Research on soybean leaf disease recognition in natural environment based on improved Yolov8

Abstract

The rapid and accurate identification of soybean diseases is critical for optimizing both yield and quality. Traditional image recognition techniques face notable limitations in terms of generalization and accuracy, particularly when tasked with identifying small-scale targets or distinguishing diseases with similar characteristics in large, heterogeneous, and complex environments. To address these challenges, this study proposes the YOLOv8-DML model for soybean leaf disease recognition. Building upon YOLOv8n, this model integrates a DWR module that replaces the high-level C2f module with C2f-DWR, enhancing feature extraction across varied receptive fields. Additionally, modifications to the neck structure incorporate a Multi-scale Enhanced Feature Pyramid (MEFP), which improves detection performance across targets of varying sizes by enabling effective multi-scale information fusion. A lightweight detection head (LSCD) is further introduced to facilitate multiscale feature interactions while reducing the overall model parameter count. Lastly, the WIoUv3 loss function is employed to place greater emphasis on small targets and moderate-quality samples, thereby enhancing detection precision. Experimental results demonstrate that YOLOv8-DML achieves a mAP50 of 96.9%, marking a 1.8% improvement over the original YOLOv8 algorithm, while also achieving an 18.6% reduction in parameters. Comparative analysis with other mainstream object detection models indicates that YOLOv8-DML delivers superior overall performance, highlighting its significant potential for effective soybean leaf disease identification.

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