画像から分類する深層学習手法を開発・検証しており、植物病害状態の取得・推定が研究の中心です。
abstractThis study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases.
Reproduction assets foundThe paper evaluates HybridViT on three public wheat leaf disease image datasets from Kaggle, cited in the reference list with explicit URLs. These are the paper-specific image inputs used for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code deposit is披露dDataset · publicAvailable: https://www.kaggle.com/datasets/olyadgetch/wheat-leaf-datasetOpen asset ↗Kaggle · olyadgetch/wheat-leaf-datasetpdf-page:51 lines:1-64Dataset · public[78] J. Jayaprakash, “Wheat Leaf Disease,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/jayaprakashpondy/wheat-leaf-diseaseOpen asset ↗Kaggle · jayaprakashpondy/wheat-leaf-diseasepdf-page:51 lines:1-64Dataset · public[79] S. Kumar, “Multiple Plant Diseases Dataset,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/samareshkumar/multipleplantdiseasesOpen asset ↗Kaggle · samareshkumar/multipleplantdiseasespdf-page:51 lines:1-64Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The global food security is severely threatened by various bacterial and fungal diseases that significantly degrade the quality, yield and productivity of wheat crop. This increases the need for an accurate and efficient system to improve wheat yield and mitigate these losses by enabling early intervention. The dataset used in this research comprises of 10,000 images from brown rust, yellow rust, powdery mildew, loose smut diseases and healthy wheat plants. The existing neural networks, ensembling and transformer-based models used for classifying wheat diseases are limited by high computational resource requirements that leads to inefficient feature extraction. These challenges are addressed by proposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer, a lightweight and optimized transformer model that enhances feature extraction while significantly reducing computational overhead. In particular, Swin-SHARP results in 82.5% reduction (48.9M to 8.5M parameters), making it an attractive solution for resource-constrained environments. The extracted features are further optimized by integrating the Swin-SHARP transformer with boosted Model-Agnostic Meta-Learning (MAML) and a weighted ensembling strategy to enhance generalization and classification accuracy. Our proposed model achieves a remarkable 98.1% accuracy, significantly outperforming existing CNN-based solutions, ensemble approaches, transformer, and deep learning models. We also cross-validated our proposed model on an unseen wheat plant diseases dataset, achieving 95.57% accuracy. Our proposed model is also compared against prominent models such as Inception-v3, ResNet-18, and VGG-16, which outperforms them by 1.6%, 1.7%, and 2.6%, respectively. The comparison with existing state-of-the-art models, including Sequential CNN, SGDR-S, Inception-v3, Cereal Conv, Darknet-53 CNN, EfficientNet B3, GLNet, CNN & SVM, Customized CNN, CaiT-YOLOv9 and MSFNet revealed that our method outperforms them by 0.6%, 5.8%, 5.3%, 0.75%, 2.8%, 2.68%, 1.42%, 1.3%, 3.31%, 3.29%, and 2.4% respectively. These results demonstrate the effectiveness and practicality of the Swin-SHARP transformer for wheat disease classification, particularly for real-time agricultural applications on mobile and embedded systems aimed at early disease detection and crop management.
Why it matches plant phenotyping methods小麦の病徴画像から植物の病害状態を推定する深層学習手法を開発し、別データセットで交差検証しており、植物表現型取得・判定が研究の中心である。
abstractproposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the authors' wheat disease dataset and analysis code are publicly available on GitHub (https://github.com/SWIN-SHARP/), which is a paper-specific, actionable asset. The paper also uses third-party public datasets (Zindi ICLR Workshop, Mundi, Watershed/GrabcCode · publicThe dataset and code used in this research have made publicly available on https://github.com/SWIN-SHARP/
SWIN-SHARP for reproducibility purposes.Open asset ↗SWIN-SHARPpdf-page:25 lines:1-104Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and efficient identification of cucumber leaf diseases is a critical step in preventing losses and facilitating timely intervention in agricultural activi-ties. However, most state-of-the-art plant disease recognition models, including those employing deep learning, often fail to identify spatial dependencies among symptomatic leaf feature regions, require high computational resources, and lack robustness in their predictions. To overcome these challenges, this paper pro-poses MobileGraph, a graph-aided deep learning model that jointly reasons local texture patterns and spatial dependencies among CNN-derived cucumber leaf feature regions using MobileNetV3 as a lightweight feature extractor. Experi-ments on a publicly available cucumber leaf disease dataset containing 5 classes and 4,000 images show that the proposed model achieves 99.75% accuracy, 99.75% macro F1-score, and 99.69% MCC, outperforming several state-of-the-art models including ResNet-152, EfficientNet-B7, DenseNet-201, ConvNeXt, and VGG16, while having a significantly lower computational cost of 0.465 GFLOPs. Explainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions. Furthermore, a proto-type mobile application illustrates the feasibility of real-time cucumber disease diagnosis for practical agricultural monitoring. These results indicate that Mobi-leGraph provides an efficient and interpretable solution for intelligent crop health surveillance.
Why it matches plant phenotyping methodsキュウリ葉の病斑という植物状態を画像から診断する深層学習手法を開発し、複数モデルとの性能比較・検証まで行っており、病害表現型の取得・推定が研究の中心である。
abstractExplainability results from Grad-CAM and LIME indicate that the model is focused on biologically important regions of plant lesions.
Reproduction assets foundThe paper's experiments use the publicly available Cucumber Disease Recognition Dataset (4,000 images, 5 classes) hosted on Mendeley Data, which is a paper-specific public phenotype/image asset. The MobileGraph source code is only available upon request, so it does not qualify as a public asset.Dataset · publicThe dataset analysed of this study, titled ”Cucumber Disease Recognition
Dataset” is publicly available in the Mendeley Data repository at
(https://data.mendeley.com/datasets/y6d3z6f8z9/1).Open asset ↗Mendeley Data · y6d3z6f8z9pdf-page:36 lines:1-71Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Early and accurate disease detection is important for increasing the agricultural output, decreasing the financial costs, and ensuring food security. Traditional diagnostic procedures take much time and effort, involve the necessity of having deep expertise, and are not always suitable for large scale farming disease detection. For this purpose, the current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images. The model utilizes several innovative architectural solutions such as depth-wise separable convolution, SE blocks, skip connections, and guided attention-based feature learning that allow enhancing the effectiveness of features extraction and decreasing computation load. Moreover, Grad-CAM is used to visualize affected areas on a map and thus increase the interpretability of the model. The suggested solution was implemented and tested on the PlantVillage dataset containing 54,305 images for 38 crop diseases out of 14 crops. The results show that the training, validation, and testing accuracies equal 97.6%, 88.3%, and 97.63%, correspondingly, along with the Macro-F1 measure of 0.867 and Micro-ROC-AUC equal to 0.99. A comparative study reveals that the presented model performs comparably well in terms of classification with lightweight structure and built-in interpretability capabilities to be applied in the mobile and edge-enabled agriculture environment. The results show that the presented approach is capable of being used as an effective and interpretable tool for diagnosing plant diseases in real-time.
Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の開発・評価が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractthe current research suggests developing an explainable lightweight CNN-based model for crop disease identification based on RGB leaf images.
Reproduction assets foundThe paper's plant-phenotyping input is the public PlantVillage leaf-image dataset (54,305 RGB images, 38 crop-disease classes), explicitly declared in the Data availability statement with a Kaggle URL. No author code, trained model, or checkpoint is deposited.Dataset · publicThe data set analyzed during current study are available in https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseaselines:366-390Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Estimating the nutritional status of rice leaves is crucial for efficient nutrient management and yield enhancement. Traditional wet lab analyses are time-consuming and labor-intensive. This study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves. The proposed framework integrates a differentiable neural search technique using polynomial function approximators and an adaptive activation mechanism, which not only provides improved predictive performance but also deals efficiently with limited training data. The model performance is evaluated across different treatments and crop growth stages using mean absolute error (MAE) and [Formula: see text] values. Experiments were conducted at the Punjab Agricultural University. The results demonstrate that the proposed model achieves MAE values in the range of 0.06-0.11 for SAS-I and 0.06-0.16 for SAS-II across eleven leaf macro/micro nutrients. To further evaluate the reliability of the predicted nutrients beyond the prediction error analysis, uncertainty estimation of nutrients is also performed. Comparative analysis shows that the proposed framework outperforms conventional deep learning baselines and machine learning methods in terms of accuracy and robustness. Furthermore, the t-SNE visualization of learned feature representations effectively clusters similar nutrient values while separating dissimilar ones. The robustness of the proposed framework is further validated through ablation studies, treatment-wise and plot-wise cross-validation, highlighting the contribution of individual components and their performance under varying field conditions. These findings highlight the proposed NAS-based framework for precise and reliable nutrient assessment in precision agriculture.
Why it matches plant phenotyping methods稲葉のマクロ・微量栄養素という植物状態をマルチスペクトル画像から推定する深層学習手法を開発し、比較検証・不確実性評価・アブレーション試験まで行っており、表現型取得・推定法が中心である。
abstractThis study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
FlowerClassificationGrowth / development / phenology
Accurate recognition of flower growth stages is important for plant phenotyping but remains challenging due to subtle visual differences and limited labeled data. This study proposes a hybrid CNN/Transformer + GCN framework for fine-grained flower growth-stage classification. A new dataset, BD Flower Growth, is introduced with 3,889 original images from eight Bangladeshi flower species, categorized into three stages (early, mid, full), forming 24 classes. The dataset is divided into training and testing sets, with augmentation applied only to the training data. Deep backbone networks are used to extract feature maps, which are transformed into graph representations and refined using Graph Convolutional Networks (GCN). A systematic ablation study is conducted by varying GCN depth (3, 5 layers), node resolution ([Formula: see text], [Formula: see text]), and graph construction methods (4-neighbour, 8-neighbour, and KNN with [Formula: see text]). Experimental results show that performance depends strongly on both backbone and graph configuration. The best performance of 97% accuracy is achieved by EfficientNetV2, DenseNet201-based hybrid models, additionally Swin Transformer model shows the largest improvement, increasing from 84% to 97% after GCN integration. Across different settings, grid-based graphs (4- and 8-neighbour) consistently provide more stable and higher performance compared to KNN graphs, while moderate GCN depth (3-5 layers) offers the best balance accuracy. Cross-dataset evaluation on the Oxford 102 Flower dataset further demonstrates the generalization capability of the proposed approach. These findings highlight the effectiveness of hybrid graph-based learning and the importance of graph configuration in improving fine-grained classification.
Why it matches plant phenotyping methods花の生育段階という植物状態を画像から分類する手法の開発・比較検証が中心で、新規データセットとアブレーションおよびクロスデータセット評価も含むため。
abstractAccurate recognition of flower growth stages is important for plant phenotyping
Early and accurate detection of apple leaf diseases is critical for sustainable agriculture, yet manual diagnosis remains time-consuming and error-prone. This study introduces a novel deep learning framework centered on a custom ContinuousLayer, a spatially adaptive convolutional layer designed to overcome the limitations of standard CNNs. This architecture automates the classification of apple leaf diseases Black rot, rust, scab, and healthy leaves with high precision. The model addresses dataset imbalance through strategic resampling, achieving uniform class distribution. The ContinuousLayer introduces spatial feature modulation using trainable Gaussian basis functions, enhancing feature extraction while penalising kernel irregularities through a hybrid composite loss function. Trained on a dataset of 3,164 images balanced via bicubic up-sampling, and evaluated on a held-out test set of 10% of the data, the model attains a 98.63% test accuracy, with F1-scores ranging from 0.98 to 1.00 across classes. Visual analysis of the confusion matrix reveals minimal misclassification, predominantly between rust and scab. Comparative evaluation against baseline architectures demonstrates the efficacy of the ContinuousLayer in capturing disease-specific spatial patterns. These results underscore the potential of integrating mathematically inspired layers into CNNs for plant pathology applications, offering a highly accurate tool for precision agriculture in controlled environments.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から分類する新規深層学習層と解析手法を開発・比較評価しており、植物病害状態の表現型抽出が中心である。
abstractThis study introduces a novel deep learning framework centered on a custom ContinuousLayer, a spatially adaptive convolutional layer designed to overcome the limitations of standard CNNs.
Reproduction assets foundThe paper's apple leaf disease image dataset (3,164 images) is explicitly stated to be publicly available on Kaggle, matching an allowed URL. No author code or model checkpoints are reported as available.Dataset · publicThe datasets analysed during the current study is publicly available in the Kaggle repository at https://www.kaggle.com/datasets/mhantor/apple-leaf-diseases.Open asset ↗Kaggle · mhantor/apple-leaf-diseaseslines:595-613Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotClassificationDisease symptoms / severity
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
Why it matches plant phenotyping methods植物画像に基づく病害分類モデルの開発・評価が研究の中心的技術貢献であり、感染植物の状態を直接推定しているため含める。
abstractThe study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics
Reproduction assets foundThe paper explicitly states that implementation code, trained models, and experimental configurations are publicly available in an authors' GitHub repository, and that the PlantifyDr plant disease dataset and IP02 pest detection dataset used in the study are available on Kaggle. These are paper-specific, public, and可直接Code · publicThe implementation code, trained models, and experimental configurations used are publicly available in: “ https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigation ”. The repository contains all necessary instructions and dependencies required to reproduce the reported experimental results.Open asset ↗https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigationlines:253-302Dataset · publicThe datasets generated and/or analyzed during the current study are available in the [PlantifyDr Dataset and Pest detection dataset] repository“ https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset ”Open asset ↗https://www.kaggle.com/datasets/lavaman151/plantifydr-datasetlines:400-436Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Convolutional Neural Networks (CNNs) are widely used for plant disease detection, yet their performance is strongly influenced by hyperparameter selection. Traditional manual tuning or random search approaches are inefficient and may lead to suboptimal solutions. In this study, we propose and evaluate four hybrid metaheuristic strategies, Ant Lion Optimizer combined with Whale Optimization Algorithm (ALO-WOA), Ant Lion Optimizer with Dragonfly Algorithm (ALO-DA), Ant Lion Optimizer with Particle Swarm Optimization (ALO-PSO), and Particle Swarm Optimization with Whale Optimization Algorithm (PSO-WOA), for automatic hyperparameter tuning of CNNs. The methods were applied to a tomato leaf disease dataset comprising 21,421 training, 4,586 validation, and 4,602 test images across 10 classes. The CNN architecture was fixed with three convolutional blocks and a tunable dropout and learning rate. The experimental results show that ALO-DA achieved the highest test accuracy of 97.83%, closely followed by ALO-WOA (97.67%) and PSO-WOA (97.52%), while ALO-PSO achieved 95.26%. These findings demonstrate that hybrid metaheuristics can effectively improve CNN hyperparameter search compared to single optimizers, balancing exploration and exploitation more efficiently. Limitations and future research directions are discussed.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類するCNNについて、ハイブリッドメタヒューリスティックによるハイパーパラメータ最適化手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractIn this study, we propose and evaluate four hybrid metaheuristic strategies, Ant Lion Optimizer combined with Whale Optimization Algorithm (ALO-WOA), Ant Lion Optimizer with Dragonfly Algorithm (ALO-DA), Ant Lion Optimizer with Particle Swarm Optimization (ALO-PSO), and Particle Swarm Optimization with Whale Optimization Algorithm (PSO-WOA), for automatic hyperparameter tuning of CNNs.
Reproduction assets foundThe paper uses the public Kaggle Tomato Leaf Disease Dataset V2 as its phenotyping image data and publishes the authors' complete hybrid metaheuristic CNN optimization pipeline (training, optimization, evaluation scripts) in a public GitHub repository. Trained models/logs are only available on request.Code · publicThe source code, trained model configurations, and experimental scripts used in this study are publicly available at: https://github.com/simarkalsi24/Hybrid-of-Optimization-Algorithm-.git The repository includes the complete training and optimization pipeline, implementations of all hybrid algorithms (ALO–DA, ALO–PSO, ALO–WOA, and PSO–WOA), as well as experiment configurations, logs, evaluation scripts, and visualization outputs to ensure full reproducibility of the reported results.Open asset ↗GitHub · simarkalsi24/Hybrid-of-Optimization-Algorithm-lines:579-588Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean production is significantly affected by crop diseases and improper pesticide use, which hinder effective disease management and reduce yield. In this study, we propose an efficient multi-task convolutional neural network (CNN) framework for the simultaneous detection of soybean seed diseases and pesticide presence from seed images. The model leverages a shared feature extraction backbone with task-specific output heads to learn complementary features for both disease classification and pesticide detection. A dataset of 429 soybean leaf images was preprocessed using normalization and augmentation techniques and split into training, validation, and testing sets. We evaluated three backbone architectures VGG19, MobileNetV3, and ConvNeXt within the multi-task framework. Experimental results demonstrate that the approach maintains computational efficiency suitable for real-world deployment while achieving high performance, with accuracies of 95%, 96%, and 97% for MobileNetV3, VGG19, and ConvNeXt, respectively. Additionally, explainable AI methods, such as Grad-CAM, highlight regions of focus for both tasks, making the model's decision-making process interpretable. This framework provides a practical tool for informed crop management and agricultural monitoring.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNN手法の開発・評価が研究の中心であり、病害表現型の画像ベース推定に該当する。
abstractwe propose an efficient multi-task convolutional neural network (CNN) framework for the simultaneous detection of soybean seed diseases and pesticide presence from seed images.
Reproduction assets foundThe paper's authors publicly released their custom analysis code (preprocessing, training, evaluation) on GitHub, matching an allowed URL. The enriched Kaggle image/annotation dataset is also public but its URL is not among the allowed URLs, so it is not listed.Code · publicThe custom code developed for this study is publicly available on GitHub at https://github.com/fikaduberie/Soybean-Disease-and-Pest (version v1.0).Open asset ↗fikaduberie/Soybean-Disease-and-Pesthtml-lines:1281-1329Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesion-profile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
Why it matches plant phenotyping methods植物病斑と病原菌感染ネットワークを対象に、ライトシート顕微鏡とトレーシング法を適応し、感染構造を定量化する指標を開発・適用しており、表現型取得法が研究の中心である。
abstractUsing light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue.
Reproduction assets foundThe paper's Data availability statement explicitly deposits metadata, data, and computer code as Supplementary Files accompanying the open-access publication (Supplementary Materials 1-5, including XLSX datasets and an untyped Supplementary Material 5 likely holding code). These are paper-specific phenotyping assets (eCode · publicMetadata, data, and computer code from this study are available in Supplementary Files included with the publication.Open asset ↗lines:147-204Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Accurate and scalable soybean crop health monitoring remains a major challenge in precision agriculture due to environment variability, inconsistent lighting conditions, and significant differences between the ground-level leaf imagery and UAV-based aerial imagery. Most existing deep learning approaches treat these two sensing modalities separately without properly exploring cross-scale feature transferability or measuring the domain gap that exists between the sensing scales. As a result, developing unified and deployment-ready crop health monitoring systems that can effectively leverage the more accessible leaf-level datasets, collected without specialized equipment or regulatory constraints, to improve UAV-scale inference remains difficult. In order to address this limitation, we propose a multi-scale soybean crop health assessment framework that integrates ground-level leaf imagery and UAV-based aerial imagery from the MH-SoyaHealthVision dataset across four health conditions, which include Healthy, Mosaic Virus, Pest attack, and Rust. CLAHE, Gray-World color constancy correction, and illumination normalization is incorporated into a structured pre-processing pipeline and further applied to reduce illumination bias and enhance cross-domain feature consistency. Six deep learning backbones were comprehensively evaluated for leaf-level classification, with MaxViT and ConvNeXt achieving the best performance. Their static weighted ensemble further improved accuracy to 87.08%. Cross-scale evaluation showed that zero-shot leap-to-UAV transfer achieved only 40% accuracy, thus highlighting the presence of a substantial domain shift. Fine-tuning improved UAV classification performance to about 97%, while a supervised contrastive learning framework specifically designed for cross-scale feature alignment further increased accuracy to approximately 98% with better convergence stability. Feature embedding analysis using PCA, t-SNE, and silhouette metrics demonstrated considerable improvements in inter-class separability (0.59 vs. 0.19) and reduced domain discrepancy (0.0336 vs. 0.114) under contrastive learning. These findings suggest that supervised alignment can generate more class-discriminative representations with lower cross-scale domain discrepancy, making them more suitable for scalable multi-scale cross-health monitoring.
Why it matches plant phenotyping methods葉およびUAV画像からダイズの健康状態・病害を推定する画像ベースの表現学習フレームワークを開発し、複数モデル、クロススケール転移、微調整、教師ありコントラスト学習を比較・検証しているため、植物表現型取得法が中心である。
abstractwe propose a multi-scale soybean crop health assessment framework that integrates ground-level leaf imagery and UAV-based aerial imagery
Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.
Why it matches plant phenotyping methodsラマン分光と機械学習により、コムギの乾燥・塩ストレス状態を非破壊的に検出・分類する方法を開発・検証しており、植物状態の取得が研究の中心である。
abstractRaman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments.
Plant leaf diseases must be detected and treated early to improve crop yield and reduce agricultural losses. However, pixel-level representations and the inability to be read limit the applicability of existing deep learning approaches to the agricultural sector. A graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease. The proposed framework models leaf pictures as a graph with nodes representing discriminative leaf areas and edges representing their spatial connection. Before creating the global context vector and classification, graph features are aggregated, and an attention weighting method is applied to refocus on disease-relevant nodes obscured by less informative background characteristics. Final disease prediction uses a multilayer perceptron classifier. A curated dataset of half-spinach and curry leaf pictures is used to assess the proposed method for fifteen illnesses and their healthy classifications. Grad-CAM-based explainable AI methods make the model predictions' most important areas clearer. The dataset and source code from this work are available on GitHub for reproducibility and openness. Experimental results reveal that the proposed AE-GNN outperforms convolutional neural networks and graph-based models in classification. Graph-structured learning, attention enhancement, and explainability create a robust and interpretable framework for multi-plant leaf disease diagnosis.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類・診断する画像解析手法を提案し、既存モデルとの比較評価と説明可能性解析を行っているため、植物フェノタイピング手法が中心である。
abstractA graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease.
Reproduction assets foundThe paper's Data availability section explicitly links a public GitHub repository containing the paper's spinach/curry leaf fungal disease image dataset used for the AE-GNN phenotyping/classification analysis.Dataset · publicData availability
The dataset is available at the link below. https://github.com/MeganathanE1990/FINAL-DISEASE-DATA-SET/tree/mainOpen asset ↗MeganathanE1990/FINAL-DISEASE-DATA-SETlines:413-463Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pearl millet is a vital crop in arid and semi-arid regions, but its productivity is significantly impacted by fungal and bacterial diseases. Traditional disease detection methods, including manual inspection and conventional machine learning techniques, often suffer from inefficiencies due to subjectivity, time consumption, and limited feature extraction capabilities. Deep learning-based segmentation models such as U-Net, SegNet, DeepLabV3, and FCN have been employed to automate disease identification, but they exhibit limitations in accurately localizing disease-affected regions, handling complex spatial patterns, and maintaining high segmentation precision. To address these challenges, this study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection. The novel framework enhances disease localization by utilizing hierarchical multi-scale feature learning, where low, mid, and high-level features contribute to superior classification accuracy. The attention mechanism prioritizes critical disease regions, reducing false positives, while Mask R-CNN enables pixel-wise segmentation, refining disease boundary identification. The proposed model is implemented using Python and TensorFlow with high-resolution image datasets. Performance evaluation demonstrates HAMNet-Mask R-CNN achieving a 99.35% Dice score, 98.80% IoU, 99.75% Precision, 99.78% Recall, and 99.65% Accuracy, outperforming U-Net (95.03% Dice, 90.53% IoU), SegNet (94.58% Dice, 89.73% IoU), DeepLabV3 (98.31% Dice, 96.69% IoU) and FCN(98.47% Dice score, 97.00% IoU). The results confirm the model's superiority in disease segmentation and classification, offering a robust and scalable solution for real-time disease monitoring in smart agriculture. The integration of hierarchical attention and spectral-spatial fusion significantly enhances detection accuracy, ensuring reliable disease identification and contributing to improved agricultural productivity.
Why it matches plant phenotyping methods真珠粟の病変領域を画像から画素単位で抽出・分類する深層学習手法を開発し、既存モデルと性能比較しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection.
Reproduction assets foundThe paper's authors explicitly state that the custom HAMNet–Mask R-CNN implementation code (preprocessing, training, evaluation, visualization) is publicly available on GitHub. The pearl millet leaf image dataset used as phenotyping input is also publicly hosted on Roboflow Universe and cited as the data source. The roCode · publicThe custom code developed for the implementation of the proposed HAMNet–Mask R-CNN framework, including data preprocessing, model training, evaluation, and visualization scripts, is publicly available in a GitHub repository. The code can be accessed at: https://github.com/ramyalaksha/Hamnet.gitOpen asset ↗https://github.com/ramyalaksha/Hamnet.gitlines:267-297Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant leaf disease detection is a critical task in precision agriculture, where reliable diagnosis under real-world conditions is essential for reducing crop losses and supporting timely intervention. Although deep learning models have achieved high classification accuracy, their performance often degrades under domain shift between controlled laboratory datasets and real-field environments, while predictive uncertainty and confidence calibration remain largely unaddressed.This study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification. The framework integrates multi-scale feature learning with Monte Carlo Dropout-based predictive uncertainty estimation and temperature-based calibration to systematically analyze model behavior in terms of accuracy, reliability, and robustness. Experiments were conducted on two complementary datasets: the New Plant Diseases Dataset (controlled conditions) and the PlantDoc dataset (field conditions), enabling bidirectional cross-domain evaluation. Results demonstrate that the proposed framework achieves superior performance, attaining 97.8% accuracy on controlled data and 93.6% on field data, while significantly improving calibration with lower Expected Calibration Error (ECE = 0.032 / 0.041), reduced Negative Log-Likelihood, and lower Brier score compared to baseline CNN and transformer models. Furthermore, the framework exhibits improved robustness under domain shift, with reduced performance degradation and stable uncertainty behavior. Overall, this study highlights the importance of integrating uncertainty estimation and calibration within a hierarchical transformer-based framework, providing a more reliable and deployment-ready solution for real-world agricultural disease diagnosis.
Why it matches plant phenotyping methods植物葉の病害状態を直接推定する不確実性-aware分類フレームワークの開発・評価が中心であり、異なる条件のデータセット間で精度、校正、頑健性を検証している。
abstractThis study presents an uncertainty-aware cross-domain evaluation framework based on a Hierarchical Vision Transformer (HViT) for plant leaf disease classification.
Reproduction assets foundThe paper's Data availability statement explicitly links the two public image datasets used for its cross-domain plant leaf disease classification experiments: the New Plant Diseases Dataset on Kaggle and the PlantDoc dataset on Dataset Ninja. No author analysis code, models, or checkpoints are reported as available.Dataset · publicThe New Plant Diseases Dataset can be obtained from Kaggle at [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset]Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetlines:360-398Dataset · publicThe PlantDoc dataset is available for download at [https://datasetninja.com/plantdoc#download]Open asset ↗plantdoclines:360-398Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plant diseases pose a major threat to global food security, significantly reducing agricultural yields. Therefore, timely diagnosis of plant diseases can help prevent food losses and support economic stability. This study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis. The feature extraction layers of each model were modified to incorporate DropBlock layers, while preserving pretrained weights from the ImageNet dataset. Images from the Plant Pathology 2021 dataset were used to fine-tune the models for multi-label classification, targeting five disease categories and a healthy label. Three experiments were conducted to evaluate model performance on the test set. First, the ResNet50 model was used to determine optimal Dropout and DropBlock probabilities. Second, these parameters were applied across all models to identify those with the best performance. Finally, twenty-three Swarm Optimization Algorithms were used to optimize classifier thresholds, improving accuracy and F1-scores. A DropBlock probability of 0.05 and a Dropout probability of 0.2 yielded superior results. Among the models, SwinV2T attained an accuracy of 90.7%, while SwinV2S achieved the highest F1-score of 91.7%, slightly outperforming the ConvNeXtT and ConvNeXtS architectures. The results demonstrated the effectiveness of DropBlock regularization and optimized classifier thresholds, highlighting the superior performance of recent architectures and optimization algorithms over their older counterparts. These findings suggest that such networks hold substantial promise for accurately identifying and diagnosing apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉画像から病害状態を分類・診断する深層学習手法を比較評価し、正則化や閾値最適化による性能改善も検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study explores the use of eight Convolutional Neural Networks and two Vision Transformers for apple leaf disease diagnosis.
Reproduction assets foundThe paper's apple leaf disease phenotyping is based on the public Plant Pathology 2021 (FGVC8) Kaggle image dataset and an authors' reorganized multi-label version publicly deposited on GitHub; both are explicitly linked in the Data availability statement. No author analysis code or trained model checkpoints are statedDataset · publicFor this research, the dataset was reorganized and extended into a multi-label format. The complete modified dataset is publicly available at: https://github.com/soroushtou/Plant-Pathology-2021---MultiLabel-Dataset.Open asset ↗Plant-Pathology-2021---MultiLabel-Datasetlines:239-262Dataset · publicThe original dataset used in this study is the publicly available Plant Pathology 2021 dataset from the FGVC8 competition available at: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8.Open asset ↗lines:239-262Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
AppleField / plotRaman / spectroscopyFruitClassificationWater status / transpiration
Visible and near-infrared (Vis/NIR) spectroscopy has been widely applied in fruit quality detection due to its advantages of rapid efficiency, non-invasiveness, and suitability for detecting opaque samples. To address the issue of whether apple watercore occurs during the growth and maturation of apples, a portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore. The device employs the AIOX2000-13 spectrometer as the detection unit, with an STM32F103VET6 ARM-based processor as the main control chip, and integrates a 4G wireless communication module to establish a stable data transmission channel between the processor and the computer. This structure ensures the efficient and stable transmission of apple spectral data and detection results, thereby meeting the need for in-field nondestructive detection of apple watercore on apple trees. The system is based on a self-designed spectral data acquisition mechanism and uses a transmission detection method to collect spectral data from 500 'Fuji' apple samples in two directions. The spectral data were preprocessed using Standard Normal Variate (SNV), and the dataset was divided using the Spectral Projection based on X-Y distances (SPXY) algorithm. Important feature wavelengths related to apple watercore were extracted by combining the Uninformative Variable Elimination method with the Successive Projections Algorithm (UVE-SPA). Subsequently, a detection model, SNV-UVE-SPA-SVM, was constructed using a Support Vector Machine (SVM) optimized by the Honey Badger Algorithm (HBA), achieving a test set accuracy of 96%. After research and analysis, Direction 1 was identified as the optimal acquisition direction, and field verification was conducted on 50 apple samples, with a detection accuracy of 94%. The results show that the detection device has the advantages of portability, high efficiency, and suitability for in-field detection, making it suitable for the rapid in-field detection of apple watercore.
Why it matches plant phenotyping methodsリンゴの水心症という植物状態を対象に、可視・近赤外分光による携帯型非破壊検出装置と解析モデルを開発し、圃場検証まで実施しており、表現型取得手法が中心である。
abstracta portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Rice is a significant food that plays a vital part in delivering nutrition to the world's population. Hence, approaches for assessing rice yield have received considerable study. The amount of rice seedlings (density) is a main agronomic module. It is related to harvest and also plays a significant part in the survival rate. Unmanned Aerial Vehicles (UAVs) are prepared with lightweight sensors, which creates a substantial effect in the field of crop phenotyping. The UAV was effectively used to measure germination rates and density in an accurate and effective method that would otherwise be laborious and expensive to obtain when compared to manual valuation. In image processing, mainly over the applications of deep learning (DL) models, there was a notable academic search for the value of UAV images for varied agricultural monitoring tasks. This work develops a Rice Seedlings for Assessing Germination Rates and Density using Aerial Images with Hierarchical Deep Network (RSAGRD-AIHDN) model. The goal of this paper is to assess germination rates and seedling density in rice fields using remote sensing (RS) or UAV-based imaging techniques for improved crop establishment monitoring. To accomplish that, the image pre-processing stage is initially applied with dual stages, such as image acquisition and pre-processing, to ensure high-quality and consistent inputs. Furthermore, the RSAGRD-AIHDN model employs the ConvNeXt method for the feature extraction process. For rice seed detection and classification, the RSAGRD-AIHDN model implements ensemble models, namely stacked autoencoder (SAE), bidirectional temporal convolution network (BiTCN), and Deep Q-Learning (DQL). The experimental assessment of the RSAGRD-AIHDN method is performed under the aerial dataset of rice seedlings. The experimentation of the RSAGRD-AIHDN method portrayed a superior accuracy value of 98.68% over existing approaches.
Why it matches plant phenotyping methodsUAV画像と深層学習モデルを用いて、イネの発芽率と苗密度という植物形質を推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThe UAV was effectively used to measure germination rates and density in an accurate and effective method
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning. The approach solves the problem of superimposed patterns of disease especially Leaf Miner, Tomato Spotted Wilt Virus (TSWV), and nutrient deficiencies through the combination of multi-domain feature learning and inter-disease similarity modeling. In contrast to conventional metric learning or contrastive learning methods that function on pairwise or triplet sample associations, CDSAL develops a class-level Cross Disease Similarity Matrix that represents structured inter-disease proximity within the embedding space. Moreover, rather than employing episodic prototype construction typical of few-shot learning, the proposed system persistently updates centroid representations throughout supervised training and incorporates similarity-aware regularization directly into the loss function. This facilitates structural embedding reshaping specifically designed for visually overlapping illness categories, beyond traditional prototype-based learning methodologies. The input images are processed through HSV based green masking, morphological cleaning, extraction of leaf contours and resizing, and using a large amount of geometric and color-space augmentation to reduce the imbalance among the classes. DenseNet121 and EfficientNet-B0 are used to obtain feature representations and class-separated centroid of latent embedding's to form a Cross Disease Similarity Matrix, where similarity-aware optimization is possible during training. Grad-CAM on the target layers offers decipherable disease-specific activation signatures. The findings of the experiments show that classification accuracy at unseen samples is 99.77% with high resilience to visual confounding. The predictions, proximity of diseases that are similar and explainable features are provided by CDSAL, thereby facilitating reliable decision-making in agricultural diagnostics.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習手法を提案し、前処理・特徴抽出・類似度学習・説明可能性を技術的中心として評価しているため。
abstractThe research proposes Cross Disease Similarity Awareness Learning (CDSAL), a robust multiclass tomato leaf disease detection framework based on high-quality and explainable deep learning.
Reproduction assets foundThe paper's plant-phenotyping inputs are two publicly available Kaggle image datasets explicitly named in the Data Availability statement: PlantVillage (emmarex/plantdisease) used as the main dataset and TomatoVillage (mamtag/tomato-village) used for ablation/field-condition experiments. No author analysis code, modelsDataset · publicThe datasets analyzed during the current study are available in the Kaggle repository. [https://www.kaggle.com/datasets/emmarex/plantdisease]Open asset ↗Kaggle · emmarex/plantdiseasehtml-lines:605-624Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Plant diseases are a serious danger to the world’s food security, because they lower agricultural output and increase economic losses. Due to subjectivity, fluctuating lighting, and environmental unpredictability, traditional visual examination techniques are frequently incorrect. The Excess Green (ExG) vegetation index and pseudo-thermal representations produced from RGB pictures are two synthetically developed complementary representations that are integrated with RGB imagery in this study’s lightweight multimodal deep learning system to address these issues. Histogram shifting and pseudo-infrared color mapping are used in a reproducible picture alteration pipeline to create the pseudo-thermal modality, which allows for extra visual signals without the need for specific thermal sensors. In order to classify plant diseases while preserving computational efficiency, the suggested framework uses MobileNetV3-Small backbones to extract modality-specific characteristics. This is followed by feature-level fusion. The publicly accessible Ginger Leaf Dataset, which includes RGB pictures of ginger leaves in four different conditions—Damage-Pest, Dehydrated, Healthy, and Leaf-blight—was used for the experiments. For training, validation, and testing, the dataset was split using a stratified 70:15:15 split. Python-based preprocessing procedures were used to create the extra modalities (ExG and pseudo-thermal representations) from the original RGB images. The experimental results show that the combination of the representations with RGB images can enhance the classification performance compared with the unimodal RGB-based models. Ablation experiments are also conducted to examine the contributions of different modalities to the overall categorization accuracy. The experimental results show that plant disease recognition can be improved with the help of efficient computing by combining lightweight convolutional neural networks with computationally generated visual representations.
Why it matches plant phenotyping methodsRGB画像からExG・疑似熱画像を生成し、植物葉の病害状態を分類するマルチモーダル手法が研究の中心であり、アブレーション評価も実施している。
titleHybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2
Reproduction assets foundThe paper's phenotyping experiments use the publicly available Ginger Leaf Dataset (RGB leaf images of four ginger leaf conditions), with a public GitHub repository and dataset website. The authors' derived ExG/pseudo-thermal representations and preprocessing scripts are only available upon request, so they do not yetDataset · publicor multispectral images
IEEE Geosci. Remote Sens. Lett. 2025
10.1109/LGRS.2025.XXXXXXX
Ulku, I., Tanriover, O. O. & Akagündüz, E. Cross-band correlation-aware interactive fusion for multispectral images. IEEE Geosci. Remote Sens. Lett.
10.1109/LGRS.2025.XXXXXXX
(2025).
10. Wong, J. Ginger Leaf Dataset. GitHub Repository (2023). https://github.com/wongjay1941/Ginger-Leaf-Dataset
11.
Bhakta I
A novel plant disease prediction model based on thermal images using modified deep convolutional neural network
Precis. Agric. 2023 24 23 39
10.1007/s11119-022-09927-x
Bhakta, I. et al. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precis.Open asset ↗https://github.com/wongjay1941/Ginger-Leaf-Datasetlines:580-681Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many countries greatly rely on agriculture as a means of livelihood and economic growth. Even the most industrialized countries need food, medicine, clothing, and shelter produced by crops. Rice is one of the most significant and widely grown crops worldwide. Nonetheless, the severely impacted crops in rice production are those of bacteria, fungi, and viruses, which decrease yield and quality. Manual disease detection is hectic, challenging, and, in most cases, inaccurate. Recent advances in deep learning and computer vision have demonstrated significant potential to improve the detection and classification of diseases. This study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases. This method consists of five key stages: image preprocessing, segmentation, augmentation, multi-feature extraction via adaptive fusion, and classification. There are five rice leaf diseases to discuss and recognize: Blight, brown spot, sheath blight, tungro, and leaf blast. The first step is global contrast enhancement, which improves image quality. After that, the segmentation is performed using Otsu's Thresholding to extract the leaf area. Then, the modified VGG16 and modified ResNet50 networks are used in parallel to extract features using a transfer-learning approach. The adaptive fusion technique combines these features to obtain a dominant, proper feature representation. Lastly, the classification is done using an adaptive fusion score technique. Experimental results show excellent performance, with class-wise Precision in the range of 95.5-100%, class-wise recall in the range of 97.4-100%, and overall test accuracy of 98.5%.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動検出・分類する深層学習ワークフローが研究の中心であり、葉領域抽出、特徴抽出、分類性能まで評価しているため、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning hybrid model for the automated detection and classification of rice leaf diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset employed in this study is accessible online at https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm.Open asset ↗Kaggle · rajeshbhattacharjee/rice-diseases-using-cnn-and-svmhtml-lines:929-951Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Today, the intelligent automation of agriculture has received much attention from researchers. One of the important factors for the success of this automation is the timely diagnosis of plant disease and making a decision appropriate to the existing conditions of the plant. Since the progress of the disease is a determining factor in the type of treatment method, the diagnosis of the severity of the disease is of particular importance. However, accurate diagnosis of plant disease progression depends on various factors, including the availability of appropriate and well-annotated training datasets for designing an efficient diagnostic system. On the other hand, the similarity of the complications of different diseases has made this work challenging. In this study, two tomato diseases, namely Bacterial Spot and Mosaic Virus, are investigated using images collected from the PlantVillage, Taiwan tomato leaves, Field-PlantVillage, and Syn-PlantVillage datasets. The disease severity levels are divided into six stages for Bacterial Spot and four stages for Mosaic Virus, and a specifically designed deep convolutional neural network is proposed for severity classification. Experimental results demonstrate that the proposed method achieves high accuracy under challenging field conditions and outperforms several state-of-the-art methods.
Why it matches plant phenotyping methodsトマト葉の病徴・病害重症度を画像から段階分類するCNN、背景除去、病斑セグメンテーションを開発しており、植物状態の取得・推定手法が中心である。
titleA parallel convolutional neural network with background removal and lesion segmentation for field plant disease severity classification
Reproduction assets foundThe paper's own severity-annotated datasets are explicitly restricted (available only on request), so no public paper-specific data asset qualifies. The authors do provide an explicit public code availability link for their proposed BaSPaC model. The Mendeley and Drive links are pre-existing external datasets cited as,Code · publicCode availability
https://github.com/m-hasheminejad/BaSPaC.Open asset ↗m-hasheminejad/BaSPaChtml-lines:878-908Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The citrus diseases are affecting the fruit production worldwide thereby posing an economical burden. Major research is moving towards finding solutions using Artificial Intelligence (AI) and Image processing methods. Due to factors like illumination variations, leaf form, and disease symptoms, image data has intrinsic uncertainties that are typically difficult for traditional machine learning techniques to handle. In this paper, the interpretability of fuzzy logic is combined with the resilience of deep learning to propose a novel Fuzzy Convolutional Neural Network (Fuzzy-CNN) architecture for the automated diagnosis of citrus leaf diseases. The hybrid method uses a Convolutional Neural Network (CNN) to obtain complex features of citrus images, and a Fuzzy Inference System (FIS) to improve the classification results. The proposed approach encodes accurate data into fuzzy sets and applies linguistic concepts to determine the severity of a disease, which will contribute to the further development of the decision. In order to test and verify the proposed approach, several experiments were carried out, which proved that Fuzzy-CNN is more effective than regular CNN models with the approximate accuracy difference approximately 1.8, and especially in cases when the symptoms of disease are not clear. To strengthen experimental validation, the proposed method is evaluated on two independent datasets, including an external benchmark dataset, imbalance-aware evaluation metrics are employed to ensure robustness and generalizability. Experimental results demonstrate consistent and statistically significant improvements over existing neuro-fuzzy and machine learning approaches. This research contributes to early detection by collaborating the potential of fuzzy neural networks and offering a flexible solution for real-time disease detection in citrus crops.
Why it matches plant phenotyping methods柑橘葉画像から病害および重症度を推定するFuzzy-CNN手法を開発し、独立データセットとベンチマークで検証しており、植物フェノタイピング手法が中心である。
abstractpropose a novel Fuzzy Convolutional Neural Network (Fuzzy-CNN) architecture for the automated diagnosis of citrus leaf diseases.
Reproduction assets foundThe paper's Data Availability statement links two public image datasets used for the citrus disease phenotyping/classification experiments (a Mendeley citrus leaves dataset and a Kaggle orange fruit dataset), and a third public Kaggle citrus disease dataset is cited as the external benchmark dataset used for validationDataset · publicThe data used in the current study is publicly available from the following links. [https://data.mendeley.com/datasets/3f83gxmv57/2]Open asset ↗data.mendeley.com · 3f83gxmv57/2html-lines:525-539Dataset · publicThe data used in the current study is publicly available from the following links. [https://data.mendeley.com/datasets/3f83gxmv57/2] [https://www.kaggle.com/datasets/sgandhi2003/orange-fruit-dataset]Open asset ↗www.kaggle.com · sgandhi2003/orange-fruit-datasethtml-lines:525-539Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Leaf hyperspectral reflectance (HSR) data have gained increasing attention due to their usage in predicting a range of leaf physiological, biochemical, structural, and photosynthetic traits using machine learning (ML) models. The PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion. However, a comprehensive evaluation of the accuracy and transferability of the PROSPECT model across a large set of species is hindered by the limited availability of ground truth data sets. Here, we employed a combination of inversion and forward simulation of the PROSPECT-D model across a broad range of species and identified four narrow wavebands linked to environmental effects. We also introduced a novel framework using partial least squares regression to enable the analysis of the transferability of the machine learning models trained base on the PROSPECT-D across species. This analysis revealed trait-specific patterns of transferability for the machine learning surrogate based on the PROSPECT-D forward model. We then extended this analysis to PROSPECT-D inversion using neural networks and developed a fast, accurate deep-learning-based surrogate inversion approach to estimate leaf traits from measured HSR data. Our data-driven framework paves the way for improving the accuracy of PROSPECT and similar mechanistic models.
Why it matches plant phenotyping methodsHSRから葉の生理・生化学・構造・光合成形質を推定する機械学習代理モデルとPROSPECT-D逆解析手法を開発・評価しており、植物形質取得法が研究の中心です。
abstractThe PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion.
To achieve efficient crop management, exact plant disease detection in leaves is required. This study proposes DeMoHybridNet (Bottleneck Reduction Fusion) for the automated classification of Corn, Apple, Citrus, and Mango crop diseases. Input images are processed through augmentation and resizing, and then features are learned using DenseNet-201 and MobileNetV2. Global Average Pooling is applied, which produces condensed features. The features are then compressed using bottleneck layers of 512 features. The features are concatenated and classified by Random Forest (RF) classifier. To further improve the performance, a hybrid meta-heuristic method called IGWO-DOA (Improved Grey Wolf Optimization-Dingo Optimization Algorithm) is used to optimize the hyperparameters of the model for better convergence and generalization. The proposed optimized model gives the classification accuracy is 98.56% for Corn, 98.99% for Apple, 97.83% for Citrus and 99.35% for Mango leaf dataset. Statistical analysis confirms its robustness and reliability, demonstrating its effectiveness for precision agriculture applications.
Why it matches plant phenotyping methods葉画像から植物の病害状態を自動分類する深層学習・特徴抽出・分類ワークフローが研究の中心であり、植物病害表現型の画像ベース推定手法に該当する。
abstractThis study proposes DeMoHybridNet (Bottleneck Reduction Fusion) for the automated classification of Corn, Apple, Citrus, and Mango crop diseases.
This paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2. The dataset, sourced from Kaggle's Plant Village repository, includes 152 images of healthy potato leaves and 1000 images each of early and late blight. The methodology covers data preparation, model architecture design, training, evaluation, and deployment. During data preparation, the data set was split into training sets (80%) and testing sets (20%), with images resized to 128x128 pixels. The Deep Learning (DL) models built using CNN with 4 different optimizers (ADAM, SGD, RMSPROP, and ADAMAX) and trained using a sparse categorical cross-entropy loss function, include multiple convolutional and pooling layers for feature extraction, and fully connected layers for classification. Early stopping was used to prevent overfitting. Model performance was assessed using accuracy, loss curves, confusion matrix, ROC curve, precision recall curve, classification report, and F1 score. In addition, we have used data augmentation to balance the dataset by increasing healthy potato leaves 6 times and the use of Ensemble Deep Learning (EDL). EDL10 which contains DL1 (CNN + ADAM), DL2 (CNN + SGD), DL3 (CNN + RMSPROP) and DL4 (CNN + ADAMX) performs best with a accuracy score of 97.0%. This highlights the importance of data balancing and the use of the ensemble classification approach for the detection of blight in Potato Leaves.
Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類するCNN・アンサンブル手法の設計、評価、データ拡張が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2.
Reproduction assets foundThe paper uses the public Kaggle PlantVillage potato leaf image dataset and archives its complete analysis source code on Zenodo with explicit availability statements and URLs.Code · publicThe complete source code is hosted in a DOI-minting repository and has been archived on Zenodo to ensure long-term accessibility and reproducibility. The code is released under an open-source license. The archived version corresponding to this publication is available at : https://doi.org/10.5281/zenodo.19624017Open asset ↗Zenodo · 10.5281/zenodo.19624017lines:252-314Dataset · publicThe datasets generated and/or analysed during the current study are available at : PlantVillage Dataset, accessed from https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.Open asset ↗Kaggle · plantvillage-datasetlines:252-314Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Maize (Zea Mays) is one of the world's most important staple crops, providing food for humans and feed for livestock. However, its production is threatened by a range of stresses, including crop diseases, which significantly reduce yields, particularly in smallholder farming systems. Traditional disease detection methods, such as visual inspection, are often labour-intensive, subjective, and prone to error, leading to delayed interventions and widespread crop losses. This study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases in a smallholder farm located in the Mopani District of Limpopo Province, South Africa. UAV-derived vegetation indices including NDVI, GNDVI, and NDRE were combined with UAV multispectral bands and the three ML algorithms, namely - support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), to first distinguish healthy from diseased plants and then to classify specific maize diseases. The SVM algorithm achieved the highest accuracy in both, distinguishing healthy and diseased crops from other land cover classes (91.73%) and in distinguishing specific diseases (89.41%). Among the diseases identified, Southern Corn Leaf Blight was classified with the highest user's accuracy, while phosphorus deficiency had the lowest user's classification accuracy. The results demonstrate the potential of integrating UAV-based multispectral imaging and ML for precision agriculture by providing timely, spatially detailed disease information that enables targeted management practices, reducing crop losses and enhancing food security for smallholder farmers.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて、トウモロコシの健全・罹病状態および具体的な葉病害を推定する手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractThis study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate detection of such diseases is crucial for improving both crop yield and quality. While numerous deep learning approaches rely solely on image data for disease identification, they often overlook the complementary value of textual information in enhancing visual analysis. To address this limitation and effectively fuse features from different modalities, we propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition. Our approach utilizes the Zhipu.ai multi-modal model to generate comprehensive textual descriptions of diseased crop leaves, including global description, local lesion description, and color-texture description. These textual descriptions are then encoded into feature embeddings, while visual features are extracted using the ShuffleNet-v2 model as the image encoder. Subsequently, a cross-attention module aligns and fuses the two modalities, and a gated fusion module enables dynamic feature selection during the fusion process. Extensive evaluations on the Soybean Disease and PlantVillage datasets demonstrate that our method outperforms existing image-based models in terms of accuracy. Specifically, our model achieves recognition accuracies of 99.04% and 99.12% on the respective datasets, surpassing the ShuffleNet-V2 model by 1.09% and 2.53%, respectively. These results highlight the effectiveness of Cross-Model learning in integrating visual and textual cues for accurate and efficient disease recognition, offering a scalable solution for crop disease diagnosis.
Why it matches plant phenotyping methods植物葉の病徴を画像と言語情報から認識する融合フレームワークを開発し、複数データセットで既存手法と比較評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a Cross-Model fusion framework based on a vision-language model that integrates cross-attention and gated fusion mechanisms for crop disease recognition.
