SCL-YOLOv8n based rice disease lightweight detection method

Abstract To address the challenges posed by complex rice disease features, low detection accuracy, and large model size, this paper, we propose slim cross-level lightweight YOLOv8n (SCL-YOLOv8n), an enhanced lightweight target detection framework based on YOLOv8n. Firstly, a novel slim-neck network architecture was designed to optimize concatenation of feature representations, thereby reducing computational cost and the number of parameters. Secondly, the receptive-field collaborative attention cross-stage partial network (RFCA-CSP) was proposed, integrating convolutional neural networks with the transformer architecture to enhance feature extraction capabilities while minimizing computational overhead. Finally, the lightweight shared-convolution with separated batch normalization and dynamic anchors (LSCSBD) detection head was incorporated to enhance the model’s computational efficiency through the implementation of techniques including shared convolution, separated batch normalization, and dynamic anchor generation. Experimental results demonstrate that the improved SCL-YOLOv8n increased the mAP50 by 5.0%. points compared with the traditional YOLOv8n. Concurrently, it decreased the parameter count to 1.93 M and the computational volume to 5.5 GFLOPs. These represent reductions of 35.7% and 31.3% respectively when compared with the original model. The SCL-YOLOv8n architecture exhibits dual advantages, it not only enhances the accuracy of object detection but also achieves substantial reductions in both the number of parameters and computational complexity. This advancement offers an effective approach for detecting rice diseases in complex backgrounds, thereby demonstrating significant potential for application in agricultural disease monitoring scenarios.