Skip to main content

ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1451078

YOLO-ACT: An Adaptive Cross-layer Integration Method for Apple Leaf Disease Detection

Provisionally accepted
Silu Zhang Silu Zhang 1*Jingzhe Wang Jingzhe Wang 2*Kai Yang Kai Yang 1Minglei Guan Minglei Guan 2
  • 1 School of Computer Science and SoftwareEngineering, University of Science and Technology Liaoning, Anshan, China
  • 2 Shenzhen Polytechnic, Shenzhen, China

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

    Apple is an important economic product in China, and the yield of apples is a primary concern for fruit growers. Apple leaf diseases are one of the main factors affecting apple growth and yield. To enhance disease detection efficiency while reducing false detection caused by complex backgrounds, lighting conditions, shooting angles, or intrinsic characteristics of the diseases, this paper proposes an Adaptive Cross-layer Integration Method for Apple Leaf Disease Detection .Based on YOLOv8s, three improved modules are designed to enhance the accuracy of apple leaf disease detection, effectively mitigating the impact of external environmental factors. This method also addresses the negative issues arising from significant feature differences or similar disease characteristics, thereby enhancing the detection performance of the model. The results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 85.1% for apple leaf disease detection, outperforming YOLOv5s, YOLOv8s, and other classical algorithms.The mAP improved by 2.8% compared to the baseline and exceeded the latest state-of-the-art model YOLOv10 by 2.2%. This approach effectively reduces both missed and false detections, significantly enhancing the detection and localization of diseases. It provides a new theoretical basis and research direction for apple disease detection.

    Keywords: Foliar disease, object detection, Feature fusion, Task-Aligned, Intelligent agriculture

    Received: 18 Jun 2024; Accepted: 05 Sep 2024.

    Copyright: © 2024 Zhang, Wang, Yang and Guan. 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:
    Silu Zhang, School of Computer Science and SoftwareEngineering, University of Science and Technology Liaoning, Anshan, China
    Jingzhe Wang, Shenzhen Polytechnic, Shenzhen, 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.