Reproduction assets foundThe paper's crop disease recognition experiments use two openly available image datasets, both with explicit public availability statements in the Data Availability section: the Soybean Disease dataset (Dryad DOI) and the PlantVillage dataset (Kaggle). No author analysis code, trained models, or generated text-annotaitDataset · publicThe datasets utilized in this study are openly accessible. The soybean dataset is available at https://doi.org/10.5061/dryad.41ns1rnj3.Open asset ↗Dryad · 10.5061/dryad.41ns1rnj3html-lines:403-424Dataset · publicThe plantvillage dataset is available at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.Open asset ↗Kaggle · plantvillage-datasethtml-lines:403-424Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Timely and accurate plant disease detection is important for enhancing agricultural productivity and promoting sustainability. The study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases. The proposed method combines fuzzy logic with multimodal data fusion to effectively address the complex interactions and uncertainties in agricultural datasets. The MAF-DNN employs a robust adaptive fuzzy framework with dynamic rule optimization and integrates Hyperspectral Imaging Data (HID) with RGB imaging data to acquire detailed spectral information and high-resolution visual cues for disease classification. The multimodal fusion enhances the model’s ability to capture intricate patterns that relate to plant health, improving the accuracy of disease classification. The experimental results showed that the MAF-DNN outperforms traditional models by achieving an accuracy of 97.8%, precision of 96.5%, recall of 98.2%, and F1-score of 97.3%. Additionally, the adaptive design reduces computational overhead, increases efficiency, and improves scalability for large-scale agricultural applications. The MAF-DNN represents a significant advancement in plant disease classification and provides a robust and efficient solution for precision agriculture.
Why it matches plant phenotyping methods植物病徴を画像から分類するマルチモーダル画像・深層学習手法の開発と性能評価が中心であり、植物の病害状態を直接推定するため。
abstractThe study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases.
Reproduction assets foundThe paper uses two public Kaggle plant disease image datasets (New Plant Diseases Dataset and CCMT Plant Disease Dataset) as its phenotyping inputs and states that the authors' custom MAF-DNN code is publicly available on GitHub, with all three URLs given in the article and matching allowed URLs.Code · publicThe custom code used to develop and evaluate the proposed Multimodal Adaptive Fuzzy Deep Neural Network (MAF-DNN) framework is publicly available at: https://github.com/skbsangeetha/MAF-DNN-Plant-disease-classificationOpen asset ↗skbsangeetha/MAF-DNN-Plant-disease-classificationhtml-lines:102-118Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro-computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision and competitive recall, resulting in strong F1-scores and average precision across a wide range of imaging conditions. While a modest precision-recall imbalance is observed in certain datasets, favoring high-confidence detections, this behavior reflects a conservative instance reconstruction strategy that prioritizes accurate boundary delineation and reduces false positive segmentation in densely packed and morphologically heterogeneous tissues. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to 3D liver images from a mouse model of hepatocellular carcinoma (HCC) to enable spatially resolved 3D nuclear phenotyping. In healthy liver tissue, nuclear DNA content and nuclear volume exhibited a tightly regulated log-log scaling relationship. In contrast, tumor-adjacent and tumor regions displayed progressive disruption of this coupling, forming spatially coherent domains of nuclear DNA-volume decoupling that are not detectable in conventional two-dimensional histology. We quantify this phenomenon using a Nuclear Decoupling Score (NDS), revealing increased nuclear instability aligned with pathological tissue remodeling highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities.
Why it matches plant phenotyping methods3D核セグメンテーション手法を開発し、多様な画像データセットでベンチマーク・検証したうえで、核形態とDNA量の定量的フェノタイピングに応用しており、植物対象ではないため本索引の対象外となる可能性はあるが、提示内容上はフェノタイピング手法研究として中心的である。
abstractAccurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy
Reproduction assets foundThe paper's newly released Zenodo deposit (10.5281/zenodo.18517324) containing raw volumes, annotations, training patches, model weights, and segmentation outputs is not among the allowed URLs, so it cannot be listed. The authors' public analysis/segmentation code repository is explicitly deposited with an authors' URLCode · publicThe source code for training and predicting nuclei segmentation using NucVerse 3D is available from https://github.com/Segovia-lab/3D-Nuclei-segmentation.git .Open asset ↗https://github.com/Segovia-lab/3D-Nuclei-segmentation.gitlines:647-728Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Accurate identification of Allium seed genotypes is essential for cultivar authentication, breeding, and fraud prevention, yet remains challenging due to morphological similarities. This study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes, including shallot, red, white, and yellow onions, bon-sorkh, and two leek varieties. A total of 700 spectra and 70 images were acquired using the Vis-NIR spectrometer and hyperspectral camera, respectively, under controlled conditions and spectral preprocessing was applied to enhance signal quality. For spectrometer data, classification models were developed using soft independent modelling of class analogy (SIMCA), artificial neural networks (ANN), and histogram-based gradient boosting (HisGB). For hyperspectral data, pixel-level spectra were used to train ANN, HisGB, and deep convolutional neural networks (1D and 2D CNNs). Among the spectrometer models, the combination of second derivative preprocessing with HisGB achieved the highest performance (F1-score: 98.52%). For HSI, HisGB yielded the highest pixel-level classification accuracy (F1-score: 97.83%; error: 2.49%), followed by 1D CNN (F1-score: 96.85%). Spatial analysis revealed that HisGB and 1D CNN produced consistent classification maps across genotypes, whereas ANN and 2D CNN exhibited higher misclassification rates, particularly for morphologically similar classes such as shallot and bon-sorkh. At image level, the hyperspectral camera outperformed the Vis-NIR spectrometer, achieving perfect classification across all models. These results demonstrate the potential of hyperspectral imaging, especially when combined with ensemble and deep learning approaches, for high-throughput, non-destructive seed sorting and genotype purity assessment. The study also emphasizes the trade-off between the lower cost but reduced precision of the Vis-NIR spectrometer and the superior accuracy offered by the hyperspectral camera.
Why it matches plant phenotyping methodsAllium種子の遺伝型識別を対象に、Vis-NIR分光およびハイパースペクトル画像取得と分類ワークフローを比較・評価しており、非破壊的な表現型取得・判別手法が研究の中心です。
abstractThis study evaluates the potential of a visible and near-infrared (Vis-NIR) spectrometer and a hyperspectral camera for non-destructive classification of seven closely related Allium genotypes
Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves. Nevertheless, to make such solutions available on the web or mobile devices one has to really think about how heavy the calculations will be, how easy the user interface should be, and also the limit on the data used. Here is a paper on a web-based applied deep learning system for disease detection in multiple crops. The system detects disease in eight crops Apple, Banana, Grape, Mango, Cauliflower, Tomato, Potato, and Corn with each crop having several disease classes and healthy samples. Three transfer-learning-based CNN architectures MobileNetV3, EfficientNetB4, and ResNet50 were compared for classification performance on the public datasets collected from PlantVillage, Kaggle, and Mendeley. Considering class-wise accuracy, prediction time, and deployment scenarios, MobileNetV3 was picked as the main model to be integrated into the system. To compensate for the differences in image quality often found in pictures taken by users, an optional super-resolution preprocessing step with Real-ESRGAN is added and quantitatively assessed. Disease prediction with spectral activation maps (Grad-CAM) enhances the model's interpretability by highlighting image areas where the disease is detected. The resulting model is embedded in a multilingual Progressive Web Application (PWA). The platform enables users to submit their crop images and receive predicted disease names and treatment options, which are generated by a Large Language Model (LLM) using structured disease metadata. The research acknowledges dataset bias and limitations in extrapolating from curated datasets to the general real-world setting although it reports very good performance of the method on the test sets. In summary, the system proposed here is intended as a practical digital agriculture decision-support tool that demonstrates deployment feasibility and raises a few issues for future validation at the field level and improvement.
Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の比較・前処理評価・実装を中心とした研究であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として中心的です。
abstractDeep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves.
Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.
Why it matches plant phenotyping methodsRGB画像指標と機械学習により、植物の干ばつ応答パターンという生理状態を分類し、蛍光測定との再現性を評価しているため、表現型取得・抽出手法が中心です。
abstractThis study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns.
Reproduction assets foundThe paper explicitly states that the authors' analysis code (data processing, feature extraction, SVM/PLS-DA modeling) is publicly deposited on Zenodo with a DOI matching an allowed URL. The phenotype datasets are only said to be in the manuscript/supplementary files, so the code deposit is the qualifying paperSpecificCode · publicThe code supporting the findings of this study, including data processing, feature extraction, and machine learning modeling is available at Zenodo: https://doi.org/10.5281/zenodo.19127295 .Open asset ↗Zenodo · 10.5281/zenodo.19127295lines:98-116Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
This study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants, a method that enhances diagnostic accuracy and enables more effective crop protection. The research was conducted in 2023 in Shandong Province, China, using two tomato cultivars - Dongfeng-1 and Gypsy. Ten greenhouse plots (2,000 m² each) and ten open-field plots (5,000 m² each) were studied, with image data collected via high-resolution cameras, multispectral sensors, and microclimate sensors, yielding approximately 20,000 annotated images. Five machine learning models were tested: convolutional neural networks (CNNs), random forests, gradient boosting, support vector machines (SVMs), and k-nearest neighbors (KNN). CNNs demonstrated superior accuracy in both greenhouse (95.2% ± 0.3) and open-field plots (92.5% ± 0.5), with corresponding AUC-ROC values of 0.96 and 0.93 (p = 0.001). The false positive rate for CNNs was 4.1% in greenhouses and 5.3% in open-field plots, while diagnostic time was shorter in greenhouses (8.3 s vs. 10.5 s). Compared to visual inspection, CNNs significantly improved diagnostic accuracy and reduced fungicide use. To ensure robustness, the models were evaluated under varying lighting and microclimate conditions. Assessments on both GPU and CPU platforms demonstrated the model's feasibility for deployment on edge devices and cloud-based systems.
Why it matches plant phenotyping methodsトマト植物の真菌病を画像・センサー観測から推定する機械学習手法を開発・比較し、精度、頑健性、計算環境で評価しており、病害状態の表現型取得が中心である。
abstractThis study explores the use of AI-driven machine learning algorithms for the early detection of fungal diseases in tomato plants
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Wheat (Triticum aestivum L.) is a globally essential cereal crop whose productivity and processing efficiency are critically influenced by the morphological traits of the grain. While biotic and abiotic stresses reduce field yields, post-harvest milling losses further diminish flour output, underscoring the importance of optimizing grain morphology for processing efficiency. This study investigates the relationships between the wheat grain shape and size parameters and their impact on milling performance outcomes to identify optimal morphological characteristics that minimize yield losses. Using a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness. Multivariate analyses through k-means clustering and principal component analysis showed two distinct morphological groups and highlighted the kernel width and uniformity as potential indicators. Strong positive correlations between size traits and negative correlations between shape descriptors emphasize the trade-offs influencing milling quality. Optimal wheat grains for enhanced the milling yield exhibited large, plump, regular kernels with high circularity and low skewness. These findings provide quantitative criteria to guide wheat breeding programs with the goal of genetically optimizing the grain morphology to improve the milling yield and processing quality, thereby contributing to global food security.
Why it matches plant phenotyping methods画像解析による小麦粒の形態形質の定量が研究の中心であり、大規模コレクションに適用して形質抽出・解析を行っているため、植物表現型手法の実質的な応用に該当する。
abstractUsing a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness.
Field / plotLeafClassificationSegmentationStress / disease detectionDisease symptoms / severity
Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food security. While much has been done recently, many deep learning-based approaches will still fail in real-world tests because of challenges such as background clutter, differences in illumination, occlusion, or the fact that visual symptoms for these diseases can be very subtle early on. Traditional CNN- and Transformer-based architectures generally lack accurate lesion localisation and interpretability, hindering their practical deployment in agricultural decision-support tools. To address these issues, we present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage. It then applies preprocessing steps guided by ROI, based on leaf segmentation from the U-Net, followed by a compact CNN-based spatial feature-extraction framework. We arrange spatial feature embeddings extracted from lesion regions into an ordered sequence and employ a Bi-LSTM with attention to model structured contextual dependencies, allowing progression-aware feature learning without requiring actual temporal image sequences. Lastly, Grad-CAM-based post-hoc explainability is employed to interpret model decisions, enabling transparent visualisation of disease-relevant regions. We conduct extensive experiments on the PlantVillage benchmark and the FieldPlant dataset and show that LDDHybridNet consistently outperforms representative CNN, transformer, and hybrid baselines across multiple evaluation metrics. Although the near-ceiling performance on PlantVillage reveals the dataset's artificial nature, the proposed framework achieves 95.37% accuracy under real-world field conditions and 92.84% on weak-lesion early-stage samples, demonstrating the method's robustness and early-stage detection potential. The performance boosts are statistically significant (P < 0.01). In general, LDDHybridNet is an interpretable and robust deep learning framework for leaf disease detection, which can support data-driven crop protection and precision agriculture applications.
Why it matches plant phenotyping methods葉の病害症状を画像から検出・局在化する深層学習手法の開発とベンチマーク評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractwe present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage.
Reproduction assets foundThe paper's phenotyping measurements are leaf disease detection experiments on two public image datasets: PlantVillage (Kaggle) and FieldPlant (IEEE Dataport), both cited with explicit public URLs. The authors' code, trained weights, and scripts are not publicly released and are available only on request, so no code/模型Dataset · public43.Hughes, D. P. & Mohanty, S. P. PlantVillage Dataset. [online] (2015). Available at: https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗PlantVillage Datasethtml-lines:657-726Dataset · public44.Moupojou, R. K., Bouachir, W., Ahamed, T. & Taki, A. H. FieldPlant: A Real-World Dataset for Leaf Disease Detection in Field Conditions. IEEE Dataport. [online] (2021). Available at: https://ieee-dataport.org/documents/fieldplant-datasetOpen asset ↗FieldPlanthtml-lines:657-726Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The leaf area index (LAI) is a key parameter for characterizing crop growth and water use efficiency. Therefore, efficient and accurate monitoring of LAI is essential for precision rice management. To overcome the limitations of traditional LAI measurement methods, which are time consuming, labor intensive, and difficult to scale, this study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models. The framework incorporates color indices (CIs) derived from RGB imagery, vegetation indices (VIs) derived from multispectral data, texture features (TIs), and texture feature indices (TFIs), and employs six machine learning algorithms to develop optimized LAI estimation models for the rice booting stage. The results indicate that at a flight altitude of 30 m, the CNN model integrating CIs and TIs achieved an accuracy of R 2 = 0.815. At 60 m, the RF model combining VIs and TFIs showed superior performance, with an R 2 of 0.866. Further integration of CIs, VIs, and TFIs at 30 m produced the best results, increasing R 2 to 0.901, reducing RMSE to 0.273, and raising RPD to above 3.0. These findings demonstrate that TFIs significantly enhance the spectral-spatial representation capability of multispectral data, thereby improving model accuracy. The combined use of CIs and VIs across different sensors compensates for the inherent limitations between spectral and spatial information, while the integration of multi-resolution TIs and TFIs effectively overcomes the constraints of single-source data. Overall, the proposed approach provides a robust and efficient solution for high-precision LAI estimation during critical growth stages of rice, offering strong support for precision agricultural management.
Why it matches plant phenotyping methodsUAV画像・マルチスペクトル特徴量と機械学習によるイネLAI推定フレームワークの開発・性能評価が研究の中心であり、植物形質の取得手法に該当する。
abstractthis study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models.
Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.
Why it matches plant phenotyping methods植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。
abstractHere, we introduce a unified quantitative framework grounded in dynamical systems theory.
Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional methods of diagnosis are time-consuming, error-prone, and inefficient. With the rapid development of AI, deep learning (DL), and IoT, there is increasing demand for combined solutions that jointly address plant health monitoring and harvest optimization in a reproducible and deployment-oriented manner. This study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages to optimize yield quality and minimize agricultural losses. The work explicitly targets improved classification reliability, broader class evaluation, rigorous validation and generation of decision-ready outputs for precision agriculture. AgroDualNet comprises two modules. The crop-disease prediction module integrates ResNet50 with a Convolutional Block Attention Module (CBAM), and a Sequential Minimal Optimization (SMO)-based SVM classifier to enhance feature learning and classification performance Several different architectural designs are benchmarked and the resultant model is tested on both a dedicated 3-class subset and a large multi-class model of the PlantVillage dataset with leakage safe protocol(augmentation applied only on training data), cross-validation, statistical significance testing as well as ablation. The fruit-ripeness module employs YOLOv8 for real-time fruit localization and MobileNetV2 for lightweight ripeness classification suitable for edge deployment and a prototype decision-support layer maps predictions to actionable recommendations. That is able to run on the edge. Experiments show that the hybrid CBAM + ResNet50 + SMO model achieves 99.6% accuracy for crop disease classification on a three-class configuration of the PlantVillage dataset and maintains consistently higher accuracy than strong baseline in a 38-class setting, with statistically significant results confirmed by McNemar's test (p < 0.001) outperforming baseline and intermediate architectures in accuracy, precision, Recall and F1-Score The fruit ripeness pipeline achieves 98.88% classification accuracy across four ripeness stages (unripe, semi-ripe, ripe, over-ripe) on a combined Kaggle and real-field apple dataset with low inference time, confirming its suitability for near real-time deployment on edge devices. Cross-validation, Statistical significance tests and ablation studies collectively validate the robustness and significance of these gains and the decision-support layer demonstrates the feasibility of converting raw predictions into interpretable, recommendation-oriented outputs. AgroDualNet provides an efficient and unified system for monitoring plant diseases and evaluating fruit ripeness, with statically validated performance across both focused and full multi-class settings, addressing two critical challenges in precision agriculture with a single extensible framework. The dual-module design of AgroDualNet, which combines disease prediction with ripeness analysis and a preliminary decision-support prototype offers a more comprehensive and practically relevant AI-driven monitoring solution than conventional single-task models. By emphasizing multi-class validation on PlantVillage, leakage-aware experimentation, statistical verification, and system-level integration, this works supports real-time, precise and automated guidance to reduce crop losses, improve harvest timing, and enable smarter farm-level decision making.
Why it matches plant phenotyping methods植物病害状態と果実成熟度を画像から推定する深層学習パイプラインの開発・比較検証が中心であり、PlantVillageおよび実圃場データで交差検証、アブレーション、統計検定を実施しているため、植物フェノタイピング手法として含める。
abstractThis study develops a new bi-phasic DL framework, AgroDualNet, that predicts crop diseases and identifies fruit ripeness stages
Reproduction assets foundThe paper's Data availability statement names two public datasets used directly in the phenotyping experiments: the PlantVillage crop-disease dataset and a Kaggle apple fruit-ripeness dataset, both with explicit Kaggle URLs matching allowed_urls. The statement also mentions implementation files, trained weights, and a Dataset · public. and V.V. wrote the main manuscript text, and K.N. prepared figures. All authors reviewed the manuscript.
Funding
There is no funding received from any organization for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are opeOpen asset ↗Kaggle · plantvillage-datasetlines:583-665Dataset · publiction for this work.
Data availability
The datasets that have been used and analysed in this study are publicly available. PlantVillage crop disease data are on kaggle ( https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ) accessed March 2026). The dataset on the ripeness of apple fruits can be found in Kaggle ( https://www.kaggle.com/datasets/mdsagorahmed/fruit-image-dataset-22-classes ) accessed March 2026). The files used to run the implementation, trained model weights, class definitions and split metadata are openly available at: 10.5281/zenodo.19051520.
Declarations
Competing interests
The authors declare no competing interests.
References
1.
George R Thuseethan S RagelOpen asset ↗Kaggle · fruit-image-dataset-22-classeslines:583-665Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Recent advancements in domain-specific classification methods have demonstrated the remarkable performance of deep learning in comparison to traditional machine learning techniques. This study develops a computationally efficient Convolutional Neural Network (CNN) model tailored for citrus plant disease classification, achieving performance comparable to that of the pretrained InceptionV3 model. A custom five-layer CNN model is constructed to classify citrus plant diseases into healthy and diseased categories using images collected from citrus orchards in Northen India. The model has been further validated using images sourced from GitHub and the Kaggle database. The proposed method surpasses classical machine learning approaches in accuracy and computational efficiency, achieving classification accuracies of 92.59%. The training time of the proposed CNN AgriVision-L5 is reduced by 50%, respectively, compared to the InceptionV3 model, demonstrating their computational efficiency. The proposed methodology offers significant advancements in plant disease management and sustainable agriculture, aligning with Sustainable Development Goals like SDG2, SDG9, and SDG12.
Why it matches plant phenotyping methods柑橘病害を画像から分類するCNNを開発し、外部画像で検証しており、植物の病害状態を推定する方法が研究の中心です。
abstractThis study develops a computationally efficient Convolutional Neural Network (CNN) model tailored for citrus plant disease classification
This study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs. Unlike prior research restricted to narrow genotypic ranges or single-image modalities, we analyzed 65 genetically diverse olive cultivars from the Tarom Olive Research Station (Zanjan, Iran). We employed a dual-image phenotyping approach, integrating high-resolution imagery of both fruits and kernels with quantitative weight metrics. This methodology enabled the extraction of critical morphological traits—including eccentricity, solidity, and shape factors—to train and validate seven Machine Learning (ML) algorithms. Our comparative analysis of Discriminant Analysis (DA), Support Vector Machine (SVM), Neural Networks (NN), and ensemble methods reveals that the DA model achieves superior performance, attaining a recall and precision of 0.98 when integrating fruit, kernel, and weight data. This significantly outperforms standard models like KNN and Naive Bayes in this domain. These findings demonstrate that combining multi-view imaging with morphological feature extraction provides a highly accurate, cost-effective tool for managing olive genetic resources and accelerating crop improvement.
Why it matches plant phenotyping methods果実・核の画像から形態形質を抽出し、機械学習モデルを比較検証する高スループット植物フェノタイピング手法が研究の中心である。
abstractThis study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs.
Accurate classification of corn leaf diseases is critical for timely detection and control of pests and diseases. By accurately recognizing different types of leaf diseases, farmers and agricultural experts can quickly take targeted control measures to reduce crop losses and safeguard corn yield and quality. Since these corn leaf disease images usually contain complex backgrounds, similar lesion features, and limited labeling data, it causes traditional convolutional neural networks (CNNs) to easily confuse the lesion region with the background, making it difficult to distinguish between different disease types. To address these limitations, we propose G-ResNet, a hybrid CNN-Vision Mamba network that enhances disease-relevant feature learning through a hierarchical feature attention module and a scale feature attention module. It was demonstrated experimentally that G-ResNet can better classify maize leaf disease images. The code is available at https://github.com/gustafmy/g_resnet.git.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から分類するCNN・Vision Mamba手法の開発と実験評価が研究の中心であり、植物病害フェノタイピングに該当する。
titleCorn or maize leaf disease classification based on CNN and vision mamba model.
Reproduction assets foundThe paper's analysis code (G-ResNet) is publicly available on GitHub with explicit availability statements, and the plant image dataset used for the disease classification experiments is a public Kaggle dataset explicitly cited in the Experiment section. The underlying data availability statement also mentions request,Code · publicSource code for the algorithms described in this paper is available at https://github.com/gustafmy/g_resnet.git.Open asset ↗gustafmy/g_resnethtml-lines:300-328Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate identification of maize diseases is crucial for safeguarding global food security. Traditional image-based methods often struggle with lighting variations, occlusions, and noise, limiting their robustness and generalisation. Multimodal approaches that integrate visual and textual information have shown promise. However, these methods frequently require manually curated textual descriptions for each image, increasing data collection costs and limiting scalability and practical implementation. To address these limitations, we proposed a maize image-text framework with Cross-Modal Category Alignment (mIT-CMCA). This approach enforces category-level alignment between image and text modalities, enabling more accurate and interpretable cross-modal mapping. First, we construct cross-modal representations by aligning image and text modalities at the category level within a shared embedding space. Second, inspired by contrastive learning, we introduce a Cross-Modal Category Alignment (CMCA) loss based on category-level textual descriptions, reducing annotation complexity. Finally, we present an Efficient Channel-Spatial Hybrid Attention (CSHA) module that preserves inter-class boundaries while incurring minimal computational overhead, thereby enhancing feature discriminability under complex conditions. Experimental results on the maize subset of the PlantVillage dataset (MPVD) show that mIT-CMCA achieves 99.48% accuracy, 99.28% precision, 99.54% recall, and 99.41% F1-score. These results represent improvements of 0.24%, 0.13%, 0.17%, and 0.15% over the strongest vision-only baseline, MaxViT_tiny. On the self-built Maize Leaf-Field dataset (MLFD), the model achieves 93.67% accuracy, 93.76% precision, 93.67% recall, and 93.71% F1-score. It uses only 8.27 million parameters, which is 71.6% fewer than MaxViT_tiny. Its model size is 32.13 MB, which is 72.3% smaller. The proposed method also outperforms comparative models in robustness experiments under artificially added perturbations. These results demonstrate that mIT-CMCA achieves a favorable balance between accuracy and efficiency, making it suitable for practical agricultural deployment.
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画像・マルチモーダル手法の開発と性能評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractwe proposed a maize image-text framework with Cross-Modal Category Alignment (mIT-CMCA).
Reproduction assets foundThe paper's maize disease identification analysis code and trained models are explicitly stated as publicly available in a GitHub repository. The phenotype image datasets (MPVD subset and self-built MLFD) are not publicly available and require contacting the corresponding author.Code · publicCode availability
The code and models are available in the GitHub repository at https://github.com/TANGFEILONG626/mIT-CMCA..Open asset ↗TANGFEILONG626/mIT-CMCAhtml-lines:673-695Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The quality of forest reproductive material is crucial for successful reforestation and afforestation. While physical seed properties like mass are known indicators of quality, the potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration. This study investigates the relationship between the seed coat color of individual Pinus sylvestris seeds, quantified in RGB (Red, Green, Blue) space using a flatbed scanner, and their subsequent germination in container nurseries. The resulting images were processed using ImageJ software to measure the mean pixel intensity (0–255) for the Red (R), Green (G), and Blue (B) channels from the segmented seed area, following the «seed–culture» passport methodology [Forestry Engineering Journal 14 | 55 (2024), 37–60]. From a population of individually tracked seeds, we compared the RGB values of germinated (N = 942) and non-germinated (N = 258) seeds after 30 days. Results from the Kolmogorov-Smirnov test showed that non-germinated seeds had significantly lower individual mass (p = 0.0045) and significantly higher pixel brightness values in the R-, G-, and B-channels (p < 0.0001) compared to germinated seeds. Normalized RGB indices also showed significant differences between groups. Our findings demonstrate that seeds with a lighter, more reflective epidermis – indicative of higher RGB brightness – are statistically associated with a lower probability of successful germination under container nursery conditions. This non-destructive, low-cost method shows significant promise for the rapid pre-sorting of Scots pine seeds. It offers a practical tool to improve the efficiency and predictability of seedling production in forest nurseries by increasing the proportion of viable seeds in sowing batches.
Why it matches plant phenotyping methods個別種子のRGB画像から種皮色を定量抽出し、発芽予測・事前選別に用いる非破壊的な表現型計測法が研究の中心である。
abstractthe potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration
Reproduction assets foundThe paper openly deposits its three core phenotyping datasets in Mendeley Data: morphometric seed data (Dataset 1), the raw VIS/RGB scanner images of individual Pinus sylvestris seeds (Dataset 2), and germination outcome data (Dataset 3). All three DOIs are listed in the Data Availability statement and match allowed URDataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/8g258nbgmf.1Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:133-160Dataset · publicThe original VIS image data of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/dt78jhyw2j.2Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:133-160Dataset · publicThe original germination data—Dataset 3—are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:133-160Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Grain number estimation plays a crucial role in agriculture, serving as a key indicator for crop yield and quality assessment. With advances in computer vision, automatic grain detection has become a significant research area, where deep learning methods have shown remarkable promise. This study proposes a vision transformer model called Swin Transformer, which leverages hierarchical attention mechanisms across shifted windows to effectively capture both local and global features of grains in complex imagery. The model achieves the highest accuracy of 98%, outperforming baseline traditional CNN (ResNet-50) and DINO models in grain counting tasks. To support and validate model performance, explainable AI (XAI) techniques such as Grad-CAM and LIME are employed, highlighting the interpretability and focus of the model on relevant grain regions. Furthermore, a comprehensive empirical analysis is conducted using multiple statistical tests to evaluate the model's robustness and generalizability across various grain morphological parameters, establishing the Swin Transformer as a powerful and interpretable solution for intelligent grain counting in agricultural data analytics.
Why it matches plant phenotyping methods画像から穀粒数を推定する深層学習手法の開発・比較検証が研究の中心であり、植物の収量関連形質を測定するため、植物フェノタイピング手法として収録する。
abstractThis study proposes a vision transformer model called Swin Transformer
Reproduction assets foundThe paper's Data availability statement names a public Kaggle dataset of wheat grain counting images used for the study's grain counting experiments. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe dataset used and/or analyzed during the current study is publicly available at: https://kaggle.com/datasets/ociule/wheat-grain-counting-100-images.Open asset ↗kaggle · ociule/wheat-grain-counting-100-imageslines:1190-1253Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wood apple (Feronia limonia L.) is an underutilized perennial fruit tree with substantial ecological, nutritional, and economic potential, yet its phenotypic diversity and trait organization remain poorly characterized. Here, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors. Trait interrelationships were examined through the complementary use of Spearman’s rank correlation and Cramér’s V association analyses, capturing both directional rank-based dependencies and scale-independent categorical linkages. Hierarchical clustering based on Gower distance separated the genotypes into three distinct phenotypic clusters, with inter-cluster dissimilarities (0.92–1.18) consistently exceeding intra-cluster variation (0.42–0.55), indicating well-supported phenotypic stratification based on cluster validation. Multiple Correspondence Analysis (MCA) explained 23.30% of total inertia across the first two dimensions, with tree growth habit, branch angle, tree shape, and fruit color emerging as the principal drivers of phenotypic differentiation. Vegetative and leaf traits formed a tightly integrated module, whereas fruit-related traits displayed weaker monotonic but persistent categorical associations, reflecting partial phenotypic independence. The strong concordance among association analyses, clustering, and MCA indicates structured patterns of coordinated and partially independent trait associations in wood apple. Overall, this study demonstrates the effectiveness of mixed-scale multivariate approaches for resolving complex trait architecture in underutilized perennial fruit crops and provides a quantitative phenotypic framework to support germplasm conservation, parent selection, and ideotype-oriented improvement of wood apple.
Why it matches plant phenotyping methods混合尺度の多変量解析を用いて植物遺伝資源の表現型構造を定量化する手法が研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll data generated or analyzed during this study are available in the article and the accompanying Supplementary Table S1.Open asset ↗lines:137-161Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
An effective framework based on deep learning (DL) is developed in this study for reliable and accurate performance. The multi-class detection of crops such as corn, tomato and potato is accurate and reliable. The aim is to improve early disease detection which guarantees good classification accuracy, strong generalization across datasets, enhanced interpretability through XAI methods and enabling realistic agricultural applications. : The study uses two large publicly available datasets of plant leaf disease images of corn, tomato and potato. The first dataset designated as D-I consists of 39,203 images belonging to 18 classes of disease and the second dataset designated as D-II consists of 65,565 images also belonging to 18 classes of disease. The two datasets contain images showing different visual scenarios and variations of disease which will help form a multi-class classifier. In this study, five DL architectures were used; InceptionNetV3, ResNet152V2, ViT, BERT, and the proposed hybrid model (ResViT-152) that combined convolutional feature extraction with transformer-based global attention. Every model was trained, validated, and tested under the same experimental setup. Cross validation and multi-phase testing assessed their performances in their capacity to learn discriminative parameters in corn, tomato and potato disease classes. The hybrid model exhibited a better performance in all test conditions. In IntraTest1, the accuracies were 99.12%, 98.94% and 99.06% for corn, tomato and potato respectively. In IntraTest2, the model achieves accuracy of 99.23% for corn, 98.97% for tomato, and 98.98% for potato on D-II. The precise percentages for the cross-tests were 96.27% (corn), 95.14% (tomato), 95.06% (potato) for CrossTest1 and 95.77% (corn), 96.22% (tomato), 96.15% (potato) for CrossTest2. The performance across datasets for all three crops is good and generalization is robust. A study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection. The experimental results verify that the proposed work provides superior performance. Generalization outperforms standard architectures in effectiveness. In addition, there is also stable performance with varying datasets. With Explainability included, the model becomes more transparent and a strong candidate for further validation toward deployment in precision agriculture, pending evaluation on real-world field datasets.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類・検出する深層学習手法の開発と、複数データセットでの交差検証・比較評価が中心であり、植物フェノタイピング手法に該当する。
abstractA study is conducted on an efficient, high-performing, and interpretable DL framework for plant leaf disease detection.
Accurate instance segmentation of mandarin fruit slices is essential for quantifying segment morphology and central core structure, which are key traits in cultivar evaluation, fruit quality assessment, and postharvest application. Manual measurement of these anatomical features, however, is time-consuming and prone to inconsistency. In this study, we developed an automated segmentation framework based on YOLOv8 to detect and semantic segment the fruit segments and the central core in high-resolution mandarin transversely cut images. The model was trained on a curated datasetderived from 58 original high-resolution cross-sectional images (5100 × 7019 pixels), which were systematically partitioned into 280 cropped sub-images (1712 × 1778 pixels), each containing a single complete citrus slice, and demonstrated excellent performance. YOLOv8 achieved near-perfect detection metrics, with bounding box metrics precision ~ 0.997, recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 ~ 0.922. Semantic segmentation accuracy was similarly strong, with precision = 0.997, Recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 = 0.965. Training and validation losses converged steadily, indicating stable learning without overfitting. The nonsignificant differences between YOLOv8-predicted measurements and ground truth data, together with low mean absolute error (MAE) values, demonstrate that the model not only performs well in semantic segmentation metrics but also maintains high accuracy in quantitative measurements, which is critical for cultivar discrimination and genetic studies. Our work provides a robust, high-precision, and reproducible framework for mandarin fruit slice phenotyping, offering significant potential for applications in agricultural research, breeding programs, and automated fruit quality evaluation.
Why it matches plant phenotyping methodsマンダリン果実スライスの形態・中心部構造を画像分割で定量する手法を開発し、精度検証と実測値比較を行っており、植物フェノタイピング手法が中心である。
abstractwe developed an automated segmentation framework based on YOLOv8 to detect and semantic segment the fruit segments and the central core
Plant diseases are a major problem for farmers around the world, reducing crop yields. The absence of expertise makes plant disease detection difficult and complicated. Plant disease detection is made easier by deep learning algorithms; however, they are computationally demanding and need huge training datasets. This research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy. Because of its architecture, the proposed network can identify leaf diseases with more accuracy by extracting the wider range of features from the images. The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories. The CNN model is trained using publicly accessible Kaggle dataset, utilising image augmentation techniques. It is evident from the simulation results that the proposed model outperforms several pre-trained, and other trending deep learning models. Proposed model achieves 99.18% classification accuracy with an average precision of 99.17% and area under the curve (AUC) of 1, making this model highly effective in leaf diseases detection. Additionally, Conv-7 DCNN achieved high FPS of 112.49, low inference time of 18.34 s, and low GFLOPS of 13.98, making it suitable for real-time applications in smart agriculture systems.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルを開発・比較しており、病害表現型の取得・分類手法が研究の中心である。
abstractThis research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy.
Reproduction assets foundThe paper's plant-phenotyping measurements (leaf disease classification of tomato, potato, and pepper-bell) are based on a publicly available Kaggle dataset explicitly named in the Data Availability statement. No author analysis code, trained model checkpoints, or other paper-specific assets are disclosed.Dataset · publicThe dataset is available online at https://www.kaggle.com/datasets/emmarex/plantdisease.Open asset ↗Kaggle · emmarex/plantdiseasehtml-lines:824-854Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
WheatSeed / grainPhysiological trait estimationGrowth / development / phenology
Multi-layer perceptron (MLP) neural networks can be used to develop accurate models for quantifying plant responses to environmental factors. This study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN. Results indicated that the MLP model could predict total germination percentage with high model accuracy, including R 2 (0.99), MSE (0.342), RMSE (0.585), and MAE (2.166) for the test data. Time to 50% germination (T50) was also accurately estimated using the MLP model (R 2 = 0.97, MSE = 26.2, RMSE = 5.11, MAE = 5.70). Water potential was identified as the most significant variable affecting total seed germination and T50, followed by salinity and temperature. Seed germination was maximum at 20.5 °C and decreased at higher and lower temperatures. The optimal temperature for T50 was 25.3 °C. Higher salinity and more negative water potential led to lower total seed germination. The results of this study can be used to develop process-based models of crop growth and development and predict total seed germination and germination time under different conditions of temperature, water potential, and salinity.
Why it matches plant phenotyping methodsANNを用いて温度・水ポテンシャル・塩分条件から発芽率とT50という植物状態を定量予測するモデルを開発・評価しており、計算的な表現型推定が研究の中心である。
abstractThis study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN.
Banana is a key staple crop in the world, but its yield is very desperately affected by the leaf diseases like Sigatoka, Fusarium Wilt, and Cordana, which bring serious losses of yield and economy. The manual disease detection methods deployed in the past are time and labor-intensive and cannot be effective in the field, so powerful automated solutions are sought. In this paper, we have presented a hybrid deep learning architecture which combines MobileNetV2 and ResNet101 with attention, dilated convolutions, multi-scale feature pooling and PCA-based feature fusion to classify banana leaf disease accurately and efficiently. The Banana and Banana Leaf, Banana Disease Recognition, and Banana LSD three benchmark datasets have been trained and evaluated following an innovative preprocessing pipeline that consists of illumination correction, background suppression, denoising, and hi-tech data augmentation. The experimental findings have shown that the presented hybrid model is always better than ten state-of-the-art deep learning models, such as VGG16, ResNet50, DenseNet121, EfficientNet-B0, and Vision Transformer (ViT). It has reached an optimal accuracy of 98.28, precision of 98.18, recall of 98.77 and F1-score of 98.43, with only 12.7 M trainable parameters and convergence rate of only 15 epochs, which makes the model both high-accuracy and computationally efficient.
Why it matches plant phenotyping methodsバナナ葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物病害フェノタイピングに該当する。
titleAttention enhanced hybrid deep learning architecture with PCA-based feature fusion for banana leaf disease detection.
Accurate and efficient grain quality assessment is critical for making informed decisions throughout the grain value chain. Early detection of disease enables actions to mitigate spread and further damage, and optimal batch mixing to fulfill specified quality requirements allows for maximizing value and minimizing scrapping. Vision based machine learning and deep learning approaches are gaining attention in the agricultural sector and are useful for the development of automated grain quality assessment. These techniques can reduce the current manual inspection load and are key for objective and precise analysis. Yet, the majority of prior studies are constrained to small or controlled and curated datasets. Practical challenges associated with real-world deployment and reliability are rarely addressed. That is the focus of this work. We present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification. The objective is to determine a CNN model that ensures high and robust classification accuracy, while elucidating and explaining how different image dataset characteristics and training parameters affect performance and reliability. We use a commercial mirror-based imaging system that captures over 90% of each kernel's surface and contrast and compare model architectures, robustness, the effect on pre-processing and image resolution. Our results show similar and high overall performance for ResNet50V2 and EfficientNetV2B0 ([Formula: see text]% accuracy), but per-class analysis indicate that the smaller classes suffer from lack of representative examples, and that most classes benefit from pre-processing including downsampling whereas others benefit from higher resolution. Interactive visualizations reveal that another contributing factor is dubious annotation and multi-class belongingness. Thus, our step-by-step analysis of CNN performance underscores the need for representative data, proper pre-processing, and class-aware evaluation to ensure trustworthy deployment in wheat grain quality assessment.
Why it matches plant phenotyping methods小麦粒画像から品質・病害クラスを推定するCNN画像解析手法の性能、頑健性、前処理、解像度、データ特性を体系的に評価しており、フェノタイピング手法が中心的である。
abstractWe present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification.
Reproduction assets foundThe paper's wheat grain image dataset has a publicly available subset deposited on Zenodo (DOI 10.5281/zenodo.17397123), explicitly stated in the Data Availability statement. The full dataset is proprietary; code is only available upon request, so no qualifying code asset.Dataset · publicA publicly available subset of the segmented wheat grain images used in this study has been deposited in Zenodo to support transparency and reproducibility. The dataset includes representative samples per class collected from instrument and can be accessed at https://doi.org/10.5281/zenodo.17397123.Open asset ↗Zenodo · 10.5281/zenodo.17397123html-lines:337-368Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
As an important economic crop, tomato is vulnerable to various diseases, and these diseases often have high visual similarity, making identification difficult. A delay in diagnosis can have a significant effect on tomato yields. Traditional manual visual inspection methods have poor accuracy, while laboratory diagnostic methods are inefficient, making them unsuitable for large-scale agricultural scenarios. To address this challenge, this study involved the collection and construction of a tomato leaf disease dataset in a real planting environment, and AutoAugment was used to achieve sample diversity and balance the number of training samples in different categories. Additionally, a dual-path ensemble network (DPEN) was proposed, which combines the multiscale feature extraction advantages of GoogLeNet with the dense connection mechanism of DenseNet121. The experimental results show that, compared with the comparison models, the DPEN achieves an identification precision of 98.80% on the self-built dataset, which is an improvement of 2.33% to 9.24%, and a reduction in the number of parameters by 7.09 M compared with GoogLeNet and 2.07 M compared with DenseNet121. The experimental results on public datasets further demonstrated the accuracy of the proposed DPEN model in identifying tomato leaf diseases in complex backgrounds. These results prove that the DPEN model can achieve precise, rapid, and efficient identification of tomato leaf diseases in complex backgrounds, providing technical support for smart agriculture applications.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から識別する深層学習手法を開発し、実環境データセットと公開データセットで精度検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstracta dual-path ensemble network (DPEN) was proposed
Climate change, particularly increasing frequency and intensity of spring frost events, poses a serious threat to viticulture by reducing yield and product quality. This study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP). A unique dataset called FGVL dataset from Sultana seedless grape vineyards in Manisa, Türkiye, following a severe frost event in April 2025. FGVL includes 418 frost-damaged grapes, 510 frost-damaged leaves, 395 healthy grapes, and 698 healthy leaves, all manually annotated by experts under natural field conditions. By integrating ASPP into YOLOv11s, proposed model improved multi-scale contextual feature extraction and achieved mAP@50 of 0.7686, demonstrating stronger performance in instance segmentation of small, overlapping, and visually similar grapevine organs. In addition, Dynamic Confidence Thresholding (DCT) strategy was introduced to improve prediction reliability in dense and visually complex vineyard scenes. Despite challenges such as background clutter, object overlap, and small target structures, model maintained stable performance with low computational demand, requiring only 6.45 GB of GPU memory. Proposed framework offers an accurate, efficient, and practically deployable early recognition system for frost damage assessment in viticulture.
Why it matches plant phenotyping methodsブドウの器官における霜害状態を画像からセグメンテーションする手法を開発・評価しており、植物の病害・障害状態の取得が研究の中心である。
abstractThis study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP).
Reproduction assets foundThe paper's Data availability statement explicitly shares the FGVL frost-damage dataset and source code in the corresponding author's public GitHub repository, matching an allowed URL.Code · publicSource code and dataset are publicly shared in GitHub repository of corresponding author. GitHub repo: https://github.com/kaanarikk/Grape-Instance-Segmentation-For-ViticultureOpen asset ↗https://github.com/kaanarikk/Grape-Instance-Segmentation-For-Viticulturelines:230-236Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The cultivation of brinjal is seriously affected by various pathogens which affect crop yield and quality hence the importance of automated disease detection systems in precision agriculture. The conventional image-based diagnostic methods fail to differentiate between similar diseases classes that look similar and this results in misdiagnosis and poor crop management. This research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time. The model combines an EMA (Efficient Multi-Scale Attention) system, IRMB (Inverted Residual Mobile Blocks), and a LDFM (Local Detail Feature Module) to obtain fine-grained lesion features, as well as a refined MPDIoU loss to achieve a better object localization. The framework was tested against a curated multi-class brinjal disease data of six fungal, two bacterial, three viral and one nematode infection. IEMA-YOLOv11 had a precision of 96.5, a recall of 95.7, mAP@50 of 95.6 and mAP@50:95 of 94.9, with only 5.12M parameters and 17.1 GFLOPs. In comparison to the current benchmarks, IEMA-YOLOv11 was superior to YOLOv8-n by a margin of 2.4% in mAP@50, which has a potential to be used on a large scale to detect brinjal disease in a sustainable manner.
Why it matches plant phenotyping methods植物病害の病斑特徴を画像から抽出・検出する深層学習手法を開発し、データセット上で性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractThis research paper advances to suggest a lightweight deep learning model, IEMA-YOLOv11 (Improved Efficient Multi-Scale Attention-based YOLOv11), to successfully identify and detect Brinjal disease successfully in real-time.
Buckwheat (Fagopyrum spp.) germplasm represents an underutilized source of morphological diversity for crop improvement. This study presents a high-throughput phenomic survey quantifying five seed morphological traits (area, length, width, circularity, roundness) across 563 RDA genebank accessions (519 common buckwheat (F. esculentum), 44 Tartary buckwheat (F. tataricum) using standardized imaging. Common buckwheat exhibits larger seeds (area: 21.12 ± 4.04 mm²) with lower coefficients of variation (CVs: 17.9%), while Tartary buckwheat shows smaller seeds (14.80 ± 3.23 mm²) with higher CVs (20.1%) and greater shape dispersion (PC2 variance: 3.73 vs. 1.06). Principal component analysis confirms species-level morphological separation and documents exploitable polymorphism, including notched/slender/round/rice morphotypes in F. tataricum. These standardized phenotypic baselines support genebank curation, accession ranking by seed size/shape extremes, and prioritization for multi-environment trials and genetic studies.
Why it matches plant phenotyping methods標準化画像を用いた大規模な種子形態形質の高スループット取得・解析が研究の中心であり、遺伝資源評価に再利用可能な表現型ワークフローとして substantive です。
abstractThis study presents a high-throughput phenomic survey quantifying five seed morphological traits (area, length, width, circularity, roundness) across 563 RDA genebank accessions
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Recent technological advancements employ imaging techniques to examine the morphological, physiological, and genetic differences among plant accessions, enhancing precision and productivity. High-throughput phenotyping serves as an essential method for selecting traits and reducing errors tied to manual data collection. However, the effects of camera-to-object distance in imaging acquisition for plant phenomics have received less attention. We analyzed the imaging parameters that define well the morphological characteristics of pepper and the effects of camera-to-object distance on the imaging of plant growth, leaf dimensions, fruit, and seed characteristics. Three camera-to-object distances (0.8, 1.0, and 1.2 m) were studied for the vegetative stage, and six camera-to-object distances (0.35-0.85 m) were used for the reproductive stages. The results demonstrated that imaging parameters such as Major (the longest line that can be drawn within the leaf) and Minor (the shortest line perpendicular to the major axis) are more effective for assessing canopy spread, while imaging Height provides a strong correlation (r = 0.9) for actual plant height measurement. An optimal camera-to-object distance of 0.8 m yielded better correlations for vegetative traits across all pepper genotypes, likely due to resolution factors at different growth stages. For fruit and seed traits, shorter distances of 0.55 m and 0.65 m were suitable. Additionally, the weights of fresh and dry fruit correlated highly with image area (r = 0.94 and 0.89, respectively, at 0.55 m). The studied pepper genotypes exhibited distinct seed characteristics, including variations in Roundness, Solidity, and Circularity. The imaging approach can accurately capture various plant characteristics and has the potential to replace traditional methods for assessing plants.
Why it matches plant phenotyping methodsRGB画像による植物形質取得を中心に、カメラ距離と撮像パラメータを最適化・検証しており、方法開発および技術検証に該当する。
titleDigitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.
Apples are one of the most economically significant and widely cultivated fruit crops worldwide, contributing substantially to food security and the horticultural economy. However, their production is frequently compromised by diseases such as Alternaria leaf spot, Apple Mosaic, Powdery Mildew, and Apple Scab, leading to significant yield losses and increased dependence on chemical control. Timely and accurate disease diagnosis is critical to minimize crop damage, reduce pesticide usage, and promote sustainable horticultural practices. This study proposes the Multilayer Transformer-based Apple Disease Classification (MTADC) model, an advanced deep learning framework designed for early and robust identification of apple leaf diseases. MTADC employs a two-stage learning approach: global feature extraction using a transformer encoder, followed by class-specific mapping through a refined classification head. By incorporating a self-attention mechanism, the model effectively suppresses background noise and enhances feature discrimination, even under variable field conditions. Unlike existing models that require well-constrained, high-quality images captured under ideal lighting and angles, MTADC is designed for deployment in real-world field conditions, allowing disease detection from diverse and unconstrained field images commonly captured by farmers. Experiments on a curated dataset comprising publicly available and field-acquired images demonstrate that MTADC achieves a classification accuracy of 96.3%, outperforming conventional convolutional models. These results highlight the model’s robustness, scalability, and potential to be an accessible tool for digital plant health monitoring and precision horticulture.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から推定する深層学習モデルを開発し、背景ノイズや圃場条件への頑健性を評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study proposes the Multilayer Transformer-based Apple Disease Classification (MTADC) model, an advanced deep learning framework designed for early and robust identification of apple leaf diseases.
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Early and accurate detection of plant diseases remains challenging due to real-field variability (e.g., illumination variations, complex backgrounds, occlusion), irregular disease patterns (e.g., subtle or non-local symptoms), limited labeled data for niche crops like arecanut, and generalization issues in uncontrolled environments. Traditional manual inspections are labor-intensive and error-prone, while existing deep learning methods—primarily grid-based CNNs and Vision Transformers—often suffer from limited spatial modeling of non-Euclidean relationships, higher computational costs, and reduced robustness to field conditions. This research introduces a novel deep learning framework for automated detection of diseases in arecanut plants, combining Graph Neural Networks (GNNs) for capturing long-range spatial relationships in leaf images with the Bat Algorithm (BA) for efficient hyperparameter optimization. The framework utilizes a curated balanced subset of 1000 arecanut images from a larger Kaggle dataset (originally 8847 images), captured under natural farm conditions and encompassing nine disease categories. Experimental results demonstrate that the proposed GNN-BA (GB) model achieves 98.45% accuracy, 96.90% precision, 94.21% recall, and 95.05% F1-score—outperforming baselines such as CNN-ViT (93.25% accuracy) and CBAM (94.10% accuracy) by 4–5% on average, while offering lower computational overhead. The model exhibits robustness across diverse environmental and leaf variations, providing a scalable, efficient solution for real-time disease monitoring to enable timely interventions and reduce crop losses in arecanut farming.
