DM-YOLO: improved YOLOv9 model for tomato leaf disease detection
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 algor...
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| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1473928/full |
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| author | Abudukelimu Abulizi Junxiang Ye Halidanmu Abudukelimu Wenqiang Guo |
| author_facet | Abudukelimu Abulizi Junxiang Ye Halidanmu Abudukelimu Wenqiang Guo |
| author_sort | Abudukelimu Abulizi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e977ec4a28f4a43a09ab369093a1066 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-5e977ec4a28f4a43a09ab369093a10662025-02-11T04:11:06ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011510.3389/fpls.2024.14739281473928DM-YOLO: improved YOLOv9 model for tomato leaf disease detectionAbudukelimu AbuliziJunxiang YeHalidanmu AbudukelimuWenqiang GuoIn 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.https://www.frontiersin.org/articles/10.3389/fpls.2024.1473928/fulltomato leaf disease detectionYOLODM-YOLOsampling methodloss function |
| spellingShingle | Abudukelimu Abulizi Junxiang Ye Halidanmu Abudukelimu Wenqiang Guo DM-YOLO: improved YOLOv9 model for tomato leaf disease detection Frontiers in Plant Science tomato leaf disease detection YOLO DM-YOLO sampling method loss function |
| title | DM-YOLO: improved YOLOv9 model for tomato leaf disease detection |
| title_full | DM-YOLO: improved YOLOv9 model for tomato leaf disease detection |
| title_fullStr | DM-YOLO: improved YOLOv9 model for tomato leaf disease detection |
| title_full_unstemmed | DM-YOLO: improved YOLOv9 model for tomato leaf disease detection |
| title_short | DM-YOLO: improved YOLOv9 model for tomato leaf disease detection |
| title_sort | dm yolo improved yolov9 model for tomato leaf disease detection |
| topic | tomato leaf disease detection YOLO DM-YOLO sampling method loss function |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1473928/full |
| work_keys_str_mv | AT abudukelimuabulizi dmyoloimprovedyolov9modelfortomatoleafdiseasedetection AT junxiangye dmyoloimprovedyolov9modelfortomatoleafdiseasedetection AT halidanmuabudukelimu dmyoloimprovedyolov9modelfortomatoleafdiseasedetection AT wenqiangguo dmyoloimprovedyolov9modelfortomatoleafdiseasedetection |