ArticleFrontiers in plant science2024
Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 60 citations in OpenAlex.
- Analysis of tomato bacterial wilt transmission dynamics with behavioral responses: A mathematical and optimal control approach.Parasite epidemiology and control · 2026Article
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
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- Brain tumor detection on magnetic resonance imaging scans using the artificial intelligence-based You Only Look Once algorithm.The Journal of international medical research · 2026Article
- A hybrid deep learning and fuzzy logic framework for robust tomato disease detection and classification.Scientific reports · 2026Article
- Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications.Frontiers in plant science · 2026Article
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- A lightweight deep learning framework for tea cultivar identification based on leaf images.Frontiers in plant science · 2026Article
- LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection.PloS one · 2026Article
- Focal-HAIN: a lightweight model with adaptive modulation and hierarchical interaction for real-time crop pest and disease monitoring.Frontiers in plant science · 2026Article
- Deep learning techniques for early detection and classification of leaf diseases in crops.Frontiers in plant science · 2026Article
- Crop leaf disease detection with additive gated convolution and hierarchical attention fusion.Scientific reports · 2025Article
- Visual-language transformer-based tomato leaf disease detection for portable greenhouse monitoring device.Plant methods · 2025Article
- Tomato Leaf Disease Detection Method Based on Multi-Scale Feature Fusion.Plants (Basel, Switzerland) · 2025Article
- Robust multiclass classification of crop leaf diseases using hybrid deep learning and Grad-CAM interpretability.Scientific reports · 2025Article
- Tomato leaf disease detection method based on improved YOLOv8n.Scientific reports · 2025Article
- Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.Scientific reports · 2025Article
- Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m.Plants (Basel, Switzerland) · 2025Article
- AG-Vision: a dual-module approach for tomato leaf disease diagnosis.Frontiers in plant science · 2025Article
- WMC-RTDETR: a lightweight tea disease detection model.Frontiers in plant science · 2025Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
When detecting tomato leaf diseases in natural environments, factors such as changes in lighting, occlusion, and the small size of leaf lesions pose challenges to detection accuracy. Therefore, this study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion. Firstly, the Convolutional Block Attention Module (CBAM) is introduced into the backbone feature extraction network to enhance the ability to extract lesion features and suppress the effects of environmental interference. Secondly, shallow feature maps are introduced into the re-parameterized generalized feature pyramid network (RepGFPN), constructing a new multi-scale re-parameterized generalized feature fusion module (BiRepGFPN) to enhance feature fusion expression and improve the localization ability for small lesion features. Finally, the BiRepGFPN replaces the Path Aggregation Feature Pyramid Network (PAFPN) in the YOLOv6 model to achieve effective fusion of deep semantic and shallow spatial information. Experimental results indicate that, when evaluated on the publicly available PlantDoc dataset, the model's mean average precision (mAP) showed improvements of 7.7%, 11.8%, 3.4%, 5.7%, 4.3%, and 2.6% compared to YOLOX, YOLOv5, YOLOv6, YOLOv6-s, YOLOv7, and YOLOv8, respectively. When evaluated on the tomato leaf disease dataset, the model demonstrated a precision of 92.9%, a recall rate of 95.2%, an F1 score of 94.0%, and a mean average precision (mAP) of 93.8%, showing improvements of 2.3%, 4.0%, 3.1%, and 2.7% respectively compared to the baseline model. These results indicate that the proposed detection method possesses significant detection performance and generalization capabilities.
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