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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1473928
This article is part of the Research Topic Plant Pest and Disease Model Forecasting: Enhancing Precise and Data-Driven Agricultural Practices View all 6 articles

DM-YOLO: Improved YOLOv9 Model for Tomato Leaf Disease Detection

Provisionally accepted
Abudukelimu Abulizi Abudukelimu Abulizi Junxiang Ye Junxiang Ye Halidanmu Abudukelimu Halidanmu Abudukelimu *Wenqiang Guo Wenqiang Guo
  • School of Information Management, Xinjiang University of Finance and Economics, Urumqi, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Tomato disease detection, YOLO, DM-YOLO, Sampling method, Loss function

    Received: 25 Sep 2024; Accepted: 20 Nov 2024.

    Copyright: © 2024 Abulizi, Ye, Abudukelimu and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Halidanmu Abudukelimu, School of Information Management, Xinjiang University of Finance and Economics, Urumqi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.