YOLO-ACT: an adaptive cross-layer integration method for apple leaf disease detection.

ABSTRACT: Apple is a significant economic crop in China, and leaf diseases represent a major challenge to its growth and yield. To enhance the efficiency of disease detection, this paper proposes an Adaptive Cross-layer Integration Method for apple leaf disease detection. This approach, built upon the YOLOv8s architecture, incorporates three novel modules specifically designed to improve detection accuracy and mitigate the impact of environmental factors. Furthermore, the proposed method addresses challenges arising from large feature discrepancies and similar disease characteristics, ultimately improving the model's overall detection performance. Experimental results show that the proposed method achieves a mean Average Precision (mAP) of 85.1% for apple leaf disease detection, outperforming the la

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11473324 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature