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Highlights

Modify the YOLOV8 backbone network to reduce model calculation complexity and parameter amount.

Abstract. In response to the challenges posed by the excessive number of parameters and the high computational complexity inherent in existing algorithms for apple leaf disease recognition, this study introduces an enhanced, lightweight detection algorithm, YOLOv8-GSSW. Building upon the YOLOv8n as the foundational model, this research innovates in several key areas. Initially, the algorithm employs a lightweight GSConv convolutional module to replace the standard convolutional modules within the backbone network, significantly reducing both the model‘s computational complexity and its parameter count. Furthermore, the incorporation of the Slim-Neck design concept into the neck network module serves to bolster the model‘s capabilities in feature processing and learning. This enhancement is further augmented by the integration of the SA attention mechanism within Slim-Neck, which amplifies the attention to target features and increases their weight by merging information across both spatial and channel dimensions. Finally, the original CIoU loss function is substituted with the WIoU loss function to address the issue of sample imbalance and accelerate the model‘s convergence. Experimental results underscore the YOLOv8-GSSW algorithm‘s superior performance, achieving a mean average precision (mAP) of 87.3% with a parameter volume of 2.7x10⁶ and a model weight of 5.4 MB. Relative to the YOLOv8n benchmark model, this represents a 15.6% reduction in parameter volume and an 11.5% decrease in model size. The modifications introduced in this algorithm not only facilitate more convenient application in practical scenarios for apple leaf disease detection but also render the algorithm well-suited for deployment on edge devices.