Why it matches plant phenotyping methodsアレカヤシ葉画像から病害状態を推定するGNN-BA画像解析手法を開発・評価しており、植物表現型（病害）の取得・推定が研究の中心である。
abstractThis research introduces a novel deep learning framework for automated detection of diseases in arecanut plants
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Agricultural productivity sustains itself by detecting diseases in the leaves of plants, especially in poor nations where economic growth is greatly affected by delayed or incorrect detection of diseases. Although high classification accuracy is achieved by the application of deep learning techniques like VGG16 and DC-GAN-based architecture, high computational complexity is still an issue. The optimization-oriented hybrid model for the classification of plant leaf diseases developed in this research emphasizes the viability of deployment and computational efficiency over algorithmic innovation. In the model, high-level semantic information is extracted from the images of the plant leaves using a ResNet50 network that was pretrained as a feature extractor. Then, the deep feature representation is reduced in size using Principal Component Analysis (PCA), which decreases the dimensions of the deep feature representation to reduce information redundancy and prevent overfitting. Finally, the multi-class illness classification is performed using a Support Vector Machine (SVM) classifier. For evaluating the model, the publicly accessible PlantVillage data set containing 38 different classes of both normal and diseased leaves was used. The model was found to achieve a training accuracy of 98.9% and a validation accuracy of 89.4% when a standard train-validation split was applied. In order to further assess the robustness of the proposed model, five-fold stratified cross-validation was carried out to attain an average accuracy of 98.63%. In an ablation study, the maximum accuracy obtained was 98.79%. As suggested by the experimental results, the balance between the accuracy of the classification and the computing economy can be achieved using the integration of deep feature extraction and dimensionality reduction with machine learning classifiers. The findings show that, even if the evaluation is done using a controlled set of data, the suggested architecture can serve as an efficient framework for creating plant disease diagnosis systems in precision agriculture with the use of effective computer resources.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習・特徴抽出・分類ワークフローが研究の中心であり、交差検証とアブレーションで技術評価も行っているため、植物フェノタイピング手法として含める。
titleA hybrid deep learning model for robust and efficient plant leaf disease detection using ResNet50, PCA, and SVM.
Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.
Why it matches plant phenotyping methodsジャガイモ葉の病害症状を圃場画像から検出・分類する深層学習法を開発し、複数モデル比較、交差検証、頑健性評価まで実施しており、植物の病害状態推定が中心的な方法論的貢献である。
abstracta deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions.
Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop-Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model's capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.
Why it matches plant phenotyping methodsレタスのバイオマス・収量という植物形質を予測する機械学習回帰モデルの開発と実験的検証が研究の中心であり、単なる農業実験のルーチン測定ではない。
abstractTo address this challenge, a robust machine learning model called UniTriRob regression model has been developed.
Proper detection and treatment of plant leaf diseases are essential factors for achieving good crop yields and ensuring food security. Convolutional Neural Networks (CNNs) have shown significant potential for classifying diseases from leaf images. Instead, most current work focuses on image-level prediction and ignores the relationship between infected leaves. This limitation somewhat constrains their use in modelling disease spread. Also, it makes them less efficient in typical field situations where disease is transmitted from plant to plant by physical contact. Moreover, existing CNN architectures do not access inter-lobar contextual information, an essential factor for early detection and control. To tackle this, we propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass. The method uses the enhanced CNN model (LDDNet), with Atrous Spatial Pyramid Pooling (ASPP) and a Channel-Spatial Attention Module (CSAM), to achieve a more precise representation across multiple scales. These embeddings are then used to construct a similarity graph, enabling a GNN to infer likely disease transmission paths among leaves. We evaluate the PlantVillage dataset on the proposed model, and the results show that it outperforms state-of-the-art CNN-based methods, achieving 98.88% classification accuracy and 98.71% F1 Score. Additionally, we were able to predict disease spread with a high AUC-ROC of 0.942 and an MCC of 0.884 using our framework. These results confirm that AdjLeafGNN can accurately model both local and relational patterns. The approach we propose is scalable and interpretable, facilitating real-time monitoring and control of diseases in precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害を分類し、隣接葉間の病害拡大を推定する深層学習・GNN手法を提案・評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractwe propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass.
Reproduction assets foundThe paper uses the public Kaggle PlantVillage leaf-image dataset as its phenotyping input and releases the complete AdjLeafGNN implementation (model, preprocessing, training, evaluation) on GitHub with a Zenodo-archived DOI.Dataset · publicthe dataset was obtained from the publicly available Kaggle distribution of the PlantVillage dataset: https://www.kaggle.com/datasets/mohitsingh1804/plantvillageTheOpen asset ↗html-lines:584-607Code · publicThe complete source code of the proposed AdjLeafGNN framework, including model implementation, training scripts, and evaluation pipeline, is publicly available. GitHub repository: https://github.com/surekhareddy123/AdjLeafGNN. A permanent archived version of the repository has been deposited in Zenodo and assigned the following DOI: 10.5281/zenodo.18862439.Open asset ↗https://github.com/surekhareddy123/AdjLeafGNN · 10.5281/zenodo.18862439html-lines:584-607Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Deep learning has improved automated plant disease detection by increasing recognition accuracy and robustness compared with traditional vision-based methods. Self-supervised learning (SSL) further reduces dependence on manual labels, but its transferability across heterogeneous agricultural datasets remains insufficiently characterized. Here, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning. PlantCLR combines SimCLR-style contrastive pretraining with a lightweight convolutional classifier to balance representation quality and deployment efficiency. Experiments on PlantVillage and Cassava Leaf Disease show strong performance, achieving 99.10% accuracy and 99.04% F1-score on PlantVillage, and 96.83% accuracy and 96.70% F1-score on Cassava. Feature embedding visualization using t-SNE and explanation maps using Grad-CAM indicate improved class separability and attention to disease-relevant regions. These results suggest that contrastive SSL can improve representation transfer while maintaining computational efficiency, supporting scalable plant disease diagnostics in practical agricultural settings. Code is available at GitHub .
Why it matches plant phenotyping methods植物病害を画像から分類するPlantCLR手法を開発し、異なるデータセット間で性能評価・検証しているため、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractHere, we evaluate a contrastive SSL pretraining and fine-tuning pipeline, termed PlantCLR, for plant disease classification under cross-dataset transfer with target-domain fine-tuning.
Reproduction assets foundThe paper's plant disease detection experiments use two publicly available image datasets with explicit Kaggle URLs in the Data availability statement. The authors also state code is available at GitHub, but no concrete URL is provided, so no code asset is included.Dataset · publicThe Cassava Leaf Disease Classification dataset is available at https://www.kaggle.com/c/cassava-leaf-disease-classificationOpen asset ↗Kaggle · cassava-leaf-disease-classificationlines:232-268Dataset · publicthe PlantVillage dataset is available at https://www.kaggle.com/datasets/emmarex/plantdiseaseOpen asset ↗Kaggle · emmarex/plantdiseaselines:232-268Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Sugarcane diseases significantly reduce crop yield and quality, posing persistent challenges to the agricultural sector. This study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection. By combining global self-attention with localized window-based attention mechanisms, the proposed model effectively captures multi-scale visual features associated with diverse disease symptoms. Experimental evaluation on a large, labeled sugarcane leaf dataset achieved a validation accuracy of 98.16% and a test accuracy of 97.06%, outperforming several convolutional neural network baselines. Additionally, a large language model (LLM) interface is employed as a post-prediction decision-support module, generating disease-specific descriptions and management suggestions based solely on the predicted disease class. This integrated framework indicates the potential effectiveness of transformer-based ensemble models combined with intelligent advisory support for practical decision-making in precision agriculture.
Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から分類するTransformerベースの手法開発と性能評価が中心であり、植物病害状態のフェノタイピングに該当する。
abstractThis study presents a novel ensemble framework that integrates Vision Transformer and Swin Transformer architectures for accurate sugarcane leaf disease detection.
Reproduction assets foundThe paper's sugarcane leaf disease image dataset (19,926 images, six classes) is explicitly stated to be publicly available on Kaggle; no author code or model checkpoints are shared.Dataset · publicThe Sugarcane Plant Diseases Dataset used in this study is publicly available on Kaggle at: https://www.kaggle.com/datasets/akilesh253/sugarcane-plant-diseases-dataset . The dataset is released for academic research and benchmarking purposes.Open asset ↗Kagglelines:112-131Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract The precise classification of plant diseases is crucial for ensuring food security for all people and boosting agricultural productivity. Although there has been significant progress in this field using deep learning approaches, cross-dataset training hasn’t drawn as much attention from researchers as intra-dataset training has. Moreover, very few models have successfully blended intra-dataset and cross-dataset training approaches. This paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations. The model improves feature extraction and classification accuracy across multiple datasets by using attention mechanisms. It was tested on five datasets (Digipathos, Northern Leaf Blight (NLB), PlantVillage, PlantDoc, and the CD&S dataset) that covered leaf diseases of both corn and potatoes. During intra-dataset training, the model achieved the highest classification accuracy of 99.38% when trained on images of potato leaves from the PlantVillage dataset. During cross-dataset training, the model exhibited the highest average classification accuracy of 82.93% for corn leaf diseases when trained on images from the CD&S dataset with their backgrounds removed. When compared to the techniques taken into consideration in this study under comparable experimental conditions, the results demonstrate improved performance. This study shows how the model may be flexible for both intra- and cross-datasets, offering a flexible way to categorize diseases that affect plants. Because of its ability to generalize across different datasets, it may be helpful in real-world agricultural applications with a wide variety of image quality and situations. This encourages the advancement of precision farming techniques and disease control.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類するCNN手法の開発・データセット間検証が中心であり、植物病害の表現型推定に該当する。
abstractThis paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations.
Reproduction assets foundThe paper's plant disease classification experiments rely on five publicly available leaf-image datasets, each cited with an explicit public access URL in the reference list: PlantVillage (GitHub), PlantDoc (GitHub), Digipathos (Embrapa), NLB (SciDB), and CD&S (OSF). No author analysis code or trained model checkpoint,Dataset · publicHughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060. Dataset accessed via GitHub: https://github.com/spMohanty/PlantVillage-DatasetOpen asset ↗GitHub · spMohanty/PlantVillage-Datasethtml-lines:1013-1082Dataset · publicDataset available at: https://github.com/pratikkayal/PlantDoc-DatasetOpen asset ↗GitHub · pratikkayal/PlantDoc-Datasethtml-lines:979-1012Dataset · publicCD&S dataset: Handheld imagery dataset acquired under field conditions for corn disease identification and severity estimation. arXiv preprint arXiv:2110.12084. Dataset available at: https://osf.io/s6ru5/files/osfstorageOpen asset ↗OSF · s6ru5html-lines:1013-1082Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model's decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .
Why it matches plant phenotyping methods果樹葉の病斑領域を画像から分割し、複数病害の重症度を定量推定する深層学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractthis research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves.
Reproduction assets foundThe paper's authors publicly release source code and trained models on GitHub, and the study analyzes two public Kaggle plant-image datasets (DiaMOS Plant and PlantVillage) used directly for the multi-disease severity phenotyping experiments.Code · publicThe source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .Open asset ↗https://github.com/mqasim0787/Multi-Disease-Severity · mqasim0787/Multi-Disease-Severitylines:1-23Dataset · publicThe datasets analyzed during the current study are available publicly in the Kaggle repository, DiaMOS dataset (1) and PlantVillage Dataset (2) 0.1. [https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset]Open asset ↗https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset · diamos-plant-datasetlines:964-977Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early detection of maize leaf diseases is essential to prevent yield losses. Existing vision-based models face challenges in real-world environments due to data imbalance, lighting variations, and interpretability. This study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings. MaizeFormerX employs multi-scale patch embeddings and a Cross-Scale Attention Fusion (CSAF) module to capture both detailed lesion textures and larger disease patterns. The CSAF output is processed through a transformer encoder stack using multi-head self-attention to model long-range dependencies. Robust preprocessing and dataset-specific augmentations were applied to improve feature extraction and address class imbalances in the Dataverse, Tanzania, and Plagues Maiz datasets. For interpretability, Grad-CAM was used for pixel-level saliency mapping in an efficient web application. When benchmarked against MobileViT, EfficientFormer, TinyViT, and Swin Transformer, MaizeFormerX achieved 97.8% accuracy on Dataverse, 97.5% on Tanzania, and 96.9% on Plagues Maiz, outperforming Swin Transformer V2 by 2–3%. Cross-domain testing yielded 88.9% accuracy when trained on Dataverse and tested on Tanzania, surpassing baseline performance by 3–6%. Class-wise analysis revealed F1 scores over 98% for Healthy and MLB classes with 6× augmentation, and over 97% for MSV. Ablation studies highlighted the significance of the cross-scale attention module for high MCC during domain shifts. This study introduces a precise, explainable, and efficient image-based method for classifying maize diseases, which could aid in more targeted crop management, reduce unnecessary agrochemical use, and promote sustainable maize production in future decision-support environments.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から分類する手法の開発・ベンチマーク・交差ドメイン検証が中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThis study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public maize leaf image datasets used for its phenotyping/disease-classification experiments (Dataverse, Tanzania/Mendeley, Plagues Maiz/figshare) and an authors' GitHub repository containing all code, preprocessing pipelines, and experimental configs. All四Dataset · publicThe datasets used in this study are publicly available and sourced from Dataverse (https://doi.org/10.7910/DVN/LPGHKK)Open asset ↗Dataverse · 10.7910/DVN/LPGHKKhtml-lines:2304-2339Code · publicAll code, preprocessing pipelines, and experimental configurations used in this work are available at: https://github.com/rezaul-h/MaizeFormerX/.Open asset ↗github · rezaul-h/MaizeFormerXhtml-lines:2304-2339Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Oranges, mandarins, bitter oranges, and lemons are examples of citrus fruits that make delicious meals and are highly nutritious. Citrus fruits suffer from a variety of infections that affect their yield. The Department of Agriculture wants to increase the production of oranges and lemons. On the other hand, several plant diseases and their advanced stages have impacted production. The quality of fruit influences market value and its financial effect. Therefore, accurate detection of ailments and their severity is crucial for improving the output and market value of oranges and lemons. To automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model. Python is used to create the ICNN model, and testing is performed using benchmark datasets from various repositories. The research presented here shows that ICNN performs better than traditional deep learning and machine learning models, such as the Convolutional Neural Network (CNN) and K-Nearest Neighbours (KNN). This illustrates how machine learning models require supplementary approaches to extract parameters from data that arrives in non-automated ways. Additionally, to improve the accuracy of their classification or prediction, deep learning models require pre-trained models. As a result, ICNN, an enhanced deep learning model that can automatically predict disease with higher accuracy than other models, represents an advancement over standard CNNs. Compared with KNN and CNN, ICNN achieves 99.69% accuracy.
Why it matches plant phenotyping methods柑橘の葉・果実の病害と重症度を画像から自動推定するCNN手法を開発し、ベンチマークデータセットで比較評価しており、植物フェノタイピング手法が中心である。
abstractTo automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public Kaggle URLs as the datasets used and analysed in the study (citrus/plant leaf disease image datasets). These are paper-specific, publicly accessible image assets directly supporting the phenotyping/disease-classification analysis. The Mendeley URL (3Dataset · publicg agricultural specialists to properly understand and accept the model’s predictions.
Author contributions
Arunapriya.R – Problem Statements, Implementation and Testing Dr.S.P.Valli – Results, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://wwOpen asset ↗kagglelines:372-388Dataset · publicts, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ]. (https:/ www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ).
Declarations
Competing interOpen asset ↗kagglelines:372-388Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice production is integral to the agricultural sector of India; over 65% of the populations are dependent on rice as their major staple. The cultivation of rice sustains this important agricultural sector; yet, there are many challenges encountered by rice producers, one of which is several types of disease that negatively impact yield and quality. Due to the fact that rice leaf smut, brown spot and bacterial leaf blights are among the most important types of diseases that can significantly reduce the yield and quality of rice, it is important to be diligent when identifying these diseases using accurate and speedy methods on an annual basis for successful and sustainable production of rice crops. As technology advances there continue to be emerging technologies such as Deep Learning (DL) as applied in agriculture to identify diseases and therefore reshape the agricultural paradigm so as to address agricultural disease challenges more readily. This research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection. The quality of the input imagery has been greatly increased using a Preceding Noise Reduction (PNR) using the Guided Filopic Diffusion (GFD) technique, retaining important characteristics of Rice Leaves (Leaf Texture) which are critical for disease classification within agricultural imaging. To evaluate the performance of our model we utilized the Dice Similarity Coefficient (DSC). This coefficient measures how much the predicted image areas representing disease overlap with the actual affected areas of the image. Therefore, DSC is a reliable way to evaluate model segmentation capability. The Rice Leaf Diseases Dataset we used to identify and classify Rice Leaf Diseases was very comprehensive. Our model achieved an accuracy rate of 98.92% and also attained the best recall, precision and F1 score.
Why it matches plant phenotyping methodsイネ葉の病害症状を画像から検出・分類・セグメンテーションする手法が研究の中心であり、植物の病害状態を直接推定している。
abstractThis research proposes a previously undemonstrated approach for identifying Rice Leaf Disease using EfficientNetV2; a Diffusion Bounded Attention method for disease detection.
Reproduction assets foundThe paper's sole data asset is the public Kaggle Rice Leaf Diseases Dataset used for all experiments; no author code or models are deposited.Dataset · publicl analysis and data collection. N.K has done the initial drafting and statistical analysis. P.R. did the investigation. All the authors of the article have read and approved the final article.
Funding
Open access funding provided by Vellore Institute of Technology.
Data availability
The rice leaf disease data are assessed using https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Upadhyay N Gupta N
Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach
Environ. Monit. Assess. 2024 196 7 610
10.1007/s10661-024-12790-0
38862723
Upadhyay, N. & Open asset ↗Kaggle · vbookshelf/rice-leaf-diseaseslines:553-627Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The objective of this study is to create a highly accurate and interpretable deep learning (DL) model for the multi-class classification of fruit using convolutional and transformer architectures. The classification performance can be enhanced by making sure that the used technique is explainable and interpretable. This research data was obtained from Kaggle which contains images of banana, grape, lemon, mango, and strawberry fruit classes. The total data was divided into 70:15:15 for training, validating and testing. To ensure consistent size and quality, all images were pre-processed before use. This study considered four pretrained models namely RegNetY-B3-GE, DarkNet53-SCSE, BEiT, and PVTv2 for performance assessment. We proposed a lightweight hybrid (convolution plus attention-based) CoAT-AgriLite model for fruit disease classification which extracts local lesion features and global context. Transferring training and data augmentation technique was utilized during training for better performance. To ensure interpretability of model decisions, Gradient-weighted Class Activation Mapping (Grad-CAM) which captures the discriminative regions from the input images for model predictions. Among all evaluated models, the proposed model achieved the highest classification accuracy of 99.37% on the testing dataset. Comparative results demonstrated that the proposed model outperformed other pretrained models in terms of precision, recall, and F1-score, confirming its robustness and effectiveness in real-world agricultural classification tasks. The experimental findings validate that the proposed model not only achieves superior classification accuracy but also provides interpretability through Grad-CAM visualizations. This hybrid framework offers a promising solution for intelligent and transparent fruit classification systems, with potential applications in precision agriculture and automated sorting systems.
Why it matches plant phenotyping methods果実病害を画像から分類する深層学習フレームワークを開発・比較し、病斑特徴の抽出とGrad-CAMによる説明性を評価しており、植物の病害状態の画像計測が中心である。
titleA hybrid convolution and attention-based framework with visual explanation for fruit disease identification.
To achieve agricultural automation, deep learning applications for early and accurate disease detection in tomato plants have been extensively developed. However, there is a fundamental trade-off between computational efficiency and diagnostic accuracy in resource-constrained agricultural edge environments. This paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential. Through evaluations of explainability, computational efficiency, and diagnostic performance, seven lightweight architectures (ShuffleNetV2, MobileNetV3-Small, SqueezeNet, MobilePlantViT, DenseNet121, ResNet50, and VGG16) are thoroughly examined. Three significant findings are derived from experiments conducted on a subset of tomato diseases in the PlantVillage dataset. First, the MobilePlantViT architecture accurately strikes the ideal balance between efficiency and performance. Second, in order to quantitatively assess the explainability of XAI models (Grad-CAM, SHAP, and LIME) and identify the best option for edge devices, we propose the perturbation stability score (PSS) metric. Third, we test CPU inference measurements to better reflect the actual scenario and find that the hybrid design effectively leverages parallel computing. According to these findings, MobilePlantViT is the ideal architecture for applications that require operation on edge devices with limited resources and achieve high diagnosis accuracy (above 99.5%).
Why it matches plant phenotyping methodsトマト病害という植物状態を画像から分類する深層学習手法について、複数モデルの精度・計算効率・説明可能性を体系的に比較評価しており、病害フェノタイピング手法の技術評価が中心である。
abstractThis paper proposes an evaluation framework for seven architectures that represent standard, efficient, and hybrid CNN structures to assess their implementation potential.
Reproduction assets foundThe authors publicly deposited their paper-specific tomato phenotype image subsets (derived from PlantVillage and expert-curated PlantDoc) on Kaggle via explicit Data Availability links. No author analysis code or trained model checkpoints are shared; ONNX Runtime is a generic library, not a paper-specific asset.Dataset · publicThe datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-plantvillage-datasetshtml-lines:710-734Dataset · publicWe enhanced the quality of the PlantDoc dataset by collaborating with experts to identify and crop regions containing disease-specific symptoms, while eliminating irrelevant image content. For long-term preservation and ease of access, we have stored copies of the datasets in the published repository. The datasets are available at the following links: https://www.kaggle.com/datasets/cthngon/tomato-plantvillage-datasets, https://www.kaggle.com/datasets/cthngon/tomato-only.Open asset ↗Kaggle · cthngon/tomato-onlyhtml-lines:710-734Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Where crop health is essential to global food security. Our focus is on early crop disease detection in the field of agriculture, especially Rice and Sugar cane leaf disease. This prompts researchers to consider quick, automated, cost-effective, precise, and efficient methods of identifying the kinds of diseases utilizing contemporary technologies like image processing, artificial intelligence (AI), and Explainable Artificial Intelligence (XAI). This paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing to standard, resizing, and normalization rice and Sugar cane images then, using feature extractor using CNN after that we using few-shot learning (FSL) techniques such as like Prototypical Networks and Model-Agnostic Meta-Learning (MAML) learning techniques for superior decision-making in smart farming systems. The experimental findings demonstrated the Accuracy and specificity of the suggested framework in identifying and effectively predicting the kind of disease. According to the results, the suggested framework outperformed the state-of-the-art benchmark algorithms in disease prediction while producing results that were plausible. With Prototypical Networks and MAML for rice leaf disease datasets, it increased by up to 97.6% and 95.27%, respectively. For effective rice disease identification, Prototypical Networks and MAML for Sugar cane leaf disease datasets increased by up to 91.68% and 90.27%, respectively. Interpretable AI-driven insights were further made possible by the combination of proposed system with Grad-CAM Explanation, which improved decision-making transparency.
Why it matches plant phenotyping methodsイネとサトウキビの葉画像から病害状態を推定する画像解析・深層学習フレームワークが研究の中心であり、植物病害フェノタイピング手法の開発・評価に該当する。
abstractThis paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing
Brown planthopper (BPH) is a serious rice pest that threatens global food security by causing yield losses of up to 80%. Conventional methods for assessing BPH infestation are labour-intensive and lack real-time precision. This study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties: Pusa Basmati-1509, Pusa Basmati-1121, and TN-1. Leaf-level spectral measurements (350–2500 nm) acquired using a portable spectroradiometer effectively differentiated BPH population severity levels. Among 28 spectral indices evaluated, Structural Insensitive Pigment Index (SIPI), Pigment Specific Normalized Difference Index (PSND) for chlorophyll b, Pigment Specific Simple Ratio (PSSR a) for chlorophyll a, and (PSSR b) for chlorophyll b, showed high sensitivity to BPH infestation. Multivariate Regression models, including Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Random Forest (RF), were developed for severity prediction. Among the tested models, RF achieved the highest accuracy for vegetation indices-based estimation (R2 = 0.99), while PLSR showed strong relationships between hyperspectral data and BPH population severity (R2 = 0.62) and key biochemical parameters, including chlorophyll (R2 = 0.84), carotenoids (R2 = 0.77), and protein (R2 = 0.84). In contrast, flavonoids exhibited weak predictability (R2 = 0.34). Field validation confirmed model robustness, with vegetation index-based predictions achieving R2 values ranging from 0.72 to 0.86. Overall, the results demonstrate the potential of hyperspectral sensing combined with machine learning for early, non-destructive detection and monitoring of BPH stress, supporting precision pest management in rice.
Why it matches plant phenotyping methodsイネ葉のハイパースペクトル計測と機械学習により、害虫被害の重症度や関連する植物生理形質を推定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractThis study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties
The intricate process of coffee blossoming, pollination transfer, and successful development is crucial for creating every exquisite cup of coffee. During the flowering stage of coffee plants, delicate white flowers with a pleasant fragrance appear briefly, providing a limited opportunity for effective pollination. At this stage, the stigma of the flower, which is the female reproductive organ, becomes receptive and prepared to receive pollen. Existing research found methods such as machine learning and image analysis for monitoring crop pollination. Manual image annotation is conducted on pollen count disregarding spatial component of pollen collection which is essential for successful pollination. However, use of these strategies on coffee flowers by their complex structure and continuous changed in flowering stages. The paper represents novel methodologies by introducing methodological approach “IoU-AI” to monitor pollen transmission and success of pollination in coffee flower. IoU-AI utilize high resolution coffee flower image accurately track and detect floral organs by offering insight to pollen transfer. IoU-AI employs deep learning models to detect and observe floral components like stigma and anthers. Further computes overlap between structures and estimate pollen transmission. The flower detection accuracy was evaluated against ground truth measurements. The accuracy of coffee flower detection ranged from 94.77% to 85.34% for flower stages ranging from 20% to 100% blooming.
Why it matches plant phenotyping methodsコーヒー花の画像から柱頭・葯を検出し、重なりに基づいて花粉伝達と受粉成功を推定する画像解析手法を開発・精度評価しており、植物状態の取得が中心です。
abstractThe paper represents novel methodologies by introducing methodological approach “IoU-AI” to monitor pollen transmission and success of pollination in coffee flower.
This paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification. This framework relies on a mathematical model based on neural networks that classifies and detects the condition of agriculture, removing the reliance on manual tasks and subjective diagnosis. This paper focuses on three main aspects of our framework: data acquisition, training and prediction. Data is collected using sensors like drones, cameras, and satellite imagery and is pre-processed to filter out noise and improve quality. The training part uses CNN to learn features from the data and become more meaningful. The prediction part of the task classifies, and diagnoses crop health through the trained model using the features. The framework accuracy for crops such as maize, potato, and wheat has been tested and yielded over 90% accuracy. The novelty of this work resides in the development of a multi-modal deep learning architecture that fuses macro-scale satellite imagery with micro-scale drone and IoT sensor data to improve diagnostic reliability. The framework was validated on a multi-source agricultural dataset using a 70% training, 15% validation, and 15% testing protocol. Experimental results demonstrate an accuracy exceeding 90% for staple crops. Using this framework can increase the visibility and quality of information maintained for crop health and improve the decision-making routine of farmers in real time. Additionally, automation of this process can significantly reduce labor costs and increase productivity per crop. Implementing this framework can contribute to precision agriculture and sustainable management practices.
Why it matches plant phenotyping methods作物の健康状態を植物の表現型・状態として推定するマルチモーダル画像・センサ基盤と深層学習手法を開発し、複数作物・データセットで検証しているため、方法が中心である。
abstractThis paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification.
Reproduction assets foundThe article's Data availability section points to a public Kaggle dataset used for the crop classification/health diagnosis experiments, matching an allowed URL. No code or model checkpoints are disclosed.Dataset · publicript. The research work was guided by Dr. B.D.K.P. The Corresponding author Shshank Chaube collaborated for review and supervision. All authors reviewed the manuscript.
Funding
Open access funding provided by Symbiosis International (Deemed University). No funds, grants, or other support was received.
Data availability
Dataset: https://www.kaggle.com/datasets/bhagvendersingh/precision-agriculture-dataset .
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
References
1. Mohyuddin, G. et al. Evaluation of machine learning approaches for preciOpen asset ↗kaggle · bhagvendersingh/precision-agriculture-datasetlines:473-545Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Amidst rising global food security challenges, early and precise detection of plant diseases has become essential-particularly for high-value crops such as mangoes. This study introduces a novel deep learning-based framework for the classification of mango leaf pathologies using advanced convolutional and hybrid neural architectures. A curated dataset of 800 high-resolution mango leaf images, collected from the Rajshahi region of Bangladesh, was preprocessed using extensive data augmentation and color space transformations to enhance generalization. Multiple models, including K-Nearest Neighbors, AlexNet, VGG16, VGG19, and EfficientNet-B7, were evaluated and compared against two proposed architectures: a custom Convolutional Neural Network (CNN) and a hybrid model integrating EfficientNet-B7, Long Short-Term Memory, and attention mechanisms. The proposed CNN model achieved 100% accuracy, precision, recall, and F1-score, outperforming all baseline models. The hybrid model achieved comparable results, demonstrating the effectiveness of combining spatial and temporal feature extraction for plant disease detection. Additionally, Grad-CAM visualizations provided interpretable diagnostic heatmaps, reinforcing the transparency and reliability of the model's predictions. The proposed framework advances state-of-the-art agricultural diagnostics by offering a scalable, interpretable, and high-performing solution for real-time disease monitoring in mango cultivation. These findings hold strong potential for improving crop surveillance and addressing food scarcity in mango-producing regions.
Why it matches plant phenotyping methodsマンゴー葉の病害状態を画像から分類するCNN・ハイブリッドモデルを開発・比較し、データセット、性能評価、Grad-CAM解釈まで含むため、植物表現型取得手法が中心である。
abstractThis study introduces a novel deep learning-based framework for the classification of mango leaf pathologies using advanced convolutional and hybrid neural architectures.
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Maize is a vital global crop, but its productivity is often threatened by plant diseases, highlighting the need for precise and timely diagnostic methods. Traditional manual inspection is inefficient and prone to errors, motivating the development of automated solutions. Recent advances in computer vision and deep learning have enabled effective automated plant disease diagnosis. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown promise in plant disease classification, CNNs struggle to capture global contextual information, and ViTs require large datasets and high computational resources. Inspired by mixture-of-experts (MoE) architectures, we propose a lightweight hybrid model that integrates CNN and ViT components, adaptively emphasizing local or global features based on input characteristics. Evaluated on a novel, real-world dataset of full maize plant images, our approach achieves 99.90% classification accuracy, significantly outperforming state-of-the-art baselines such as MobileViT, PiT, EdgeNeXt, and DeiT. These results demonstrate that lightweight hybrid architectures can deliver high-performance disease diagnosis suitable for practical agricultural deployment. The code is available at: https://www.github.com/sabermehdipour/MXiT .
Why it matches plant phenotyping methodsトウモロコシ全身画像から病害状態を推定する軽量CNN-ViT手法を開発・評価しており、植物表現型取得・判定が中心的です。
abstractwe propose a lightweight hybrid model that integrates CNN and ViT components
Reproduction assets foundThe paper's authors' MXiT analysis code is publicly available via a GitHub URL stated in the abstract, and the PlantVillage image dataset used for evaluation is publicly available. The Plant Scanner maize dataset is paper-specific but only available upon request, so it is listed as request_only.Code · publicThe code is available at: https://www.github.com/sabermehdipour/MXiT.Open asset ↗sabermehdipour/MXiThtml-lines:1-77Dataset · publicThe PlantVillage dataset is publicly available (https://github.com/spMohanty/PlantVillage-Dataset).Open asset ↗spMohanty/PlantVillage-Datasethtml-lines:707-785Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Precise and timely prediction of wheat yield is pivotal for ensuring global food security and optimizing agricultural management practices, particularly through advanced remote sensing and machine learning techniques. In this study, wheat yield was accurately estimated by leveraging remote sensing-derived soil and vegetation indices. Yield data from 189 study points were collected, and Sentinel-2 (10-meter resolution) imagery from the Tillering and Anthesis growth stages was used. The models incorporated 25 variables, including 16 optical indices (e.g., NDVI, SAVI, MSAVI2) and three topographic factors. The key novelty of this research is the rigorous comparison of the predictive value and synergistic contribution of Sentinel-1 Synthetic Aperture Radar (SAR) data when integrated with Sentinel-2 optical indices within machine learning frameworks. Three machine learning approaches (Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF)) were employed and evaluated using 70% training and 30% testing subsets. Results revealed that the RF model, leveraging data from the Anthesis phenological stage, exhibited superior performance in wheat yield estimation, achieving an R² of 0.92 and an RMSE of 0.14 ton ha -1 for the training set, and an R² of 0.90 and an RMSE of 0.29 ton ha -1 for the testing set. To enhance model accuracy, Sentinel-1 radar data were integrated into the RF framework. This addition reduced the training set RMSE to 0.13 ton ha -1 but increased the testing set RMSE to 0.33 ton ha -1 , with R² values remaining stable at 0.92 and 0.90 for the training and testing sets, respectively. Variable importance analysis indicated that optical soil and vegetation indices were the dominant predictors. Although the inclusion of Sentinel-1 SAR data offered additional insights, it did not outperform the predictive capacity of optical indices. These findings validate the efficacy of the combined Sentinel-2 remote sensing approach for generating reliable wheat yield forecasts approximately 50 days prior to harvest.
Why it matches plant phenotyping methodsSentinel-1/2リモートセンシングと機械学習による小麦収量という植物形質の推定が中心で、モデル比較・評価と予測精度検証を実施している。
abstractwheat yield was accurately estimated by leveraging remote sensing-derived soil and vegetation indices.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Early identification of bean leaf diseases, particularly Angular Leaf Spot and Bean Rust, is vital for ensuring crop productivity and global food security, especially within smallholder farming systems where disease outbreaks can rapidly escalate and cause severe yield losses. Conventional disease identification through visual inspection is labor-intensive, subjective, and highly dependent on expert knowledge, making it impractical for large-scale agricultural monitoring. Although recent deep learning-based approaches have demonstrated impressive accuracy in plant disease classification, their inherent “black-box” nature significantly limits real-world adoption, as farmers and agronomists often lack the ability to understand, trust, or act upon unexplained predictions. To address these challenges, this study proposes an automated and explainable disease diagnostic framework based on a Vision Transformer (ViT-B/16) architecture optimized through transfer learning from ImageNet. Unlike traditional convolutional neural networks that primarily focus on localized features, the Vision Transformer processes images as a sequence of flattened patches and leverages self-attention mechanisms to capture long-range dependencies and global contextual patterns across the entire leaf surface. This global representation enables the model to detect subtle and spatially distributed disease symptoms that are often overlooked by CNN-based approaches. To further enhance transparency and interpretability, GradCAM + + is integrated into the framework as an explainable artificial intelligence (XAI) mechanism. This method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions, thereby establishing a human-interpretable validation loop for farmers, agronomists, and domain experts. The proposed framework was evaluated on the publicly available I-Bean dataset, achieving a validation accuracy of 97.52% along with strong precision, recall, and F1-score performance. The generated GradCAM + + visualizations consistently demonstrate the model’s sensitivity to true diseased regions, reinforcing both the reliability and trustworthiness of its predictions. By combining high-capacity global feature learning with visual explainability, the proposed approach offers a scalable, transparent, and practical solution for real-world precision agriculture. This framework not only enhances diagnostic accuracy but also bridges the critical gap between model performance and user trust, enabling informed decision-making and timely disease management in modern farming environments.
Why it matches plant phenotyping methods画像から豆葉の病害症状を分類・可視化する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格。
abstractThis method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions
Reproduction assets foundThe paper uses the publicly available I-Bean bean leaf disease image dataset (Healthy, Angular Leaf Spot, Bean Rust) and points to it via a Data availability DOI (10.21227/4k7y-vs03), which is an allowed URL. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPSP and SNT: Problem Formulation and MethodologyAS and DS: Implementation and VisualizationMVV PK and KB: Original Draft and Supervision.
Funding
Open access funding provided by Symbiosis International (Deemed University). This research received no external funding.
Data availability
[https://dx.doi.org/10.21227/4k7y-vs03]
Declarations
Competing interests
The authors declare no competing interests.
The authors declare that they have no conflict of interest.
References
1.
Wang Y Wang Q Su Y Jing B Feng M
Detection of kidney bean leaf spot disease based on a hybrid deep learning model
Sci. Rep. 2025 15 1 11185
10.1038/s41598-025-93742-7
40169647
POpen asset ↗10.21227/4k7y-vs03lines:325-415Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices.
Why it matches plant phenotyping methodsイチゴ果実の成熟度という植物器官の状態を、MIR分光と特徴選択・分類器で非破壊推定する方法を開発し、交差検証や統計検定で性能評価しており、フェノタイピング手法が中心である。
abstractThis study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification.
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available at the authors' GitHub release URL. The spectral dataset itself is not public and is available only from the corresponding author on request.Code · publicCode availability
The code is available publicly on:
https://github.com/RabihAssaf89/RabihAssaf-codes/releases/tag/v1.0.Open asset ↗RabihAssaf89/RabihAssaf-codes · v1.0html-lines:822-851Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Agriculture and global food security are critically dependent on accurate and timely identification of plant diseases and pests. Traditional approaches to disease identification rely heavily on visual inspection and expert knowledge, which frequently lack the accuracy, speed, and scalability needed to address growing agricultural challenges. Early and precise disease detection enables proactive interventions that can prevent widespread crop damage and reduce excessive pesticide use, thereby supporting sustainable agricultural practices. Artificial intelligence, particularly deep learning methods, has emerged as a transformative solution for automated plant disease diagnosis. Convolutional neural networks (CNNs) have demonstrated remarkable capabilities in image classification tasks, evolving from individual architectures to sophisticated ensembles and transferring learning models. However, existing CNN-based research on rice disease identification has typically focused on a limited number of disease classes, restricting their practical applicability in real-world agricultural settings. This study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification. We utilized a dataset comprising seven of the most common rice diseases, significantly expanding the scope beyond previous studies. The model employs transfer learning with pre-trained ImageNet weights and is optimized using the Adam optimizer with carefully tuned hyperparameters. The experimental evaluation on an independent test set demonstrates that our proposed model achieves an overall accuracy of 97.9%, with individual disease classification accuracy ranging from 94% to 99.67%. The model exhibits balanced performance across multiple metrics, including precision (96.2%), recall (97.97%), and F1-score (97%), confirming its robustness and generalizability. These results establish DenseNet121 as a highly effective framework for automated rice disease diagnosis, offering a practical tool for enhancing agricultural productivity and food security.
Why it matches plant phenotyping methodsイネ葉の画像から病害状態を分類する深層学習手法が研究の中心であり、独立テストセットによる性能評価も実施しているため、植物病害フェノタイピング手法として収載する。
abstractThis study addresses these limitations by implementing DenseNet121, an advanced CNN architecture known for its efficient feature reuse and gradient flow, for comprehensive rice disease classification.
Reproduction assets foundThe paper's rice disease classification experiments use the public Kaggle Paddy Disease Classification dataset (8030 images, 7 disease classes), explicitly cited and linked by the authors in the Data Availability Statement. No author code or trained model checkpoints are disclosed.Dataset · publicThe data presented in this study are available in Kaggle42.Open asset ↗Kagglehtml-lines:487-556Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Plant diseases continue to pose a significant threat to worldwide food security, resulting in notable yield reductions and economic consequences. Automated disease diagnosis through machine learning has arisen as a potential solution; nevertheless, current methods frequently have difficulty in capturing both detailed local attributes and overarching contextual patterns found in plant leaf images. This study presents a thorough comparative examination of conventional and deep learning methods—such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), YOLO, Support Vector Machines (SVMs), and Random Forests—for the classification of multi-class plant diseases. To overcome the constraints of individual CNN and transformer models, a new hybrid framework that integrates EfficientNet-B7 for strong spatial feature extraction with a Vision Transformer (ViT-B16) for comprehensive contextual modeling is suggested. The system is assessed on an extensive dataset consisting of 21,534 images covering 38 classes of plant diseases and healthy specimens. Experimental findings show that the suggested hybrid model reaches an accuracy of 98.13%, surpassing standalone CNN baselines and other rival models, while consistently achieving high precision, recall, and F1-scores for all classes. The results emphasize the success of combining convolutional and transformer-based models for scalable and precise plant disease detection, aiding the creation of smart decision-support systems for precision farming.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ハイブリッド画像解析手法を提案し、複数手法との比較評価と大規模データセットでの検証を行っており、フェノタイピング手法が中心である。
abstractAutomated disease diagnosis through machine learning has arisen as a potential solution
Reproduction assets foundThe paper's plant disease image dataset (New Plant Diseases Dataset on Kaggle) and the authors' complete hybrid CNN–ViT implementation (GitHub repository with Zenodo DOI) are both publicly and explicitly available.Dataset · publicThe data that support the findings of this study are openly available in the New Plant Diseases Dataset at Kaggle [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data].Open asset ↗Kaggle · new-plant-diseases-datasethtml-lines:296-327Code · publicThe source code, including model architecture, training scripts, evaluation routines, and Google Colab notebooks for inference, is hosted on GitHub at: https://github.com/mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosis.Open asset ↗GitHub · mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosishtml-lines:296-327Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract The safety of the global food supply depends heavily on effective crop management, making early diagnosis of plant diseases vital for improving agricultural productivity. This proposal outlines the development of an intelligent irrigation system that utilizes machine learning and the Internet of Things (IoT) for the early detection of sugarcane leaf diseases and assessment of their impact on crop yield. The system gathers and analyzes data on soil temperature, humidity, and leaf characteristics—specifically changes in texture and color—using high-resolution photography from unmanned aerial vehicles (UAVs) and IoT-connected sensors. To enhance feature extraction and classification, the system employs a non-linear growing self-organizing map (NG-SOM) embedded within the hidden layers of an artificial neural network (ANN). This advanced model effectively identifies complex patterns in the collected data. Compared to traditional classification methods, this approach achieves a sugarcane disease detection accuracy of 95.6% and reduces false positives by 18.3%. It has been tested on multiple disease types, including red rot, smut, and rust. Additionally, the integration of early diagnosis with intelligent irrigation shows a strong correlation with optimized crop production. Predictive modeling of disease progression based on early detection improves output projections by 22.4%, demonstrating the system’s value in precision agriculture. By merging UAV imaging, sensor-based monitoring, and advanced machine learning, this approach offers a promising solution for proactive crop disease management and sustainable yield enhancement in sugarcane farming.
Why it matches plant phenotyping methodsUAV画像、IoTセンサー、機械学習を統合し、サトウキビ葉の色・テクスチャから病害状態を検出する方法の開発と評価が中心である。
abstractThe system gathers and analyzes data on soil temperature, humidity, and leaf characteristics—specifically changes in texture and color—using high-resolution photography from unmanned aerial vehicles (UAVs) and IoT-connected sensors.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Despite their relevance to postharvest engineering and cultivar improvement, the genotype- and environment-dependent variation of the physical and geometrical traits of onion bulb remains poorly characterized in the Republic of Korea. The study evaluated these traits in six commercial onion cultivars grown across two distinct production regions (Muan and Changnyeong), using a randomized complete block design with three replications. A standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions, including polar and equatorial diameters, neck and bulb thickness. Linear mixed models were used to partition genotype (G), location (L), and G x L interaction effects. Most traits exhibited significant G, L, and G×L effects, indicating strong environmental sensitivity alongside genetic control Combined heritability estimates were high for bulb thickness (0.83), bulb weight (0.66), and diameter- and area-related traits (0.54–0.68), while broad-sense heritability across locations was consistently high (0.71–0.99), particularly for single bulb weight and size traits. Spring Breeze, Katamaru, and Healthy Q consistently produced larger bulbs, while Cheonjujeok and Eomji Nara exhibited smaller bulb dimensions. Trait responses varied markedly between environments, with changes ranging from reduction of approximately 80% to increases exceeding 160%, highlighting pronounced genotype × environment interactions. Hierarchical cluster heatmap analysis revealed strong associations among bulb size–related traits and distinct genotype groupings, with clear location-dependent differences in trait expression between Muan and Changnyeong. These findings demonstrate the utility of image-based phenotyping for robust environment-aware assessment of onion bulb geometry and provides a quantitative basis for region-specific cultivar selection, postharvest system design, and future multi-site breeding evaluations.
Why it matches plant phenotyping methods画像取得とImageJによる形質抽出を組み合わせた標準化フェノタイピングワークフローが、タマネギ球の形態形質測定の中心的手法として明示されているため。
abstractA standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions
Potato leaf diseases, if left undetected, threaten food security in agricultural economies and cause substantial crop losses. To address this critical challenge, we developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD), a variant of SSD that integrates multiscale feature fusion and Squeeze-and-Excitation attention to improve fine-grained lesion detection. The enhanced model processes high-resolution images (512×512 pixels) and analyzes leaves at ten magnification levels, enabling it to identify even minor signs of infection. The inclusion of Squeeze-and-Excitation filters allows the system to focus more effectively on characteristic disease patterns, increasing detection precision. After scanning the leaves, the system applies advanced image processing techniques to localize disease regions and assess their severity. We evaluated EF-SSD using 2,500 labeled potato leaf images representing healthy plants and cases of early and late blight. The proposed model achieved a mean Average Precision (mAP) of 97% at 0.5 IoU, an F1-score of 95%, and an Intersection over Union (IoU) of 89%, outperforming advanced detectors such as YOLOv5, YOLOv8, RetinaNet, and Faster R-CNN across all metrics. It also delivers real-time inference at 47 FPS, confirming its suitability for on-field deployment. An ablation study further demonstrates the effectiveness of SE blocks and extended feature hierarchies in enhancing detection accuracy. These outcomes highlight EF-SSD’s potential as a reliable, efficient, and scalable tool for smart agriculture and early crop disease management.
Why it matches plant phenotyping methodsジャガイモ葉の病斑を画像から検出・局在化し、病害の重症度を評価する深層学習手法を開発・検証しており、植物表現型取得が中心である。
abstractwe developed an AI-based system called the Enhanced Single Shot Multibox Detector(EF-SSD)
Reproduction assets foundThe paper's Data Availability statement explicitly provides a public GitHub repository with the authors' analysis/training code and a public Google Drive link to the custom 2,500-image potato leaf disease dataset with Pascal VOC annotations used in this study.Code · publicThe code implemented in this study is openly available at the following GitHub repository: https://github.com/bhavanisravan/potato-leaf-diseases-code.Open asset ↗bhavanisravan/potato-leaf-diseases-codehtml-lines:414-439Dataset · publicThe dataset used for training and evaluation can be accessed at: https://drive.google.com/drive/folders/1Yin9zp0gQKwqJ0LD3GWdLq_V_idLO2bG?usp=sharing.Open asset ↗html-lines:414-439Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Medicinal plants including Ocimum tenuiflorum L. (Tulsi), Azadirachta indica A. Juss. (Neem), and Kalanchoe pinnata (Lam.) Pers. (Patharkuchi) are essential sources of bioactive compounds, yet leaf diseases threaten their yield and phytochemical integrity. This study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks, achieving 99.72% accuracy, 1.00 macro F1-score, and AUC = 1.00 across 12 disease classes (1,000 images/class post-augmentation) using only 9.38 M parameters and 2.50 GFLOPs. Five-fold cross-validation yielded 99.74% ± 0.14% accuracy, with rapid convergence and no overfitting. Explainable Artificial Intelligence (XAI) via Gradient-weighted Class Activation Mapping (Grad-CAM) (mean intensity: 0.1664-0.2702), Local Interpretable Model-agnostic Explanations (LIME), and t-distributed Stochastic Neighbor Embedding (t-SNE) (silhouette score: 0.87) confirmed biologically meaningful attention on pathological regions. External validation on the independent BD-MediLeaves dataset (8 classes, 8,000 samples) achieved 99.42% accuracy and 0.99 macro F1. With 6.98 ms/image inference latency and 35.81 MB memory, LSeTNet enables real-time, edge-based deployment. It significantly outperforms DenseNet169 (95.56%), ViT-B16 (95.61%), and LW-CNN+SE (95.39%) ([Formula: see text], paired t-tests), establishing a transparent, efficient, and generalizable benchmark for precision phytopathology and sustainable medicinal plant cultivation.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するCNN・Transformer手法を開発し、交差検証、外部データセット、既存モデルとの比較で検証しており、植物フェノタイピング手法が中心である。
abstractThis study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks
Reproduction assets foundThe paper publicly releases its primary medicinal leaf image dataset (MedicinalLeaf-12) on Mendeley Data, uses a public external validation dataset (BD-MediLeaves, also on Mendeley), and provides full training/evaluation code for LSeTNet on GitHub. All three are paper-specific, public, and actionable.Dataset · publicThe primary dataset used in this study is available in the Mendeley Data Repository: https://data.mendeley.com/datasets/ncg7kk3gwx/1 .Open asset ↗Mendeley Data · ncg7kk3gwx/1lines:712-750Code · publicThe full training and evaluation code for the proposed LSeTNet model is publicly available on GitHub at: https://github.com/mdtuhinkhan101/LSeTNet .Open asset ↗GitHub · mdtuhinkhan101/LSeTNetlines:712-750Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Southern Corn Leaf Blight (SCLB, also called Maize Leaf Blight, MLB), caused by Bipolaris maydis (teleomorph: Cochliobolus heterostrophus), severely limits maize yield under favourable conditions. Rapid detection and precise interventions are essential for sustainable production. We present an AI-driven framework integrating deep learning diagnostics, precision fungicide application, and a digital decision support system (DSS) for field-level SCLB management. Thirteen machine learning (ML) and deep learning (DL) algorithms were evaluated, with VGG16 achieving the highest performance (accuracy 97.0%, precision 0.98, recall 0.96, F1-score ≥ 0.97, AUC-ROC = 1.00). Feature extraction analysis highlighted VGG16’s ability to capture hierarchical disease-specific patterns (score = 0.95), and error- and variance-based assessment confirmed minimal prediction errors (MAE = 0.06, RMSE = 0.16, Explained Variance = 0.90, MBD = − 0.02). Confusion matrix analysis revealed only a small number of misclassifications (4 false negatives and 9 false positives), demonstrating excellent generalization. Grad-CAM heatmaps, t-SNE visualization, and learning curves confirmed lesion-focused predictions and feature separability. Two-year field trials (2023 and 2024) validated precision fungicide application (Azoxystrobin 18.2% + Difenoconazole 11.4% SC), reducing disease severity to ≈ 10% PDI (86.2% reduction) and increasing grain yield to 83.7 q/ha (C: B ratio 1:2.41). The Streamlit-based DSS provides actionable, real-time advisories, offering a scalable AI platform for automated disease detection and precision agriculture in maize. The proposed framework can be extended to other foliar diseases and integrated with IoT-based sensing for region-wide advisory systems.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を深層学習で検出・分類する方法と、その性能検証および現地試験での検証が中心であり、植物病害フェノタイピング手法に該当する。
abstractWe present an AI-driven framework integrating deep learning diagnostics, precision fungicide application, and a digital decision support system (DSS) for field-level SCLB management.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Aluminium (Al) toxicity is a potential constraint to maize productivity in acidic soils, primarily due to its inhibitory effect on root growth during its early establishment. In the present study, a hydroponic screening protocol was standardized using Modified Magnavaca-II solution at the seedling stage and applied to 250 tropical maize inbred lines. Five root traits-total root length (TRL), root surface area (RSA), root volume (RV), average root diameter (AD), and number of root tips (NRT)-were quantified using WinRHIZO. To assess differential tolerance, the Relative Root Tolerance Index (RRTI)-a ratio-based metric comparing root performance under stress versus control-was calculated along with percent reduction for all traits. Protocol optimization with seven elite inbreds exposed to graded AlCl₃ concentrations (0-1500 µM) identified 300 µM AlCl₃ at 11 days post-germination as optimal for differentiating genotypic responses. Under this optimized condition, the 250 inbreds showed highly significant genotypic variation and genotype × treatment interactions. Stress significantly reduced most root traits by 10-40%, while improving the average root diameter, indicating compensatory thickening. Substantial variability was observed for both RRTI and percent reduction indices, ranging from 3.83 to 533.88. Principal component analysis and composite indices identified IMR292, IMR592, IMR463, IMR621, IMR546, IMR534, IMR629 and IMR395 as tolerant due to high TRL, RSA and NRT under stress, while IMR388, IMR33, IMR58, IMR349 and IMR446 were highly susceptible. The tolerant inbreds offer promising genetic resources for breeding Al-tolerant maize, while the optimized hydroponic system provides a robust, scalable framework for future phenotyping and genetic dissection studies.
