ArticleFrontiers in plant science2023
Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- MSP-Net: An Effective Multi-Scale Feature-Aware Detection Network for the Detection of Tomato Leaf Diseases.Plants (Basel, Switzerland) · 2026Article
- Enhancing leaf disease classification using GAT-GCN hybrid model.Frontiers in plant science · 2025Article
- HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection.Sensors (Basel, Switzerland) · 2024Article
- IoT-based system of prevention and control for crop diseases and insect pests.Frontiers in plant science · 2024Article
- Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion.Frontiers in plant science · 2024Article
- Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.Frontiers in plant science · 2024Review
- DM-YOLO: improved YOLOv9 model for tomato leaf disease detection.Frontiers in plant science · 2024Article
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2 authors.
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Abstract
To address the challenges of insufficient accuracy in detecting tomato disease object detection caused by dense target distributions, large-scale variations, and poor feature information of small objects in complex backgrounds, this study proposes the tomato disease object detection method that integrates prior knowledge attention mechanism and multi-scale features (PKAMMF). Firstly, the visual features of tomato disease images are fused with prior knowledge through the prior knowledge attention mechanism to obtain enhanced visual features corresponding to tomato diseases. Secondly, a new feature fusion layer is constructed in the Neck section to reduce feature loss. Furthermore, a specialized prediction layer specifically designed to improve the model's ability to detect small targets is incorporated. Finally, a new loss function known as A-SIOU (Adaptive Structured IoU) is employed to optimize the performance of the model in terms of bounding box regression. The experimental results on the self-built tomato disease dataset demonstrate the effectiveness of the proposed approach, and it achieves a mean average precision (mAP) of 91.96%, which is a 3.86% improvement compared to baseline methods. The results show significant improvements in the detection performance of multi-scale tomato disease objects.
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