ArticleFrontiers in plant science2024
DM-YOLO: improved YOLOv9 model for tomato leaf disease detection.
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 21 papers.
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21 citing papers in PubMed.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases.Journal of Zhejiang University. Science. B · 2026Article
- Spectrally optimised YOLOv10s-SeqOpt framework for real-time UAV-based early detection of avocado foliar diseases in indian orchards.Scientific reports · 2026Article
- GBR-DETR: A Real-Time Tomato Leaf Disease Detection Model for Edge Device Deployment.Sensors (Basel, Switzerland) · 2026Article
- MSP-Net: An Effective Multi-Scale Feature-Aware Detection Network for the Detection of Tomato Leaf Diseases.Plants (Basel, Switzerland) · 2026Article
- Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments.Frontiers in plant science · 2026Article
- BR-YOLOv9: a multi-scale fusion framework for robust weed recognition in rice paddy fields.Frontiers in plant science · 2026Article
- A study on plant disease and pest detection and counting based on multi-scale enhancement and cross-scale fusion.Frontiers in plant science · 2026Article
- LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection.PloS one · 2026Article
- Article
- A federated learning with Large-Small Kernel Attention Network for image classification.Frontiers in plant science · 2026Article
- Article
- A lightweight and generalizable deep learning framework for early detection of rice leaf diseases in complex field environments.Scientific reports · 2025Article
- Crop leaf disease detection with additive gated convolution and hierarchical attention fusion.Scientific reports · 2025Article
- Tomato Leaf Disease Detection Method Based on Multi-Scale Feature Fusion.Plants (Basel, Switzerland) · 2025Article
- BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm.Sensors (Basel, Switzerland) · 2025Article
- SSD-YOLO: a lightweight network for rice leaf disease detection.Frontiers in plant science · 2025Article
- YOLO-Citrus: a lightweight and efficient model for citrus leaf disease detection in complex agricultural environments.Frontiers in plant science · 2025Article
- Real-time detection method for Litchi diseases and pests based on improved YOLOv5s.Frontiers in plant science · 2025Article
- Research on detection and counting method of wheat ears in the field based on YOLOv11-EDS.Frontiers in plant science · 2025Article
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4 authors.
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Abstract
In natural environments, tomato leaf disease detection faces many challenges, such as variations in light conditions, overlapping disease symptoms, tiny size of lesion areas, and occlusion between leaves. Therefore, an improved tomato leaf disease detection method, DM-YOLO, based on the YOLOv9 algorithm, is proposed in this paper. Specifically, firstly, lightweight dynamic up-sampling DySample is incorporated into the feature fusion backbone network to enhance the ability to extract features of small lesions and suppress the interference from the background environment; secondly, the MPDIoU loss function is used to enhance the learning of the details of overlapping lesion margins in order to improve the accuracy of localizing overlapping lesion margins. The experimental results show that the precision (P) of this model increased by 2.2%, 1.7%, 2.3%, 2%, and 2.1%compared with those of multiple mainstream improved models, respectively. When evaluated based on the tomato leaf disease dataset, the precision (P) of the model was 92.5%, and the average precision (AP) and the mean average precision (mAP) were 95.1% and 86.4%, respectively, which were 3%, 1.7%, and 1.4% higher than the P, AP, and mAP of YOLOv9, the baseline model, respectively. The proposed detection method had good detection performance and detection potential, which will provide strong support for the development of smart agriculture and disease control.
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