Why it matches plant phenotyping methodsアルミニウム耐性評価のための高スループット根系表現型測定プロトコルを標準化・最適化し、250系統へ適用しているため、表現型取得法が研究の中心です。
abstracta hydroponic screening protocol was standardized
Productivity and quality of food are crucial for populations around the world. However, food faces challenges due to the threats of fruit diseases, which lead to poor food quality. Therefore, early detection and classification of fruit diseases are important to help farmers detect and overcome these diseases, thereby improving food quality and productivity. One of the biggest challenges in the agriculture field is classifying and detecting fruit diseases using traditional manual visual grading. As a result, deep learning and computer vision models have emerged as new methods for visual grading, offering higher accuracy in classification and detection. This study proposes deep learning models for fruit disease detection and classification in the early stages. Five deep learning models are used: Convolutional Neural Network (CNN), DenseNet121, EfficientNetB3, Xception, and ResNet50. These models are applied to detect six types of fruit diseases, including orange, grape, mango, guava, apple, and banana plant diseases. Image preprocessing and data augmentation techniques were employed for image processing. The results show accuracies of 96.25%, 99.14%, 96.17%, 94.06%, 96.72%, and 99.33% for the CNN, EfficientNetB3, ResNet50, DenseNet121, ResNet50, and EfficientNetB3 models, respectively, for detecting orange, grape, mango, banana, guava, and apple plant diseases. We compared our models with other deep learning models, and the model that utilized image preprocessing and data augmentation techniques demonstrated higher accuracy and performance. We recommend the EfficientNetB3 model for fruit disease detection based on these results.
Why it matches plant phenotyping methods果実植物の病害状態を画像から検出・分類する深層学習手法の開発と比較が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis study proposes deep learning models for fruit disease detection and classification in the early stages.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abiotic stress tolerance is a critical trait in plant breeding programs aimed at developing climate-resilient crop varieties. The accurate identification and selection of stress-tolerant genotypes require comprehensive evaluation using multiple mathematical indices. However, the manual calculation of these indices from large-scale experimental datasets is time-consuming, error-prone, and computationally demanding. Here, we present PTSIonline (Plant Tolerance and Sensitivity Indices online, http://87.107.144.237 ), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research. The platform implements 18 widely recognized stress evaluation indices including Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Superiority Index (SI), Abiotic Tolerance Index (ATI), Stress Susceptibility Percentage Index (SSPI), Relative Efficiency Index (REI), Modified Stress Tolerance Indices (K₁STI, K₂STI), Stress Distribution Index (SDI), Drought Index (DI), and Stress Non-Productivity Index (SNPI). PTSIonline features an intuitive user interface that accepts standard experimental data formats and generates comprehensive statistical outputs, visualizations, and genotype rankings within seconds. Comparative analysis demonstrates that PTSIonline provides the most extensive index coverage among available online tools while maintaining computational efficiency suitable for high-throughput phenotyping programs. The platform eliminates computational barriers in stress tolerance research, enabling researchers and plant breeders to rapidly identify superior genotypes from diverse germplasm collections. PTSIonline represents a significant advancement in computational tools for crop improvement under changing environmental conditions.
Why it matches plant phenotyping methods作物の収量データから耐性・感受性指標を計算し、遺伝子型順位付けを行うウェブ型解析プラットフォームが研究の中心であり、植物表現型解析向けの再利用可能なソフトウェアとして収録対象です。
abstractwe present PTSIonline (Plant Tolerance and Sensitivity Indices online, http://87.107.144.237 ), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research.
Water deficit, salinity, and cadmium (Cd) contamination have generated an environmental problem worldwide, leading to damages to plant growth due to alteration in their metabolism. This study aimed to classify enzymatic and non-enzymatic antioxidant systems in Micro-Tom (MT) plants when subjected to two intensities (moderate and severe) of water deficit, salinity, and Cd exposure. The experimental design was a completely randomized 3 × 2 factorial, with the first factor representing the stress agents (water deficit, salinity, and Cd) and the second factor indicating stress intensities (moderate and severe), along with a control group. After an acclimation period, plants were exposed to 10 days of stress. Water deficit treatments were imposed using solutions adjusted to osmotic potentials of − 0.40 MPa and − 1.00 MPa; salinity stress was established with nutrient solutions containing 40 mM or 120 mM NaCl; and Cd stress was induced using nutrient solutions with 0.25 mM or 0.5 mM CdCl₂. Laboratory analyses included lipid peroxidation, hydrogen peroxide content, proline accumulation, protein quantification, and enzyme extraction. Descriptive analyses, and a Spearman’s correlation, identified the behavior of enzymatic and non-enzymatic systems for each stress agents and intensities, enabling the selection of key influencing factors. A factorial analysis of variance was performed to assess the mean differences among the treatments (α = 0.05) for enzymatic, non-enzymatic systems, MDA and, H₂O₂. Using this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high. Variations in antioxidant response and stress biomarkers were detailed, with proline and superoxide dismutase identified as the primary variables of significance across stress indicators. Furthermore, the model achieved robust classification performance with Matthew’s correlation coefficients exceeding 0.80 in the extreme classes; however, it encountered limitations in distinguishing between classes with closely proximate values. The findings indicate the capability of the decision tree to classify stress levels in plants.
Why it matches plant phenotyping methods植物の抗酸化・生理指標から非生物的ストレス強度を推定・分類する決定木モデルが中心で、性能評価も行っているため、植物状態の計算的フェノタイピングに該当します。
abstractUsing this data, a decision tree model classified the stresses into four levels: low, low-medium, medium-high, and high.
Field / plotLeafObject detectionStress / disease detectionDisease symptoms / severity
Timely localization and diagnosis of crop lesions are critical for disease control and reducing pesticide use. However, in-field lesions often resemble leaf textures, vary widely in scale, and suffer from lighting and shadow interference-making simultaneous high accuracy and lightweight inference challenging. We propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition. Central to our design is Wavelet Channel Recalibration (WCR), a DWT-based downsampling module: discrete wavelet transform naturally provides multi-resolution, time-frequency localized representations that explicitly separate low-frequency approximations from high-frequency edge/texture details. WCR fuses high- and low-frequency components and enhances feature representation through their frequency-domain complementarity, thereby preserving semantic and fine texture information during resolution reduction with negligible extra cost. We also introduce PS-C2f, which integrates Pinwheel-shaped convolutions into C2f to better capture tiny lesion details via multi-directional, irregular kernels, and replace SPPF with Dynamic Group Attention Pooling (DGAP) for efficient multi-scale context aggregation. On our PlantDoc_boost dataset, WGA-YOLO improves over YOLOv8n by 3.02 and 2.85% points, while reducing parameters and FLOPs by ~ 0.18 M and ~ 0.3G, demonstrating improved inference efficiency and deployment friendliness while maintaining strong detection performance in field scenarios.
Why it matches plant phenotyping methods植物葉の病斑を画像から検出・診断するYOLO改良手法の開発が中心であり、病害状態の画像ベース表現型計測に該当する。
abstractWe propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition.
Reproduction assets foundThe paper's PlantDoc_boost dataset (the annotated crop-disease image dataset constructed and analyzed in this study) is explicitly stated to be publicly released on the authors' GitHub repository. No author analysis code or trained model checkpoints are stated as available. The Roboflow corn and tomato datasets are preDataset · publicThe PlantDoc_boost dataset used and analyzed in this study is publicly available from the project repository at http://github.com/YongChaoLiang/PlantDoc_boost/tree/master.Open asset ↗YongChaoLiang/PlantDoc_boosthtml-lines:693-708Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early and precise diagnosis of diseases in tomato plants is critical in ensuring productivity in agriculture and reducing losses caused by diseases. Classical classification approaches, however, are often limited by different image quality, different lighting, low resolution, and imbalanced classes. In order to confront these issues, this paper suggests a hybrid ensemble model that integrates the advantages of deep learning, fuzzy logic, and a generative model to classify diseases successfully. The suggested approach combines three strong convolutional neural networks, ResNet-50, EfficientNet-B0, and DenseNet-121, into the adaptive ensemble system. Individual models are combined to form the final decision depending on the predictive accuracy and confidence level. The use of fuzzy logic to refine intelligently the decision-making process provides more flexibility than the decisions of the static ensemble methods. A Conditional Generative Adversarial Network (C-GAN) is used to alleviate the problem of class imbalance and overfitting through the production of multiple synthetic images of high quality. This, in turn, significantly enhances the generalization of models and even gives a balanced representation of the disease’s classes. The hybrid structure achieved a classification accuracy of 99.19% when tested on the PlantVillage dataset and outperformed the traditional ensemble and classical techniques. The results highlight the potential of the hybrid approach for real-world agricultural applications. This offers a scalable, accurate, and intelligent solution for automated plant disease diagnosis. This study contributes a novel, interpretable, and performance-driven model that can support sustainable agriculture through timely and precise disease management in tomato crops.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する深層学習・ファジー論理・生成モデルの統合手法を開発し、PlantVillageで性能評価しているため、植物病害フェノタイピング手法が中心です。
abstractthis paper suggests a hybrid ensemble model that integrates the advantages of deep learning, fuzzy logic, and a generative model to classify diseases successfully.
Reproduction assets foundThe paper uses four public tomato leaf image datasets (PlantVillage, Tomato Leaves, PlantDoc, Tomato-Village) plus two Mendeley-hosted datasets for robustness evaluation, and declares a public GitHub repository of C-GAN-generated tomato crop images under Code availability. No trained model checkpoints or analysis code/Dataset · publicTomato Leaves Dataset
https://www.kaggle.com/datasets/ashishmotwani/tomato?
Over 20,000 samples of tomato leaves with 10 diseases and 1 healthy class are availableOpen asset ↗html-lines:399-523Dataset · publicThe “Tomato-Village” dataset: The “Tomato-Village” dataset is designed to improve tomato disease detection in real-world agricultural conditions. It is available at the following link.: https://github.com/mamta-joshi-gehlot/Tomato-VillageOpen asset ↗Tomato-Villagehtml-lines:1009-1039Dataset · publicMendeley Data: This dataset contains images of tomato leaves afflicted with distinct diseases gathered under several conditions. This is available on link: https://data.mendeley.com/datasets/zfv4jj7855/1Open asset ↗html-lines:1009-1039Dataset · publicGTLD: The images in the dataset were taken with a DSLR and a quality mobile phone. Some images were taken in direct sunlight, while others were captured in shaded areas beneath the plants. This is available on link: https://data.mendeley.com/datasets/2bdfjb99k5/1Open asset ↗html-lines:1009-1039Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.
Why it matches plant phenotyping methodsチリ葉の病害を画像から検出・分類する深層学習手法の開発、比較評価、データセット構築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対象範囲に該当する。
abstractthe present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves.
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement
Pollen and spore morphology provides essential taxonomic reference data for floristic and environmental studies in tropical regions, where modern comparative datasets remain limited. This study documents the morphological characteristics of pollen and spores recovered from a shallow soil profile in a degraded mixed deciduous forest within Sri Nan National Park, northern Thailand. Using a non-acetolysis extraction protocol and systematic sub-sampling of a 30-cm profile, pollen and spores representing 37 plant families were identified, including lycophytes, bryophytes, monilophytes, gymnosperms, and angiosperms. Spore-producing taxa, particularly monilophytes, dominate the assemblage, while angiosperm pollen includes both arboreal and non-arboreal elements. More than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions. The resulting dataset expands the regional palynological reference framework for northern Thailand and tropical Southeast Asia and supports consistent taxonomic identification in palynological, floristic, and comparative paleoecological studies, particularly in human-impacted forest-agriculture mosaics.
Why it matches plant phenotyping methods植物の花粉・胞子形態を標準化して記載し、高解像度画像を含む再利用可能な地域参照データセットを構築しており、形態取得・記述が研究の中心です。
abstractMore than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions.
Segmentation of hyperspectral image data is a well-established technique in remote sensing. While it is commonly applied to individual field crops, its use for individual trees is less prevalent. Conifers are crucial in forestry, and assessing physiological status, or genetic diversity is required for effective early-age treatment in nurseries and hyperspectral imaging (HSI) combined with high-throughput phenotyping (HTP) offers faster and non-destructive evaluation. NDVI-based thresholding is sufficient for detection of leaves with large projection areas, but needles of conifers present challenges due to spatial resolution constraints and increased proportion of border pixels. This study monitored the offspring of three locally adapted Scots pine (Pinus sylvestris L.) populations, representing distinct upland and lowland ecotypes. This study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings. Using a K-means algorithm, 23 hyperspectral centroids were successfully derived and subsequently classified into ten biologically distinct groups. Random forest classification model effectively differentiated Scots pine seedlings based on origin during water stress and recovery periods. This study highlights the potential of hyperspectral imaging and machine learning in evaluating the physiological state of conifer seedlings, demonstrating promising applications in forest tree physiology research and tree breeding.
Why it matches plant phenotyping methods個体のマツ苗を分離・セグメンテーションするハイパースペクトル画像処理パイプラインを開発し、機械学習で生理状態や由来を評価しており、表現型取得手法が中心である。
abstractThis study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings.
Reproduction assets foundThe paper's Data availability statement explicitly deposits demonstration hyperspectral sample data on Zenodo and the segmentation/classification scripts on GitHub; both are paper-specific, public, and actionable. Full experimental data is request-only and not listed as a public asset.Dataset · publicDemonstration sample data and their accompanying descriptions are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17167809).Open asset ↗Zenodo · 10.5281/zenodo.17167809lines:161-192Code · publicThe scripts developed for this study are available on GitHub at: https://github.com/JCepl/Pine-hyperspectral-image-segmentaionCompleteOpen asset ↗GitHub · JCepl/Pine-hyperspectral-image-segmentaionCompletelines:161-192Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Sensor-based datasets often have limited features because continuous sensor deployment is expensive and complex. This study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets. The research utilizes two sensor-based tomato disease datasets - TomEBD and TPMD, which have been collected in real-time. To address the dataset imbalance, the KMeans-SMOTE technique is applied. Feature extraction is performed using reformed triangular and gaussian membership functions, where all parameters are computed solely from the training data to prevent information leakage and biased evaluation. The enhanced datasets are classified using two optimized models: Optimized Kernel Extreme Learning Machine (OKELM) and Optimized Radial Basis Function Neural Network (ORBFNN), both tuned using the Optuna framework. The proposed technique is further validated on eight benchmarking non-plant-based datasets. Among all models, the TMF-ORBFNN achieved the highest accuracy across both plant-disease and benchmark datasets. Further, statistical analysis using the Friedman test and post-hoc Bonferroni-Dunn test showed that TMF-ORBFNN performed significantly differently from its counterparts. The time complexity of the proposed approach has also been analysed. The proposed MFFE technique provides effective feature extraction in small, sensor-based datasets without class-variable dependency. Enhancing and classifying plant-disease datasets using the proposed TMF-ORBFNN model will help farmers take timely actions to prevent crop diseases and reduce pesticide use.
Why it matches plant phenotyping methods植物病害データから病害状態を抽出・分類する特徴抽出法と分類ワークフローが研究の中心であり、センサベースの植物病害フェノタイピング手法として適格です。
abstractThis study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets.
Evapotranspiration (ET) is a key component of the hydrological cycle and is critical for determining crop water requirements. Accurate ET estimation is essential for improving irrigation efficiency, particularly under increasing water scarcity and climate variability. Conventional approaches such as the soil water balance, empirical formulations, the FAO Penman-Monteith method, eddy covariance flux towers, lysimeters, and scintillometers each have limitations related to spatial representativeness, accuracy, or operational cost. Unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors offer a high spatial resolution and cost-effective alternative for field-scale assessment of surface energy balance components and ET. In this study, a field experiment was conducted on maize during rabi season of 2022-23 under two irrigation regimes based on depletion of available soil moisture (20% DASM and 40% DASM). UAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes. Spatiotemporal analysis showed that the 20% DASM treatment (400 mm) resulted in a 1.7 °C lower land surface temperature, a 16.5% higher NDVI, and an 11% increase in daily ETa compared with the 40% DASM treatment (316 mm), which experienced water stress and a 20% reduction in seasonal ETa. The UAV-based METRIC estimates of daily ETa showed strong agreement with that of Penman-Monteith (PM) combination approach (R² = 0.84; RMSE = 0.22 mm day⁻¹; MAPE = 6.1%), with a slight underestimation of seasonal ETa (-7%). Agreement with the soil water balance method ranged from - 3% to + 3%, demonstrating the capability of the approach to capture irrigation-induced variability in ETa and surface energy fluxes. Overall, the results highlight the potential of UAV-based METRIC for spatiotemporal assessment of crop evapotranspiration and surface energy dynamics to support precision irrigation management.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とMETRICモデルにより、トウモロコシの蒸発散量・表面エネルギーフラックスを取得し、複数手法との一致性を検証している。植物キャノピーの生理状態の定量が研究の中心であり、単なる灌漑試験のルーチン測定ではない。
abstractUAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes.
This study presents an automated framework for early blight detection in tomato plants using a modified MobileNet architecture. Addressing the limitations of traditional labor-intensive methods, this study proposes a two-stage pipeline combining (1) transfer learning with depthwise separable convolutions for efficient feature extraction and (2) a meta-learned ensemble of Random Forest, SVM, and Gradient Boosting classifiers to handle real-world variability in lighting and environmental conditions. The approach introduces two custom convolutional layers (Custom_Feature_Extraction_Block) that improve F1-score by + 3.8 points over the MobileNet baseline, with the ensemble contributing an additional + 2.1 points. Evaluated on a balanced PlantVillage dataset (1,982 images) with extensive augmentation to simulate variable lighting and orientations, the system achieved up to 100% accuracy with selected classifiers on a held-out validation subset of 30 images under controlled conditions. To assess generalization, we further validated the framework on an independent dataset (tomato_dataset_v2, 30, 609 images, 10 classes) containing field-acquired tomato leaf images, where the model attained 94.5% accuracy, confirming robustness beyond control environments. Comparative analysis with 10 recent methods demonstrates superior accuracy-efficiency trade-offs, offering practical on-device decision support for smallholder farmers. The framework’s lightweight design (4.2 M parameters, 23 ms/image on Raspberry Pi 4) and validated scalability underscore its potential for mobile and drone-based agricultural deployment. This addresses critical needs in global food security through accessible plant disease detection.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出する深層学習パイプラインを開発し、独立データセットで性能検証しており、植物病害状態の表現型取得が中心である。
abstractThis study presents an automated framework for early blight detection in tomato plants using a modified MobileNet architecture.
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Bitter gourd, an important crop with both economic and medicinal value, requires precise identification of fruit shape and tubercle phenotypes to enhance breeding efficiency. To address the low efficiency and high subjectivity of traditional methods, this study proposes an improved YOLOv8-CEFC model for high-throughput automatic detection of the bitter gourd fruit shape and tubercle characteristics. First, the model integrates the ConvNeXt V2 module into the backbone network, combined with a Fully Convolutional Masked Autoencoder (FCMAE) framework and Global Response Normalization (GRN) layers to enhance feature extraction capabilities. Second, an Efficient Multi-scale Attention (EMA) mechanism is introduced, capturing local tubercle textures and global fruit shape contours simultaneously through a parallel dual-branch structure, while also improving the model’s robustness against cluttered backgrounds and environmental noise. Finally, Focal-CIoU Loss is incorporated to replace CIoU Loss, reducing the impact of class imbalance on model accuracy. The results show that the model achieves precision, recall, mAP50, mAP50-95, and F1 scores of 93.9%, 94.4%, 96.3%, 93.6%, and 94.15%, respectively, which represent improvements of 2.0%, 3.5%, 1.1%, 3.4%, and 2.75% compared to the original YOLOv8n model. The performance gain of the model was further examined using the bootstrap method, which confirmed that the improvement is statistically significant. Further validation through confusion matrix analysis, PR curves, and ablation experiments confirms the effectiveness of the improvements. Compared to other mainstream YOLO models, YOLOv8-CEFC demonstrates more accurate identification, better stability, and higher detection efficiency. The proposed improved YOLOv8-CEFC model provides an efficient solution for phenotypic analysis in Bitter Gourd breeding and holds significant importance for advancing the intelligentization of crop breeding.
Why it matches plant phenotyping methods苦瓜果実形状とこぶ形質を高スループットに自動認識するYOLOv8改良モデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractthis study proposes an improved YOLOv8-CEFC model for high-throughput automatic detection of the bitter gourd fruit shape and tubercle characteristics.
Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI-IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.
Why it matches plant phenotyping methodsヤシの生体特徴から健康状態を分類する機械学習手法を開発・比較し、分類性能を評価しているため、植物状態推定が中心的な方法的貢献である。
abstractThis study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies.
Abstract Plant diseases pose a critical threat to global food security, agricultural sustainability, and farmer livelihoods, particularly in regions with limited access to advanced diagnostic technologies. Traditional methods of disease detection rely heavily on manual inspection, which is time-consuming, error-prone, and often results in delayed interventions. This paper presents a novel, solar-powered autonomous robotic system designed to detect plant diseases in real time using deep learning and IoT technologies. The proposed system integrates a high-resolution imaging unit, IoT-based environmental sensors, and an onboard processing module based on Raspberry Pi. Deep CNNs, trained on diverse datasets including PlantVillage, are used for accurate disease classification, while soil moisture and temperature sensors provide contextual environmental data to support diagnosis. The robot’s mobility, powered by solar energy, allows for continuous field monitoring with minimal human intervention. Experimental results demonstrate the system’s high classification performance, achieving 99.39% training accuracy, 97.47% validation accuracy, and 97.13% testing accuracy. Furthermore, the model achieved 99.63% overall accuracy, with a Precision of 99.40%, a Recall/Sensitivity of 99.56%, an F1-score of 99.46%, and a Specificity of 99.99% across multiple disease classes. These results highlight the robustness of the proposed approach in real-world agricultural conditions, enabling reliable disease detection and monitoring. The integration of cloud-based monitoring enables farmers to receive real-time alerts and insights, supporting timely and informed decision-making. This cost-effective, scalable, and environmentally sustainable solution has the potential to transform precision agriculture by enhancing early disease detection, reducing pesticide overuse, and improving crop yield and health.
Why it matches plant phenotyping methods植物病害状態を画像と深層学習で検出するロボット型フェノタイピング基盤が研究の中心であり、技術性能も評価している。
abstractThis paper presents a novel, solar-powered autonomous robotic system designed to detect plant diseases in real time using deep learning and IoT technologies.
Reproduction assets foundThe paper's Data Availability statement explicitly points to the public Kaggle PlantVillage leaf-disease image dataset used to train the CNN models, which is a paper-specific, publicly accessible phenotyping image asset. No author code, models, or field-collected data are deposited.Dataset · publicThe data presented in this study are available in [kaggle and roboflow] at [ [https://www.kaggle.com/datasets/emmarex/plantdisease](https:/www.kaggle.com/datasets/emmarex/plantdisease) ], reference number [46].Open asset ↗kaggle · emmarex/plantdiseasehtml-lines:319-384Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precise and timely identification of cotton leaf diseases is essential for sustaining crop yield and quality, yet manual inspection remains time-consuming, labor-intensive, and prone to error. Existing automated approaches are limited by insufficient dataset diversity, inconsistent evaluation practices, limited use of explainable AI (XAI), and high computational cost. To address these challenges, we propose an attention-enhanced CNN ensemble, namely CottonLeafNet, which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets. CottonLeafNet achieves state-of-the-art performance, obtaining 98.33% accuracy, a macro F1-score of 0.9833, Cohen's kappa of 0.9800, a mean PPV of 0.9838, and an NPV of 0.9967 on Dataset D1, with an inference time of 0.51 s per image. On Dataset D2, it reaches 99.43% accuracy, a macro F1-score of 0.9942, Cohen's kappa of 0.9924, a mean PPV of 0.9943, and an NPV of 0.9981, with a 0.40 s inference time. Moreover, a unified eight-class dataset created by merging both datasets yields a test accuracy of 99.08%. Robustness analysis under artificially induced class imbalance further confirms the model's stability, with consistently strong macro F1-scores. To evaluate the generalization capability of the proposed CottonLeafNet, we conducted cross-dataset experiments, and the results indicate that the model maintains moderate performance even when trained and tested on different datasets. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations demonstrate that CottonLeafNet reliably attends to disease-relevant regions, enhancing interpretability. Finally, real-time feasibility is validated through a web-based deployment achieving ≈1 s inference per image. These results establish CottonLeafNet as an accurate, robust, interpretable, and computationally efficient solution for automated cotton leaf disease diagnosis.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する分類手法を開発・評価しており、植物病害フェノタイピングが中心的である。
abstractwe propose an attention-enhanced CNN ensemble, namely CottonLeafNet, which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets.
Reproduction assets foundThe paper's plant-phenotyping inputs are three publicly available Kaggle cotton leaf disease image datasets (D1, D2, and cross-dataset D3) explicitly named in the Data availability statement. No author analysis code, trained model checkpoints, or supplementary code repository is disclosed in the supplied blocks.Dataset · publicThe datasets analyzed during the current study are publicly available in the Kaggle repository. Dataset D1 can
be accessed atOpen asset ↗Kagglepdf-page:17 lines:68-84Dataset · publicThe dataset used for
cross-dataset testing is publicly available at:Open asset ↗pdf-page:17 lines:68-84Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate localization of plant growth points is essential for precision agriculture applications, including electro-weeding and laser weeding. While crop and weed detection has been extensively studied, existing methods focus primarily on object-level recognition and often neglect fine-grained growth point localization. To address this limitation, we propose a novel training strategy, epoch-based prior annealing (EPA), which incorporates the excess green minus excess red (ExG-ExR) index as prior knowledge and introduces schedule factor and gain factor to effectively steer keypoint regression. The experimental results show that incorporating EPA improves keypoint localization performance, with mAP50 increasing by 0.024 and mAP50:95 by 0.011, while maintaining bounding box detection performance. The parameter sensitivity experiments confirmed that both excessively strong and weak guidance can hinder training. Furthermore, analysis of parameters and computational cost shows that the additional overhead introduced by the EPA strategy accounts for less than 0.5% of the total, and be considered negligible. In summary, the proposed EPA strategy significantly improves the accuracy, robustness, and generalizability of plant and growth point detection models, offering a practical and scalable solution for precision agricultural applications.
Why it matches plant phenotyping methods植物の生長点を画像から局在化する学習戦略を開発し、その性能を実験的に検証しており、植物器官の表現型取得が中心です。
abstractwe propose a novel training strategy, epoch-based prior annealing (EPA), which incorporates the excess green minus excess red (ExG-ExR) index as prior knowledge and introduces schedule factor and gain factor to effectively steer keypoint regression.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicYou can access the dataset used in this study at the following links: https://github.com/cropandweed/cropandweed-dataset.Open asset ↗cropandweed/cropandweed-datasethtml-lines:497-528Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet), which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM), is proposed to enhance the model's learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習モデルと新規活性化関数を開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet), which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases.
Reproduction assets foundThe paper's phenotyping inputs are two public leaf-image datasets used directly for its classification experiments: the Mendeley Potato Leaf Disease dataset (explicitly deposited with URL) and the PlantVillage dataset via Kaggle (explicitly linked in Data availability). The authors' PLDNet code is only promised 'upon' Dataset · publicsis. A.M initially drafted the paper, and all the authors (A.M, A.C, and N.A) reviewed and edited the paper.
Funding
Open access funding provided by University of Inland Norway. INN have subscription for open-access (OA) publication in Scientific Reports, Nature.
Data availability
The dataset used in this study is available at: https://data.mendeley.com/datasets/ptz377bwb8/1 and https://www.kaggle.com/datasets/emmarex/plantdisease .
Code availabilityOpen asset ↗Mendeley · ptz377bwb8/1lines:1390-1403Dataset · publicthors (A.M, A.C, and N.A) reviewed and edited the paper.
Funding
Open access funding provided by University of Inland Norway. INN have subscription for open-access (OA) publication in Scientific Reports, Nature.
Data availability
The dataset used in this study is available at: https://data.mendeley.com/datasets/ptz377bwb8/1 and https://www.kaggle.com/datasets/emmarex/plantdisease .
Code availabilityOpen asset ↗Kaggle · emmarex/plantdiseaselines:1390-1403Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Real-time plant disease diagnosis employs new technologies to identify and detect plant diseases while they occur, thus allowing a rapid response that reduces crop loss and improves healthier agricultural practices. This work improves plant health monitoring using early detection to maximize yield and reduce loss. Typical procedures for diagnosing plant diseases involve sampling or visual inspection and are slow, labor-intensive, and subject to human error. These procedures are not suitable for widespread adoption in the field of crop systems where the scale of diagnostics requires real-time, scalable, and accurate reporting of problems. The Plant Disease Diagnosis using Deep Learning (PDD-DL) framework, through Convolutional Neural Networks (CNNs), analyzes plant images to automatically diagnose plant diseases in real time. This model is faster, more trustworthy, and more scalable in diagnosis than traditional methods of diagnosis. The research presents the validation of the model based on common, popular crops; however, the application includes a wide array of crops. The system may be retrained for specific disease classes depending on agricultural requirements. CNNs will certainly provide effective image analysis, accurately differentiating healthy from sick plants, and permitting continuous monitoring for preemptive measures in the classification of plant diseases. The proposed model performed with an overall accuracy of 98.32%, precision score of 97.85%, recall value of 98.14%, F1-score of 97.99%, and real-time inference speed of 42.6 ms per image. As a result, the study's findings improve accuracy and speed in diagnosing plant disease, which aids in precision agriculture and sustainable plant health management.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNN診断手法の開発・検証が研究の中心であり、植物の病害表現型を直接評価しているため含める。
abstractThe Plant Disease Diagnosis using Deep Learning (PDD-DL) framework, through Convolutional Neural Networks (CNNs), analyzes plant images to automatically diagnose plant diseases in real time.
Real-time, accurate assessment of crop conditions is key to effective decision-making in precision agriculture. This study proposes an enhanced deep-learning framework that jointly investigates YOLOv8 and the newly released YOLOv11 object-detection architectures for multi-class leaf-health monitoring. A curated dataset of 5000 high-resolution images annotated as healthy, stressed, or damaged was collected across diverse species, growth stages, and lighting conditions. An end-to-end training pipeline was developed featuring extensive geometric, colour, cut-out, and mosaic augmentations; transfer-learning from COCO weights; and GPU-accelerated fine-tuning for 50 epochs. To underpin reproducibility, we provide a compact mathematical formulation (15 equations) that details bounding-box prediction, objectness scoring, class-probability estimation, and the composite CIoU-based loss. On the held-out test set YOLOv11 achieves a mean Average Precision of 93.3% (mAP@0.5) and 76.5% (mAP@0.5:0.95), surpassing YOLOv8 (92.0%/75.2%). Precision–Recall AUC improves from 0.931 to 0.947, while small-object recall rises by 3.4 pp. Inference latency is 15 ms per image on an RTX 3060 (YOLOv11) versus 12 ms for YOLOv8, maintaining real-time throughput (> 60 FPS). An ablation study confirms that full augmentation yields an additional + 1.3 pp mAP gain. Qualitative analyses illustrate tighter bounding boxes and fewer misclassifications between stressed and damaged classes with YOLOv11. These findings demonstrate that YOLOv11’s architectural refinements deliver measurable accuracy gains with only a modest computational overhead, making it preferable where detection fidelity is paramount. Remaining challenges occlusions, visually ambiguous symptoms, and domain shift are analysed, and mitigation strategies (multi-spectral inputs, temporal modelling, and edge-side quantisation) are proposed. The proposed framework, validated with meticulous metrics and consistent mathematical approaches, this framework creates a dependable baseline for AI-driven plant health monitoring in advanced agricultural ecosystems.
Why it matches plant phenotyping methods植物の葉の健康状態（healthy、stressed、damaged）を画像から推定するYOLOベースの手法を開発・比較し、データセット、学習パイプライン、精度、速度、アブレーションで技術評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes an enhanced deep-learning framework that jointly investigates YOLOv8 and the newly released YOLOv11 object-detection architectures for multi-class leaf-health monitoring.
Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7 (650, 428) , RSI8 (663, 422) , and RSI9 (671, 450) , performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R 2 , RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.
Why it matches plant phenotyping methods冬小麦の植物・葉の含水量および水分ストレスを、VIS/NIRと冠層温度のデータ融合および機械学習で推定する手法が研究の中心であり、植物生理状態の定量的フェノタイピングに該当する。
abstractThis study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat
Mangrove biomass is a key indicator for quantifying carbon cycling in blue-carbon ecosystems, yet conventional approaches face significant challenges. To improve large-scale mangrove biomass assessment and provide a baseline for targeted conservation, present study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys, and applies it to Chonburi, Thailand. In 2023, total mangrove aboveground biomass in Chonburi Province was 145.24 kt, with a mean AGB density of 101.61 Mg/ha, slightly below the global mangrove average. Long-term records reveal an initial decline followed by post-2015 recovery to about 85% of the 1996 level. Relative to the conventional plot-satellite model, the AGB T/F~U~S framework substantially improves estimation performance and reduces prediction error (ΔR²≈0.47; ΔRMSE ≈ 66.03 Mg/ha), and remains robust under limited training data, with accuracy gains saturating once plot numbers exceed a moderate threshold. These results demonstrate that multi-scale upscaling provides a transferable pathway for mangrove biomass mapping in data-scarce regions and offers a practical baseline for blue-carbon accounting and targeted restoration planning.
Why it matches plant phenotyping methodsUAV-SfM、SAR、MSIと現地調査を統合し、マングローブの地上部バイオマスを推定するマルチスケール手法を開発・比較評価しており、植物形質の取得・推定が研究の中心です。
abstractpresent study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys
Cassava leaf disease detection plays a critical role in safeguarding crop yield and supporting sustainable farming practices in regions where cassava is a primary food source. The task remains challenging due to visually similar disease symptoms, variability in lesion shapes and colors, inconsistent lighting conditions in field images, and overlapping infections. Existing deep learning and hybrid vision models, although effective in controlled environments, often suffer from high computational demands and limited capability to jointly capture fine lesion textures and long-range spatial relationships. To address these limitations, this research introduces Lite-RGA-GTNet, a lightweight residual graph-augmented graph-transformer network with spectral-directional preprocessing and progressive token pruning. The approach integrates RGB data with directional gradient and vegetation index maps, employs residual graph reasoning before attention layers, and fuses local-global features through hierarchical graph-transformer modules to produce compact yet context-rich representations. Experimental evaluation on a benchmark cassava leaf image dataset, consisting of five classes including healthy and diseased samples, demonstrates that Lite-RGA-GTNet achieves 96.84% accuracy, 96.25% precision, 96.72% recall, and 96.48% F1-score, surpassing existing models such as CassNet and LeafXFormer by up to 2.65% in accuracy, while maintaining an average inference time of 14 ms-indicating its suitability for real-time agricultural deployment.
Why it matches plant phenotyping methodsカッサバ葉の画像から病徴・病害状態を推定する深層学習手法を開発し、ベンチマークデータセットで性能評価しているため、植物表現型取得・推定が中心である。
abstractExperimental evaluation on a benchmark cassava leaf image dataset, consisting of five classes including healthy and diseased samples, demonstrates that Lite-RGA-GTNet achieves 96.84% accuracy
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Monitoring wheat growth, as one of the most important food grain sources for human nutrition, and forecasting yields are done through different phenological phases. Reliable estimates on yields play a crucial role in securing sufficient food supplies for the world's growing population. Currently, farmers estimate a wheat yield during the later stages of growth and are often biased in this process. Plant breeding scientists use a more accurate approach that collects data on the number of wheat ears manually counted at various locations throughout the field. A sufficiently precise count of wheat ears is one of the most important parameters for reliable early-stage prediction of wheat yield. To support the development of an affordable and trustworthy automated wheat ear detection approach, this work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties. Additionally, it evaluates six deep learning models for wheat ear detection. Among the F-RCNN-based models, RetinaNet, YOLOv8, and a Vision Transformer-based detector, RT-DETR, achieved the highest mean Average Precision (mAP@50) of 91%, with significantly higher computational complexity. BioS-Wheat complements Global Wheat Head Detection datasets, introducing a meaningful shift in data complexity with high sowing density and minimal row spacing, emphasizing the impact of agronomic diversity on model performance by an increased object occlusion and dense spatial arrangements. Enriched and agronomically diverse datasets support model robustness at different varieties, growth stages, and locations. This work offers a good baseline for establishing the procedure for image crowdsourcing, further dataset expansions, and model improvements.
Why it matches plant phenotyping methods小麦穂の画像検出による個体群形質推定を対象とし、注釈付きデータセットの構築と複数モデルの評価が中心であるため、表現型計測手法として収載する。
abstractthis work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties.
Agriculture is a cornerstone of the economies of many countries, and wheat is a staple cereal crop that sustains nearly half of the worldwide population. However, production of wheat is highly vulnerable to biotic stress such as pathogens and pests, as well as adverse environmental conditions. These factors significantly affect yield and quality, posing critical threats to food security and economic resilience. Conventional disease detection methods often involve intense human labor, prolonged procedures, and are predisposed to subjectivity. Therefore, the development of an automated, accurate, and real-time disease monitoring system is imperative for modern precision agriculture. We propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery. The framework leverages the pre-trained VGG-16 convolutional neural network for deep feature extraction via a transfer learning approach. These features are subsequently classified using Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), XGBoost, and Bernoulli Naïve Bayes (BNB) algorithms of machine learning. The model is trained and evaluated on a custom-curated dataset, containing wheat diseases: stripe rust, powdery mildew, scab (Fusarium head blight), and yellow dwarf. Evaluation of experiments demonstrates that the classification performance is enhanced significantly through our hybrid approach, with accuracy ranging from 74 to 97%, precision from 73 to 96%, and recall from 73 to 95.7%. The SVM-based variant of the model achieved the highest performance, yielding 96% precision, 95.7% recall, 96% F1-score, and 97% accuracy. The proposed two-phase fine-tuned system demonstrates its effectiveness and efficiency in detecting multiple wheat diseases. The MDDM-WD model offers a resource-efficient and scalable approach for early disease detection, supporting informed decision-making for farmers, agronomists, and policymakers in advancing sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像からコムギの病徴・病害状態を直接推定する深層学習フレームワークを開発・評価しており、植物表現型取得法が中心です。
abstractWe propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery.
Reproduction assets foundThe paper's wheat disease image analysis is based on two public datasets (DAE-Mask GitHub dataset and a Kaggle multi-class crop disease image dataset), explicitly declared as open-source and publicly available in the Data Availability statement. No author code or trained model is released.Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://github.com/YcZhangSing/Dataset-of-DAE-Maskhtml-lines:699-732Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-imageshtml-lines:699-732Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Effective pest and disease detection plays a crucial role in minimizing crop losses and improving decision-making in precision agriculture. Among the most destructive pests affecting maize crops globally is the Fall Army Worm (FAW), known for its rapid spread and high impact on yield. Existing detection practices often rely on manual scouting, which can be inefficient, labour intensive and prone to human error. This study proposes a novel deep learning based framework for the automatic classification of FAW infested and healthy maize crops by integrating RGB and thermal image modalities. The core objective is to enhance detection accuracy through multimodal image fusion. A hybrid DNN-ViT model is introduced, combining two complimentary pipelines: (i) feature-level fusion, where CNN extracted features from RGB and thermal images are fused and classified using a Deep Neural Network (DNN) and (ii) image-level fusion, where a 6 channel RGB-thermal image is directly processed using a modified Vision Transformer (ViT). Experimental results demonstrate that the fused model achieved superior performance with an accuracy of 0.98, precision, recall and F1-score of 0.98 and AUC-ROC of 0.98 on the test set, outperforming models trained on RGB-only, thermal-only and unfused data. The ablation study confirms the effectiveness of multimodal fusion, with the no-fusion model showing significantly lower performance (accuracy-0.60 and AUC-ROC-0.67). This work highlights the benefits of integrating complementary data sources for robust crop health monitoring. Future research will explore enhanced fusion strategies, environmental robustness and field level deployment to validate the model's practical applicability.
Why it matches plant phenotyping methodsRGB・熱画像融合によるFAW被害・健全状態の画像判定モデルを開発し、融合方式や性能を比較検証しているため、植物の健康状態を取得する方法が中心である。
abstractThis study proposes a novel deep learning based framework for the automatic classification of FAW infested and healthy maize crops by integrating RGB and thermal image modalities.
Reproduction assets foundThe paper's paired RGB/thermal maize FAW image dataset is publicly deposited on Figshare (part of a peer-reviewed data publication), and the authors' custom Python analysis code is released as a public supplementary file (Supplementary Code.zip) with explicit availability language. The Figshare URL matches an allowed, Dataset · publicThe dataset has been made publicly available in the Figshare Data repository as a part of a peer reviewed data publication54. Detailed information on data acquisition, sensor specifications, environmental conditions and annotation protocols is provided in the associated data article. The dataset can be accessed at: https://figshare.com/s/677d2384ba6e02db9230 (10.6084/m9.figshare.28388018).Open asset ↗Figshare · 10.6084/m9.figshare.28388018html-lines:324-345Code · publicThe custom python code developed for this study is available as supplementary file (“Supplementary Code.zip”) and includes all scripts necessary to reproduce the multimodal feature fusion, image-level fusion and ablation experiments described in the manuscript. The dataset used is publicly available on Figshare. All dependencies are listed within the code file. Readers can execute the python script to reproduce the reported results.Open asset ↗html-lines:324-345Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly available datasets, the PlantVillage Potato Leaf Disease (2,152 images) dataset and the Kaggle Mango Leaf Disease dataset (4,000 images). Images were pre-processed, augmented, and split into training and testing datasets (80:20), to enable better model generalization. Four deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, Residual Networks (ResNet), and EfficientNet, were evaluated in the context of multi-class disease classification. The baseline CNN achieved a training accuracy of 93.67% and a testing accuracy of 92.61%, with balanced precision and recall (92.5%), thus providing a very strong feature extraction and classification capability. AlexNet showed moderate performance (91.3% training, 90.2% validation), and a very small overfitting was observed. ResNet had an efficient convergence, and attained 96.7% validation accuracy in just a few epochs, thus pointing out the advantage of residual connections in the context of deeper learning. EfficientNet surpassed all the other architectures, since it reached a training accuracy of 98.2% and a validation accuracy of 97.8%, with very small loss (≈ 0.015) and no overfitting, thus proving to have the best generalization ability. The models demonstrated stability and discriminative ability with the support of confusion matrices and accuracy and loss plots produced on an epoch-wise basis. Therefore, the findings indicate that DL models can be adapted for real-time and accurate plant disease diagnosis, establishing a pathway for early remediation, and supporting precision agriculture. The research establishes the opportunity for EfficientNet to be considered a promising solution for scalable smart farming.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法を開発・比較し、複数データセットで精度を検証しており、植物の病害状態の取得・推定が研究の中心である。
abstractdeep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (PlantVillage potato, mango leaf disease) and states that all code, preprocessing scripts, dataset splits, and model artifacts are publicly available in a GitHub repository (also archived on Zenodo). All three are paper-specific, public, and actionable.Dataset · publicThe datasets analyzed during the current study are available in (https://www.kaggle.com/datasets/aarishasifkhan/plantvillage-potato-disease-dataset)Open asset ↗html-lines:473-503Code · publicAll code, preprocessing scripts, dataset splits, and model artifacts used in this study are publicly available in the GitHub repository at: [https://github.com/logeswarig/PROJECT_1].Open asset ↗logeswarig/PROJECT_1html-lines:473-503Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出する深層学習手法の開発と、独立データセットによる性能・汎化性検証が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractthis study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection.
Reproduction assets foundThe paper's rice leaf disease detection dataset was curated from three publicly available repositories (one Kaggle, two Roboflow), and the cross-species generalization used two additional public Roboflow datasets (tomato and potato leaf diseases). All five URLs are explicitly listed in the article as data sources. No作者Dataset · publicData Sources: The dataset utilized in this study was curated and screened from the following publicly available online repositories:.Open asset ↗html-lines:110-216Dataset · publicThe Tomato Leaf and Potato Leaf disease datasets were acquired from public domain resources. The dataset links are: Tomato Leaf Diseases: https://universe.roboflow.com/dyploma/tomato-leaf-diseases-4xa5iOpen asset ↗dyploma/tomato-leaf-diseases-4xa5ihtml-lines:747-783Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
WheatAerial / UAVMultispectral / hyperspectralGrowth / development / phenologyLeaf traits
Wheat growth monitoring plays a vital role in agricultural decision-making and food security. This study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques. Based on preprocessed Sentinel-2 satellite images and measured wheat leaf area index (LAI) data, a set of 11 vegetation indices-such as NDVI, NDRE, and RVI-were selected and ranked through Pearson correlation analysis. A comprehensive index system was then constructed by selecting the top eight indices using a stepwise optimization approach. Three machine learning models-Linear Regression (LR), Backpropagation Neural Network (BPNN), and XGBoost-were applied to evaluate the performance of the index system, with the Particle Swarm Optimization (PSO) algorithm employed to optimize each model. The results demonstrate that the PSO-optimized XGBoost model achieved the highest accuracy (R² = 0.94, MSE = 0.075), exhibiting strong stability and robustness to data fluctuations. These findings suggest that the proposed approach provides a reliable solution for wheat growth monitoring.
Why it matches plant phenotyping methods衛星リモートセンシングと機械学習により小麦のLAIを推定する監視手法の開発・評価が研究の中心であり、植物キャノピー形質の取得方法を扱っている。
abstractThis study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques.
Sustainable precision agriculture has become increasingly vital for enhancing crop productivity, minimizing environmental impact, and ensuring global food security. Potato leaf diseases, such as blight, pose significant threats to crop yield. The accurate and timely detection of potato leaf diseases is critical for minimizing yield losses. This study proposes a pair a lightweight MobileNetV3 classifier with a MapReduce-style data pipeline that parallelizes preprocessing and batch inference across nodes. The model utilizes a dataset comprising 2152 images categorized into three classes. The preprocessing pipeline includes image resizing, normalization, and data augmentation to enhance model generalization. MobileNetV3 is employed for high-level feature extraction and classification, while MapReduce enables parallel processing and efficient handling of large datasets. The experimental results achieved a detection accuracy of 98.6% across the training phase, 96.9% in the validation phase, and 96.8% in the testing phase, and testing sensitivity (95.3%), Specificity (97.7%), and F1-Score (96.4%) While training for this dataset is performed on GPU, the MapReduce pipeline makes the system horizontally extensible for larger deployments and continuous image ingest. We report per-class confusion matrices and standard clinical metrics, and analyze when MapReduce provides throughput gains versus a single-node baseline. The proposed model significantly outperforms several state-of-the-art methods, as validated through statistical measures such as sensitivity, specificity, and misclassification rate. Its high accuracy, scalability, and robustness make it suitable for large-scale agricultural disease monitoring and precision farming applications.
Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から推定する分類手法と、MapReduceによる推論パイプラインを中心に開発・評価しており、植物フェノタイピング手法として適格。
abstractThis study proposes a pair a lightweight MobileNetV3 classifier with a MapReduce-style data pipeline that parallelizes preprocessing and batch inference across nodes.
TomatoGreenhouseMultimodalRGB / grayscaleMultispectral / hyperspectralFruitClassificationSegmentationGrowth / development / phenology
Computer vision and multispectral imaging have increasingly become essential tools in modern precision agriculture. Accurate ripeness assessment is critical for yield optimization, reducing post-harvest losses, and enabling automated harvesting systems. However, traditional RGB-based approaches struggle to differentiate subtle maturity changes, and existing solutions often fail under varying lighting, occlusion, or cultivar-specific conditions. To address these challenges, this study focuses on the integration of complementary spectral cues for reliable tomato ripeness evaluation. The work utilizes a curated RGB-NIR tomato dataset comprising 224 hyperspectral samples, processed into aligned multimodal image pairs with balanced ripeness categories.The proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues. The novelty of the methodology lies in the dynamic cross-attention mechanism, which learns inter-modal dependencies between RGB and NIR signals for enhanced ripeness interpretation. Performance metrics including accuracy, precision, recall, F1-score, mIoU, and AUC were used to comprehensively evaluate the system. Experimental results demonstrate that TomatoRipen-MMT significantly outperforms all baseline RGB-only, NIR-only, and fusion methods, achieving 94.8% classification accuracy and 82.6% mIoU. These findings establish the effectiveness of multimodal Transformers for robust, high-precision fruit maturity assessment in controlled and greenhouse environments.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を、RGB・NIR画像融合とTransformerで推定する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThe proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues.
