DM-YOLO: improved YOLOv9 model for tomato leaf disease detection.

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.

Plant phenotyping relevance

トマト葉の病斑を画像から検出するYOLO改良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フェノタイピング手法に該当する。

Code and data availability

The paper's tomato leaf disease detection experiments use a public Roboflow dataset explicitly cited by the authors as the dataset used in this study. The ultralytics YOLOv5 repository is a generic third-party library, not a paper-specific asset, and no author analysis code or trained model checkpoint is reported as a.