ArticleScientific reports2023
Recent advances in plant disease severity assessment using convolutional neural networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 89 citations in OpenAlex.
- A knowledge-driven unified framework for plant disease classification and severity grading via domain adaptation.Plant phenomics (Washington, D.C.) · 2026Article
- Sensor-Based Cross-Modal Spatiotemporal Alignment and Causal Profit Modeling for Agricultural Input Optimization.Sensors (Basel, Switzerland) · 2026Article
- Tomato leaf disease and severity prediction using multi-task learning.BMC plant biology · 2026Article
- A parallel convolutional neural network with background removal and lesion segmentation for field plant disease severity classification.Scientific reports · 2026Article
- Optimized Lightweight U-Net and YOLACT framework for multi-disease severity detection in pome fruit leaves.Scientific reports · 2026Article
- PlantPathNet: a novel network based on cross-layer feature integration for plant disease classification.Frontiers in plant science · 2026Article
- LMP-PM: a lightweight multi-path pruning method for plant leaf disease recognition.Frontiers in plant science · 2026Article
- YOLOv11-AIU: a lightweight detection model for the grading detection of early blight disease in tomatoes.Plant methods · 2025Article
- Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (Plants (Basel, Switzerland) · 2025Article
- Earlier quantification of rice blast impact via instantaneous chlorophyll fluorescence.Plant methods · 2025Article
- Location-guided lesions representation learning via image generation for assessing plant leaf diseases severity.Plant phenomics (Washington, D.C.) · 2025Article
- Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV.Plants (Basel, Switzerland) · 2025Article
- A precision grading method for walnut leaf brown spot disease integrating hierarchical feature selection and dynamic multi-scale convolution.Frontiers in plant science · 2025Article
- Article
- Assessing Huanglongbing Severity and Canopy Parameters of the Huanglongbing-Affected Citrus in Texas Using Unmanned Aerial System-Based Remote Sensing and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Revolutionizing crop disease detection with computational deep learning: a comprehensive review.Environmental monitoring and assessment · 2024Review
- Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- RAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss.Frontiers in plant science · 2024Article
- Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.