Reproduction assets foundThe paper's phenotyping analysis is built on a publicly available USDA/NAL hyperspectral tomato dataset, explicitly linked in the Data Availability statement with an exact URL match. No author code or model checkpoints are disclosed.Dataset · publicThe dataset analyzed in this study is publicly available at the https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_b_Hyperspectral_Imaging_Analysis_for_Early_Detection_of_Tomato_Bacterial_Leaf_Spot_Disease_b_/26046328.Open asset ↗26046328html-lines:1038-1053Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Timely detection of crop diseases in large, heterogeneous agricultural fields is difficult, as aerial imagery is often corrupted by illumination, weather, and crop-stage variations. This paper introduces AgroVisionNet, an AI-powered drone and computer vision approach that synthesises high-resolution drone imagery with in-field IoT/environmental sensor data to enhance early disease detection. The core of the proposed model is a hybrid CNN-Transformer backbone to extract spatial and contextual data from drone images, and an adaptive fusion layer to fuse time-aligned sensor readings and to make a decision, using visual and environmental evidence. Particularly, a multimodal drone–sensor dataset is collected across multiple crops and field conditions. Beyond widely used deep models for plant/crop disease identification, such as VGG16, ResNet50, Inception V3, and DenseNet121, experiments are conducted using the same training and evaluation framework. It is shown that AgroVisionNet achieves higher classification accuracy and F1-score, while inference remains feasible on an NVIDIA Jetson Nano using TensorFlow Lite. Moreover, by generating Grad-CAM plots, the study demonstrates that the proposed approach identifies disease-affected areas and, in this sense, provides interpretable information required by agronomists. These outcomes suggest that AI-based crop health tracking can be robust and field-ready by integrating drone imagery, sensor fusion, and edge computing.
Why it matches plant phenotyping methods植物の病害状態をドローン画像とセンサーから推定する手法を開発し、データセット収集、比較評価、エッジ実装、可視化まで行っており、病害フェノタイピング手法が中心である。
abstractThis paper introduces AgroVisionNet, an AI-powered drone and computer vision approach that synthesises high-resolution drone imagery with in-field IoT/environmental sensor data to enhance early disease detection.
Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350-2500 nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700-2200 nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-related biochemical variation. Compared to conventional destructive methods, NIRS provides a non-invasive, rapid, and scalable alternative for nitrogen assessment in field conditions. This is the first study to demonstrate NIRS-based discrimination of nitrogen levels across multiple rice genotypes, offering new avenues for genotype-informed fertilization strategies and improved nitrogen use efficiency (NUE). The results support the objectives of the Green Campus Initiative at the Alliance Bioversity International & CIAT and contribute to broader Sustainable Development Goals (SDGs 2, 12, 13, and 15), promoting data-driven, environmentally responsible nutrient management in rice production.
Why it matches plant phenotyping methodsNIRSとケモメトリクスによりイネ葉窒素含量を非破壊推定・分類する手法が研究の中心であり、植物形質の取得と技術性能を評価している。
abstractThis study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes
Abstract The development of accurate and efficient plant disease classification systems is vital for addressing the challenges of climate change and the growing global demand for food. This study presents $$\hbox {V}^2$$ PlantNet, a novel lightweight multi-class classification model based on a modified MobileNet architecture, designed to detect plant leaf diseases across a diverse range of crop types. $$\hbox {V}^2$$ PlantNet employs depthwise separable convolutions to significantly reduce model complexity without compromising accuracy. The architecture integrates Batch Normalization (BN) and Rectified Linear Unit (ReLU) activation after each convolutional layer, while a multi-stage design enhances feature extraction and overall performance. Despite its compact size, comprising only 389,286 parameters and requiring just 1.46 MB of memory, $$\hbox {V}^2$$ PlantNet achieved up to 99% training accuracy, with validation and test accuracies of 97% and 98%, respectively. Across most classes, precision, recall, and F1-scores ranged from 0.97 to 1.0, demonstrating consistent and robust generalization across diverse plant species. These architectural innovations enable $$\hbox {V}^2$$ PlantNet to outperform larger models such as ResNet-50 and Inception V3 in terms of computational efficiency, owing to its smaller model size (1.46 MB), reduced parameter count (389,286), and faster inference time (0.676 s), offering a scalable solution for real-time plant disease detection in precision agriculture.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する軽量な深層学習手法を開発し、精度・計算量・推論速度を比較評価しており、フェノタイピング手法が中心である。
abstractThis study presents $$\hbox {V}^2$$ PlantNet, a novel lightweight multi-class classification model based on a modified MobileNet architecture, designed to detect plant leaf diseases across a diverse range of crop types.
Reproduction assets foundThe paper's Data Availability statement explicitly links the PlantVillage dataset used to train and evaluate PlantNet, hosted publicly on Kaggle. No author code or trained model checkpoint is deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Kaggle repository (see link to dataset: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset).Open asset ↗Kaggle · plantvillage-datasethtml-lines:1649-1677Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Grape cluster compactness is a key trait that influences fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years, with marker effects ranging from 7.6% to 22.1%. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.
Why it matches plant phenotyping methodsSAMを用いた画像解析パイプラインで個々の果粒を分割し、サイズ・形状と房のコンパクトネスを算出する方法が、研究の主要な技術的要素として明示されている。
abstractAdditionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention.
Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts producing slow processes and human errors. Automated detection methods establish low accuracies, limited scalability and real time applications. To tackle the research issue, this study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms into a single action to achieve ultimately real time detection of accurate diseases the combination helps identify both the high detection speed of YOLOv5 along with Faster-RCNN regional proposal accuracy. The new method is that the compilation of these two modern object detection methods has been compiled and designed specifically for detecting leaf disease across varying environmental conditions. Notable contributions to this method include increases in classification accuracy, processing speed, real time detection making these methods suitable for farmers agronomists and sensor deployment. CLD-Net integrates YOLOv5 and Faster R-CNN, combining real-time detection capability with precise classification, to deliver robust cotton leaf disease identification. Experimental validation on a curated dataset of cotton leaf images demonstrates the superiority of CLD-Net, achieving an accuracy of 96.7%, which surpasses that of traditional models. These results confirm the potential of the proposed approach to revolutionize crop disease detection, leading to timely intervention and increased yield.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から推定する深層学習手法を開発し、画像データセットで性能検証しており、植物フェノタイピング手法が中心です。
abstractthis study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle cotton leaf disease image dataset used for the CLD-Net experiments; no author code or trained model deposit with a public URL is provided despite a mention of 'reproducible code and trained models'.Dataset · publicThe data used in this research are available in the following links: https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset.Open asset ↗Kaggle · seroshkarim/cotton-leaf-disease-datasethtml-lines:541-573Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Early and accurate detection of sugarcane leaf diseases is critical for improving crop productivity and reducing economic losses in the agricultural sector. Timely interventions enable sustainable crop management and better resource use. In this study, we propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN). The MADRN model integrates dense residual learning and multi-scale attention mechanisms to effectively capture fine-grained, disease-specific features and address challenges related to domain variability and complex data patterns. Two datasets are used to evaluate the model: a Kaggle dataset and a blended dataset created by combining Kaggle images with those from the Bangladesh Sugarcrop Research Institute (BSRI), simulating real-world conditions. All images undergo preprocessing steps, including resizing, normalization, and data augmentation, before training. Additionally, several baseline models (CNN, VGG16, MobileNetV2, and XceptionNet) are fine-tuned and compared with the MADRN model. Experimental results demonstrate that MADRN consistently outperforms baseline models in accuracy, precision, recall, and F1-score across both datasets, achieving up to 94.78% accuracy on the Kaggle dataset and 92.25% on the blended dataset. These findings highlight MADRN's superior ability to learn discriminative features and generalize effectively across diverse data sources, making it a promising tool for precision agriculture and disease management. To facilitate practical implementation, a web-based application is developed, enabling real-time and user-friendly disease detection. This research lays a strong foundation for the development of accurate, scalable, and practical disease classification tools that can support sustainable agricultural practices.
Why it matches plant phenotyping methodsサトウキビ葉の画像から病害状態を推定する深層学習手法を開発し、複数データセットとベースラインで比較検証しているため、植物フェノタイピング手法が中心です。
abstractwe propose a deep learning-based approach for sugarcane leaf disease classification that leverages a novel architecture, the Multi-scale Attention-based Dense Residual Network (MADRN).
Reproduction assets foundThe paper's Kaggle sugarcane leaf disease image dataset (2521 images, five classes) is a public, paper-specific phenotyping image asset with an explicit URL in the Data Availability statement. The BSRI field images are only available upon request, and no author analysis code or trained model is deposited.Dataset · publicg and preparation.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The data supporting the findings of this study are publicly available and can be accessed through the following sources. Kaggle Sugarcane Leaf Disease Dataset [https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset](https:/www.kaggle.com/datasets/nirmalsankalana/sugarcane-leaf-disease-dataset) (accessed Jan. 15, 2025). Bangladesh Sugarcrop Research Institute (BSRI): Data available upon request. For BSRI data inquiries, please contact the corresponding author, Dr. Md. Shamim Reza.
Declarations
CompetinOpen asset ↗Kagglelines:278-299Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model's architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.
Why it matches plant phenotyping methods植物ストレス状態を土壌水分とクロロフィルから推定するSAM-L・XAI・LSTM統合手法が研究の中心であり、植物の生理状態を対象とした計算的フェノタイピング手法に該当する。
abstractIn this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content.
Reproduction assets foundThe paper states its plant-stress phenotyping data came from a publicly available Kaggle dataset ('Real-Time Plant Health Insights: Simulated Biosensor Data for AI-Driven Monitoring'), used directly for the SAM-L/XAI stress-prediction experiments. Code is only available on request, so it does not qualify as a public,作者Dataset · publicThe dataset used in this study was sourced from Kaggle and was publicly available.Open asset ↗Kagglepdf-page:16 lines:1-70Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniques to enhance classification accuracy. Real-time leaf images are collected from various agricultural environments to capture a wide range of conditions. To improve image quality and segmentation precision, the Contextual Image Enhancement Wiener Filter (CIEWF) is employed for effective noise reduction. Data augmentation is performed using a Generative Adversarial Network (GAN), increasing dataset diversity and improving model generalization. A novel Region of Interest-based Multi-Dimensional Attention Network (ROI-MDAN) is developed to identify and segment critical disease-affected areas within the leaves. For robust feature extraction, the MSFNet-CAM model is proposed, leveraging parallel multi-scale features and incorporating Coordinate Attention to enhance feature fusion and improve classification performance. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to interpret the model's decision-making process by highlighting the influential regions contributing to disease classification. Experimental results validate the effectiveness of the proposed approach, setting a new benchmark for AI-assisted plant disease diagnosis.
Why it matches plant phenotyping methods葉画像から病徴部位を分割・分類し、植物病害状態を推定する画像解析手法の開発が中心であるため、植物フェノタイピング手法として含める。
abstractA novel Region of Interest-based Multi-Dimensional Attention Network (ROI-MDAN) is developed to identify and segment critical disease-affected areas within the leaves.
The prevalence of diseases in wheat crops poses a significant threat to global food security, as it reduces yield and quality. Addressing these challenges is critical for sustainable agriculture. This study proposes and evaluates a hybrid deep learning (DL) model, EffiXB3, which combines Xception and EfficientNetB3 architectures, enhanced with edge-aware features, to improve disease classification in wheat crops. EffiXB3 employs a dual-input stream architecture, where one stream processes structural features, while the other incorporates textural features through Canny edge detection. The performance of individual models, Xception and EfficientNetB3, was assessed alongside the hybrid EffiXB3 model in a multi-class classification task involving five wheat leaf categories: Blast, Brown Rust, Healthy, Leaf Blight, and Septoria. Xception and EfficientNetB3 achieved classification accuracies of 95% and 93%, respectively. The proposed EffiXB3 model outperformed both, achieving an accuracy of 98.5%. The integration of edge-aware features substantially improved robustness and classification performance, particularly in differentiating visually similar disease patterns. The findings demonstrate the effectiveness of hybrid DL models with edge feature integration in diagnosing agricultural diseases. EffiXB3 offers a promising approach for enhancing disease detection in wheat, contributing to improved crop management and food security.
Why it matches plant phenotyping methodsコムギ葉の病徴を画像から分類する深層学習手法を開発・評価しており、植物の疾病状態推定が中心的な方法論的貢献である。
abstractThis study proposes and evaluates a hybrid deep learning (DL) model, EffiXB3, which combines Xception and EfficientNetB3 architectures, enhanced with edge-aware features, to improve disease classification in wheat crops.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Crop leaf disease detection plays a crucial role in ensuring healthy crop growth and improving food security. Disease features are often small and have blurry edges, while background interference is strong, making precise detection a significant challenge. Although YOLO-based methods perform well in object detection, they still struggle to effectively handle the extraction of lesion details and background noise interference when applied to crop leaf disease detection. To address these challenges, this study introduces an innovative crop leaf disease detection approach built upon the YOLO, named AG-HAF. This method proposed two modules, the additive gated convolutional unit (AGCU), which introduces a gating mechanism to dynamically adjust the importance of features, enhancing the detection of small and blurry lesions, improving nonlinear feature modeling, and suppressing irrelevant background interference. Hierarchical Attention Fusion Module (HACFM), which utilizes a hierarchical attention mechanism to optimize the fusion of multi-scale features, enhancing the representation and semantic information of disease regions, and further improving the model's adaptability to complex backgrounds. Ablation and comparative experiments show that AG-HAF outperforms existing methods across various metrics, particularly excelling in disease detecting in complex backgrounds and small lesions, demonstrating significant potential for practical applications.
Why it matches plant phenotyping methods植物葉の病斑を画像から検出・推定する手法を新規開発し、比較実験とアブレーションで性能検証しているため、植物病害状態の画像ベース表現型計測が中心である。
abstractTo address these challenges, this study introduces an innovative crop leaf disease detection approach built upon the YOLO, named AG-HAF.
Seed germination is a critical phase strongly affected by abiotic stresses including drought and artificial seed ageing. Traditional indices like Germination Percentage (GP) and Mean Germination Time (MGT) often fail to capture complex stress responses and priming efficacy. This study introduces eight novel indices that quantitatively measure distinct physiological mechanisms: The Priming Efficiency Index (SPEI), Stress Performance Stability Index (SPSI), Germination Recovery Ratio (SGRR), and Combined Vigor Index (SCVI), among others. Tested on wheat under drought stress and priming treatments, the indices demonstrated 34.2% improvement in germination recovery with gibberellin priming compared to 25.8% with hydro-priming. The SCVI showed a 20.7% enhancement in integrated seedling performance, while SGRR achieved complete stress recovery (1.004) with gibberellin treatment. Validation across triticale and pumpkin revealed consistent performance, with cross-species correlations exceeding 0.89. Statistical analyses confirmed the novel indices' superior discriminatory power, requiring 37.6% smaller sample sizes than traditional metrics while maintaining 94% rank stability under data perturbations. These indices provide robust, mechanistically informed tools for precision phenotyping in breeding programs and seed technology research.
Why it matches plant phenotyping methods発芽・幼植物性能を定量化する新規指標を開発し、複数作物で性能検証しており、表現型測定法が研究の中心である。
abstractThis study introduces eight novel indices that quantitatively measure distinct physiological mechanisms
Agricultural productivity remains seriously threatened by the attacks of plant diseases, even though it is the bedrock of global food security. These diseases, if ignored, can lead to massive crop losses and economic setbacks. Therefore, the development of accurate and efficient plant disease detection systems is essential to preserve plants and promote sustainable agricultural practices. This study examined the potential of ensembled deep learning models that incorporates feature-level fusion to enhance the robustness and accuracy of plant disease detection. Specifically, we utilized pre-trained deep learning architecture (VGG16, Residual Network 50 (ResNet50), and GoogleNet (InceptionV3)) to extract distinctive features representations from plant leaf images. These extracted features were combined to enhance the performance of our ensemble model. For disease classification, the fused features were passed through a Dense layer with 128 units and ReLU activation, followed by a SoftMax classification layer to predict the probabilities of each plant disease class. Experiments were conducted using the New Plant Diseases Dataset. It contains 87,867 image samples of various plant disease species for 38 classes and 14 different crop species. The ensemble model achieved notable results, with an accuracy of 97.0%. This reveals the capability of feature-fusion ensembled learning in improving detection stability and accuracy. The knowledge and application of feature fusion in disease detection can help ensure more accurate, timely, and eco-friendly interventions. This, in turn, can support sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習分類法の開発・評価が研究の中心であり、植物表現型（病害状態）の取得手法に該当する。
abstractThis study examined the potential of ensembled deep learning models that incorporates feature-level fusion to enhance the robustness and accuracy of plant disease detection.
Vegetable pea (Pisum sativum L.) is a nutritionally rich food source with a balanced profile of macronutrients and micronutrients, contributing multiple health benefits and plays a crucial role in combating nutritional deficiencies. Its nutritional diversity encompassing high range of protein, starch, soluble sugars, and phenolic content, renders it an ideal candidate for nutritional profiling, which is essential for mining Nutri-dense accessions. Near-infrared reflectance spectroscopy (NIRS) is a valuable alternative to conventional methods for nutritional profiling, offering rapid, accurate, less laborious, cost-effective, and non-destructive analysis with the capability to measure multiple parameters simultaneously for large-scale germplasms. This investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid with spectral pre-processing done by standard normal variate (SNV) and detrending (DT) using 90 vegetable pea (both marketable and mature stages) dried seed flour. The best-performing models were developed for moisture content (0.938, 0.469, 3.989), protein (0.931, 0.709, 3.063), starch (0.814, 1.312, 2.317), amylose (0.847, 0.646, 2.556), TDF (0.932, 0.652, 3.473), phenol (0.925, 0.078, 3.538), TSS (0.918, 0.231, 3.494), and phytic acid (0.898, 0.095, 2.358) corresponding to coefficient of determination (RSQ), corrected standard error of prediction (SEP(C)), and ratio of performance to deviation (RPD), respectively. This study presents the first report on the development of NIRS based prediction models using MPLS method for multi-trait assessment across different developmental stages in diverse vegetable pea germplasm, exhibiting high-throughput capability of the models in an economical and precise way.
Why it matches plant phenotyping methodsNIRSとmPLSによる植物種子の栄養形質を非破壊・高スループットに推定する予測モデルを開発し、性能指標で評価しているため、植物形質取得法が中心です。
abstractThis investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid
Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges. Through two diverse and comprehensive image datasets, the model performs well for both fruit 13,602 images and leaf 11,199 images classification. OptiNet-B3 optimizes learning in low computational budget by integrating Mish activation, Convolutional Block Attention Module (CBAM), Group Normalization, and knowledge distillation. Great care in preprocessing and augmenting data was taken to improve generalization. Comparison with state-of-the-art models-including DenseNet121, ResNet50, MobileNetV3, and InceptionV3-based models-reveals that OptiNet-B3 substantially outperforms in terms of accuracy, with 98.12% and 99.23% on the fruit and leaf datasets, respectively. Due to its light-weight architecture, real-time deployment for in-field diagnosis on mobile and edge devices is much more feasible. The results underscore the potential of explainable, AI-driven tools in transforming plant disease management practices.
Why it matches plant phenotyping methods果実・葉の画像から植物病害を分類するモデルを開発し、複数データセットと既存モデルとの比較で性能検証しているため、植物状態の画像ベース表現型推定が中心です。
abstractThis paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges.
Domain adaptation in agricultural settings has traditionally focused on 2D imagery, leaving a significant gap in the robust application of 3D sensing technologies for plant monitoring and classification. In this paper, we propose an adversarial unsupervised domain adaptation framework for 3D point cloud classification in agriculture, addressing the domain shift between controlled (Crops3D) and real-world (Pheno4D) datasets. Our approach leverages a PointNet-based feature extractor, a domain discriminator trained with a Gradient Reversal Layer (GRL), and an entropy minimization objective to ensure confident predictions on the unlabeled target domain. Extensive experiments demonstrate that our method achieves a classification accuracy of 97% on the target domain, with strong per-class F1 scores, despite significant sensor and environmental differences between datasets. We also evaluate model performance in real-time scenarios and discuss deployment feasibility on edge devices. This work highlights the potential of 3D domain adaptation in precision agriculture and paves the way for more generalizable plant phenotyping models.
Why it matches plant phenotyping methods3D点群分類のための敵対的ドメイン適応フレームワークを開発・評価しており、植物モニタリングおよび植物フェノタイピングへの適用が中心である。
abstractwe propose an adversarial unsupervised domain adaptation framework for 3D point cloud classification in agriculture
Reproduction assets foundThe paper uses two public 3D plant point cloud datasets as its source (Crops3D) and target (Pheno4D) domains; both are explicitly described as publicly available. No author code, trained models, or repository URLs are provided in the supplied blocks.Dataset · publicThe Crops3D dataset was collected by researchers at the Leibniz Institute of Agricultural Engineering and
Bioeconomy (ATB Potsdam) and made publicly available for academic research under an open data license.Open asset ↗pdf-page:5 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Existing disease discovery in papaya leaves is most significant in achieving yield and profitability stability in the tropics but has proven difficult in the presence of deficiencies in manual exploration and tailored crop models in crop-AI systems. Therefore, this study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves. For real-world deployment in scarce-resource farming contexts, PapayaNet adopts batch norm and hierarchical attention steps in five convolution stages and accelerates both computational celerity and discriminability. Trained on 6618 manually annotated orchard images sourced from orchards in Bangladesh at a very high resolution, it has a 98.79% classification accuracy, all of which was realized using 483,926 parameters and an average infer time of 0.01 s, which is significantly better when evaluated using EfficientNetB6, DenseNet121, and VGG16. XAI methods, including Grad-CAM and LIME, showed model decisions towards the biologically informative parts of the leaf, thus boosting interpretability and user confidence. Systematic ablation analysis also confirmed the importance of distributed attention in ensuring robust generalization towards visually similar disease classes. An in-browser diagnostic portal deployed using Gradio provides intra-browser predictive deployment and interpretability overlay in real time, thus inviting field practicability. Given its low-latency inference and minimal computational footprint, PapayaNet is well-suited for integration into edge devices and drone platforms, offering a scalable solution for real-time in-situ crop health monitoring. This study advances the field of precision agriculture by delivering a crop-specialized, explainable, and deployable AI system for sustainable management of papaya diseases.
Why it matches plant phenotyping methodsパパイヤ葉の病害・健全状態を画像から分類するCNN手法を開発し、データセット、比較評価、アブレーション、実運用ポータルまで中心的に扱っているため、植物病害フェノタイピング手法に該当する。
abstractthis study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves.
Reproduction assets foundThe paper's papaya leaf image dataset is publicly deposited on Mendeley Data with an explicit availability statement and authors' URL; no code or model checkpoint availability is stated.Dataset · publiccript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The dataset analysed of this study, titled ”Healthy and Unhealthy Papaya Leaf Images from Bangladeshi Orchards,” is publicly available in the Mendeley Data repository at ( https://data.mendeley.com/datasets/44p8v6ywsm/1 ).
Competing interests
The authors declare no competing interests.
References
1. Sandhu, G. K. & Kaur, R. Plant disease detection techniques: A review. In 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019 34–38 (2019). 10.1109/ICACTM.2019.8776827
2.
Ngugi LC Abelwahab M AboOpen asset ↗Mendeley Datalines:622-669Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In accordance with human life, agriculture has main role in it, and in addition to that most people are involved in some kind of agricultural activity either in a direct or indirect manner. Moreover, the agricultural sectors acquired a major role in supplying better quality food and thus made the greatest attribution to the growth of populations and economics. But, the disease over the crop has influenced the growth of the corresponding species and thus requires an earlier diagnosis of plant disease by utilizing the most adequate and automatic detection approach for improving the quality of the production of food as well as to reduce the loss in economic. But, there are no techniques in the conventional system for identifying the disease in diverse crops in the agricultural environment. In modern times, deep learning approaches have acquired tremendous enhancement in the identification of image categorization as well as the object detection system. For precise detection of plant disease, an improved classification model is developed. Initially, from the standard publicly available database, the images of the plants are aggregated. The gathered images are segmented using Dilated, Adaptive, and Attention-based Mask Recurrent Convolutional Neural Networks (DAA-MRCNN). Then, it is fed into a hybrid classification phase, where the new model namely Dilated, Adaptive, and Attention-based Multiscale DenseNet termed as (DAA-MDeNet) for classification. The classifier performance is improved by optimizing the parameter in Mask RCNN and Multiscale DenseNet using the hybrid optimization algorithm named African Vulture and Lemur Optimizer (AVLO). When compared with the other model, a superior performance is shown in the proposed model.
Why it matches plant phenotyping methods植物画像から病害をセグメンテーション・分類する深層学習手法を開発しており、罹病状態の推定が中心的な方法論的貢献である。
abstractFor precise detection of plant disease, an improved classification model is developed.
Reproduction assets foundThe paper uses the public PlantifyDr Kaggle dataset of plant disease images and provides the authors' implementation code on GitHub with explicit availability statements.Dataset · publica total of 12,500 images in it from 10 different plant types, where the 10 different types are considered as 10 individual datasets. (1) Apple, (2) Cherry, (3) Citrus, (4) Corn, (5) Grape, (6) Peach, (7) Pepper, (8) Potato, (9) Strawberry, and (10) Tomato. It contains a total of 37 as plant diseases. It was collected through “ https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset ”: “Access Date: 2023-08-09”.
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\setlength{\oddsidemargin}{-Open asset ↗lines:103-129Code · publicThis research did not receive any specific funding.
Data availability
In case of benchmark data: The data underlying this article are available in the dataset link as: https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset .
Code availability
The code for the implementation of the developed model is available at the link https://github.com/kalicharan8u/Plant-Disease-Detection-using-Mask-RCNN-with-Multiscale-DenseNet - and it has been given in Section " Simulation setup ".
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Ashourloo D Matkan AA Huete A Aghighi H Mobasheri MR
Developing an index for detection and identification of disease stages
IOpen asset ↗lines:1529-1559Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Pomegranate cultivation faces significant challenges due to fruit diseases that significantly impact crop yield and farmer income. Traditional methods for disease detection are often slow and prone to errors, delaying timely intervention. This paper proposes a deep learning-based system for automatic, multi-class disease classification in pomegranates using transfer learning. A dataset comprising 5099 annotated images was used to train and evaluate several CNN models, including DenseNet121, EfficientNetB0V2, MobileNetV2, ResNet50, VGG16, and InceptionV3. DenseNet121 emerged as the top performer, achieving an accuracy of 99.35%. To enhance practical value, a novel Healthy-Based Deviation Scoring (HBDS) method was developed to estimate disease severity using Grad-CAM ++ for lesion localization and Mahalanobis distance-based scoring, followed by Gaussian Mixture Model clustering. The severity predictions of the system were verified against manually labeled images, and the system has shown superior accuracy compared to pixel-based methods. Also, a recommendation module was integrated using a retrieval-augmented language model, which provides disease-specific treatment suggestions based on the predicted severity. The complete pipeline is implemented as a user-friendly web application that delivers real-time diagnosis, severity estimation, and actionable treatment plans, which offer a practical and scalable solution for modern precision agriculture.
Why it matches plant phenotyping methods画像から植物病害の症状・重症度を推定する手法を開発し、手動ラベルおよび既存法と検証しているため、植物フェノタイピング手法が中心です。
abstracta novel Healthy-Based Deviation Scoring (HBDS) method was developed to estimate disease severity using Grad-CAM ++ for lesion localization and Mahalanobis distance-based scoring
Cotton, as a globally important economic crop, requires early and accurate disease detection to ensure stable yield and promote sustainable development. However, due to the small size of certain leaf lesions, traditional detection methods often suffer from missed or false detections. To address this issue, we propose an improved YOLOv8-based model, CM-YOLO, aimed at enhancing the detection performance for small cotton leaf disease targets. Specifically, the SS2D module from VMamba is introduced into the backbone network to achieve comprehensive feature extraction through multi-directional scanning. Furthermore, the MSDA module is embedded prior to the SPPF module to reduce performance degradation caused by redundant computations and to enhance the model's focus on critical small targets. Finally, the original bounding box loss function is replaced with DIoU, enabling precise localization of small targets by optimizing anchor center point distances and accelerating model convergence. Experimental results demonstrate that CM-YOLO achieves superior performance in cotton leaf disease detection, with an mAP50 of 0.933 and a recall of 0.891. Compared with state-of-the-art methods, YOLOv8n and YOLOv11n achieve mAP50 values of 0.874 and 0.930, respectively, both lower than CM-YOLO, thereby validating the effectiveness of the proposed method. Additionally, generalization experiments indicate that the model maintains high detection accuracy and robustness across different plant datasets, highlighting its strong applicability in complex scenarios and providing a valuable reference for intelligent agricultural disease detection research.
Why it matches plant phenotyping methods綿花葉の病斑という植物の病害状態を画像から検出・局在化するYOLOベース手法を開発し、性能比較と汎化実験で検証しており、フェノタイピング手法が中心である。
abstractwe propose an improved YOLOv8-based model, CM-YOLO, aimed at enhancing the detection performance for small cotton leaf disease targets.
Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion. A robust environmental monitoring system continuously captured key factors including air and soil temperature, humidity, light intensity, CO 2 concentration, soil moisture, and soil electrical conductivity. These variables were fed into a Random Forest regression model enhanced with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) for interpretability. Results revealed that soil temperature (~ 21.8 °C), light intensity, and soil electrical conductivity were the most influential drivers of fruit expansion, each exhibiting distinct threshold behaviors, and the proposed IoT-XAI framework achieved R 2 = 0.82 with an MSE of 0.0046, confirming both predictive accuracy and interpretability. Our approach transforms raw sensor data into actionable insights for precision climate and fertigation management, supporting sustainable smart agriculture through interpretable machine learning.
Why it matches plant phenotyping methodsトマト果実の膨張という植物形質を対象に、IoTセンシングと機械学習・XAIによる推定および環境要因解析を中心的に行っているため、フェノタイピング手法の応用として含める。
abstractThis study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion.
To reduce losses from agriculture as well as enhance food security, we propose a three-stage deep ensemble for early citrus disease diagnosis from actual-field images of oranges (n = 2,240) as well as lemons (n = 208). To prevent leakage, augmentation is strictly enforced following splitting (70:30 stratified) following curation, normalisation, resizing by 224 × 224. Our contribution comes from combining state-of-the-art deep features by adding explicit texture priors. Namely, we add Local Binary Patterns (LBP) as well as Grey-Level Co-occurrence Matrix (GLCM) descriptors for micro-textures of lesions (e.g., stippling, scab rims around them, chlorosis encircled by veins) as well as second statistics (e.g., contrast, homogeneity, entropy) that CNNs/ViTs tend to discount by virtue of their small size coupled with variable-field data. These hand-crafted signals are z-score normalised as well as PCA-compressed for overfit protection as well as removal of collinearity then combined by deep embeddings. InceptionV3 (90% lemon) as well as DenseNet121 (93% orange) are the best of the five pretraining CNNs (ResNet50, DenseNet121, VGG16, InceptionV3, EfficientNetB0) that we test at Stage-1. The best CNNs are enlisted with a Vision Transformer (ViT) at Stage-2 for capture of long-range contextual capture improving upon Stage-1 by 98% (lemon) as well as 97% (orange). t-SNE confirms class separation while Stage-3 employs a multiclass SVM over the combined description that achieves 99% (lemon) while holding at 97% (orange) at another curation. The pipeline outperforms single-backbone variants, minimises variance while remaining lightweight enough for deployment, thus showing that LBP + GLCM texture priors compressed by PCA but combined by CNN/ViT features substantially enhance robustness plus generalisation for in-orchard citrus disease testing.
Why it matches plant phenotyping methods柑橘の実画像から病徴・病害状態を推定する深層学習画像解析パイプラインを開発・比較評価しており、植物フェノタイピング手法が中心である。
abstractwe propose a three-stage deep ensemble for early citrus disease diagnosis from actual-field images of oranges
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum , Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L⁻¹) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.
Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を高解像度で追跡・定量するドロplet型マイクロ流体プラットフォームが研究の中心であり、植物状態の取得・解析手法を開発・評価している。
abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Agricultural production is still quite susceptible to plant diseases, despite the fact that it is essential to both economic growth and food security. Yellow rust can lower wheat yields by 20–30%, red rust by 5–10%, and anthracnose by up to 60% in crops including cotton and mango. For losses to be minimized, early and precise detection is therefore crucial. Preprocessing, segmentation, feature extraction, and classification are all included in this study’s machine learning-based framework for detecting various crop leaf diseases. To test the model, 9,111 carefully chosen images that were balanced through augmentation were employed. The novelty of this work lies in combining bilateral filtering and GraphCut segmentation with texture-based feature extraction and a systematic comparison of multiclass SVM kernels across a multi-crop dataset. Experimental results using stratified 5-fold cross-validation show that the linear kernel SVM achieved the best performance, with 99.0% accuracy, 98.6% precision, 98.7% recall, and 98.6% F1-score–outperforming earlier SVM-based approaches. These findings demonstrate the effectiveness of kernel selection and preprocessing in enhancing disease classification and provide a strong basis for future comparisons with deep learning methods to build scalable and reliable plant disease detection systems.
Why it matches plant phenotyping methods植物葉の画像から病徴・病害状態を推定する画像解析手法が研究の中心であり、前処理、セグメンテーション、特徴抽出、SVM分類の比較と検証を行っているため含める。
abstractPreprocessing, segmentation, feature extraction, and classification are all included in this study’s machine learning-based framework for detecting various crop leaf diseases.
Fractional vegetation cover (FVC) is an important indicator of crop growth and a key parameter in vegetation modeling. Unmanned aerial vehicles (UAVs) equipped with RGB cameras offer a practical and cost-effective alternative to labor-intensive field surveys and multispectral imaging for FVC estimation. In this study, 18 segmentation methods, derived from the combination of six vegetation indices and three thresholding algorithms, were applied to UAV imagery of sugar beet fields during the 2022 growing season. The methods were validated using ground truth data collected from 30 plots across four growth stages. Results indicated that the Excess Green (ExG), Green Leaf Index (GLI), and Red-Green-Blue Vegetation Index (RGBVI), when combined with Otsu and Ridler-Calvard (RC) thresholding, generally provided the most accurate segmentation of vegetation cover. In particular, ExG with Otsu and RC achieved the highest accuracy (NRMSE = 5.1%, R² = 0.96), whereas ExGB with the Two-Peaks method showed the weakest performance (NRMSE = 42.3%, R² = 0.34). Statistical analyses confirmed that ExG-based approaches demonstrated stronger correlations with field measurements compared to other methods. These findings suggest that ExG in combination with Otsu or RC can be considered a promising option for UAV-based estimation of sugar beet vegetation cover, although further validation under different environmental and crop conditions is recommended.
Why it matches plant phenotyping methodsUAV画像の植物領域分割手法を開発・比較し、地上真値で検証してサトウダイコンの植生被覆率を推定しており、表現型取得手法が研究の中心です。
abstract18 segmentation methods, derived from the combination of six vegetation indices and three thresholding algorithms, were applied to UAV imagery of sugar beet fields
The integration of advanced tools and techniques has significantly boosted agricultural productivity. Wheat crops, which are vital for global food security, are often susceptible to various bacterial and viral diseases, considerably impacting both yield and quality. Efficient disease detection is crucial for effective treatment and yield optimization. This study presents an innovative approach that combines a multi-level contrast enhancement framework with a novel transformer-based architecture for the rapid and precise diagnosis of wheat crop leaf diseases. By employing contrast enhancement techniques, we enhance the visual quality of wheat crop images, facilitating improved feature extraction. Incorporating Vision Transformers (ViTs) enhances computational efficiency, enables multi-scale feature extraction, and reduces dimensionality. We implement and evaluate our proposed models on two publicly available wheat datasets, utilizing three variants of ViT: a modified ViT with seven-block transformers, a pre-trained ViT-16-Tiny, and a modified ViT with seven transformer blocks and skip connections. Our results demonstrate classification accuracies of 98.90% for the modified seven-block ViT, 97.50% for the ViT-16-Tiny model, and 97.90% for the ViT seven-block with skip connections. A comparative analysis with state-of-the-art techniques reveals that our proposed techniques outperform existing methods in terms of accuracy, precision, sensitivity, False Negative Rate, and the total number of learnable parameters.
Why it matches plant phenotyping methodsコムギ葉の病徴を画像から分類する画像処理・Vision Transformer手法の開発と評価が中心であり、植物の病害状態を直接推定するため、方法論文として含める。
abstractThis study presents an innovative approach that combines a multi-level contrast enhancement framework with a novel transformer-based architecture for the rapid and precise diagnosis of wheat crop leaf diseases.
Accurately identifying the growth stages of rice seedlings is crucial for managing factory nurseries and ensuring consistent seedling quality. This study introduces PGL-ShuffleNetV2, a lightweight and advanced model designed for efficient and accurate recognition of rice seedling growth stages. The proposed model achieves a streamlined architecture by: 1). removing the second 1 × 1 convolution in the downsampling block's right branch. 2) Reducing the repetition of basic units for improved efficiency. Additionally, the GELU activation function replaces ReLU to enhance nonlinear representation capabilities, and a parallel weighted hybrid attention module (PWMAM) is incorporated to improve feature extraction. Experimental results demonstrate that PGL-ShuffleNetV2 achieves a remarkable 98.80% recognition accuracy and a 98.82% F1 score, with a compact model size of just 0.84 MB. Its optimal balance between accuracy and parameter efficiency makes it highly suitable for deployment on resource-constrained devices, enabling effective monitoring and management of rice seedlings in factory nursery environments. Based on the advantages of this model, this study further applied it to rice seedlings under laser supplementary lighting conditions to investigate the impact of laser on the growth stages of seedlings, providing technical support for the application of laser technology in intelligent seedling cultivation.
Why it matches plant phenotyping methodsイネ幼苗の生育段階を画像等から認識する軽量モデルを開発し、精度・F1スコアを評価したうえで補光条件の生育段階評価に適用しており、植物表現型取得手法が中心である。
abstractThis study introduces PGL-ShuffleNetV2, a lightweight and advanced model designed for efficient and accurate recognition of rice seedling growth stages.
Timely and correct identification of diseases in the apple leaf is also important in protecting crop production and sustaining agriculture. This paper introduces E-YOLOv8, a lightweight improved version of YOLOv8, that can be implemented in real-time and with a limited resource base. The model has three key contributions: (1) GhostConv and C3 fusion to reduce redundant feature extraction and computational cost, (2) CBAM attention and a specifically designed FPN to maximize multi-scale feature fusion and small-lesion detections, and (3) large-scale evaluation on datasets of apple leaf disease, as well as ablation experiments and operational testing on edge devices to verify the accuracy and viability of this model. In experiments, E-YOLOv8 reaches 93.9mAP0.5 using 5.3 GFLOPs and 1.8 M parameters, a 33.9x factor smaller than that of YOLOv8l. These results indicate that E-YOLOv8 has achieved better performance than recent state-of-the-art detectors and is still applicable to practical real-world agricultural tasks.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する深層学習手法を開発し、データセット評価、アブレーション、エッジデバイス試験で検証しているため、植物病害状態の表現型取得が中心である。
abstractThis paper introduces E-YOLOv8, a lightweight improved version of YOLOv8, that can be implemented in real-time and with a limited resource base.
Salinity is one of the major abiotic stresses affecting the growth and yield of wheat crops, particularly in arid and semi-arid regions, where irrigation water or soil with high salt content is often present. With increasing soil salinization and abrupt climate change at the global level, identifying salt-tolerant wheat genotypes has become crucial. The present study aimed to characterize and screen the salt tolerance of 25 wheat genotypes at 25, 52, 69, 90, and 118 Days After Sowing (DAS), under field conditions using thermography and bio-physiological parameters. Wheat genotypes were irrigated with saline irrigation water (with threshold EC of 4dSm/m) and performances of the genotypes were monitored using thermal image-based indices e.g., CWSI (Crop Water Stress Index), IG (index of Stomatal Conductance) and bio-physiological parameters i.e., Photosynthesis (Pn), Stomatal conductance (Ig), Transpiration rate, Leaf Area Index (LAI), Normalized difference vegetation index (NDVI), Relative water content (RWC), Total leaf chlorophyll, Membrane stability index (MSI), Osmotic pressure (OP) of leaf, Leaf Na and K. With these biophysical parameters, a new screening index named as Normalized Salinity Stress Tolerance Index (NSSTI) was developed using different multivariate analysis e.g., Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA). Based on the criteria developed in this study, NSSTI could classify the 25 wheat genotypes for salinity stress into: 6 - tolerant, 16 - moderate, and 3 - sensitive genotypes. DA confirmed the classification by NSSTI with 92-100% accuracy based on canonical discriminant functions. Further, thermal image-derived CWSI and IG differentiated tolerant and sensitive genotypes across all DAS under salt stress conditions. Irrespective of different DAS, NSSTI showed significant (p < 0.01) correlation with CWSI (0.70-0.83) and IG (0.78-0.84). The study also identified transpiration rate, RWC, OP, NDVI, and Pn as important parameters to characterize and screen wheat genotypes under salinity stress conditions at different DAS. The newly developed index - NSSTI, exhibited significant (p < 0.01) correlations with wheat yield (0.76-0.84) and biomass (0.73-0.82), indicating the usefulness of NSSTI in evaluating and screening wheat genotypes for salt tolerance. The identified wheat genotypes and key bio-physiological traits can be used in breeding programs to develop advanced salt-tolerant wheat lines. In future, the newly developed salinity stress index NSSTI would play a potential role in the screening and selection of salt-tolerant wheat genotypes under field conditions.
Why it matches plant phenotyping methods熱画像からCWSI・気孔コンダクタンス指標を抽出し、多変量解析で新規の耐塩性スクリーニング指標NSSTIを開発・検証しており、表現型取得と解析手法が研究の中心である。
abstractusing thermography and bio-physiological parameters
Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses and economic challenges for farmers. Manual disease identification is slow and often unreliable, necessitating the development of automated disease detection methods. In this study, we propose ConViTSE, a lightweight hybrid deep learning architecture specifically designed for black gram leaf disease classification. ConViTSE integrates ConvMixer, Vision Transformer (ViT), and Squeeze and Excitation (SE) blocks to effectively extract and refine both local and global features. The model introduces Local Channel Attention Refinement (LCAR) and Global Channel Attention Refinement (GCAR) modules to enhance feature representation at different hierarchical levels. Extensive studies show that ConViTSE achieves a leading classification accuracy of 99.30% on the black gram dataset, outperforming traditional deep learning models. Furthermore, ConViTSE exhibits robust cross-domain generalization, achieving accuracies of 98.75% for rice, 98.20% for maize, and 95% for wheat, highlighting its potential for widespread adoption in precision agriculture. ConViTSE enhances disease detection accuracy while remaining computationally efficient, making it a practical tool for real-time disease management in diverse agricultural environments.
Why it matches plant phenotyping methods黒豆葉の病徴を画像から分類する深層学習手法の開発が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格です。
abstractwe propose ConViTSE, a lightweight hybrid deep learning architecture specifically designed for black gram leaf disease classification.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe Black Gram Plant Leaf Disease (BPLD) dataset from Mendeley Data49 is used in this work to classify leaf
diseases into five classes: anthracnose, healthy, leaf crinkle, powdery mildew, and yellow mosaic.Open asset ↗Mendeley Datapdf-page:14 lines:1-40Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment. While microscopy is widely used for this purpose, manual analysis is labor-intensive and time-consuming that limit large scale studies. To overcome this, we introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement. Our approach provides a comprehensive analysis of stomatal morphology by examining both stomatal pores and guard cells. A key finding is the introduction of stomatal angles as a novel phenotyping trait, which can offer deeper insights into stomatal function. We developed a model using a carefully annotated dataset that accurately segments and analyzes stomatal guard cells from high-resolution images. Additionally, our study introduces a new opening ratio metric, calculated from the areas of the guard cells and the stomatal pore, providing a valuable morphological descriptor for future physiological research. This scalable system significantly enhances the precision and efficiency of large-scale plant phenotyping, offering a new tool to advance research in plant physiology.
Why it matches plant phenotyping methods深層学習による気孔画像解析と、気孔形態・角度・開口率の自動推定手法を開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement.
Abstract The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. Currently, most studies use deep learning models for feature extraction and identification of crop leaf disease and pest images. However, these methods are often seen as “black box” model, making it difficult to interpret the basis for their specific decisions. To address this issue, we propose an intrinsically interpretable crop leaf disease and pest identification model named C ontrastive P rototypical Part Net work (CPNet). The idea of CPNet is to find the key regions that influence the model’s decision by calculating the similarity values between the convolutional feature maps and the learnable latent prototype feature representations. Moreover, because the limited availability of data resources for crop leaf disease and pest images, we employ a supervised contrastive learning strategy to capture the similar information between examples in one class and contrast them with examples in other classes. Finally, we evaluate our approach on four publicly available datasets, and the experimental results demonstrate that our proposed CPNet not only achieves improvements in performance over baseline methods across multiple datasets, but also provides interpretable evidence for crop leaf disease and pest identification.
Why it matches plant phenotyping methods作物葉の病害状態を画像から推定する解釈可能なコンピュータビジョン手法を開発し、複数データセットで評価しており、病害表現型の取得・分類手法が中心です。
titleAn interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning
Reproduction assets foundThe paper evaluates CPNet on four public leaf disease/pest image datasets. Two are directly linked in the Data availability statement: the Wheat Plant Diseases dataset (Kaggle) and the Dataset for Crop Pest and Disease Detection (Mendeley), both paper-specific image assets used for the phenotyping-style classification/Dataset · publicnstruction Project of Anhui Science and Technology University (Grant No. XK-XJGY002), the Anhui Provincial Department of Education Natural Science Major Project (Grant No. 2023AH040276).
Data availability
The data used in this study is publicly available at the following link: https://data.mendeley.com/datasets/bwh3zbpkpv/1 and https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases .
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Xing S Lee HJ
Crop pests and diseases recognition using DANet with TLDP
Comput. Electron. Agric. 2022 199 107144
10.1016/j.compag.2022.107144
Xing, S. & Lee, H. J. Crop pests and diseases recognition using DANetOpen asset ↗Kaggle · kushagra3204/wheat-plant-diseaseslines:1594-1660Dataset · publicversity (Grant No. FZ230122 ), the key Discipline Construction Project of Anhui Science and Technology University (Grant No. XK-XJGY002), the Anhui Provincial Department of Education Natural Science Major Project (Grant No. 2023AH040276).
Data availability
The data used in this study is publicly available at the following link: https://data.mendeley.com/datasets/bwh3zbpkpv/1 and https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases .
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Xing S Lee HJ
Crop pests and diseases recognition using DANet with TLDP
Comput. Electron. Agric. 2022 199 107144
10.1016/j.compag.2022.107144
Xing, S. & Lee, HOpen asset ↗Mendeley · bwh3zbpkpv/1lines:1594-1660Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops. A custom-curated dataset was developed, comprising over 1,800 images acquired through smartphone cameras and foldscope devices under natural lighting conditions. The proposed CNN model achieved a classification accuracy of over 95%, with a disease quantification accuracy of 90.5%, calculated using pixel-level segmentation of infected regions. Experimental results revealed infection percentages ranging from 0.68% in early-stage cases to 13.98% in severely affected samples, enabling precise disease severity analysis. The framework includes a MATLAB-based graphical user interface (GUI) for real-time visualization of classification results and severity scores. Training convergence was demonstrated with a mini-batch loss reduction from 1.0879 to 0.0094 over 200 iterations, and classification confidence scores exceeding 90% for most disease categories. In addition to software implementation, the model was synthesized for hardware deployment using FPGA, demonstrating less than 5% LUT and 1% register usage for 512 × 512 images, ensuring resource-efficient performance in IoT environments. This work introduces a scalable, field-deployable tool for crop health monitoring, with potential to enhance sustainable farming practices through timely disease management.
Why it matches plant phenotyping methodsイネ・ジャガイモの病害画像を用いて感染領域と病害重症度を定量化するCNN・画像処理・GUI・FPGA実装を開発しており、植物表現型（病害状態）の取得・抽出が研究の中心です。
abstractThis study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops.
Field / plotLeafStress / disease detectionStress response / toleranceWater status / transpiration
Plants are exposed to atmospheric conditions for extended periods, allowing the observation of their color, texture, and internal structure to infer the surrounding atmospheric conditions. High ozone concentrations, particularly in urban areas, are harmful to plants. Optical coherence tomography (OCT) is a non-destructive method to evaluate samples' internal structure and optical properties, allowing potential measurements without any influence on the sample. This study aimed to estimate the ozone damage on plant leaves by measuring the OCT of white clover (Trifolium repens), an indicator plant of ozone influence, collected from different areas. Initially, to compare the influence of ozone and the impact of transportation from sampling regions, the temporal changes in water transpiration from cutting leaves were evaluated. Next, the leaves of white clover collected from various regions were measured using OCT. Changes on OCT images in light intensity, layer thickness, and texture (contrast, correlation, energy) within the leaves were analyzed to compare the presence or absence of the stresses (ozone and water stresses). The first experiment confirmed that the trends in Energy changes in the OCT images were opposite for ozone and water stress, with ozone increasing Energy due to widespread tissue disruption that blurred texture patterns, and water stress decreasing it due to localized shrinkage. In the second experiment, similar to the first experiment, a decrease in the intensity of the palisade tissue, an increase in thickness, and an increase in Energy were observed in regions with particularly high ozone concentrations. These results confirmed that OCT image analysis could detect specific plant changes due to ozone and water stress. This study demonstrates the potential of in-situ measurements of plants using OCT to infer the environmental conditions to which the plants are exposed.
Why it matches plant phenotyping methodsOCT画像から葉の内部構造・テクスチャ・組織厚などの植物状態を抽出し、オゾンおよび水ストレスを検出する手法が研究の中心であるため。
abstractThese results confirmed that OCT image analysis could detect specific plant changes due to ozone and water stress.
Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by presenting a novel multimodal deep learning algorithm. The algorithm leverages EfficientNetB0 for image-based disease classification and utilizes Recurrent Neural Networks (RNN) to predict disease severity based on environmental data. By integrating visual and climatological inputs, our model addresses the limitations of unimodal systems, enhancing classification accuracy and interpretability. The model achieved a disease classification accuracy of 96.40% and a severity prediction accuracy of 99.20%. Additionally, the use of LIME and SHAP explainable AI techniques improves the understanding of disease severity classification outcomes. The contributions of this study align with precision agriculture practices and advance the resilience of local food systems, particularly in economies heavily dependent on tomato production. The proposed approach has the potential to mitigate the impacts of plant diseases and enhance food security by utilizing innovative technological solutions.
Why it matches plant phenotyping methodsトマト病害の画像分類と病害重症度推定を行うマルチモーダル手法が研究の中心であり、植物状態の測定・推定に直接関わるため含める。
abstractpresenting a novel multimodal deep learning algorithm
Wheat protein content is a major determinant of its usage and value. Current methods require wet labs that may be difficult to access and are not real-time. To overcome this, Hyperspectral imaging (HSI) has been reported for estimating the protein content of wheat seeds with the advantage that it is real-time, does not require wet labs, and has high accuracy. However, these models have been developed and validated for a small range of protein content, and without considering cultivation regions. This paper reports the extension of the use of HSI for protein estimation for a wider range of protein content and for wheat cultivated in different regions. Hyperspectral images of 621 wheat samples from five regions in India were acquired in the 900-1700 nm wavelength range. The reference protein content of each sample was determined using the Kjeldahl method, with values ranging from 9.5 to 17.25%. Mean spectra were extracted from the hyperspectral images to develop deep learning and conventional machine learning methods, which were validated through 5-fold cross-validation. The experiments showed that the one-dimensional convolutional neural networks (1D-CNN) performed the best, with the coefficient of determination (R²) of 0.9972, root mean square error (RMSE) of 0.0771, and the ratio of performance to deviation (RPD) of 18.81 for the prediction set. This shows that a 1D-CNN model trained using mean spectra can accurately estimate the wheat protein content. This has the advantage of not requiring a wet lab, and being potentially real-time, which could benefit the farmers, traders, and food industry.
Why it matches plant phenotyping methods小麦種子のタンパク質含量という植物形質を、ハイパースペクトル画像と1D-CNNで推定する手法の拡張・検証が中心であり、交差検証による性能評価も実施している。
abstractThis paper reports the extension of the use of HSI for protein estimation for a wider range of protein content and for wheat cultivated in different regions.
Abstract The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention mechanisms, are hypothesized to provide accurate and efficient classification of such deficiencies for real-time agricultural applications. In this study, multiple convolutional neural networks (CNNs) and mobile-friendly architectures, including ResNet50, VGG16, NASNetMobile, and MobileNet variants (V1, V2, V3), were evaluated using transfer learning on a curated banana leaf deficiency dataset. To improve robustness and prediction accuracy, modified classification layers and ensemble strategies–initially average ensembling and later a NAS-guided dynamic attention weighting mechanism were employed. This optimization resulted in a novel lightweight model, NASMobV2 (NASNetMobile + MobileNetV2), capable of both classifying nutrient deficiencies and assessing their severity levels. The proposed model achieved a validation accuracy of 98.57%, outperforming baseline and state-of-the-art counterparts in precision, recall, and F1 score. To improve generalization, banana crop diseases along with an additional Coffee crop dataset were included for evaluation. Finally, the practical utility of the model was demonstrated by deploying the trained system in both mobile and web applications, enabling farmers and agronomists to perform fast and accurate diagnostics directly in the field.
Why it matches plant phenotyping methodsバナナ葉画像から栄養欠乏と重症度を推定する軽量深層学習モデルを開発・評価し、実運用アプリにも展開しており、植物状態の取得・推定手法が中心である。
titleA neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves.
Reproduction assets foundThe paper's banana leaf nutrient-deficiency image dataset is publicly available on Mendeley Data and was directly used for the phenotyping/classification measurements. No author analysis code or trained model checkpoints are explicitly deposited; the GitHub/Streamlit links are deployment apps rather than deposited codeDataset · publiclidation and editing in addition to overall supervision.
Funding
Open access funding provided by Vellore Institute of Technology. We thank our Management “Vellore Institute of Technology, Vellore” for open access funding support.
Data availability
An openly available repository (Mendeley dataset) was used to perform this study;(https://data.mendeley.com/datasets/7vpdrbdkd4/1), Request for any data or materials shall be addressed to the author(sudhakar.m2020@vitstudent.ac.in).
Declarations
Competing interests
The authors declare that they have no competing interests.
References
1.
Sherefu A Zewide I
Review paper on effect of micronutrients for crop production
J. Nutr. Food Process. 2021
10.31Open asset ↗Mendeley · 7vpdrbdkd4lines:1245-1307Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
The increasing need for smart agriculture in the twenty-first century has increased demand for computer-vision-based disease recognition systems encompassing small and large application areas. In recent years, one of the most promising agricultural research areas has been the automated identification of plant diseases using computer vision. Chlorosis is one of the most common diseases of green leaves. It causes the leaves to turn yellow. The severity of chlorosis (chlorophyll decrease) can be perceived by observing the degree of yellowness in the leaves. One of the most challenging tasks in computer vision-based disease diagnosis is severity estimation with precision. This research has developed a novel approach with a high degree of accuracy to detect the disease-affected lesion areas and the degree of severity. The proposed method involves multiple steps, including initial optimization of superpixel algorithm parameters, feature extraction, feature selection, classification, and disease severity estimation. An evolutionary superpixel-based method has been proposed for grouping different colour patches on the leaf. To detect the presence of yellowness, texture features from several categories of superpixels are extracted using color-GLCM techniques. In this work, a multi-swarm Cuckoo search-based feature selection approach has been proposed and utilized to reduce the feature set designed using the color-GLCM measures. Subsequently, the reduced feature set has been employed to classify the superpixels into four distinct categories based on the degree of yellowness. The proposed PQCSAF has been tested with the chlorosis-affected images of Pongamia pinnata leaves. The proposed system has been trained using four classifiers: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and multi-layer perceptron (MLP). For categorization of the superpixels according to the four chlorosis stages, the DT, KNN, SVM, and MLP obtained average classification accuracies of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Finally, the severity index of chlorosis for the whole leaf is reported based on the weighted scores of the superpixels based on their categories. The proposed method demonstrates applicability for its robustness and detection accuracy, as indicated by comparison studies with existing literature. The proposed method can be used to measure chlorosis severity for different types of plants and various leaf diseases. Due to its adaptive qualities, the proposed model has the potential to be applied to on-field AI edge devices in future.
Why it matches plant phenotyping methods葉画像からクロロシスの病変領域と重症度を定量推定する画像解析手法を開発しており、植物状態の取得・抽出が研究の中心である。
abstractThis research has developed a novel approach with a high degree of accuracy to detect the disease-affected lesion areas and the degree of severity.
The agriculture sector plays a pivotal role in the growth of the global economy, but remains highly susceptible to prediction errors, particularly in disease identification. To address the limitations of existing approaches, this study proposes a deep learning-based framework for the classification of medicinal plant leaf diseases. "Medicinal plant leaf images are collected from the standard data source. These images undergo a pre-processing phase that includes filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE) to enhance visual quality. Subsequently, an adaptive thresholding mechanism is employed for precise leaf segmentation. For effective disease recognition, deep feature extraction is carried out using a customized Multi-Scale VGG16 architecture," capturing diverse features such as color, shape, and texture. These heterogeneous features are then subjected to a weighted fused feature selection process, where feature weights are optimized using a novel Hybridized Zebra with Krill Herd Optimization (HZKHO) algorithm. The optimized feature set is input to the disease classification stage, which employs an Attention-based Dilated Adaptive DenseNet (A-DADensenet) model to produce accurate classification results. The proposed model achieves an impressive classification accuracy of 90.69%, thereby demonstrating its effectiveness in accurately identifying diseased medicinal plant leaves. "The integration of deep learning with a hybrid optimization technique" significantly enhances the model's classification performance, proving its potential for real-world agricultural applications.
Why it matches plant phenotyping methods薬用植物葉の画像から病害を分類する画像ベースの表現型推定手法を開発しており、葉の前処理・分割・特徴抽出・分類モデルが研究の中心である。
abstractthis study proposes a deep learning-based framework for the classification of medicinal plant leaf diseases.
Reproduction assets foundThe paper's medicinal plant leaf disease classification uses a public Mendeley Data image dataset (3838 soursop leaf images, six classes), explicitly cited with URL and access date in the Data availability statement. No author code or trained model is deposited.Dataset · publicThe medicinal plant leaf images are garnered from the dataset of Mendeley Data that is available in the data
source of https://data.mendeley.com/datasets/hjrhrt5hs8/2 with access date: 2025-07-21.Open asset ↗Mendeley Data · hjrhrt5hs8/2pdf-page:5 lines:1-34Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemical composition of grapes, has become a key technology for developing non-destructive testing devices. In this paper, we collected 2,880 randomly labeled multispectral images of Sunshine Rose grapes with a Changguang Yuchen MS600 PRO multispectral camera and measured the sugar content (in Brix values) of the labeled grapes with a handheld refractometer, using data exclusively from this grape variety. To address noise and misalignment issues in the multispectral images, we proposed preprocessing methods including Gaussian denoising and ECC (Enhanced Correlation Coefficient) algorithm registration. Based on a ResNet-50 residual network, we constructed a grape sugar content prediction regression model Improved-Res with SE (Squeeze-and-Excitation) attention modules, DSC (Depthwise Separable Convolutions), and Inception modules. The model's performance was evaluated by MSE (Mean Squared Error), MAE (Mean Absolute Error), and R 2 (R-Square) metrics. We compared the performance of four feature extraction methods combined with four traditional machine learning models, as well as seven deep learning models. The results showed that among traditional machine learning methods, the combination of color histogram feature extraction and the XGBoost regression achieved the best performance, with MSE, MAE, and R 2 of 1.35, 0.90 Brix, and 0.78, respectively. Among deep learning methods, the ResNet-50 model demonstrated the best performance, with MSE, MAE, and R 2 of 0.95, 0.96 Brix, and 0.84, respectively. Effective improvements of SE attention module, depthwise separable convolutions, and Inception module in the ResNet-50 model was confirmed through ablation experiments: the proposed Improved-Res model achieved MSE, MAE, and R 2 of 0.49, 0.55 Brix, and 0.92, respectively, which significantly outperformed traditional machine learning methods and classical deep learning models.
Why it matches plant phenotyping methodsブドウ果実の糖度という植物形質をマルチスペクトル画像から非破壊推定する前処理・深層学習モデルを開発し、複数手法との比較とアブレーション検証を行っており、フェノタイピング手法が中心である。
abstractdeveloping non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms.
MaizeSeed / grainClassificationGrowth / development / phenology
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneous sensor data and predict multiple seed phenotype characteristics simultaneously. The framework integrates genetic algorithms with particle swarm optimization for real-time parameter adjustment, addressing the complex relationships between electromagnetic treatment conditions and seed quality outcomes. Experimental validation using three corn varieties (Zhengdan 958, Xianyu 335, and Jingke 968) demonstrates significant performance improvements, with optimized treatment protocols achieving 12.8% enhancement in germination rates and 17.7% improvement in vigor indices compared to untreated controls. The multi-objective deep learning model achieved 93.7% prediction accuracy with 91.2% recall rate, outperforming conventional optimization approaches. The adaptive parameter optimization strategy successfully balanced competing objectives including treatment effectiveness, energy efficiency, and processing time while maintaining robust performance across different seed batches. This research provides a comprehensive solution for intelligent seed treatment systems, offering substantial potential for advancing precision agriculture and sustainable crop production technologies.
Why it matches plant phenotyping methods深層学習モデルによる種子形質の予測と技術検証が研究の中心であり、単なる処理効果の測定にとどまらない。
abstractA hybrid CNN-LSTM network architecture was developed to process heterogeneous sensor data and predict multiple seed phenotype characteristics simultaneously.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Laboratory / benchtopMicroscopyMultimodalCell / cellular structureRootTissueTrackingVisualization / data management
Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.
Why it matches plant phenotyping methodsTHG/3PFによる根の構造を高時空間分解能でラベルフリー取得するイメージング手法を開発・実証しており、植物表現型取得が中心である。
abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution
During rice cultivation, common rice diseases and pests such as Rice blast, Bacterial blight, Brown-planthopper and Leaf-folder will significantly affect the yield and quality. The current model is limited to detecting rice diseases or pests alone, and faces challenges such as the diversity of disease and pest traits, small detection targets, uneven light and complex background shading in paddy fields, resulting in low accuracy and adaptability of the model. In this study, a YOLO-DP (Diseases and Pests) model based on YOLOv8n model was proposed to detect fifteen common rice diseases and pests under complex conditions. First, the Triplet Attention mechanism is introduced into the network's Backbone to achieve cross-dimensional interaction between channels and spatial dimensions. Then, GLSA (the Global to Local Spatial Aggregation) module is used to improve the Neck of YOLOv8n, enhancing the effectiveness of feature representation. The WTConv (Wavelet Transform Convolution) is used to improve the C2f-BottleNeck of the original model, expanding the network's receptive field. Finally, the loss function is replaced with EIoU (Enhanced Intersection over Union) to reduce the position offset and shape mismatch of the predicted boxes. Experimental results demonstrate that the model achieves an average accuracy of 80.9%, a recall rate of 74.4%, a Mean Average Precision mAP50 of 77.8% and mAP95 of 50.1%, significantly outperforming the original YOLOv8n and mainstream detection models such as TOOD, Faster R-CNN and RT-DETR. This model exhibits exceptional performance in detecting rice diseases and pests in complex environments, providing robust technical support for rice growth monitoring and offering insights for the detection of other crop diseases.
Why it matches plant phenotyping methods画像からイネの病害状態を検出するYOLOモデルの開発・性能評価が研究の中心であり、病害症状という植物状態の推定に該当する。
abstractIn this study, a YOLO-DP (Diseases and Pests) model based on YOLOv8n model was proposed to detect fifteen common rice diseases and pests under complex conditions.
Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99.60% and 100% as Top_1 and Top_5 accuracy, respectively. The method also achieved a recall of 99.53%, a precision of 99.53%, and an F1 score of 99.60%. During 10 trials, the method consistently performed with an average. Top_1 and Top_5 accuracy of 98.41% and 100% respectively, recall 98.53%, precision 98.39% and F1 score 98.42%.This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.
Why it matches plant phenotyping methodsYOLOv8による葉画像からの植物病害認識が研究の中心で、交差検証による性能評価も実施しているため、植物病害状態を推定するフェノタイピング手法として含める。
abstractThis study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition.
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models will perform accurate disease detection through the identification of spatial patterns. However, they cannot predict the diseases at the early stages due to the lack of temporal information. Temporal observations will help perform continuous monitoring and detect minute changes in the crops at the early times. To tackle this problem, we proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively. The SSDHR network uses multi-branch convolution kernels to extract distinct discriminative characteristics rather than conventional leaf-based indicators. It incorporates spatial, and temporal-based attention mechanism Symmetric Fusion Attention (SFA) to improve feature selection and XGBoost (XGB) classifier for better stability. According to experimental findings, the suggested framework achieves a 99.25% accuracy rate in identifying and classifying 13 paddy classes, including normal, blast, hispa, tungro, white stem borer, brown spot, leaf roller, downy mildew, yellow stem borer, bacterial leaf blight, bacterial leaf streak, black stem borer, and bacterial panicle blight.
Why it matches plant phenotyping methodsイネ病害の症状を空間・時間画像データから検出・分類する深層学習フレームワークを提案し、その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。
abstractwe proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively.
Reproduction assets foundThe paper's phenotyping inputs are the publicly available Paddy Doctor image dataset (16,225 annotated paddy disease images) hosted on IEEE DataPort, with an explicit dataset link and data availability statement. No author analysis code or trained models are shared.Dataset · publicThe dataset used in our study was obtained from the publicly available repository titled “Paddy Disease and Pest Image Dataset” on IEEE Data Port. The dataset comprises 16,225 high-quality images across 13 classes, including 12 paddy disease and pest categories along with healthy samples.Open asset ↗IEEE Data Porthtml-lines:118-200Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurately detecting and counting potatoes during early harvest is essential for estimating yield, automating sorting, and supporting data-driven agricultural decisions. However, field environments often present practical challenges-such as soil occlusion, overlapping tubers, and inconsistent lighting-that hinder robust visual recognition. In response, we introduce SCG-YOLOv8n, a compact and field-adapted detection framework built upon the YOLOv8n architecture and specifically tailored for small-object detection in real-world farming conditions. The model incorporates three practical enhancements: a C-SPD module that preserves spatial detail to improve recognition of partially buried tubers; an S-CARAFE operator that reconstructs fine-scale features during upsampling; and GhostShuffleConv layers that reduce computational overhead without sacrificing accuracy. Through extensive field-based experiments, SCG-YOLOv8n consistently outperforms YOLOv5n and its base version across all key metrics. Float16 quantization compresses the model to 3.2 MB, enabling real-time inference on Android devices. We also developed PotatoDetector, a mobile application that demonstrates stable performance in field trials, achieving an RMSE of 1.38 and [Formula: see text] of 0.96 in counting tasks. These results suggest that SCG-YOLOv8n offers a practical and scalable tool for precision agriculture, with potential applicability to other root and tuber crop monitoring scenarios.
Why it matches plant phenotyping methodsジャガイモ塊茎の画像検出・計数を行うモデルとモバイル実装を開発し、圃場で性能検証している。塊茎数という植物器官形質の取得が中心であり、単なる収量測定ではない。
abstractwe introduce SCG-YOLOv8n, a compact and field-adapted detection framework built upon the YOLOv8n architecture and specifically tailored for small-object detection in real-world farming conditions.
Reproduction assets foundThe paper's custom potato image dataset is not publicly available (available only from the corresponding author on request), but the authors provide a public GitHub repository for the SCG-YOLOv8n analysis code with an explicit availability statement and URL.Code · publicCode availability
Code can be found at https://github.com/AiXia520/SCG-YOLOv8n.git.Open asset ↗https://github.com/AiXia520/SCG-YOLOv8n.githtml-lines:359-392Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Accurate frost detection on leaf surfaces is critical for agricultural monitoring, yet existing methods struggle with segmentation errors caused by complex backgrounds (blurred, soil, weeds) and subtle frost-leaf texture differences. To address this, we propose MCGE-Frost, a multi-component gradient enhancement method that integrates color space analysis with gradient fusion theory. The algorithm extracts gradient features from individual color channels (HSV, Lab), applies adaptive weighting to enhance frost-leaf boundary contrast, and employs morphological filtering to suppress background noise. Experiments on leaf images demonstrate that MCGE-Frost achieves a total algorithmic error segmentation rate of 3.29%, significantly outperforming ExG (8.63%), OTSU (8.98%), and HSV (11.98%). The method reduces computational complexity by 40% compared to deep learning-based approaches while maintaining robustness across diverse backgrounds. MCGE-Frost achieves 0.8 s/image processing on GPU-accelerated systems, balancing accuracy and efficiency for edge deployment. Additionally, it improves the intelligence of frost quantification with minor manual calibration. This advancement supports real-time frost monitoring in precision agriculture, providing actionable insights for frost protection and crop management.
Why it matches plant phenotyping methods葉面の霜を画像から検出・定量する画像処理手法を開発し、既存手法との性能比較と精度評価を行っており、植物状態の取得方法が研究の中心である。
abstractwe propose MCGE-Frost, a multi-component gradient enhancement method that integrates color space analysis with gradient fusion theory.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning models, particularly artificial neural networks (ANN), in predicting wheat disease severity with high accuracy. The field experiment was conducted over two consecutive rabi growing seasons (2023 And 2024) using a randomized block design with four sowing dates to investigate critical weather-disease relationships for two key wheat pathogens: Puccinia striiformis f. sp. tritici (yellow rust) and Blumeria graminis f. sp. tritici (powdery mildew). Weekly assessments of disease severity were combined with meteorological data and analyzed using ANN and regularized regression models. The ANN model demonstrated superior predictive accuracy for yellow rust and powdery mildew, achieving R-squared values (R 2 of 0.96 And 0.98 for calibration And 0.93 And 0.95 for validation, respectively. Random Forest (RF) models also exhibited robust performance with R 2 values of 0.97 And 0.98 for calibration And 0.93 And 0.90 for validation for yellow rust and powdery mildew, respectively. In contrast, Elastic Net, Lasso, and Ridge regression models showed comparatively moderate predictive capabilities. Principal component analysis (PCA) explained the key meteorological variables influencing disease incidence, with evapotranspiration, temperature, wind speed and humidity emerging as critical factors. Disease prediction is an important aspect of developing a decision support system, and it makes farmers make informed decisions to optimize production.
Why it matches plant phenotyping methods小麦の病害重症度という植物状態を対象に、機械学習モデルによる予測手法を構築し、複数モデルの精度を検証しているため、方法開発・検証が中心です。
titlePredicting crop disease severity using real time weather variability through machine learning algorithms.
Hazelnut (Corylus avellana L.) is one of the most appreciated and cultivated nuts in temperate areas. Producers are now facing an increasing demand and industries need to select high-yielding and fine-quality cultivars. In this context, a challenge to take up is the development of a rapid, non-destructive and high-resolution method to study the growth and differentiation dynamics of floral reproductive organs, to limit yield losses especially in response to climate adaptation. In this study, we scanned mixed buds from the hazelnut cultivar Tonda di Giffoni from anthesis to fruit formation by micro-computed tomography (Micro-CT). We reconstructed in three dimensions (3D) the spatial arrangement of flowers within the glomerulus, characterized the position and configuration of ovules, ovaries and funiculus as well as observed the formation of the embryo during the early developmental stages. The proposed approach enables precise volume measurements of ovaries, ovules, and embryos. It helps identify abortive ovules early and track developmental stages, such as embryo formation. Unlike traditional 2D methods, this approach captures growth patterns more accurately, supporting research on fruit development, crop quality, and genetic studies. Overall, it provides a powerful tool for advancing reproductive biology research of hazelnuts and improving hazelnut cultivation.
Why it matches plant phenotyping methodsヘーゼルナッツの生殖器官をMicro-CTで非破壊・高解像度に3D再構成し、器官体積や発達段階を定量化する手法が研究の中心であるため。
abstractthe development of a rapid, non-destructive and high-resolution method to study the growth and differentiation dynamics of floral reproductive organs
Mango is a fruit of great economic importance in India. India is the top mango-producing nation in the world, accounting for over half of global mango output. In order to determine the production capability of the insured orchards, a complete inventory is carried out in situ every three years. The inventory includes counting number of trees, grouping them into yield categories, and assessing damaged ones. Satellite Remote Sensing proves to be a vital tool for estimating ecological parameters such as population density, tree health, volume, biomass, and carbon sequestration rates. The significance of tree counting extends beyond orchard evaluations, playing a vital role in environmental protection, agricultural planning, and crop yield forecast. unfortunately, conventional tree counting methods often require very expensive feature engineering, which leads to more errors as well as lower overall optimization. In order to overcome these obstacles, deep learning-based methods have been used to count trees, exhibiting cutting-edge results in this crucial activity. This paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data. The proposed model, named Bi-directional Feature Pyramid Network (BiFPN)-YOLOv8m an improved version of YOLOv8, employs object detection to effectively separate, locate, and count mango trees with in orchards. A dataset of 1700 training and 300 testing images of mango orchards with trees of various ages is used to evaluate the various YOLOv8 variants, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, including YOLOv9, YOLOv10, and BiFPN-YOLOv8m, with a focus on computational efficiency, accuracy, and speed. Experimental findings show that, even under difficult circumstances, the proposed method continuously outperforms state-of-the-art techniques.
Why it matches plant phenotyping methods衛星画像からマンゴー樹木を分離・位置推定・計数する深層学習手法を開発・評価しており、植物個体数という観測可能な形態・構造形質の抽出が中心である。
abstractThis paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data.
Reproduction assets foundThe paper's satellite remote sensing image dataset used for mango tree counting is publicly deposited on GitHub per the Data Availability Statement. No separate analysis code or trained model checkpoint is explicitly deposited.Dataset · publicRemote Sensing Image Data that support the findings of this study have been deposited in the GitHub. The url to the data uploaded is https://github.com/lbirla/Mango_tree_satellite_data.Open asset ↗https://github.com/lbirla/Mango_tree_satellite_datahtml-lines:497-525Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Terahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively, for example over the course of the day or at the onset of drought stress. By studying a wildtype and a genetically modified variant of Arabidopsis thaliana, we observed significant differences in their dehydration dynamics. For a better understanding of the underlying processes, we modelled this behaviour with a simple rate equation model, compared the results with the experimental data and correlated our model with the biological regulatory mechanisms. In particular, under drought stress, we found an almost three times ([Formula: see text]) higher maximal stomatal opening in the mutant than in the wildtype. Over the course of the day, the degree of stomatal opening shows an exponential decrease with a half-life [Formula: see text] of [Formula: see text]2.6 h in the wildtype and [Formula: see text]0.8 h in the mutant.
Why it matches plant phenotyping methodsTHz分光法による植物体内水分量と気孔開閉 dynamics の非破壊・経時的測定が研究の中心であり、水分状態・生理形質を定量化するフェノタイピング手法としてモデル検証も行っている。
abstractTerahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively
Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques. The study emphasizes the significance of timely disease diagnosis in citrus crops, which are vital for global food security and economic stability. The methodology begins with image quality enhancement through Vector-Valued Anisotropic Diffusion (VAD) and morphological filtering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning (DL) architectures-VGG19, AlexNet, and Xception-using a fuzzy rank-based scoring mechanism built on nonlinear transformations (exponential, tanh, and sigmoid functions) to address prediction uncertainty and model bias. A comprehensive dataset of lemon leaf diseases, consisting of 1354 images across nine classes, was utilized for training and evaluation. Experimental results using five-fold cross-validation demonstrate that the proposed model achieves superior performance with an average accuracy of 96.51%, outperforming conventional ensemble and state-of-the-art approaches. The results validate the proposed NL-FuRBE as an effective, automated, and cost-efficient tool for precision agriculture and early disease diagnosis in citrus farming.
Why it matches plant phenotyping methods柑橘葉の病徴を画像から検出・分類する手法を新規開発し、画像処理、アンサンブル分類、交差検証で技術性能を評価しているため、植物病害状態の表現型取得が中心である。
abstractThis paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques.
Reproduction assets foundThe paper's lemon leaf disease image dataset (1354 images, nine classes) is publicly deposited on Mendeley Data with explicit availability statement and DOI; no author code or models are shared.Dataset · publicThe dataset used during the current study are available in the Mendeley Data repository under the title “Comprehensive Lemon Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture” (DOI: 10.17632/44nrn4593f.1), https://data.mendeley.com/datasets/44nrn4593f/1.Open asset ↗Mendeley Data · 10.17632/44nrn4593f.1html-lines:805-842Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models. Eight pre-trained deep learning models - ResNet50, InceptionResNetV2, DenseNet 201, InceptionV3, EfficientNetB0, Xception, VGG16 and AlexNet,were evaluated using a three-stage methodology. First, the models are assessed using traditional classification metrics. Second, Local Interpretable Model-agnostic Explanations (LIME) is employed to visualize and quantitatively evaluate feature selection using metrics such as Intersection over Union (IoU) and the Dice Similarity Coefficient (DSC). Third, a novel overfitting ratio metric is introduced to quantify the reliance of the models on insignificant features. In the experimental analysis, ResNet50 emerged as the most accurate model, achieving 99.13% classification accuracy as well as the most reliable model demonstrating superior feature selection capabilities (IoU: 0.432, overfitting ratio: 0.284). Despite the high classification accuracies, models such as InceptionV3 and EfficientNetB0 showed poor feature selection capabilities with low IoU scores (0.295 and 0.326) and high overfitting ratios (0.544 and 0.458), indicating potential reliability issues in real-world applications. This study introduces a novel quantitative methodology for evaluating deep learning models that goes beyond traditional accuracy metrics, enabling more reliable and trustworthy AI systems for agricultural applications. This methodology is generic and researchers can explore the possibilities of extending it to other domains that require transparent and interpretable AI systems.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する深層学習モデルについて、性能と説明可能性を評価する三段階の方法論を開発・検証しており、植物病害状態のフェノタイピング手法が中心である。
abstractThe main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models.
Reproduction assets foundThe paper uses the public Kaggle rice leaf disease dataset and explicitly states that the authors' analysis code is publicly available on GitHub and MATLAB File Exchange via short URLs, both of which are in the allowed URL list.Dataset · publicChinna Gopi Simhadri: Formal analysis, investigation, resources, data collection, writing an original draft, Review & editing. All authors have read and agreed to the published version of the manuscript.
Data availability
In this work, the publicly available dataset was used. The data set is available through the link to Kaggle https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset.
Code availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be Open asset ↗Kaggle · dedeikhsandwisaputra/rice-leafs-disease-datasetlines:1332-1347Code · publicode availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be accessed via the following links: Github: https://shorturl.at/Github_Quant_XAI MATLAB File Exchange link: https://shorturl.at/MATLAB_Quant_XAI
Declarations
Competing interestsOpen asset ↗GitHublines:1332-1347Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning. In the proposed study, extensive experiments were conducted to develop an optimized Bayesian Weighted Ensemble CNN model for the detection of potato leaf blight. First, multiple CNN architectures were trained using different optimizers: ADAM (DL1), SGD (DL2), RMSProp (DL3), and ADAMAX (DL4), evaluating their individual performance. To mitigate class imbalance, data augmentation techniques were applied, increasing the number of healthy leaves by 6 times. In addition, fuzzy image enhancement was implemented to improve feature extraction and classification accuracy. Bayesian optimization was then used to determine the optimal weights for a deep ensemble model, exploring 11 possible model combinations. The final EDL7 ensemble model (DL1 + DL2 + DL3), optimized through Bayesian optimization, achieved the highest accuracy of 97.94%, outperforming individual models. Furthermore, the ensemble model achieved a precision of 0.981, recall of 0.983, and an F1 score of 0.982, ensuring a well-balanced trade-off between precision and recall. These results highlight the effectiveness of Bayesian-optimized ensemble learning in improving potato blight detection, making it a robust and reliable solution for agricultural disease classification.
Why it matches plant phenotyping methodsジャガイモ葉の病徴を画像から分類するCNN・画像強調・ベイズ最適化アンサンブルを中心に開発・評価しており、植物病害状態の取得手法が主題である。
abstractwe propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based approach that optimizes CNN models through Bayesian optimization and ensemble learning.
Reproduction assets foundThe paper's potato leaf blight detection models were trained on a public Kaggle dataset, explicitly declared in the Data availability statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe datasets generated and analyzed during the current study are available in the Kaggle repository https://www.kaggle.com/datasets/muhammadardiputra/potato-leaf-disease-datasetOpen asset ↗Kaggle · muhammadardiputra/potato-leaf-disease-datasetlines:308-345Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The rising food demand and challenges with respect to the climate have made precision agriculture (PA) vital for sustainable crop production. This study presents an IoT-based smart greenhouse platform tailored for tomato farming, integrating environmental sensing and deep learning. The system employs ESP32-based wireless sensors to collect real-time data on soil moisture, temperature, and humidity; this data is transmitted to a cloud dashboard (ThingsBoard) for remote monitoring. A Raspberry Pi equipped with a Pi Camera and a YOLOv8 model classifies tomato ripeness stages-green, half-ripened, and fully ripened-using real greenhouse images. Model optimizations, including quantization, pruning, and TensorRT, improved inference speed by 35% while maintaining 52.8% classification accuracy during our initial stage of the project. Energy profiling revealed daily consumption of 8.91 Wh for the ESP32 sensors and 78 Wh for the Raspberry Pi. This prototype demonstrates real-time monitoring, high model precision, and practical energy insights, paving the way for multi-node scalability and edge AI enhancements. Future work will explore incorporating Edge TPU for faster on-device processing, LoRa for low-power, long-distance data transfer, and automated control of irrigation and ventilation systems to realize a fully autonomous smart greenhouse.
Why it matches plant phenotyping methodsトマト果実の成熟段階という植物状態をカメラ画像とYOLOv8で推定し、モデル最適化・精度・推論速度を評価しているため、画像ベースの表現型取得が中心的です。
abstractA Raspberry Pi equipped with a Pi Camera and a YOLOv8 model classifies tomato ripeness stages-green, half-ripened, and fully ripened-using real greenhouse images.
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.
Why it matches plant phenotyping methods植物葉の病害状態を画像から認識する新規深層学習手法を提案・比較評価しており、植物フェノタイピング手法が中心である。
abstractIn this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants.
Abstract Crop disease is a significant challenge in agriculture, requiring quick and precise detection to safeguard yields and reduce economic losses. Traditional diagnostic methods are slow, labor-intensive, and rely on expert knowledge, limiting scalability for large-scale operations. To overcome these challenges, a novel architecture called Mob-Res , combining residual learning with the MobileNetV2 feature extractor, is introduced in this work. Despite having only 3.51 million parameters, Mob-Res is lightweight and well-suited for mobile applications while delivering exceptional performance. The proposed model is assessed using two benchmark datasets: Plant Disease Expert , consisting of 199,644 images across 58 classes, and PlantVillage , with 54,305 images across 38 classes. Through a rigorous training strategy, Mob-Res demonstrates robust performance, achieving 97.73% average accuracy on the Plant Disease Expert dataset and 99.47% on the PlantVillage dataset. The cross-domain validation rate ( CDVR ) is computed to assess its cross-domain adaptability, with the model showing competitive results compared to other pre-trained models. Additionally, Mob-Res outperforms prominent pre-trained CNN architectures, surpassing ViT-L32 while maintaining a significantly lower parameter count and achieving faster inference times. The proposed model enhances interpretability by utilizing Gradient-weighted Class Activation Mapping ( Grad-CAM ), Grad-CAM++ , and Local Interpretable Model-agnostic Explanations ( LIME ). These techniques provide visual insights into the neural regions influencing the predictions. The experimental results conducted in the current work highlight Mob-Res as a promising solution for automated plant disease detection, supporting large-scale agricultural operations and advancing global food security.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNを開発し、複数データセットで性能比較・検証しているため、画像ベースの植物フェノタイピング手法が中心である。
titleA lightweight and explainable CNN model for empowering plant disease diagnosis
Reproduction assets foundThe paper's authors publicly host the Mob-Res architecture code on GitHub, and the study directly uses two public plant-disease image datasets (PlantVillage and the Mendeley Sugarcane Leaf Disease Dataset) for its phenotyping/classification experiments. The Plant Disease Expert dataset (Kaggle) is also used but its URLCode · publicThe code of the architecture
is available at https://github.com/Chiranjit369/Mob-Res.Open asset ↗Chiranjit369/Mob-Respdf-page:6 lines:1-71Dataset · publicwe have used a benchmark Sugarcane Leaf Disease Dataset as mentioned in Section Experiments on field
dataset which can be accessed at https://data.mendeley.com/datasets/9424skmnrk/1.Open asset ↗pdf-page:13 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The biodiversity function of the desert steppe ecosystem faces many challenges under the pressure of climate change and human activities. Accurate and efficient assessment of plant diversity is critical for guiding desert steppe restoration efforts. However, desert steppe vegetation has sparse leaves and sparse distribution. It is difficult to accurately distinguish micro-vegetation types based on a single spectrum, vegetation index or texture feature, and the resolution of satellite remote sensing cannot meet the needs of high-precision diversity assessment. To this end, this study proposed a novel method for assessing plant diversity index in degraded desert grassland based on multimodal UAV hyperspectral data and Encoder-CNN. Through experiments on different modal feature combinations, spatial spectra, vegetation indices and texture features were targeted and fused. Channel Attention Fusion (CAF) was introduced into Encoder to achieve cross-layer "soft" residual fusion, the Encoder and CNN models were fused to construct a global-local co-expression structure, and finally the quantitative calculation of the plant diversity index at the pixel level was realized. The results show that the vegetation types determined by the fusion of multimodal data and deep learning are consistent with the existing species, dominant species and sub-dominant species of the actual community, and the calculated diversity index results are also consistent with the actual situation. The use of multimodal data combining spatial spectral features with index features, combined with the Encode-CNN model, can provide the most accurate information on community composition. The overall accuracy of sparse vegetation classification can reach 90.01%, and the average accuracy can reach 85.23%, which is better than single mode or traditional 3DCNN, VIT models. This study demonstrates the application potential of UAV hyperspectral multimodal technology and deep learning in the assessment of desert steppe plant diversity, providing important technical support for ecological protection and conservation.
Why it matches plant phenotyping methodsUAVハイパースペクトルとEncoder-CNNを用いて、植物多様性指数を画素レベルで定量推定する手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。
abstractthis study proposed a novel method for assessing plant diversity index in degraded desert grassland based on multimodal UAV hyperspectral data and Encoder-CNN.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes used in this study are available at https://github.com/15204718180/encoder-cnn.Open asset ↗15204718180/encoder-cnnpdf-page:17 lines:56-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Apple leaf diseases significantly impair the photosynthetic efficiency and growth quality of apple trees, leading to reduced fruit yields. Existing methods for disease detection and severity classification struggle to quickly and accurately segment and quantify diseased areas on leaves, particularly in complex backgrounds. To address this issue, we propose a method for assessing the severity of apple leaf diseases based on a combination of improved HRNet and DRL-watershed algorithms. First, we selected HRNet_w32 as the backbone feature extraction network and incorporated a Normalization Attention Mechanism (NAM). Then, we combined the Dice Loss and Focal Loss functions to construct an enhanced HRNet based semantic segmentation model for pixel-level segmentation of both apple leaf and diseased regions. Furthermore, the segmented leaf and disease regions were further optimized using the DRL-watershed algorithm to distinguish overlapping leaf regions. Experimental results demonstrate that the modified HRNet model achieved a mean intersection over union (mIoU) of 88.91% and a mean pixel accuracy (mPA) of 94.13%, representing improvements of 8.77 and 7.25% points, respectively, over the original HRNet. The disease severity assessment accuracy reached 97.65%. This study not only accurately segments apple leaves and diseased areas, but also effectively addresses the impact of complex backgrounds and leaf overlap on disease severity assessment, providing a solid scientific basis for disease management strategies.
Why it matches plant phenotyping methodsリンゴ葉の病斑領域を画像から分割・定量し、病害重症度を推定する深層学習・DRL-Watershed手法が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractExisting methods for disease detection and severity classification struggle to quickly and accurately segment and quantify diseased areas on leaves
The quality and quantity of maize yields are declining as a result of several structural issues with Ethiopia's traditional maize producing system. The lack of soil fertility, which is frequently hard to see visually from the maize leaves, is a major reason for this decline. An automated approach to identify and categorize fertility problems in maize plants is desperately needed to address this issue. The goal of this study is to develop a model for the recognition and classification of fertilizer treatment for maize based on maize leaf images, using deep learning algorithms to facilitate and improve the recognition and early control of fertilizer treatment for maize. The datasets utilized for this study were collected from various farming areas in the East Gojjam Zone, specifically the Hulet Ejju Enessie Woreda, comprising 4000 images of normal and deficient maize leaves. Through data augmentation techniques, this dataset was expanded to 16,000 images. A Convolutional Neural Network (CNN) with VGG16 and VGG19 architectures, along with a SoftMax classifier, was employed to analyze and classify the images into eight distinct categories based on their characteristics. Image enhancement and segmentation were performed using Gaussian filtering and Canny edge detection techniques. Hyperparameters, including image size, epoch number, batch size, dataset training and testing split ratio, and learning rate, were used to enhance the model's performance. The experiments with an image size of 224 × 224, 60 epochs, a batch size of 32, an 80/20 dataset split ratio, and a learning rate of 0.001 showed significant improvements in the classification model's performance. Ultimately, the best result was achieved with an accuracy of 95% in VGG16. VGG16 performed better than VGG19 in predicting fertilizer treatment for maize due to its lower complexity, which minimizes the risk of overfitting and enhances generalization, especially with smaller datasets.
Why it matches plant phenotyping methodsトウモロコシ葉画像から肥料欠乏・施肥状態を分類する画像解析・深層学習手法の開発が研究の中心であり、植物の生理状態を直接推定している。
abstractThe goal of this study is to develop a model for the recognition and classification of fertilizer treatment for maize based on maize leaf images, using deep learning algorithms
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants. This dataset was uniformly split into training, validation, and test sets to obtain consistent and unbiased model evaluations. The data pre-processing also involved pre-processing steps suitable for DL architectures to keep the input the same among all the models.We use several state-of-the-art pre-trained ConvNets models for the baselines, such as EfficientNetV2, ConvNeXt, Swin Transformer, and Vi-Transformer (ViT), to have an outlook on the performance. A new ConvNet-ViT hybrid model combines the ConvNet and ViT layers for local feature extraction and maintaining the global context. The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. Its outstanding performance of the state-of-the-art pre-trained top models positions itself as a solid model for practical agricultural use. Fusing the ConvNet and transformer frameworks jointly is beneficial for improving classification performance in image-based disease detection work.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法の開発と比較検証が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。
abstractThe key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves.
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model's efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset's class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet's robustness and reliability in automated plant 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し、交差検証で性能を評価しており、植物フェノタイピング手法が中心である。
abstractThis study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves.
Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genotype-by-environment interactions. Permutation-based feature importance analysis further revealed that planting date had a more significant impact on trait variation than genotype. To identify optimal combinations of genotype and planting date for maximizing morphological traits, the RF model was integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). According to the RF-NSGA-II optimization results, the optimal values, including 26 branches per plant, a growth period of 176 days, 116 bolls per plant, and 1517 seed numbers per plant, were achieved with the Qaleganj genotype planted on May 5. Collectively, these findings highlight the potential of integrating machine learning and evolutionary optimization algorithms as powerful computational tools for crop improvement and agronomic decision-making.
Why it matches plant phenotyping methods形態形質を予測する機械学習モデルを開発し、RFとMLPの性能比較・検証を行っているため、計算的な形質推定が研究の中心である。
abstracta machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits
Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease recognition under small-sample conditions, such as the difficulty in capturing multi-scale features, the minuteness of features, and the weak adaptability of traditional data augmentation methods. It proposes a solution that combines a multi-scale feature hybrid fusion architecture with data augmentation. The innovation of this study lies in the following four dimensions: (1) Utilize generative models to enhance the cross-category data balancing ability under small-sample conditions and enrich the sample information in the dataset. (2) Innovatively propose the LVT Block, a multi-scale information perception hybrid module based on the Ghost and Transformer structures. This module can effectively acquire and fuse multi-scale information and global information in the feature map. (3) Use the dense connection method to combine the LVT Block and the MARI Block to propose a new architecture, the DLVT Block. By fusing multi-scale information and global information, it improves the richness of feature information. It also uses the MARI to enhance the model's perception of disease areas and constructs an end-to-end lightweight model, DLVTNet, using the DLVT Block. Experiments show that this method achieves an average recognition rate of 98.48% on the New Plant Diseases Dataset. The number of parameters is reduced to 42.7% of that of MobileNetV4, and it maintains an accuracy of 96.12% in the tomato leaf disease test. This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. It provides a new method system with strong interpretability and excellent generalization performance for disease detection.
Why it matches plant phenotyping methodsブドウ葉の病害領域を画像から認識する軽量モデルを開発しており、植物の病害状態を直接推定する手法が研究の中心である。
titleLightweight grape leaf disease recognition method based on transformer framework.
This study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs). Sesame is a crucial oilseed crop that faces significant challenges from various diseases, including phyllody and bacterial blight, which adversely affect crop yield and quality. The objective of this research is to develop a robust and accurate model for identifying these diseases, leveraging the strengths of three state-of-the-art CNN architectures: ResNet-50, DenseNet-121, and Xception. The proposed ensemble model integrates these individual networks to enhance classification accuracy and improve generalization across diverse datasets. A comprehensive dataset of sesame leaf images, representing healthy, phyllody, and bacterial blight conditions was utilized to train and evaluate the models. The ensemble approach achieved an impressive overall accuracy of 96.83%, demonstrating superior performance in accurately classifying the different leaf conditions. The results highlight the effectiveness of combining multiple deep learning models, which allows for the extraction of diverse feature representations and decision-making strategies. This thesis also discusses the advantages of the ensemble methodology, including improved robustness to variations in disease symptoms and enhanced adaptability to changing agricultural practices. The findings of this research have significant implications for precision agriculture. They offer a reliable tool for the early detection and classification of sesame diseases. By enabling timely interventions, this ensemble-based framework can contribute to the sustainability and productivity of sesame cultivation, ultimately supporting food security and agricultural resilience.
Why it matches plant phenotyping methodsゴマ葉画像から病害状態を推定するCNN分類手法の開発と評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs).
Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato. About 8850 leaf images have been acquired for this work from five different publicly available datasets on Kaggle. All the acquired images were pre-processed by applying four different image pre-processing operations. The number of images in the training dataset was tripled for better model performance by applying five different augmentation operations. The augmented dataset was then used to train YoloV5, YoloV8, and the proposed YOLO-LeafNet. The performance of all three models is evaluated in terms of recall, precision, and Mean Average Precision (mAP). The YoloV5 attained a precision of 0.861, recall of 0.868, mAP50 of 0.944, and 0.815 of mAP50-95, and YoloV8 attained 0.977 precision, 0.975 recall, 0.984 of mAP50, and mAP50-95 of 0.915, whereas the proposed the YOLO-LeafNet attained precision of 0.985, recall of 0.980, mAP50 of 0.990, and mAP50-95 of 0.940. The experimental results reveal that the proposed YOLO-LeafNet outperformed YOLOv5 and YOLOv8 in terms of all performance metrics.
Why it matches plant phenotyping methods葉画像から植物病害を検出する深層学習手法を開発し、既存モデルと性能比較しており、植物状態の画像ベース表現型取得が中心である。
abstractIn this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato.
The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73-[Formula: see text] overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70-[Formula: see text] with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to [Formula: see text] with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.
Why it matches plant phenotyping methodsFTIR・生化学データからジャガイモ植物の vigor を予測し、圃場間・年次間で予測性能を検証しており、植物形質の取得・推定法が中心です。
abstractinterested in predicting it at scale
Reproduction assets foundThe paper's Data Availability statement points to a public 4TU.ResearchData deposit containing the seed tuber and plant canopy (drone-derived vigor) datasets plus the Python code needed to reproduce the regression results — a paper-specific, publicly actionable asset.Dataset · publicBoth the seed tuber and plant canopy datasets are available at http://doi.org/10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55e . The data also includes the Python code necessary to reproduce the results of regression.Open asset ↗10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55elines:267-336Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract This paper introduces an efficient IoT-based framework for predicting crop damage within smart agricultural systems, focusing on the integration of Internet of Things (IoT) sensor data with advanced machine learning (ML) and ensemble learning (EL) techniques. The primary objective is to develop a reliable decision support system capable of forecasting crop health status classifying crops as healthy, pesticide-damaged, or affected by other stressors while addressing a critical challenge: the presence of missing data in real-time agricultural datasets. To overcome this limitation, the proposed approach incorporates robust data imputation strategies using both traditional ML methods and powerful EL models. Techniques such as K-Nearest Neighbors, linear regression, and ensemble-based imputers are evaluated for their effectiveness in reconstructing incomplete data. Furthermore, Bayesian Optimization is applied to fine-tune EL classifiers including XGBoost, CatBoost, and LightGBM (LGBM), enhancing their predictive performance. Extensive experiments demonstrate that XGBoost outperforms all other models, achieving an average sensitivity of 88.1%, accuracy of 89.56%, precision of 83.4%, and F1-score of 84.8%. CatBoost and LGBM also deliver competitive results, with CatBoost achieving 90.50% accuracy and LGBM reaching 90.23%. In addition, the imputation capability of the XGBoost model is validated through a low Mean Squared Error (MSE) of 0.0213 and a high R-squared (R 2 ) value of 0.99, confirming its effectiveness for both prediction and data recovery tasks. The key contributions of this innovative work include the design of a low-cost, power-efficient, and scalable crop damage prediction system, the integration of real-time IoT data with optimized ensemble learning, and a comprehensive evaluation of imputation techniques to enhance model robustness. This framework is particularly suited for deployment in resource-constrained agricultural environments, advancing the field of smart farming through intelligent, data-driven solutions.
Why it matches plant phenotyping methodsIoTセンサーデータから作物の健康・損傷状態を推定する機械学習ワークフローが研究の中心であり、植物状態の取得・分類法として実質的に評価されている。
abstractThe primary objective is to develop a reliable decision support system capable of forecasting crop health status classifying crops as healthy, pesticide-damaged, or affected by other stressors
Venus flytrap (Dionaea muscipula) leaves exhibit an exceptionally rapid closing motion that occurs within one second. The rapid closure of outwardly curved leaves is thought to be driven by snap-buckling instability-a rapid transition of an elastic system from one state to another. However, the ability of leaves that do not curve outward to also close suggests that the mechanics of leaf closure are complex and need to be understood using three-dimensional (3D) kinematics. We therefore developed a 3D reconstruction method to quantify the curvatures and displacements of leaf blades using two high-speed cameras. We then reconstructed a 3D surface mesh of the leaf, which revealed that the changes in curvature are spatiotemporally heterogeneous. We inferred the stretching and curvature elastic energies of the reconstructed surface, determining that the mechanical forces associated with in-plane deformation become significant in the peripheral regions of the leaf. This was true among different samples; however, the components of the energy profiles varied for each sample. The novelty of this study is that we could infer the elastic energy and the corresponding mechanical forces during closing motion. Our mechanical inference method will be useful for examining the deformation processes of various curved plant structures.
Why it matches plant phenotyping methods植物葉の3D画像再構成により、曲率・変位・弾性エネルギーを定量化する手法を開発しており、植物形態・運動の表現型取得が研究の中心である。
abstractWe therefore developed a 3D reconstruction method to quantify the curvatures and displacements of leaf blades using two high-speed cameras.
Rice Bacterial Leaf Blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), is a major threat to rice production due to its rapid spread and widespread impact. Early detection and stage-specific classification of BLB are essential for timely intervention, particularly in complex environments with cluttered backgrounds and overlapping symptoms. This study introduces RCAMNet, a novel multi-task framework designed for accurate classification and severity analysis of BLB. The proposed approach begins by generating multiclass segmentation masks using three candidate methods: MultiClass U-Net, DeepLabv3, and Detectron2 (used here for its instance segmentation capability). In the second phase, a dual-path attention mechanism is employed. The Convolutional Block Attention Module (CBAM) is independently applied to both the RGB image and its corresponding segmentation mask to emphasize important visual and spatial features. Enhanced features are fused and fed into a lightweight MobileNetV2 classifier for disease severity prediction. RCAMNet achieved a test accuracy of 96.23%, outperforming conventional raw image-based models (89.58%). Interpretability is enhanced through Grad-CAM visualizations. RCAMNet demonstrates robust performance in classifying BLB severity across diverse environmental conditions, confirming its real-world deployment potential. Additionally, the proposed framework supports the development of edge device-compatible solutions, enabling real time monitoring and improved disease management in precision agriculture.
Why it matches plant phenotyping methodsイネ葉の画像から病害の重症度を推定するセグメンテーション・深層学習手法を中心に開発・評価しており、植物状態の画像ベース表現型計測に該当する。
abstractThis study introduces RCAMNet, a novel multi-task framework designed for accurate classification and severity analysis of BLB.
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in crops is highly important. As the population grows, the demand for rice also rises. Therefore, disease management is vital in rice cultivation, and rapid identification of rice diseases is critical for timely pesticide application and effective control. Consequently, there is a need to boost agricultural productivity by adopting new technologies. Deep learning is a popular area of research in various fields. This research aims to design and propose a new automated model using a deep learning model for the disease identification and categorization of paddy leaves. The system follows a structured workflow comprising several stages: image acquisition, pre-processing, feature extraction, feature selection, and classification. Images of paddy leaves were obtained from the paddy doctor dataset hosted on Kaggle. The data is pre-processed by choosing the RoIs, labelling, enhancement, and segmentation using adaptive thresholding and grouped using K-means clustering. The MobileNetV3 model, a pre-trained transfer learning approach, extracted colour, shape, and texture features. The vital features are selected using the hybrid Genghis Khan Shark Optimization (GKSO) with Simulated Annealing (SA) algorithm. The chosen features are subsequently fed into the CatBoost for disease classification. The deep learning techniques introduced for disease identification and classification have been compared with various conventional classifiers, and the system's performance has been validated using metrics such as accuracy, sensitivity, and F1-score. Performance investigations prove that the technique efficiently yields a higher accuracy of 98.52%, outperforming state-of-the-art techniques.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動推定する深層学習ワークフローが研究の中心であり、画像取得・前処理・特徴抽出・分類と性能検証を含むため、植物フェノタイピング手法として含める。
abstractThis research aims to design and propose a new automated model using a deep learning model for the disease identification and categorization of paddy leaves.
Tomato leaf disease detection is critical in precision agriculture for safeguarding crop health and optimizing yields. This study compares the latest YOLO architectures, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, using the Tomato-Village dataset, which contains 14,368 images across six disease classes. All models are trained under identical settings to ensure a fair evaluation based on precision, recall, mean Average Precision, training time, and inference speed. Results show that YOLOv11 consistently outperforms the other architectures, achieving the highest accuracy with competitive training times and acceptable latency. YOLOv10, YOLOv8, and YOLOv12 also deliver strong results, with YOLOv12n emerging as the most effective lightweight model for resource-constrained environments. In contrast, YOLOv9 demonstrates the weakest performance, requiring more training time and exhibiting higher latency. Overall, YOLOv11 is positioned as the most effective solution for tomato leaf disease detection, providing a strong benchmark for future advancements in agricultural technology.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定するYOLO手法を複数比較し、精度・推論速度・学習時間で技術評価しているため、植物フェノタイピング手法が中心である。
abstractThis study compares the latest YOLO architectures, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, using the Tomato-Village dataset
Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques demand a large quantity of data to train the models for diagnosis and further classification. Few-shot learning has surfaced to remove the drawbacks of deep learning methods. Therefore, the proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning. Metric Learning calculates the distance to measure the similarity between the data points. Hence, addressing the challenge of limited labeled data, GRCornShot effectively classifies healthy and corn diseases. Furthermore, the Gabor filter is incorporated into the backbone network ResNet-50 to extract the texture features and to enhance the classification performance. The experiments show the promising application of few-shot learning in agronomic applications, providing a robust solution for detecting corn diseases precisely with minimal data requirements. Using a 4-way 2-shot, 3-shot, 4-shot, and 5-shot learning strategy, GRCornShot achieves impressive accuracy of 96.19%, 96.54%, 96.90%, and 97.89%, respectively.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から分類する少数ショット深層学習手法を開発し、精度を評価しており、植物フェノタイピング手法が中心である。
abstractthe proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning.
Reproduction assets foundThe paper's corn disease detection experiments use two publicly available image datasets (Roboflow corn-disease and the Kaggle corn/maize leaf disease dataset), explicitly linked in the Data Availability statement. No author analysis code or trained model checkpoint is deposited.Dataset · publiction, Writing—Original draft preparation. J.S. Conceptualization of this study, Editing, Supervision. S.B. Conceptualization of this study, Editing, Supervision. S.K. Funding, Editing, Supervision. All authors reviewed the manuscript.
Data availability
Publicly available datasets were used in this study which can be found here: https://universe.roboflow.com/final-enlye/corn-disease and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset.
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Competing interests
The authors declare no competing interests.
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JayakOpen asset ↗Roboflow · final-enlye/corn-diseaselines:355-376Dataset · publication of this study, Editing, Supervision. S.B. Conceptualization of this study, Editing, Supervision. S.K. Funding, Editing, Supervision. All authors reviewed the manuscript.
Data availability
Publicly available datasets were used in this study which can be found here: https://universe.roboflow.com/final-enlye/corn-disease and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jayakrushna Sahoo, Sivaiah Bellamkonda and Sumit Kumar contributOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:355-376Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning. To address class imbalance and improve generalization, the study adopts a holistic training strategy that integrates data augmentation, stratified data splitting, and class weighting, alongside transfer learning. The model is evaluated on the PlantVillage (PV) dataset and a curated Apple PV (APV) dataset and compared against EfficientNet-B0, EfficientNet-B3, Inception-v3, ResNet50, and VGG16 models. The fine-tuned model demonstrates outstanding test accuracies of 99.69% and 99.78% for classifying plant diseases using the APV and PV datasets, respectively. The fine-tuned model outperforms EfficientNet-B0, EfficientNet-B3, and VGG16 on both datasets and shows superior performance compared to Inception-v3 and ResNet-50 on the PV dataset. Both EfficientNet-B0 and the fine-tuned model demonstrate the lowest memory consumption and floating-point operations per second (FLOPs). Also, as compared to the EfficientNet-B0 model, the fine-tuned model achieves an 11% increase in accuracy on the APV dataset and a 49.5% accuracy improvement on the PV dataset, with approximately a 7-8% increase in both memory usage and FLOPs. The fine-tuned model thus emerges as an effective solution for plant leaf disease classification, delivering outstanding accuracy with optimized memory consumption and FLOPs, making it suitable for resource-constrained environments. This study demonstrates that fine-tuned CNN approaches, when combined with transfer learning, advanced data pre-processing, and architectural optimizations, can significantly enhance the accuracy of diseased leaf classification in crops with efficient implementation in limited-resource settings.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から分類するCNN手法の開発・比較評価が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractThis paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe PV dataset used for this research work is taken from: https://data.mendeley.com/datasets/tywbtsjrjv/1Open asset ↗tywbtsjrjv/1lines:334-374Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Tomato growing points and flower buds serve as vital physiological indicators influencing yield quality, yet their detection remains challenging in complex facility environments. This study develops an improved YOLOv8 model for robust flower bud detection by first constructing a comprehensive multi-environment dataset covering 10 typical growing conditions with enhanced annotations. Three key innovations address YOLOv8's limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. Ablation experiments confirm these modifications synergistically improve adaptability to scale and environmental variations, achieving 97.8% mAP@0.5 (+ 0.5%) and 85.1% mAP@0.5:0.95 (+ 5.1%) with 11% fewer parameters. Practical deployment on agricultural robots in operational greenhouses demonstrated 93.6% detection accuracy, validating the model's effectiveness for precision agriculture applications. The proposed system achieves an optimal balance of accuracy, speed, and lightweight design while providing immediately applicable solutions for automated tomato monitoring.
Why it matches plant phenotyping methodsトマトの生長点・花蕾という植物器官を対象に、複雑な環境での画像検出モデルを開発し、データセット構築、アブレーション、実環境ロボットでの検証まで行っており、植物フェノタイピング手法が中心である。
abstractThis study develops an improved YOLOv8 model for robust flower bud detection by first constructing a comprehensive multi-environment dataset covering 10 typical growing conditions with enhanced annotations.
With the increasing demand for precision agriculture, automatic detection of tomato leaf diseases has become a critical technological challenge in smart agriculture. Among various diseases, Tomato Yellow Virus Leaf, due to its unique pathological characteristics, presents a particularly challenging identification target. Traditional image recognition methods often fail to meet the high-precision detection requirements for this disease, leading to delayed responses in disease control by farmers, which severely impacts tomato yield and quality. To address this issue, this paper proposes an optimized YOLOv8n algorithm, incorporating a C2f-DynamicConv optimization module. By dynamically adjusting the weights of convolutional kernels, the model can adapt to the characteristics of different input data, thereby enhancing its ability to represent diverse features. Additionally, we introduce the SimAM attention mechanism, which enhances the model's focus on key areas by weighting the feature map, significantly improving the accuracy of disease detection while filtering out irrelevant features and enhancing sensitivity. During the upsampling process, we adopt the Dysample upsampling operator, optimizing the quality of feature map reconstruction and improving detection resolution through a refined upsampling strategy. To better address the bounding box regression problem in object detection, we incorporate the GIoU loss function. Compared to traditional loss functions, GIoU performs excellently in handling bounding box overlap and positional accuracy, further improving the model's detection performance. Experimental results show that the improved model achieves an average precision of 81.8%, precision of 77.1%, and recall of 77.4%. Compared to existing methods, our approach shows significant advantages in detection accuracy, localization precision, and model computational efficiency, achieving improved detection performance on the tomato leaf disease dataset.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出する改良YOLOv8n手法を開発し、精度・再現率・位置特定性能を評価しており、植物の病害状態の取得が研究の中心である。
abstractExperimental results show that the improved model achieves an average precision of 81.8%, precision of 77.1%, and recall of 77.4%.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData availability
The datasets can be downloaded in https://github.com/weilaibot/sanyau_02.git.Open asset ↗weilaibot/sanyau_02pdf-page:20 lines:1-65Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yields and food supply chains. Despite the progress in artificial intelligence, particularly deep learning, challenges persist in real-world applications due to environmental noise, varying light conditions, and other complicating factors that hinder detection accuracy. This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. By integrating convolutional operations with self-attention mechanisms, AttCM-Alex effectively addresses the variability in light intensity and image noise, ensuring robust performance. To simulate practical agricultural scenarios, the study employs bilinear interpolation for image dimension adjustment and introduces Salt-and-Pepper noise. Additionally, the model's robustness was evaluated by varying image brightness levels by ±10%, ±20%, and ±30%. Experimental results demonstrate that AttCM-Alex significantly outperforms traditional models, particularly in scenarios involving fluctuating light conditions and noise interference. The model achieved a peak detection accuracy of 0.97 with a 30% increase in image brightness and maintained an accuracy of 0.93 even with a 30% decrease in brightness, highlighting its robustness and reliability. The findings affirm the AttCM-Alex model as a powerful tool for real-world agricultural applications, capable of enhancing disease detection systems' accuracy and efficiency. This advancement not only supports better crop management practices but also contributes to sustainable agriculture and global food security.
Why it matches plant phenotyping methods植物画像から病害を検出・分類する深層学習手法を開発し、照明変動やノイズ条件で性能評価しており、病害状態の取得・推定が研究の中心である。
abstractThis study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe proposed model codes are publicly available at: https://github.cOpen asset ↗pdf-page:5 lines:1-71Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop losses and improving agricultural yield. This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of the Convolutional Neural Networks (CNNs) and Swin Transformers, enabling the extraction of both local and global features from plant images. This is achieved through the implementation of an inception architecture and cross-channel feature learning, which collectively enhance the information necessary for detailed feature extraction. Comprehensive experiments were conducted using five distinct datasets: PlantVillage, Plant Pathology 2021 competition dataset, PlantDoc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.96% on the PlantVillage dataset, 99.22% on iBean, 86.89% on AI2018, and 77.54% on PlantDoc. These results underscore the model's robustness and its capacity to generalize across various datasets and real-world conditions. The high accuracy and F1 scores, in conjunction with low loss values, further validate the model's efficacy in learning discriminative features. The ST-CFI model signifies a substantial advancement in the early and accurate detection of plant diseases, serving as a valuable instrument for precision agriculture. Its capacity to integrate CNNs and Transformers within a unified framework enhances the model's feature extraction capabilities, resulting in improved accuracy in the identification of plant diseases. This study concludes that the ST-CFI model effectively addresses plant disease detection challenges, with significant implications for agricultural sustainability and productivity.
Why it matches plant phenotyping methods葉画像から植物病害を識別する深層学習手法を提案し、複数データセットで性能評価しているため、植物状態の画像ベース表現型推定が中心である。
abstractThis paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicAll data sets in the paper are public data sets, and the corresponding data sets can be downloaded according to
the references provided.Open asset ↗pdf-page:18 lines:1-26Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible image indices. A total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models. Out of 143 genotypes screened based on final disease severity 3, 18, 18, 17, 34 and 53 genotypes were grouped in immune, resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible categories, respectively. Model performances was evaluated using R 2 , d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. Results revealed that 34 indices exhibited significant correlations (p 2 and d-index values exceeding 0.92 and 0.98, respectively, in calibration, and 0.88 and 0.96 in validation, underscoring their effectiveness in predicting YMD severity using RGB images only. Random Forest (RF), Cubist, XGBoost (XGB), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM) emerged as the five top-performing models for predicting YMD severity using visible indices in yard-long beans. These findings hold practical implications for timely disease management strategies, expediting breeding programs, and aiding policy planners and farmers in making well-informed decisions.
Why it matches plant phenotyping methodsRGB画像から可視画像指標を抽出し、機械学習で植物病害の重症度を推定・検証する手法が研究の中心であり、植物状態の定量的フェノタイピングに該当する。
abstractA total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models.
One of the primary challenges leading to a significant reduction in agricultural production is the prevalence of diseases affecting citrus plants. Prevention and monitoring the spread of citrus plant diseases is crucial for maintaining citrus production. This decrease in productivity adversely affects the overall economy. The essential step for enhancing the quality of fruit production and promoting economic growth involves the classification and identification of leaf diseases in the early stage. In this work, a multi-kernel CNN model with attention mechanism is used for classification of citrus plants diseases is proposed. Initially, the input image is pre-processed for resizing the images as the images are obtained from different datasets. After resizing the image, the feature extraction process is carried out by the pretrained convolutional neural networks. In the next step, the two attention mechanisms multi kernel channel attention and spatial attention is used. These two attention mechanisms are used for obtaining spatial and channel attention feature maps. Finally, the classification process is carried out to classify the normal and diseased cases. The test accuracy results shows that our model surpasses the other models in terms of its classification performance.
Why it matches plant phenotyping methods柑橘葉画像から病害状態を分類するCNN手法が研究の中心であり、植物の病徴・状態を画像から推定するため、植物フェノタイピング手法として含める。
abstractIn this work, a multi-kernel CNN model with attention mechanism is used for classification of citrus plants diseases is proposed.
Studies on plant ecotoxicology focus essentially on growth and biochemical processes, often overlooking anatomical and morphological alterations that may occur post-germination. These changes, however, offer valuable insights into early environmental stress, enabling proactive intervention and mitigation strategies. Thus, this study aimed to identify and characterize morphological and anatomical alterations during early seedlings' development, using a new Visual PhytoToxicity assessment (ViPTox) approach. This visual scoring system categorizes the alterations recorded into severity levels, offering a simple, reproducible method for assessing phytotoxicity based on observable changes in plant structure. A standard germination assay with Lactuca sativa was conducted using potassium dichromate (PD, reference compound) at 0.00, 100.5, 120.6, 144.7, 173.6, 208.3, and 250.0 mg/L. Standard endpoints, including germination rate, seedling size, and fresh and dry weight were evaluated alongside a novel ecotoxicological approach. Based on the seedling effects observed, a dichotomous key with a scoring system was defined using a classification range from 0 (normal seedling) to 10 (no germination - maximum damage), providing insights into the severity of observed alterations (e.g., absence of roots and/or leaves (score 9), chlorosis and necrosis (score 8), atrophy (score 7, 6, and 5), deformations (score 4 and 3), reduction of size (score 2 and 1)), in order to calculate the phyto-morphological damage (PMD). Considering the standard endpoints, no significant alterations were observed in L. sativa germination. However, a significant decrease in seedling size (> 20 %) and fresh weight (> 50 %) was observed, after exposure to the highest PD concentrations (173.6, 208.3, and 250.0 mg/L). Regarding the ViPTox approach, PMD was observed in all concentrations ≥ 120.6 mg/L of PD. Significant effects were observed even at lower PD concentrations (120.6 and 144.7 mg/L) where phyto-morphological damages (e.g., atrophy and deformations) were quantified, while standard endpoints were unaffected. ViPTox presents a reproducible, non-invasive, and cost-effective approach to evaluate seedling responses to environmental stress, complementing traditional assessment techniques while providing crucial insights that support proactive intervention and effective mitigation strategies.
Why it matches plant phenotyping methodsViPToxという幼植物の形態・解剖学的変化を可視スコア化する新規で再現可能な表現型評価法の開発・検証が研究の中心である。
abstractusing a new Visual PhytoToxicity assessment (ViPTox) approach.
The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions such as inconsistent lighting, leaf deformities, and subtle distinctions between disease symptoms. To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. Additionally, the integration of ancillary convolutional layer to this architecture enhances the ability of the model to detect subtle disease variations. To further improve the adaptability, the proposed method incorporates a preprocessing and data augmentation techniques. Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. The proposed approach achieved an accuracy of 99.89% and a recall rate of 99.87%, demonstrating its suitability for crop classification with minimal computational overhead. Ablation studies further validate the significant contributions of the spatial attention module and the ancillary convolutional layer to the overall performance of the proposed model.
Why it matches plant phenotyping methods植物病葉の画像から病害状態を分類する深層学習手法を提案し、既存手法との比較とアブレーション評価を行っており、病害表現型の取得・推定手法が中心である。
abstractwe introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA).
Cotton, the backbone of global textile production, demands sustainable agricultural practices to ensure fiber, food, and environmental security. Cotton crop play an essential role in farming economies; however, production is sometimes affected by various diseases that harm production. We proposed a methodology that uses formal modeling and verification for requirements confirmation to improve the monitoring and detection of cotton crop diseases. The correct information and requirements about disease symptoms can improve disease monitoring and prediction. The Temporal Logic of Action (TLA+) is used to construct a mathematical model to verify requirements by providing disease symptoms and then model checking to ensure correctness properties. Using model checking in TLA + ensures the reliability and correctness of disease symptom detection. We consequently used deep learning models to predict cotton diseases, i.e., Aphids, Armyworms, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy leaf. Our results show that the Convolutional Neural Network (CNN) model achieved an overall accuracy of 98.7% with class-specific accuracy ranging from with F1-scores across all classes (e.g., 0.90 for Powdery Mildew and 0.87 for Army Worm).
Why it matches plant phenotyping methods綿花葉の病徴を画像から深層学習で分類・検出する手法が研究の中心であり、植物の病害状態を推定するため、植物フェノタイピング手法として収録対象。
abstractWe proposed a methodology that uses formal modeling and verification for requirements confirmation to improve the monitoring and detection of cotton crop diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publictoolbox modules, are publicly available on GitHub at https://github.com/abdulpk/MuhammadMusa for furtherOpen asset ↗https://github.com/abdulpk/MuhammadMusapdf-page:18 lines:1-64Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Climate change exacerbates the challenges of maintaining crop health by influencing invasive pest and disease infestations, especially for cereal crops, leading to enormous yield losses. Consequently, innovative solutions are needed to monitor crop health from early development stages through harvesting. While various technologies, such as the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI), have been used, portable, cost-effective, and energy-efficient solutions suitable for resource-constrained environments such as edge applications in agriculture are needed. This study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability. The system consists of a high-definition camera for real-time plant image acquisition, a Raspberry-Pi 5 integrated with the Tiny-LiteNet model for edge processing, and a GSM/GPRS module for cloud communication. The experimental results demonstrated that Tiny-LiteNet achieved up to 98.6% accuracy, 98.4% F1-score, 98.2% Recall, 80 ms inference time, while maintaining a compact model size of 1.2 MB with 1.48 million parameters, outperforming traditional CNN architectures such as VGGNet-16, Inception, ResNet50, DenseNet121, MobileNetv2, and EfficientNetB0 in terms of efficiency and suitability for edge computing. Additionally, the low power consumption and user-friendly design of this smart device make it a practical tool for farmers, enabling real-time pest and disease detection, promoting sustainable agriculture, and enhancing food security.
Why it matches plant phenotyping methods植物画像から病害・害虫状態を推定するエッジ撮像・CNNシステムの開発が研究の中心であり、植物状態のフェノタイピング手法に該当する。
abstractThis study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability.
As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional farming methods struggle with efficiently managing nutrients and water while ensuring high-quality products, leading to resource wastage and food safety concerns. This study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems, thereby enhancing resource management and product quality. We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. The model was validated with real-time data from aeroponic systems, showing good predictive performance, particularly for fresh weight and total leaf area. In contrast, predictions of nitrate content and water consumption were less accurate, due in part to smaller training datasets and limitations of the physics-based component under soilless conditions. Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability.
Why it matches plant phenotyping methods植物の生体重と葉面積という形態・成長形質を推定するハイブリッド計算モデルを開発し、実データで検証しており、形質推定手法が研究の中心である。
abstractThis study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems
Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs) have revealed promise in leaf disease detection and classification. This research proposes PLA-ViT, or Precision Leaf Analysis with Vision Transformers, to improve agricultural monitoring. Vision Transformers (ViTs) outperform other neural networks because they employ self-attention to find global contextual information. The approach uses data augmentation, normalization, and bilateral filtering to increase generalization and image quality. Transfer learning using pre-trained ViTs reduces computing load and improves feature extraction. The model may be adjusted by hyperparameter tuning and adaptive learning rate scheduling for robust performance with minimal overfitting. In experiments, PLA-ViT outperforms other neural network-based models regarding detection accuracy, disease localization performance, inference time, and computational complexity. By attaching the system to IoT sensors, stakeholders may observe farms in real time and take timely measures like pesticide treatment or plant isolation. This novel method shows that transformer-based designs might help progress in precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害を検出・分類・局在化するVision Transformer手法の開発と性能比較が研究の中心であり、植物の病害状態を直接推定している。
abstractThis research proposes PLA-ViT, or Precision Leaf Analysis with Vision Transformers, to improve agricultural monitoring.
Detecting rice leaf diseases is essential for agricultural stability and crop health. However, the diversity of these diseases, their uneven distribution, and complex field environments create challenges for precise, multi-scale detection. While YOLO object detection algorithms show strong performance in automated detection, their feature extraction capabilities remain limited in complex agricultural settings. Moreover, their high computational demands hinder deployment on resource-constrained devices, necessitating further optimization.To overcome these issues, This paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF). The LEDH enhances detection speed by simplifying the network structure while maintaining accuracy, reducing computational demands. The MSPPF improves the model's ability to capture intricate details of rice leaf diseases at various scales by fusing multi-level feature maps. On the RiceDisease dataset, G-YOLO surpasses YOLOv8n with 4.4% higher mAP@0.5, 3.9% higher mAP@0.75, and a 13.1% increase in FPS, making it well-suited for resource-constrained devices due to its efficient design.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出する新規YOLOアーキテクチャを開発し、データセット上で精度・速度を評価しており、病害フェノタイピング手法が中心である。
abstractThis paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF).
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food security and the incomes of farmers. Citrus plants, recognized for their nutritional benefits and economic significance, are especially vulnerable to diseases such as citrus greening, Black spot, and Citrus canker. Due to technological advancements, image processing and Deep learning algorithms can now detect and classify plant diseases early on, which assists in preserving crop health and productivity. The proposed work enables farmers to identify and visualize multiple diseases affecting citrus plants. This study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf. It is achieved using the RSL (Residual Squeeze & Excitation LeakyRelu) Linked-TransNet multiclass segmentation model. The proposed model stands out in its ability to address major limitations in existing models, including spatial inconsistency, loss of fine disease boundaries, and inadequate feature representation. The significance of this proposed RSL Linked-Transnet model lies in its integration of hierarchical feature extraction, global context modeling via transformers, and precise feature reconstruction, ensuring superior segmentation accuracy and robustness. The results of the proposed RSL Linked-TransNet architecture reveal average values of 0.9755 for accuracy, 0.0660 for loss, 0.9779 for precision, 0.9738 for recall, and 0.9308 for IoU. Additionally, the model achieves a mean F1 score of 0.7173 and a mean IoU of 0.7567 for each disease class in images from the test dataset. The segmentation results are further utilized to identify the prime disease affecting the leaves and evaluate disease severity using the prime disease classification and severity detection algorithm.
Why it matches plant phenotyping methods柑橘葉画像から病斑をセグメンテーションし、主要病害と重症度を推定する手法の開発が中心であり、植物の病害状態を直接測定するため、植物フェノタイピング手法として適格。
abstractThis study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf.
Pests and diseases significantly impact the growth and development of crops. When attempting to precisely identify disease characteristics in crop images through dialogue, existing multimodal models face numerous challenges, often leading to misinterpretation and incorrect feedback regarding disease information. This paper proposed a large language model for multimodal identification of crop diseases and pests, which can be called LLMI-CDP. It builds up on the VisualGLM model and introduces improvements to achieve precise identification of agricultural crop disease and pest images, along with providing professional recommendations for relevant preventive measures. The use of Low-Rank Adaptation (LoRA) technology, which adjusts the weights of pre-trained models, achieves significant performance improvements with a minimal increase in parameters. This ensures the precise capture and efficient identification of crop pest and disease characteristics, greatly enhancing the model's application flexibility and accuracy in the field of pest and disease recognition. Simultaneously, the model incorporates the Q-Former framework for effective modal alignment between language models and image features. Through this approach, the LLMI-CDP model is able to more deeply understand and process the complex relationships between language and visual information, further enhancing its performance in multimodal recognition tasks. Experiments are carried out in the homemade datasets, The results demonstrate that the LLMI-CDP model surpasses five leading multimodal large language models in relevant evaluation metrics, confirming its outstanding performance in Chinese multimodal dialogues related to agriculture.
Why it matches plant phenotyping methods作物画像から病害・害虫の状態を識別するマルチモーダル手法を開発し、自作データセットで既存モデルと比較評価しており、植物の病害状態の取得・推定が中心です。
titleA large language model for multimodal identification of crop diseases and pests.
By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad Kavous University Research Farm on three planting dates (19 November, 19 January, and 19 March), with three replicates in 2018/2019 and 2019/2020. The study employed RReliefF, MRMR, and F-Test feature selection algorithms to identify essential phenotype traits and molecular markers. Subsequently, Decision Tree, Random Forest, Neural Network, and Gaussian Process Regression models were compared using MAE, RMSE, and R2 metrics. The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. The results indicated that the Neural Network model accurately predicted powdery mildew disease resistance in barley lines. The evaluation based on high R2 values, as well as low MAE and RMSE, highlighted the efficacy of these models in identifying significant phenotype traits and molecular markers associated with disease resistance. The findings demonstrate machine learning models' potential in accurately predicting powdery mildew disease resistance in Barley. The neural network model specifically showed excellent results in this area because it managed to identify critical phenotypic traits and molecular markers very well. This research highlights the importance of combining AI with molecular markers for improved disease resistance and other desirable crop traits during plant breeding.
Why it matches plant phenotyping methods機械学習モデルの比較・最適化により、オオムギのうどんこ病抵抗性という植物状態を予測する計算手法が研究の中心であり、単なる生物学的実験の測定結果ではない。
abstractBy employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L).
Agriculture and its yields are indispensable to human life all over the planet. It is an essential part of many countries' economies and without it the world's population can not be fed. As such, guaranteeing harvest with minimal loss is a primary objective. One factor that heavily contributes to loss in crop harvesting are plant diseases, which often affect crops and their leaves. A plant's leaf often carries symptoms that indicate whether or not a plant is infected, but traditional manual approaches to identifying these symptoms are tedious and laborious. Additionally the process of manually spotting diseases can be rather slow in a field where urgency and fast identification are very important. The sooner a disease gets identified, the sooner countermeasures can be carried out. To improve both the accuracy with which diseases can be recognized, as well as increasing the speed at which this can be carried out, deep learning methods have proven useful. Recently the field of plant disease recognition has seen a big uptick in the application of various convolutional neural network (CNN) models for the automatic classification of diseases. There exist many different highly-capable models at this time. There also exists a range of plant leaf disease classification image datasets containing different plants and diseases. However, there seems to be no consensus on which model is best suited to handle this task and the same can be said for the datasets. To the best of our knowledge, prior work has used a wide range of different models with different datasets in the way of feasibility studies, but without comprehensively identifying which models are best used in this field. In this work we test a large number of state-of-the-art CNN models on a wide range of openly available datasets to asses their performance and to identify models that are best suited for this field, in order to be able to built better models, and even new foundation models, based on these findings. 23 models have been tested on 18 datasets, both using transfer-learning and transfer-learning with additional fine-tuning added, for five iterations each. Transfer-learning allows models to utilize knowledge obtained from other previous tasks to be used for new tasks, reducing training time and lowering the need for training data. The experiments result in a total of 4140 having been trained for this work. All results will be compared and contextualized in order to find the best models architecture for plant leaf disease classification as well as assessing which datasets are well suited for this task.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する深層学習モデルについて、多数のモデルと公開データセットを比較評価するベンチマーク研究であり、病害表現型の抽出手法が中心です。
abstractIn this work we test a large number of state-of-the-art CNN models on a wide range of openly available datasets to asses their performance and to identify models that are best suited for this field
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-of-the-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉画像から病害の有無・種類を推定する画像ベースの病害表現型計測法をYOLOモデルで開発・評価しており、手法が研究の中心です。
abstractThis study proposed a system designed to identify and monitor plant diseases using a transfer learning approach.
Reproduction assets foundThe paper's Data Availability statement provides a public Google Drive link to the study data and a public GitHub repository, and the study's input images come from the public Roboflow 'Detecting Diseases' dataset. These are paper-specific, publicly accessible assets supporting the plant disease detection experiments.Dataset · publicone who inspired our work.
Author contributions
BSM: conceptualization and drafting, HSN: data collection and analysis. MNW: Methodology and review. NOF: Drafting and interpretation. CCZ: Writing and method. HDA: writing and Data Collection; EMO: Drafting, Supervision and Editing.
Data availability
The data can be accessed via: https://drive.google.com/file/d/1kA_JWhHQhyzzuzlpzppK2nNTtbiR2N77/view . https://github.com/Sachinthana-Lokuyaddage/Plant_Disease_Detection_Using_Transfer_Learning_with_ResNet50 .
Declarations
Competing interests
The authors declare no competing interests.
Consent to participate
All authors consent to participate.
Footnotes
Publisher’s note
Springer Nature remains neuOpen asset ↗lines:209-247Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer architecture, crafted to adeptly handle 256 × 256 pixel images across three color channels (RGB). Beginning with an input layer complemented by a Zero Padding 2D layer to preserve spatial dimensions, our model ensures the retention of crucial geographical information across its depth. The innovative use of a convolutional layer with 64 7 × 7 filters, followed by batch normalization and Rel U activation, allows for the extraction and representation of intricate patterns from the input data. Key to our model's design is the incorporation of residual blocks, facilitating the learning of deeper networks by alleviating the vanishing gradient problem. These blocks combine Conv2D layers, batch normalization, activation layers, and shortcut connections, ensuring robust and efficient feature extraction at various levels of abstraction. The GlobalAveragePooling2D layer towards the model's end succinctly summarizes the extracted features, preparing the model for the final classification stage. This stage features a dropout layer for regularization, a dense layer with 512 units for further pattern learning, and a final dense layer with 8 units and a soft max activation function, producing a probability distribution across different disease classes. Our model's architecture is not just a testament to the sophistication of modern deep learning techniques but also highlights the novelty of applying such complex structures to the challenges of agricultural disease detection. We utilized a datasets consisting of 4000 high-resolution images of tea leaves, encompassing both diseased and healthy states, meticulously captured in the tea gardens of Pathantula, Sylhet, Bangladesh. Employing the Canon EOS 250d Camera ensured detailed representation crucial for training a robust deep learning model for disease detection in tea plants. By achieving remarkable accuracy in identifying diseases in tea leaves, this research not only sets a new benchmark for precision in agricultural diagnostics but also opens avenues for future innovations in the field of precision agriculture.
Why it matches plant phenotyping methods茶葉画像から病害状態をCNNで分類する手法の開発が中心であり、植物の病害状態を直接推定する画像ベース・フェノタイピング研究である。
titleTowards precision agriculture tea leaf disease detection using CNNs and image processing.
Turmeric leaf disease detection is essential to maintain the crop health and optimize the yield. Through early identification disease can be controlled and its relevant economic losses can be avoided. However, the existing methods for leaf disease detection exhibit limitations in extracting complex leaf features which leads to lower classification accuracy. Also, the existing models often struggle to process the fine details in turmeric leaves which further reduces the reliability in real-world applications. The objective of this research is overcome these limitations through a novel leaf disease detection model which incorporates Vision Transformer (ViT) with hybrid Falcon-Bowerbird Optimization (FBO). The proposed approach aimed to attain improved feature extraction abilities which enhances the overall performance of turmeric leaf diseases detection process. In the first step turmeric images are preprocessed histogram equalization to highlight the complex features like leaf texture and color intensity then the image is divided into non-overlapping patches. The Vision Transformer process each patch as a token through a self-attention mechanism so that the most relevant patches can be processed to extract the essential features. The Hybrid Falcon-Bowerbird Optimization further enhance the convergence speed and fine-tune the hyperparameters to attain improved detection performances. Using turmeric leaf disease dataset, the performance of the proposed model is evaluated through metrics like precision, recall, F1-score and accuracy. With an accuracy of 97.03%, the proposed model outperforms AlexNet which achieved 95.5%, and optimized MobileNetv3 which achieved 96.8%. The proposed hybrid optimized ViT model attained superior performance in turmeric leaf disease detection in terms of accuracy compared to existing techniques.
Why it matches plant phenotyping methodsウコン葉の画像から病害状態を推定するVision Transformer手法の開発・性能比較が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractThe objective of this research is overcome these limitations through a novel leaf disease detection model which incorporates Vision Transformer (ViT) with hybrid Falcon-Bowerbird Optimization (FBO).
There is a consensus on the role of protein conformational changes within the photosynthetic antenna that alter the spectral properties of the embedded pigments during regulated heat dissipation, but despite this, the molecular mechanisms involved are still poorly understood. The mechanisms, associated with the quenching of excessive energy, are however commonly seen in vitro as 'red spectral forms' of Chlorophyll a or red-shifted and broadened absorbance behaviour. Similar mechanisms are expected to occur in vivo, but so far, the spectral absorbance changes have not been described in detail at the whole plant canopy level. Here we derive the dynamic changes in surface absorbance features from canopy reflectance of tomato plants (Solanum lycopersicum L.), under increasing light exposure and drought. Specific features in the green (520 nm-peak) and the red-edge (695 nm-peak) region could indicate the quick activation of quenched conformational states under low light conditions, for all plant canopies. Under additional drought stress, further red-shifted and broadened absorbance changes appear, suggesting another conformational change. The latter changes disappeared upon drought recovery. Observing these antenna-related mechanisms from proximal sensing demonstrates the promising potential of imaging spectroscopy to detect the stepwise tuning of regulated energy dissipation of plants in a non-destructive way.
Why it matches plant phenotyping methodsキャノピー反射スペクトルから植物の光防御・エネルギー散逸状態を非破壊的に推定するイメージング分光法を中心に扱っており、単なる生理測定ではない。
abstractHere we derive the dynamic changes in surface absorbance features from canopy reflectance of tomato plants (Solanum lycopersicum L.), under increasing light exposure and drought.
Agricultural diseases pose significant challenges to plant production. With the rapid advancement of deep learning, the accuracy and efficiency of plant disease identification have substantially improved. However, conventional convolutional neural networks that rely on multi-layer small-kernel structures are limited in capturing long-range dependencies and global contextual information due to their constrained receptive fields. To overcome these limitations, this study proposes a plant disease recognition method based on RepLKNet, a convolutional architecture with large kernel designs that significantly expand the receptive field and enhance feature representation. Transfer learning is incorporated to further improve training efficiency and model performance. Experiments conducted on the Plant Diseases Training Dataset, comprising 95,865 images across 61 disease categories, demonstrate the effectiveness of the proposed method. Under five-fold cross-validation, the model achieved an overall accuracy (OA) of 96.03%, an average accuracy (AA) of 94.78%, and a Kappa coefficient of 95.86%. Compared with ResNet50 (OA: 95.62%) and GoogleNet (OA: 94.98%), the proposed model demonstrates competitive or superior performance. Ablation experiments reveal that replacing large kernels with 3×3 or 5×5 convolutions results in accuracy reductions of up to 1.1% in OA and 1.3% in AA, confirming the effectiveness of the large kernel design. These results demonstrate the robustness and superior capability of RepLKNet in plant disease recognition tasks.
Why it matches plant phenotyping methods植物病害状態を画像から認識する深層学習手法の開発と比較検証が研究の中心であり、植物フェノタイプ（病害状態）の抽出に該当する。
abstractthis study proposes a plant disease recognition method based on RepLKNet
Reproduction assets foundThe paper's plant disease image dataset (Plant Diseases Training Dataset, 95,865+ images across 61 disease categories) is explicitly stated to be publicly available on Kaggle, with the exact URL matching an allowed URL. No author analysis code or trained model checkpoints are reported.Dataset · publicThe dataset used for this model is called the Plant Diseases Training Dataset, available on Kaggle at https://www.kaggle.com/code/gpiosenka/efficentnet-mobilenet-f1-s-93-93/input .Open asset ↗Kagglelines:83-103Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Horse gram (Macrotyloma uniflorum (Lam.) Verd.) is an underutilised legume from the Indian subcontinent. Being a nutritious legume, it plays an important role in human nutrition in developing countries like India. Conventional assessment of nutritional traits, are labour and time intensive for screening of huge germplasm, hence alternative and rapid technique for conventional method for the determination of nutritional components of horse gram flour is needed. NIRS can be used for this purpose as it gives rapid and precise results for most of the plant products. In this study, a highly diverse collection of 139 horse gram accessions was utilized to generate reference data. Prediction models were developed for protein, starch, TSS, phenols, and phytic acid using MPLS regression method with spectral preprocessing using SNV-DT to remove scatter effects and baseline noise. Models were optimized for derivatives, gap selection, and smoothening and evaluated using different statistics including RSQ, bias and RPD. The RSQ and RPD for the best fit models obtained were protein (0.701; 1.85), starch (0.987; 4.03), TSS (0.800; 4.06), phenols (0.778; 2.15) and phytic acid (0.730; 1.88) indicating developed models are good for screening large number of germplasm collections and market samples. Statistical analyses, including paired t-tests, correlation, and reliability assessments, validated the strength of these models. This study represents the first report introducing a rapid, multi-trait evaluation approach for horse gram germplasm, highlighting its high predictive accuracy for pre-breeding applications. High throughput germplasm screening can be done through these developed models to identify trait-specific germplasm, which can be recommended to develop healthy products and thus can also be recommended for production in the farmer field simultaneously.
Why it matches plant phenotyping methodsNIRSによる種子由来試料の栄養形質を推定する予測モデルを開発・最適化・検証しており、育種用 germplasm の高速形質評価が中心である。
abstractPrediction models were developed for protein, starch, TSS, phenols, and phytic acid using MPLS regression method with spectral preprocessing using SNV-DT to remove scatter effects and baseline noise.
This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress. Two field experiments were conducted with three irrigation regimes: 100% (B100), 75% (B75), and 50% (B50) of the full irrigation requirements. Various measured parameters including, wet biomass (WB), dry biomass (DB), canopy moisture content (CMC), soil plant analysis development (SPAD), and soil water content (SWC) as well as seed yield (SY) were evaluated. The results showed that the highest values for WB, DB, CMC, SWC, and SY were achieved under B100, while the highest SPAD values were achieved under B75. The study also found that most of the RGB image indices (RGBIs) and spectral reflectance indices (SRIs) exhibited a linear relationship with the measured parameters and SY, with R² values ranging from 0.34 to 0.95. In contrast, SPAD showed a significant quadratic relationship, with R² values ranging from 0.34 to 0.79. Additionality, the newly developed SRIs demonstrated 5-40% higher correlations compared to the best-performing published SRIs across all measured parameters and SY. ANNs using RGBIs and SRIs separately demonstrated high prediction accuracy with R 2 values ranging from 0.79 to 0.97 and 0.86 to 0.97, respectively. Combining the RGBIs and SRIs, the ANNs achieved higher prediction accuracy, with R² values ranging from 0.88 to 0.99 across different parameters. In conclusion, this study demonstrates the effectiveness of using SRIs and RGBIs with ANNs as practical tools for managing the growth and production of dry bean crops under deficit irrigation.
Why it matches plant phenotyping methodsRGB画像指標・スペクトル反射指標とANNを用いて、乾燥豆のバイオマス、水分、SPAD、収量などの植物形質を定量・予測する手法を開発し、相関および予測精度を評価している。形質取得・推定法が研究の中心である。
abstractusing remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress
Abstract There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルと、検出・処置用のIoTシステム／ハードウェアを中心的に開発・評価しているため、植物フェノタイピング手法として含める。
abstractit presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops
Reproduction assets foundThe paper's plant-phenotyping measurements (ResNet50 training/validation/testing on 25,940 leaf images) are based on the public New Plant Diseases Dataset from Kaggle, explicitly stated by the authors. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned.Dataset · publicThe used dataset is available on the Kaggle repositoryOpen asset ↗Kagglepdf-page:7 lines:1-60Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Modern agriculture increasingly relies on technologies that enhance farmers' efficiency and economic growth. One challenge is the accurate identification of disease-affected plants, whose characteristics like structure, size, texture, and color can vary significantly. While there are existing methods to detect and classify these diseases, challenges such as image noise, hyper-parameter selection, and over-fitting can impede prediction accuracy. This paper introduces a hybrid fruit fly and simulated annealing-optimized Faster R-CNN (FS-FRNet) for improved plant leaf disease identification and classification. Our novel FS-FRNet method integrates a Wiener filter for de-noising and a super-resolution method to enhance image quality. By hybridizing the fruit fly optimization algorithm and simulated annealing, the Faster R-CNN's hyper-parameter issues are addressed, and the convergence rate is improved. We applied the FS-FRNet to identify and classify tomato plant diseases like early blight, yellow leaf curl, Septoria leaf, mosaic virus, and late blight. Experimental outcomes on the Plant Village dataset show that our method outperforms existing techniques, achieving 98.3% accuracy, 98.04% precision, and 98.11% recall, thus confirming its efficacy for reliable detection of tomato plant leaf diseases.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から検出・分類する手法を開発し、データセット上で性能評価しているため、植物表現型（病害状態）の取得・推定が中心である。
abstractThis paper introduces a hybrid fruit fly and simulated annealing-optimized Faster R-CNN (FS-FRNet) for improved plant leaf disease identification and classification.
Complex pest and disease features appearing during the growth of wheat crops are difficult to capture and can seriously affect the normal growth of wheat crops. The existing methods ignore the full pre-interaction of deep and shallow features, which largely affects the accuracy of identification. To address the above problems and needs, we rethink the feature representation and attention mechanism in intelligent recognition of wheat leaf diseases and pests, and propose a representation and recognition network (RReNet) based on the feature attention mechanism. RReNet captures key information more efficiently by focusing on complex pest and disease characteristics and fusing multi-semantic feature information. In addition, RReNet further enhances the perception of complex disease and pest features by using four layers of detection units and fast IoU loss function, which significantly improves the accuracy and robustness of wheat leaf disease and pest recognition. Tests on a challenging wheat leaf pest and disease dataset with twelve pest and disease types show that RReNet achieves precision, recall and mAP as high as 94.1%, 95.7% and 98.3% respectively. Also, ablation experiments proved the effectiveness of all parts of the proposed method.
Why it matches plant phenotyping methods小麦葉の病害・害虫状態を画像から認識する深層学習ネットワークを提案し、データセット上で性能評価とアブレーション実験を行っているため、植物状態の取得・推定手法が中心である。
abstractTests on a challenging wheat leaf pest and disease dataset with twelve pest and disease types show that RReNet achieves precision, recall and mAP as high as 94.1%, 95.7% and 98.3% respectively. Also, ablation experiments proved the effectiveness of all parts of the proposed method.
In complex farmland environments, wheat canopy coverage is insufficient at the tillering stage, posing a considerable challenge to the accurate extraction of its canopy using UAV(unmanned air vehicle) remote sensing images. In this paper, an end-to-end semantic segmentation method based on visible light (RGB) and thermal infrared (TIR) images, Tiff-SegFormer, which fused spectral features and temperature features effectively, is proposed for accurate pixel-level classification of winter wheat at the tillering stage images taken by UAV to segment the wheat canopy and background. Tiff-SegFormer utilizes hierarchical feature representation and efficient self-attention in the encoder stage to extract features of detail contours of RGB images and temperature changes of TIFF images, respectively. In the decoder stage, the features are concatenated and then the channel and spatial attention mechanisms are superimposed, aiming to further improve the segmentation accuracy and efficiency of winter wheat at the tillering stage in UAV remote sensing images. The results show that Tiff-SegFormer can achieve accurate segmentation of wheat canopy and background from UAV images of winter wheat at the tillering stage (mIoU = 84.28%, mPA = 88.97%, accuracy = 94.55%). In order to verify the efficiency of the proposed method, Tiff-SegFormer is compared with four widely used semantic segmentation methods, all of which show better performance. The four methods are UNet, DeepLabv3+, HRNet, SegFormer and four-channel (RGB + TIFF) Segformer. The generalization test shows that the proposed Tiff-SegFormer also achieves better performance than other comparison methods (mIoU = 84.94%, mPA = 91.46%, accuracy = 94.71%). Tiff-SegFormer provides a robust and efficient tool for segmenting winter wheat canopy from UAV remote sensing images of winter wheat at the tillering stage, and has great potential in applications (model implementation and results can be found at https://github.com/wylSUGAR/Tiff-SegFormer ).
Why it matches plant phenotyping methodsUAVのRGB・熱赤外画像から冬コムギのキャノピーを抽出するセマンティックセグメンテーション手法を開発し、複数手法との比較および汎化性能検証を行っており、植物状態の取得方法が中心である。
abstractan end-to-end semantic segmentation method based on visible light (RGB) and thermal infrared (TIR) images, Tiff-SegFormer, which fused spectral features and temperature features effectively, is proposed for accurate pixel-level classification of winter wheat at the tillering stage images taken by UAV to segment the wheat canopy and background.
Reproduction assets foundThe paper publicly releases the UAV RGB/TIR winter wheat tillering-stage image dataset and the TIR-to-TIFF conversion code via two author GitHub repositories. The Tiff-SegFormer model repository is referenced but its URL is not among the allowed URLs, and labelme is a generic third-party tool, so neither is included.Dataset · publicThe image can be found at
https://github.com/wylSUGAR/wheat_tillering_stage.Open asset ↗wylSUGAR/wheat_tillering_stagepdf-page:2 lines:56-74Code · publicthe TIR image was
converted into a TIFF image (the code can be found at https://github.com/wylSUGAR/TIR_DJ_tiff)Open asset ↗wylSUGAR/TIR_DJ_tiffpdf-page:2 lines:56-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional farming methods have caused significant environmental damage, highlighting the need for sustainable practices like nutrition management. This paper addresses the emerging integration of artificial intelligence (AI) in agriculture, focusing on the specific challenge of growth stage classification of strawberry plants for optimized nutrition management. While AI has been successfully applied in various agricultural domains, such as plant stress detection and growth monitoring, the precise classification of strawberry growth stages remains underexplored. Accurate growth stage identification is vital for timely nutrient application, directly impacting yield and fruit quality. Our research identifies common gaps in existing literature, including limited or inaccessible datasets, outdated methodologies, and insufficient benchmarking. To overcome these shortcomings, we introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions. We then benchmark multiple state-of-the-art models on this dataset, finding that EfficientNetB7 achieves a testing accuracy of 0.837-demonstrating the promise of AI-driven approaches for precise and sustainable nutrient management in horticulture.
Why it matches plant phenotyping methodsイチゴの生育段階という植物状態を画像データセットで分類し、複数モデルをベンチマークすることが研究の中心であるため、植物フェノタイピング手法として収載する。
abstractwe introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions.
In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled samples, which are then incorporated to further train the original model, resulting in a new model. However, if these pseudo labels contain substantial errors, the resulting model's accuracy may drop, potentially falling below the performance of the initial model. To tackle the problem, we propose an Ambiguity-Aware Semi-Supervised Learning method for Leaf Disease Classification. Specifically, we present a per-disease ambiguity rejection algorithm that eliminates ambiguous results, thereby enhancing the precision of pseudo labels for the subsequent semi-supervised training step and improving the precision of the final classifier. The proposed method is evaluated on two public leaf disease datasets of coffee and banana across various data scenarios, including supervised and semi-supervised settings, with varying proportions of labeled data. The results indicate that our semi-supervised method reduces the reliance for fully labeled datasets while preserving high accuracy by utilizing the ambiguity rejection algorithm. Additionally, the rejection algorithm significantly boosts precision of final classifier on both coffee and banana datasets, achieving rates of 99.46% and 100.0%, respectively, while using only 50% labeled data. The study also presents a thorough set of experiments and analyses to validate the effectiveness of the proposed method, comparing its performance against state-of-the-art supervised approaches. The results demonstrate that our method, despite using only 50% of the labeled data, achieves competitive performance compared to fully supervised models that use 100% of the labeled data.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する半教師あり学習法と曖昧性除去アルゴリズムを開発し、コーヒー・バナナの公開データセットで評価しており、病害表現型の抽出手法が研究の中心である。
abstractwe propose an Ambiguity-Aware Semi-Supervised Learning method for Leaf Disease Classification.
Positron Emission Tomography (PET) is a valuable tool for plant imaging, but its accuracy can be compromised by positron range effects. This study improves PET accuracy using the GATE Monte Carlo simulation tool to estimate and correct these effects. The GATE model was validated for the Siemens Biograph Vision system using the NEMA NU 2-2018 protocol, showing alignment with experimental data. Deviations were within 9% for sensitivity and 3% for peak Noise Equivalent Count Rate (NECR). Different isotopes ( 18 F, 11 C, 15 O, and 30 P) and plant phantom properties were analyzed for their impact on reconstructed images. A sixfold enhancement was observed for 15 O and a threefold improvement for 11 C when a magnetic field was applied to the plant phantom. Our findings suggest that integrating PET with magnetic resonance imaging can help address Positron range effects in plant imaging. This study provides valuable insights into PET imaging and offers refined methodologies for clinical and plant-centric research. Our research validates the use of GATE Monte Carlo simulation for Biograph Vision and advances our understanding of Positron range phenomena and potential mitigation strategies for precise PET Plant imaging.
Why it matches plant phenotyping methods植物PET画像の精度向上、モンテカルロ補正、装置検証を中心とする植物イメージング手法研究であり、植物状態の取得方法が中核です。
abstractThis study improves PET accuracy using the GATE Monte Carlo simulation tool to estimate and correct these effects.
Global food security depends on tomato growing, but several fungal, bacterial, and viral illnesses seriously reduce productivity and quality, therefore causing major financial losses. Reducing these impacts depends on early, exact diagnosis of diseases. This work provides a deep learning-based ensemble model for tomato leaf disease classification combining MobileNetV2 and ResNet50. To improve feature extraction, the models were tweaked by changing their output layers with GlobalAverage Pooling2D, Batch Normalization, Dropout, and Dense layers. To take use of their complimentary qualities, the feature maps from both models were combined. This study uses a publicly available dataset from Kaggle for tomato leaf disease classification. Training on a dataset of 11,000 annotated pictures spanning 10 disease categories, including bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, spider mites, target spot, yellow leaf curl virus, mosaic virus, and healthy leaves. Data preprocessing included image resizing and splitting, along with an 80-10-10 split, allocating 80% for training, 10% for testing, and 10% for validation to ensure a balanced evaluation. The proposed model with a 99.91% test accuracy, the suggested model was quite remarkable. Furthermore, guaranteeing strong classification performance across all disease categories, the model showed great precision (99.92%), recall (99.90%), and an F1-score of 99.91%. With few misclassifications, the confusion matrix verified almost flawless classification even further. These findings show how well deep learning can automate tomato disease diagnosis, therefore providing a scalable and quite accurate solution for smart agriculture. By means of early intervention and precision agriculture techniques, the suggested strategy has the potential to improve crop health monitoring, reduce economic losses, and encourage sustainable farming practices.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する深層学習手法を開発し、複数疾患カテゴリで性能評価しており、植物病害表現型の取得・推定が中心である。
abstractThis work provides a deep learning-based ensemble model for tomato leaf disease classification combining MobileNetV2 and ResNet50.
Early detection of nutrient deficiencies is crucial for optimizing crop yields and ensuring sustainable agricultural practices. This study presents a novel application of the YOLOv8s object detection model for identifying nitrogen, phosphorus, and potassium deficiencies in soybean plants. Employing a unique dataset from a long-term nutrient-deficient field maintained for over 40 years, we trained and evaluated the model on 6,020 red, green, and blue images of soybean leaves exhibiting nutrient stress conditions. The YOLOv8s model achieved exceptional performance, with a mean average precision (mAP@0.5) of 99.18% during training and 98.51% for validation. Precision rates for individual nutrient deficiencies ranged from 90.03 to 96.54%, with highly accurate potassium deficiency detection. The model demonstrated robust generalization across diverse field conditions, processing images in 3.46 ms each, making it suitable for real-time applications. This research significantly advances the field of precision agriculture by providing a fast, accurate, and scalable method for detecting early nutrient deficiency in soybean crops, potentially revolutionizing fertilizer management practices and contributing to more sustainable farming systems.
Why it matches plant phenotyping methods大豆葉の栄養欠乏状態を画像から検出するYOLOv8s手法を開発・評価しており、表現型取得とモデル性能検証が研究の中心である。
abstractThis study presents a novel application of the YOLOv8s object detection model for identifying nitrogen, phosphorus, and potassium deficiencies in soybean plants.
Multispectral / hyperspectralLeafPigment / colour / senescenceWater status / transpiration
The mainstream radiation transport models represented by the PROSPECT model regard the internal substances of the leaf as uniformly distributed. The influence of the uneven distribution of substances inside the leaf on the spectra of the leaf was not considered. The PIOSL-3 model proposed in this study assumes that the leaf is composed of three layers of optical properties. The particle swarm optimization algorithm (PSO) was used to determine the distribution proportion of biochemical parameters including chlorophyll, water and dry matter in each layer of leaves. In this paper, the LOPEX and ANGERS datasets were used to verify the spectral simulation effect of the PIOSL-3 model, and it can be seen from the optimization results of the parameters that the structural parameters in the leaves of different plant types are higher in the upper layer, and the distribution of chlorophyll also shows similar characteristics, while water and dry matter mainly exist in the lower layer of the leaves, and some plants also show different characteristics. In terms of simulated spectra, the PIOSL-3 model reduced RMSE mean values by 1.78, 0.39, 6.12, and 0.9, and SAM mean values by 0.07, 0.0094, 0.2267, and 0.03 compared to the PROSPECT model on the LOPEX and ANGERS datasets, respectively. The hypothesis of layered simulation is feasible, and the proposal of PIOSL-3 model provides a new idea for modeling the leaf radiation transmission process.
Why it matches plant phenotyping methods葉のハイパースペクトル反射をシミュレーションする新規モデルを開発し、既存モデルおよびデータセットでスペクトル再現性を検証しており、植物形質取得に関わる方法が中心である。
abstractThe PIOSL-3 model proposed in this study assumes that the leaf is composed of three layers of optical properties.
The South Khorasan Province in Iran is the main producer of seedless barberry, accounting for 98% of the country's production. This has led to significant economic growth in the region. However, the cultivation of barberry is threatened by the rust fungus Puccinia arrhenatheri, which causes witches' brooms on Berberis vulgaris L. var. asperma. Our research aims to detect infected leaves containing this fungal pathogen using deep learning (DL)-based artificial intelligence (AI) techniques on an available dataset. We captured healthy and infected barberry foliage images and used conventional laboratory methods to label them. We developed a convolutional neural network (CNN) deep learning model using TensorFlow's Keras API to detect and classify barberry broom rust disease. A cross-validation technique is used to check the robustness of the proposed model. The results imply that the proposed model successfully distinguished between healthy specimens and those affected by broom rust disease. The model achieved an impressive accuracy rate of 98% in automatically identifying the disease type and its severity. This interdisciplinary research demonstrates the practical application of AI in agriculture, providing timely intervention strategies to protect crop yields and maintain economic viability in the face of plant diseases.
Why it matches plant phenotyping methods植物葉の画像から感染状態と病害の種類・重症度を推定するCNN手法を開発し、交差検証で頑健性を評価しており、病害表現型の取得・推定が中心である。
abstractWe captured healthy and infected barberry foliage images and used conventional laboratory methods to label them.
Rapid, non-destructive, lightweight and accurate diagnosis of early stage nutrient deficiency in rice is essential for both yield and quality. Traditional diagnostic methods often exhibit low efficiency, reduced accuracy, and a lack of timeliness. To address these issues, a diagnostic method for the early detection of nitrogen, phosphorus, and potassium deficiencies in rice, based on multimodal integration and knowledge distillation, is proposed. In this study, the late rice variety 'Huanghuazhan rice' was selected as the experimental subject for field trials. First, leave images of rice plant were captured using a scanner, and some data preprocessing techniques were utilized to extract image samples from the leaf tip areas of the top one leaf, the top two leaf and the top three leaf. Second, the teacher model was obtained through transfer learning, fine-tuning training and model fusion. The custom neural network model was heuristically customized based on the conventional model. The teacher model then performs knowledge distillation on the custom neural network model, resulting in a lightweight model with high accuracy and low memory consumption, which serves as a feature extractor. Finally, the multimodal features were input into LightGBM for training and the rice nutrient deficiency recognition model, S-RiceNet-D-LightGBM (SRDL), was constructed. The experimental results demonstrate that the SRDL model is an efficient, lightweight model characterized by high accuracy and low memory consumption. It achieved an accuracy score of 0.9501, a macro precision score of 0.9501, a macro recall score of 0.9499, and a macro F1 score of 0.9500, outperforming the VGG16, ResNet101, DenseNet169, InceptionNetV3, MobileNetV2, second only to the performance of the ensemble model. The memory footprint is 23.6 MB, which is slightly higher than that of the MobileNetV3S model. This study provides new insights and viable avenues for the practical implementation of a lightweight model designed for the intelligent diagnosis of crop nutrient deficiency.
Why it matches plant phenotyping methodsイネ葉画像から栄養欠乏状態を推定する軽量な画像・機械学習診断法を開発し、性能評価しているため、植物表現型取得・推定法が研究の中心である。
abstracta diagnostic method for the early detection of nitrogen, phosphorus, and potassium deficiencies in rice, based on multimodal integration and knowledge distillation, is proposed.
As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments' findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.
Why it matches plant phenotyping methods大豆種子表面の欠陥という植物器官の状態を、画像処理と改良YOLOv9で抽出・検出する手法開発が研究の中心であり、性能評価も行っている。
abstractwe propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc.
Reproduction assets foundThe paper's soybean seed defect detection study uses a public Kaggle dataset of 4,388 soybean seed images (intact, broken, skin-damaged, spotted), explicitly stated in the Data Availability statement. No author analysis code or trained model checkpoints are deposited; makesense.ai is a generic annotation tool, not a deDataset · publicuthors reviewed and approved the final manuscript.
Funding
This work was supported in part by the Special Support Plan for High level Talents in Zhejiang Province (2021R52019), and the Education Department of Hainan Province (Hnky2024-18).
Data availability
The data used in this article can be downloaded from the following link https://www.kaggle.com/datasets/warcoder/soyabean-seeds .
Competing interests
The authors declare no competing interests.
Footnotes
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Contributor Information
Xia Yu, Email: 100170@hainnu.edu.cn.
Qi Dai, Email: daiqi@zstu.edu.cn.
ReferenOpen asset ↗Kaggle · warcoder/soyabean-seedslines:388-411Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images distributed across 9 disease classes, including Black spot, Canker, Greening, Scab, Melanose, and healthy examples of fruits and leaves. Both InceptionV3 and DenseNet121 achieved a test accuracy of 99.12%, with a macro average F1-score of approximately 0.986 and a weighted average F1-score of 0.991, indicating exceptional performance in terms of precision and recall across the majority of the classes. ResNet50 and EfficientNetB0 attained test accuracies of 84.58% and 80.18%, respectively, reflecting moderate performance in comparison. These research results underscore the promise of modern convolutional neural networks for accurate and timely detection of citrus diseases, thereby providing effective tools for farmers and agricultural professionals to implement proactive disease management, reduce crop losses, and improve yield quality.
Why it matches plant phenotyping methods柑橘の葉・果実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、植物病害表現型の取得・分類が研究の中心である。
abstractwe evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images.
Plant pathogens and pests hinder general plant health, resulting in poor agricultural yields and production. These threaten global food security and cause environmental and economic shortages. Amidst the available existing heavy deep learning (DL) models, there is an increasing demand for computation resources, memory constraints, delayed interface time, unscalable deployment, increased training time, higher data requirements, and inflexibility. To solve all these challenges, this study presents a robust and lightweight "AgarwoodNet" DL model. The research introduces and uses a new raw curated Agarwood pest and disease dataset (APDD) with 14 classes and 5,472 Agarwood leaf images from Brunei and the Turkey Plant Pests and Diseases (TPPD) dataset with 4,447 images categorized into 15 diverse classes of six plants. MATLAB deep learning toolbox was used to train the DL architectures. The performance assessment parameters considered Cohen's Kappa, specificity precision, F1 scores, and recall. The proposed AgarwoodNet achieved impressive Macro-average performance of 0.9666, 0.9714, and 0.9859 in Precision, Recall, and F1 Scores, respectively, and 0.9859 on Kappa when tested on APDD. More so, the model attained 95.85%, 96.13%, and 95.90% in testing using TPPD and 96.84% on Kappa with the model size of 37 megabytes, making it a lightweight model in relation to the pre-trained convolutional neural network a considerably heavy, others twice the proposed model. This model size is considerably light and can be implemented on low-memory devices, thus supporting sustainable agricultural applications that are precise and accurate in classifying and detecting plant diseases and diseases.
Why it matches plant phenotyping methods植物葉画像から病害・害虫状態を分類・検出する軽量深層学習モデルを開発し、複数データセットで性能評価しており、植物状態の取得・推定手法が中心である。
abstractthis study presents a robust and lightweight "AgarwoodNet" DL model.
Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases 'Crown Root', 'Tillering', 'Mid Vegetative', 'Booting', 'Heading', 'Anthesis', and 'Milking', comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.
Why it matches plant phenotyping methods圃場画像からコムギの生育段階という植物状態を認識する深層学習手法を開発・比較・検証しており、画像データセット、モデル性能比較、交差検証が研究の中心である。
abstractthis study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition.
Cassava is a tuberous edible plant native to the American tropics and is essential for its versatile applications including cassava flour, bread, tapioca, and laundry starch. Cassava leaf diseases reduce crop yields, elevate production costs, and disrupt market stability. This places significant burdens on farmers and economies while highlighting the need for effective management strategies. Traditional methods of manual disease diagnosis are costly, labor-intensive, and time-consuming. This research aims to address the challenge of accurate disease classification by overcoming the limitations of existing methods, which encounter difficulties with the complexity and variability of leaf disease symptoms. To the best of our knowledge, this is the first study to propose a novel dual-track feature aggregation architecture that integrates the Residual Inception Positional Encoding Attention (RIPEA) Network with EfficientNet for the classification of cassava leaf diseases. The proposed model employs a dual-track feature aggregation architecture which integrates the RIPEA Network with EfficientNet. The RIPEA track extracts significant features by leveraging residual connections for preserving gradients and uses multi-scale feature fusion for combining fine-grained details with broader patterns. It also incorporates Coordinate and Mixed Attention mechanisms which focus on cross-channel and long-range dependencies. The extracted features from both tracks are aggregated for classification. Furthermore, it incorporates an image augmentation method and a cosine decay learning rate schedule to improve model training. This improves the ability of the model to accurately differentiate between Cassava Bacterial Blight (CBB), Brown Streak Disease (CBSD), Green Mottle (CGM), Mosaic Disease (CMD), and healthy leaves, addressing both local textures and global structures. Additionally, to enhance the interpretability of the model, we apply Grad-CAM to provide visual explanations for the model's decision-making process, helping to understand which regions of the leaf images contribute to the classification results. The proposed network achieved a classification accuracy of 93.06%.
Why it matches plant phenotyping methods葉画像から病徴・病害状態を分類する深層学習手法の開発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 research aims to address the challenge of accurate disease classification
Reproduction assets foundThe paper uses the public Kaggle cassava leaf disease classification dataset (Makerere AI Lab images) as its phenotyping input, with an explicit data availability statement pointing to Kaggle. No author analysis code, trained model checkpoints, or supplementary repository is disclosed; the Kaggle URL itself is not in aDataset · publicThe datasets generated and/or analyzed during the current study are available in Kaggle.Open asset ↗Kagglepdf-page:14 lines:1-78Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the difficulty of the conventional methods. Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. Four distinct datasets—Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf—were used in this investigation. The experimental results we received are as follows: for the Banana Leaf dataset, the combination of Inception v3 with SVM proved good with an Accuracy of 91.9%, Precision of 92.2%, Recall of 91.9%, F1 score of 91.6%, AUC of 99.6% and MCC of 90.4%, FFor the Custard Apple Leaf and Fruit dataset, the combination of VGG19 with kNN with an Accuracy of 99.1%, Precision of 99.1%, Recall of 99.1%, F1 score of 99.1%, AUC of 99.1%, and MCC of 99%, and for the Fig Leaf dataset with Accuracy of 86.5%, Precision of 86.5%, Recall of 86.5%, F1 score of 86.5%, AUC of 93.3%, and MCC of 72.2%. The Potato Leaf dataset displayed the best performance with Inception v3 + SVM by an Accuracy of 62.6%, Precision of 63%, Recall of 62.6%, F1 score of 62.1%, AUC of 89%, and MCC of 54.2%. Our findings explored the versatility of the amalgamation of ML and DL techniques while providing valuable references for practitioners seeking tailored solutions for specific plant diseases.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する深層学習・機械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 research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy
The decision support system for agro-technology transfer (DSSAT) is a worldwide crop modeling platform used for crops growth, yield, leaf area index (LAI), and biomass estimation under varying climatic, soil and management conditions. This study integrates DSSAT with satellite remote sensing (RS) data to estimates canopy state variables like LAI and biomass. For LAI estimation, Moderate Resolution Imaging Spectroradiometer (MODIS) product (MCD15A3H for LAI and MOD17A2 / MOD17A3 products for biomass) are used. Field data for Sheikhupura district is provided by National Agriculture Research Council (NARC) and used for the calibration and validation of the model. The results indicate strong agreement between the DSSAT and RS derived estimates. Correlation coefficients (R²) for LAI varied from 0.82 to 0.90, while for biomass ranged from 0.92 to 0.99 over two farms and two growing seasons (2012-2014). The index of agreement (D-index) ranged from 0.79 to 0.96 across the two farms and two growing seasons (2012-2014) affirming the model's durability. However, the biomass estimated from RS data is underestimated due to saturation phenomenon in the optical RS. The performance metrics, comprising the coefficient of residual mass (CRM) and normalized root mean square error (nRMSE), further substantiate the approach utilized. This study will help decision and policymakers and researchers to apply geospatial techniques for the sustainable agriculture practices.
Why it matches plant phenotyping methods衛星リモートセンシングと作物モデルを統合し、LAI・バイオマスという植物キャノピー形質を推定して現地データで校正・検証しており、形質取得手法が中心的です。
abstractThis study integrates DSSAT with satellite remote sensing (RS) data to estimates canopy state variables like LAI and biomass.
Strawberry grading by picking robots can eliminate the manual classification, reducing labor costs and minimizing the damage to the fruit. Strawberry size or weight is a key factor in grading, with accurate weight estimation being crucial for proper classification. In this paper, we collected 1521 sets of strawberry RGB-D images using a depth camera and manually measured the weight and size of the strawberries to construct a training dataset for the strawberry weight regression model. To address the issue of incomplete depth images caused by environmental interference with depth cameras, this study proposes a multimodal point cloud completion method specifically designed for symmetrical objects, leveraging RGB images to guide the completion of depth images in the same scene. The method follows a process of locating strawberry pixel regions, calculating centroid coordinates, determining the symmetry axis via PCA, and completing the depth image. Based on this approach, a multimodal fusion regression model for strawberry weight estimation, named MMF-Net, is developed. The model uses the completed point cloud and RGB image as inputs, and extracts features from the RGB image and point cloud by EfficientNet and PointNet, respectively. These features are then integrated at the feature level through gradient blending, realizing the combination of the strengths of both modalities. Using the Percent Correct Weight (PCW) metric as the evaluation standard, this study compares the performance of four traditional machine learning methods, Support Vector Regression (SVR), Multilayer Perceptron (MLP), Linear Regression, and Random Forest Regression, with four point cloud-based deep learning models, PointNet, PointNet++, PointMLP, and Point Cloud Transformer, as well as an image-based deep learning model, EfficientNet and ResNet, on single-modal datasets. The results indicate that among traditional machine learning methods, the SVR model achieved the best performance with an accuracy of 77.7% (PCW@0.2). Among deep learning methods, the image-based EfficientNet model obtained the highest accuracy, reaching 85% (PCW@0.2), while the PointNet + + model demonstrated the best performance among point cloud-based models, with an accuracy of 54.3% (PCW@0.2). The proposed multimodal fusion model, MMF-Net, achieved an accuracy of 87.66% (PCW@0.2), significantly outperforming both traditional machine learning methods and single-modal deep learning models in terms of precision.
Why it matches plant phenotyping methodsイチゴのRGB-D画像から重量・サイズを推定する画像／点群解析手法を開発し、複数モデルと比較評価しており、植物形質取得が中心である。
abstractthis study proposes a multimodal point cloud completion method specifically designed for symmetrical objects
Field / plotWhole plant / canopy / plot / fieldCountingSegmentationGrowth / development / phenology
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and decrease the purity of the crops are referred to here as weeds. A total of 2100 weed images were utilized to train the DCNN model in this study, including 500 images from the original dataset and 1600 images from the Crop Weed Field Image Dataset (CWFID), which includes broadleaf, a monocot, and dicot weeds. This research has proposed proposes hybrid Convolutional Neural Network models (HCNN) which have amalgamated the feature of the SegNet and U-Net CNN model for weed image segmentation. This work uses segmentation masks to exclude background and foreground vegetation to investigate weed growth and density estimation. To boost the identification weight of the weed leaf, furthermore, it has presented four distinct modified pooling layers and reduced the pooling layer of the classic segmentation model and loss function. According to the experimental results, our proposed algorithms achieved the best accuracy of 98.95%. The evaluation of financial misfortunes and impact due to weeds in farming is a critical perspective of considering which makes a difference in formulating suitable management methodologies against weeds.
Why it matches plant phenotyping methods雑草画像のセグメンテーションを基盤に、雑草の成長と密度を推定する深層学習手法を開発しており、植物状態の取得・抽出が研究の中心である。
abstractThis research has proposed proposes hybrid Convolutional Neural Network models (HCNN) which have amalgamated the feature of the SegNet and U-Net CNN model for weed image segmentation.
Common beanLeafStress / disease detectionDisease symptoms / severity
Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages, are computationally expensive and do not always yield optimal results. Moreover, reliable datasets for kidney bean leaf spot disease remain scarce. To address these challenges, this study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset, filling a significant gap in the field. Based on this dataset, a novel hybrid deep learning model framework is proposed, which integrates deep learning models (EfficientNet-B7, MobileNetV3, ResNet50, and VGG16) for feature extraction with machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, and Stochastic Gradient Boosting) for classification. By leveraging the Optuna tool for hyperparameter optimization, 16 combined models were evaluated. Experimental results show that the hybrid model combining EfficientNet-B7 and Stochastic Gradient Boosting achieves the highest detection accuracy of 96.26% on the KBLD dataset, with an F1-score of 0.97. The innovations of this study lie in the construction of a high-quality KBLD dataset and the development of a novel framework combining deep learning and machine learning, significantly improving the detection efficiency and accuracy of kidney bean leaf spot disease. This research provides a new approach for intelligent diagnosis and management of crop diseases in precision agriculture, contributing to increased agricultural productivity and ensuring food security.
Why it matches plant phenotyping methods腎豆葉の病斑を画像から検出・分類するデータセット構築と深層学習手法開発が研究の中心であり、植物の病害状態を直接推定するため。
abstractthis study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset
Corn seed breeding is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for corn kernel recognition and classification has great potential in terms of constructing environmentally friendly agriculture, and saving manpower. Existing segmentation methods that utilize U-shaped architectures typically operate by processing images in discrete pixel-based segments. This approach often overlooks the finer pixel-level structural details within these segments, leading to models that struggle to preserve the continuity of target edges effectively. In this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI, which aims to integrate MSADI module into the encoder and decoder of the VMUnet architecture. Our VMUnet-MSADI model benefits from self-attention computation in VMUnet and multi-scale coding to effectively model non-local dependencies and context relationships at the scale layer, thus improving the segmentation quality of different images. Unlike previous Unet-based improvement schemes, the proposed VMUnet-MSADI adopts a multiscale convolutional attention module coding mechanism at the depth level and an efficient multiscale deep convolutional decoder at the spatial level to extract coarse-grained features and fine-grained features at different semantic scales and effectively avoid the loss of information at the target boundary to improve the quality and accuracy of target segmentation. We introduce a Visual State Space (VSS) block to capture a wide range of contextual information and a Detail Infusion Block (DIB) to enhance the fusion of low-level and high-level features, which further fills in the remote contextual information during the up-sampling process. Comprehensive experiments were conducted on open-source datasets and the results demonstrate that the VMUnet-MSADI model excels in the task of corn kernel segmentation. The model achieved a segmentation accuracy of 95.96%, surpassing the leading method by 0.9%. Compared to other segmentation models, our method exhibits superior performance in both accuracy and loss metrics. Extensive comparative experiments conducted on various benchmark datasets further substantiate that our approach outperforms the state-of-the-art models. Code, pre-trained models and data processing protocols are available at https://github.com/corbining/VMUnet-MSADI .
Why it matches plant phenotyping methodsトウモロコシ種子画像から不健全カーネルを抽出する新規セグメンテーション手法の開発・ベンチマークが中心であり、画像ベースの植物器官状態の表現型取得に該当する。
abstractIn this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI
Reproduction assets foundThe paper reports corn kernel segmentation experiments and explicitly states that code, pre-trained models, and data processing protocols are publicly available at the authors' GitHub repository, which is a paper-specific actionable asset. The corn kernel dataset itself is described as open-source from GaoZhe Tech but,Code · publicCode, pre-trained models and data processing protocols are
available at https://github.com/corbining/VMUnet-MSADI.Open asset ↗corbining/VMUnet-MSADIpdf-page:1 lines:1-57Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector.
Why it matches plant phenotyping methods綿花の病害症状を画像から認識する深層学習手法の比較・評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用。
abstractThis study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models.
Rice is susceptible to various diseases, including brown spot, hispa, leaf smut, bacterial leaf blight, and leaf blast, all of which can negatively impact crop yields. Current disease detection methods encounter several challenges, such as reliance on a single dataset that diminishes accuracy, the use of complex models, and the limitations posed by small datasets that hinder performance. To overcome these challenges, this paper presents a novel hybrid deep learning (DL) approach for classifying rice leaf diseases. The proposed model leverages two distinct datasets: the Rice Leaf Diseases Dataset and the Rice Disease Images Dataset. It enhances image quality through two advanced techniques: Upgraded Weighted Median Filtering (Up-WMF) to minimize noise and Aligned Gamma-based Contrast Limited Adaptive Histogram Equalization (AG-CLAHE) to improve image contrast. Features from these images are extracted using methods such as Discrete Wavelet Transform (DWT), Gray Level Run Length Matrix (GLRLM), and deep learning-based VGG19 features. To optimize model performance, the most significant features are selected using the Bio-Inspired Artificial Hummingbird (BI-AHB) method, which streamlines complexity. Classification of rice diseases is conducted using a new model known as the Dual Branch Convolutional Graph Attention Neural Network (DB-CGANNet). This model demonstrates remarkable performance, achieving 98.9% accuracy on rice leaf disease dataset and 99.08% on Rice diseases image, surpassing existing techniques. The proposed methodology enhances disease detection accuracy, facilitating improved management of rice crops and contributing to increased agricultural productivity.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から分類する深層学習手法の開発が中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。
abstractthis paper presents a novel hybrid deep learning (DL) approach for classifying rice leaf diseases.
Cacao is a globally significant crop, but its production is severely threatened by diseases, particularly Black Pod Rot (BPR) caused by Phytophthora spp. Understanding plant-pathogen interactions, especially stomatal responses, is crucial for disease management. Machine learning offers a powerful, yet largely untapped, approach to analyze and interpret complex plant responses in plant biology and pathology, particularly in the context of plant-pathogen interactions. This study explores the use of machine learning to analyze and interpret complex stomatal responses in cacao leaves during pathogen interactions. We investigated the impact of the black pod rot pathogen (Phytophthora megakarya) and a non-pathogenic fungus (Rhizoctonia solani) on stomatal aperture in two cacao genotypes (SCA6 and Pound7) under varying light conditions. Image analysis revealed diverse stomatal responses, including no change, opening, and closure, that were influenced by the interplay of genotype, pathogen isolate, and light conditions. Notably, SCA6 exhibited stomatal opening in response to P. megakarya specifically under a 12-hour light/dark cycle, suggesting a light-dependent activation of pathogen virulence factors. In contrast, Pound7 displayed stomatal closure in response to both P. megakarya and R. solani, indicating the potential recognition of conserved Pathogen-Associated Molecular Patterns (PAMPs) and a broader defense response. To further analyze these interactions, we employed machine learning techniques to predict stomatal area size. Our analysis identified key morphological features, with size-related traits being the strongest predictors. Shape-related traits also played a significant role when size-related traits were excluded from the prediction. This study demonstrates the power of combining image analysis and machine learning for discerning subtle, multivariate traits in stomatal dynamics during plant-pathogen interactions, paving the way for future applications in high-throughput disease phenotyping and the development of resistant crop varieties.
Why it matches plant phenotyping methodsカカオ葉の気孔開度を画像解析で測定し、機械学習で気孔面積を予測する手法が研究の中心であり、病原体応答の表現型解析および将来の高スループット病害フェノタイピングへの応用を実証している。
abstractThis study explores the use of machine learning to analyze and interpret complex stomatal responses in cacao leaves during pathogen interactions.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant diseases can damage specific parts of leaves for better readability during the farming process. Plants refer to various types of crops, including fruits and vegetables. During the production phase of healthy crops, plant diseases often begin by infecting the leaves. Leaves, being exposed, are more vulnerable to disease than other plant parts. When affected by disease, crop yield decreases, leading to economic loss. Hence, early disease identification model is required and deployed in an automated computerized way. The analysis have shown that multiple approaches were executed to detect the disease, still it suffers from pitfalls like inadequate feature extraction, handcrafted features, computation burden, complexities and so on. To improve the process, an efficient method is developed for detecting various plant diseases by different learning method. Firstly, the different plant leaf data were gathered from UCI, Kaggle web sources and benchmarks. The unwanted noise in the input leaf images are pre-processed by using median filter. Subsequently, the affected or abnormal region was segmented by the adaptive hybrid K-means with fuzzy C-means clustering (AHKM-FCM); the parameter tuning is also done by improved random variable-based water strider algorithm (IRV-WSA). Finally, the segmented region was subjected into the Transfer Learning Network that was processed with Efficient-net, ResNet and Densenet, in which fine tuning of weight was accomplished by using the IRV-WSA. The model was analyzed and computed across divergent measurements. Classification output results from the proposed IRV-WSA-ETLNet model include 94.853% accuracy, 94.750% sensitivity, 94.888% specificity, and 96.068% F1 Score. Additionally, this system uses less computing time 24.378 ms. Compared to previous methods, the findings of the proposed model demonstrate improved classification rates and help the farmer to increase crop production.
Why it matches plant phenotyping methods葉画像から病変・異常領域を分割し、深層学習で植物病害を分類する画像解析手法の開発が中心であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として含める。
abstractTo improve the process, an efficient method is developed for detecting various plant diseases by different learning method.
Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R 2 = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.
Why it matches plant phenotyping methodsドローン熱画像から作物の樹冠温度と水ストレス指数を抽出する手法を開発し、センサー測定との相関で検証しており、植物状態の取得法が中心である。
abstractA novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI.
Rice serves as a fundamental staple for a significant portion of the global population, playing an essential role in ensuring food security worldwide. However, the continuous threat of various diseases risks both yield and quality. Detecting these diseases at an early stage is very important for effective management of these risks. This research introduces a novel approach for rice disease detection using the fusion vision boosted classifier (FVBC), integrating VGG19 for feature extraction and LightGBM for classification. The meticulously curated dataset comprises 2627 rice leaf images, categorized into training, validation, and test sets for robust model evaluation. The FVBC model achieves impressive accuracies of 97.78% on the training set, 97.5% on the validation set, and 97.6% on the test set, demonstrating its efficacy in disease detection. The model's performance compared with other classifiers, including Softmax, highlights its superiority. Hyperparameter tuning, such as learning rate and tree depth for LightGBM, was crucial for optimizing model performance. The proposed FVBC model offers a non-invasive, scalable solution for early disease detection, empowering farmers to implement timely interventions and enhance agricultural productivity.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する分類手法を開発・評価しており、植物病害表現型の取得が研究の中心である。
abstractThis research introduces a novel approach for rice disease detection using the fusion vision boosted classifier (FVBC), integrating VGG19 for feature extraction and LightGBM for classification.
Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson's algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where [Formula: see text]=0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.
Why it matches plant phenotyping methodsブドウ葉のクロロフィル含量という植物形質を、ハイパースペクトル計測と前処理・特徴選択・機械学習で推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。
abstractTo address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations.
Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in developing countries. Symptoms such as chlorosis, structural deformities, and wilting, characterize these diseases. However, early identification can be challenging due to symptoms similarity. Researchers using artificial intelligence (AI) for plant disease classification, challenges like data imbalance, symptom variability, real-time performance, and costly annotation hinder accuracy and adoption. This work introduced a novel approach using the You Only Look Once (YOLO) deep learning model, chosen for its exceptional accuracy and speed. The study focuses on analyzing YOLO models, specifically YOLOv3 and YOLOv4, to identify fruit plant diseases. This work examines healthy peach and strawberry leaves, as well as peach leaves affected by bacterial spots and strawberry leaves with scorch disease. These models underwent thorough training using data from the publicly accessible Plant Village dataset. The simulation results were highly promising, numerically YOLOv3 model achieved 97% accuracy and a Mean Average Precision (mAP) of 92%, within a total detection time of 105 s. In comparison, the YOLOv4 model outperformed, with a 98% accuracy and an impressive mean average precision of 98%, all while completing the detection process in just 29 s. YOLOv4 demonstrated lower complexity, significantly faster, and more precise performance, especially in detecting multiple items. Serving as an efficient real-time detector, it holds the potential to transform plant disease diagnosis and mitigation strategies, ultimately leading to increased agricultural productivity and enhanced financial outcomes for developing nations.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像から識別するYOLO手法の開発と性能比較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 work introduced a novel approach using the You Only Look Once (YOLO) deep learning model
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Plant Village dataset on Kaggle (4,222 peach/strawberry leaf images across four classes), used to train and evaluate YOLOv3/YOLOv4 disease-detection models. No author analysis code, trained model checkpoints, or paper-specific supplements are publicly statedDataset · publicThis study utilizes data from the publicly available Plant Village dataset 41 , accessible on Kaggle.Open asset ↗Kagglelines:139-155Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定するCNN・特徴選択・セグメンテーション手法を提案し、ベンチマークデータセットと既存手法で性能比較しており、表現型取得・推定法が研究の中心である。
abstractFor disease classification in rice crops, we utilize a Convolutional Neural Network (CNN).
CowpeaSoybeanRootMorphology / geometry measurementSegmentationRoot system architecture
A simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes (adzuki bean, mung bean, cowpea, and soybean) based on two-dimensional images. Four different thresholding methods; Otsu, Gaussian adaptive, mean adaptive and triangle threshold were used to know the effect of thresholding in root trait estimation and to optimize the accuracy of root trait estimation. The results generated by the algorithm applied to 400 legume root images were compared with those generated by two separate software (WinRHIZO and RhizoVision), and the algorithm was validated using ground truth data. Distance transform method was used for estimating SA, AD, and RV and ConnectedComponentsWithStat function for TRL estimation. Among the thresholding methods, Otsu thresholding worked well for distance transform, while triangle threshold was effective for TRL. All the traits showed a high correlation with an R² ≥0.98 (p < 0.001) with the ground truth data. The root mean square error (RMSE) and mean bias error (MBE) were also minimal when comparing the algorithm-derived values to the ground truth values, with RMSE and MBE both < 10 for TRL, < 6 for SA, and < 0.5 for AD and RV. This lower value of error metrics indicates smaller differences between the algorithm-derived values and software-derived values. Although the observed error metrics were minimal for both software, the algorithm-derived root traits were closely aligned with those derived from WinRHIZO. We provided a simple Python algorithm for easy estimation of legume root traits where the images can be analyzed without any incurring expenses, and being open source; it can be modified by an expert based on their requirements.
Why it matches plant phenotyping methods根の二次元画像から主要形質を抽出するPythonアルゴリズムを開発し、既存ソフトウェアおよびグラウンドトゥルースで検証しており、植物フェノタイピング手法が研究の中心です。
abstractA simple Python algorithm was used to estimate the four major root traits: total root length (TRL), surface area (SA), average diameter (AD), and root volume (RV) of legumes
Reproduction assets foundThe authors publicly release their Python root-trait analysis source code together with the 400 legume root images and validation images on GitHub, as stated in the article text and Data availability statement. The Zenodo DOI cited for ground-truth images is a third-party dataset from Rose and Lobet (2018), i.e., citedCode · publicThe source code along with the root images and the validation images can be downloaded from ( https://github.com/AG9843/Legume-Root-Analysis.git ).Open asset ↗AG9843/Legume-Root-Analysislines:65-75Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
AppleFruitClassificationObject detectionGrowth / development / phenology
The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.
Why it matches plant phenotyping methods画像と最適化アルゴリズムを用いてリンゴ果実の成熟度を推定する手法が中心であり、果実の状態を直接評価する植物フェノタイピングとして適格。
abstractThis paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images.
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Traditional methods to diagnose these diseases are time-consuming, prone to error, and require expert knowledge, making automated solutions highly preferred. This paper explores the application of advanced deep learning techniques to detect as well as classify cassava leaf diseases which includes EfficientNet models, DenseNet169, Xception, MobileNetV2, ResNet models, Vgg19, InceptionV3, and InceptionResNetV2. A dataset consisting of around 36,000 labelled images of cassava leaves, afflicted by diseases such as Cassava Brown Streak Disease, Cassava Mosaic Disease, Cassava Green Mottle, Cassava Bacterial Blight, and healthy leaves, was used to train these models. Further the images were pre-processed by converting them into grayscale, reducing noise using Gaussian filter, obtaining the region of interest using Otsu binarization, Distance transformation, as well as Watershed technique followed by employing contour-based feature selection to enhance model performance. Models, after fine-tuned with ADAM optimizer computed that among the tested models, the hybrid model (DenseNet169 + EfficientNetB0) had superior performance with classification accuracy of 89.94% while as EfficientNetB0 had the highest values of precision, recall, and F1score with 0.78 each. The novelty of the hybrid model lies in its ability to combine DenseNet169's feature reuse capability with EfficientNetB0's computational efficiency, resulting in improved accuracy and scalability. These results highlight the potential of deep learning for accurate and scalable cassava leaf disease diagnosis, laying the foundation for automated plant disease monitoring systems.
Why it matches plant phenotyping methodsカッサバ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性能比較が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractThis paper explores the application of advanced deep learning techniques to detect as well as classify cassava leaf diseases
Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current studies have primarily focused on detecting and counting crop plants during the early stages of low coverage, there is limited investigation into the later stages of high coverage, which could impact the accuracy of forecasting yield. High canopy coverage and severe occlusion in later stages pose significant challenges for plant detection and counting. Therefore, this study evaluated the performance of the Count Crops tool and a deep learning (DL) model derived from early-stage unmanned aerial vehicle (UAV) imagery in detecting and counting Konjac plants during the high-coverage growth stage. Additionally, the study proposed an approach that integrates the DL model with Konjac location information from both early-stage and high canopy coverage stage imagery to improve the accuracy of recognizing Konjac plants during the high canopy coverage stage. The results indicated that the Count Crops tool outperformed the DL model constructed solely from early-stage imagery in detecting and counting Konjac plants during the high-coverage period. However, given the single stem and erect growth characteristics of Konjac, incorporating the DL model with the location information of the Konjac plants achieved the highest accuracy (Precision = 98.7%, Recall = 86.7%, F1-score = 92.3%). Our findings indicate that combining DL detection results from the early growth stages of Konjac, along with plant positional information from both growth stages, not only significantly improved the accuracy of detecting and counting plants but also saved time on annotating and training DL samples in the later stages. This study introduces an innovative approach for detecting and counting Konjac plants during high-coverage periods, providing a new perspective for recognizing and counting other crop plants at high-overlapping growth stages.
Why it matches plant phenotyping methodsUAV画像と深層学習を用いて植物個体の検出・計数手法を評価・改良しており、植物表現型の取得方法が研究の中心です。
abstractthis study evaluated the performance of the Count Crops tool and a deep learning (DL) model derived from early-stage unmanned aerial vehicle (UAV) imagery in detecting and counting Konjac plants during the high-coverage growth stage.
Using real-time and precise detection methods for maize leaf disease can significantly reduce economic losses in agriculture. Practical implementation often faces challenges such as the large volume of leaf disease data, low identification accuracy, and inefficiencies in production environments. To address these issues, this study introduces YOLO-MSM, a maize leaf disease detection algorithm that integrates multi-scale variable kernel convolution. In the YOLO MSM algorithm, we introduce an innovative convolutional method, MKConv (Multi-scale Variable Kernel Convolution), which offers diverse parameter configuration options and adapts flexibly to sample shapes with specific data characteristics. This design significantly enhances the network's overall performance. Additionally, to highlight critical features and mitigate the influence of environmental noise, we develop the C2f-SK module, leveraging the SK (Selective Kernel) attention mechanism to optimize feature extraction and representation. The loss function is optimized using MPDIoU (Minimum Point Distance Intersection over Union) to enhance the algorithm's capability in accurately locating densely occluded targets. The findings from the experiments indicate that the YOLO MSM algorithm reaches a real-time detection rate of 279.56 fps. In comparison to the baseline algorithm, the algorithm improves the precision and recall by 0.66% and 1.61%, respectively. Moreover, YOLO MSM algorithm is effectively lightweight compared to the series of cutting-edge algorithm models, which are only 5.4 MB in size, and the number of parameters and Flops are also reduced significantly. Therefore, YOLO MSM algorithm has an obvious light-weight advantage, which can achieve a good balance between precision and speed, and lay a theoretical foundation for identifying and detecting leaf disease on mobile devices.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から検出するYOLO-MSMアルゴリズムを開発し、精度・速度・軽量性を評価しているため、植物病害フェノタイピング手法が中心である。
abstractthis study introduces YOLO-MSM, a maize leaf disease detection algorithm that integrates multi-scale variable kernel convolution.
Drought stress induces a range of physiological changes in plants, including oxidative damage. Ascorbic acid (AsA), commonly known as vitamin C, is a vital non-enzymatic antioxidant capable of scavenging reactive oxygen species and modulating key physiological processes in crops under abiotic stresses like drought. Chickpea (Cicer arietinum L.), predominantly cultivated in drought-prone regions, offers an ideal model for studying drought tolerance. We explored the potential of AsA phenotyping to enhance drought tolerance in chickpea. Using an automated phenomics facility to monitor daily soil moisture levels, we developed a protocol to screen chickpea genotypes for endogenous AsA content. The results showed that AsA accumulation peaked at 30% field capacity (FC)-when measured between 11:30 am and 12:00 noon-coinciding with the maximum solar radiation (32 °C). Using this protocol, we screened 104 diverse chickpea genotypes and two control varieties for genetic variability in AsA accumulation under soil moisture depletion, identifying two groups of genotypes with differing AsA levels. Field trials over two consecutive years revealed that genotypes with higher AsA content, such as BDNG-2018-15 and PG-1201-20, exhibited enhanced drought tolerance and minimal reductions in yield compared to standard cultivars. These AsA-rich genotypes hold promise as valuable genetic resources for breeding programs aimed at improving drought tolerance in chickpea.
Why it matches plant phenotyping methodsチックピーの内生AsA含量を測定・スクリーニングするプロトコルを開発し、自動フェノミクス施設で多数遺伝子型に適用しているため、植物フェノタイピング手法が中心的です。
abstractWe explored the potential of AsA phenotyping to enhance drought tolerance in chickpea.
Timely detection of plant diseases is crucial for agricultural safety, product quality, and environmental protection. However, plant disease detection faces several challenges, including the diversity of plant disease scenarios and complex backgrounds. To address these issues, we propose a plant disease detection model named PYOLO. Firstly, the model enhances feature fusion capabilities by optimizing the PAN structure, introducing a weighted bidirectional feature pyramid network (BiFPN), and repeatedly fusing top and bottom scale features. Additionally, the model's ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. Finally, the MHC2f mechanism is designed to enhance the model's ability to perceive complex backgrounds and targets at different scales by utilizing its self-attention mechanism for parallel processing. Experiments demonstrate that the model's mAP value increases by 4.1% compared to YOLOv8n, confirming its superiority in plant disease detection.
Why it matches plant phenotyping methods植物病害を画像から検出するPYOLOモデルを開発し、YOLOv8nとの性能比較で検証しているため、植物の病害状態を推定する画像ベースの表現型計測手法が中心である。
abstractwe propose a plant disease detection model named PYOLO.
Reproduction assets foundThe paper's authors publicly release the YOLOv8-based PYOLO/YOLO-ESC model code used for plant disease detection via a GitHub release, matching an allowed URL. No separate phenotype dataset or trained checkpoint is explicitly deposited in the supplied text.Code · publicThe improvements and execution process of the YOLOv8 code discussed in this article are available on GitHub, and can be downloaded from https://github.com/WANG9711/my-source-code/releases/tag/yolov8 under the file name yolov8.zip.Open asset ↗WANG9711/my-source-code · yolov8lines:193-288Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant's root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.
Why it matches plant phenotyping methodsダイズ根上の線虫シスト数とサイズという植物病害抵抗性関連形質を、画像から自動検出・計測するソフトウェアを開発しており、表現型取得手法が研究の中心です。
abstractwe have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously.
Reproduction assets foundThe paper's SCN cyst phenotyping assets are publicly available: the authors' Nemacounter analysis software on GitHub, two annotated cyst image datasets on Roboflow (bounding-box and segmentation/area annotations), and the authors' trained YOLOv5-xl model (cystmodel.pt) on Iowa State's Box. The SAM weights and ultralyptCode · publicThe Nemacounter software can be downloaded here: https://github.com/DjampaKozlowski/NemaCounter and we provide an installation manual and utilization manual as supplementary data.Open asset ↗DjampaKozlowski/NemaCounterlines:65-70Dataset · publicThe complete dataset is accessible on the Roboflow website at: https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2Open asset ↗lines:118-138Dataset · publicAll training datasets are available on Roboflow website at : https://universe.roboflow.com/iowa-state-university-cwvqa/cystnewboundingboxv2 and https://universe.roboflow.com/iowa-state-university-cwvqa/cyst-detectors-area.Open asset ↗lines:139-197Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model's performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity.
Why it matches plant phenotyping methods可視・熱画像と生物物理形質を統合した機械学習による、萎凋病条件下の遺伝子型別早期収量予測を開発・評価しており、形質推定手法が中心である。
abstractML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters.
Plants are essential at all stages of living things. Plant pests, diseases, and symptoms are most regularly visible in plant leaves and fruits and sometimes within the roots. Yet, their diagnosis by experts in the laboratory is expensive, tedious, and time-consuming if the samples involve laboratory analysis. Failure to detect early plant symptoms and diseases is the core biotic cause of increased plant stresses, structure, health, reduced subsistence farming, and threats to global food security. To mitigate these problems at a social, economic, and environmental level, inappropriate herbicide application reduction and early plant disease detection and classification (PDDC) are significant solutions in this case. Advancements in transfer learning techniques have resulted in effective results in smart farming and have become extensively used in disease identification and classification research studies. This study presents a novel hybrid inception-xception (IX) using a convolution neural network (CNN). The presented model combines inception and depth-separable convolution layers to capture multiple-scale features while reducing model complexity and overfitting. In contrast to ordinary CNN architectures, it extends the network for better feature extraction, improving PDDC performance that demands diverse feature competencies. It further presents a real-time artificial intelligence (AI) application available in MATLAB, Android, and Servlet to automatically identify and classify diseases based on the leaf environment using improved CNN, machine learning (ML), and computer vision techniques. To assess the presented IX-CNN model performance, different classifiers, namely, support vector machine (SVM), decision tree (DT) and random forest (RF), were used. The experiments used six datasets, including PlantVillage, Turkey Disease, Plant Doc, Rice Disease, RoCole, and NLB datasets. Plant Doc, PlantVillage, and Turkey Disease datasets demonstrated an accuracy of 100%. Rice Disease, RoCole, and NLB attained an accuracy of 99.79%, 99.95%, and 98.64%, respectively.
Why it matches plant phenotyping methods葉の画像から植物病害を自動検出・分類するCNN手法を開発し、複数データセットで性能評価しているため、植物表現型（病徴・病害状態）の取得・推定が中心である。
abstractThis study presents a novel hybrid inception-xception (IX) using a convolution neural network (CNN).
Cigar leaf is a special type of tobacco plant, which is the raw material for producing high-quality cigars. The content and proportion of nicotine and other composite substances of cigar leaves have a crucial impact on their quality and vary greatly with the time of harvest. Hyperspectral remote sensing technology has been widely used in the field of crop monitoring because of its advantages of large area coverage, fast information acquisition, short cycle turnover, strong real-time performance and high efficiency. Therefore, it is important to accurately monitor nicotine content of field crops in a timely manner in the production of high-quality cigar leaf. To this end, this study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition. MSC, SG, and SNV were combined and applied to the raw data. The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. An inversion model of the content of nicotine was established, and the model was evaluated for accuracy. The main research conclusions are as follows: (1) With the increase in the rate of application of nitrogen fertilizer, the nicotine content of cigar leaves increased; (2) Processing data by the CARS, SPA, and UVE methods reduces the degree of data redundancy and information co-linearity in the screening of the content of nicotine sensitive bands; (3) The MSC-SNV-SG-CARS-BP model has the best predictive accuracy on the nicotine content. The prediction accuracy of the testing set was R 2 = 0.797, RMSE = 0.078,RPD = 2.182.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像からタバコ葉のニコチン含量を推定する取得・前処理・機械学習ワークフローを構築し、精度評価しており、植物形質の計測手法が中心である。
abstractthis study set out to measure crop reflectance spectra acquired by UAV drones from tobacco field crops by hyperspectral image acquisition.
Indian agriculture is vital sector in the country's economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers' livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management. To recover from this and, address these challenges, this study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection. Deep learning architectures, like convolutional neural network, can autonomously learn and extract complicated characteristics and patterns from huge datasets. Our research, conducted on mango and groundnut leaves collected during field visits in western Maharashtra and supplemented by online datasets, demonstrates a CNN model that achieves an impressive 96% accuracy as compared to machine learning techniques that follow tedious feature extraction. Furthermore, image processing contributes to enhancing the dataset through normalization, resizing, and augmentation for better classification results. Overall, CNN can continuously improve and adapt its performance through iterative training, resulting in higher accuracy rates and reduced false positives in contrast to conventional machine learning methods.
Why it matches plant phenotyping methodsCNNによる植物葉の病害状態分類と画像前処理・性能比較が研究の中心であり、植物病害表現型の取得・推定手法に該当する。
abstractthis study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants. Incorporating transformer encoder blocks offers enhanced capability in capturing intricate spatial dependencies within leaf images, promising agricultural sustainability and food security. By providing farmers and farming stakeholders with a reliable tool for rapid disease detection, our model facilitates timely intervention and management practices, ultimately leading to improved crop yields and mitigated economic losses. Through extensive comparative analyses on various datasets and filed tests, the proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98) for Grpae leaves across different datasets including bell pepper and tomato. Furthermore, including DSC layers enhances the computational efficiency of the model while maintaining expressive power, making it well-suited for real-time agricultural applications. The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習モデルを開発し、複数データセットで比較評価するとともにエッジデバイスへ実装しており、植物病害状態の取得・推定手法が研究の中心である。
abstractthe proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98)
Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model's predictions.
Why it matches plant phenotyping methodsバナナ個体の健全・萎凋病状態を航空・地上画像から推定する深層学習モデルを開発・評価しており、植物病害状態の画像ベースフェノタイピング手法が中心です。
abstractWe developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms.