A CIW-YOLOv8n-based method for cotton leaf disease detection and recognition

doi:10.14088/j.cnki.issn0439-8114.2025.10.032

Abstract

Abstract: To address the issues of low accuracy and insufficient detection efficiency in cotton leaf disease detection, the YOLOv8 model was improved in terms of both model structure and detection efficiency, leading to the construction of the CIW-YOLOv8n model.The C2f-ConvNeXtv2 module was introduced into the backbone network, which dynamically adjusted the convolution kernels based on the input image, thereby enhancing the model’ adaptability to diverse image content and the accuracy of feature extraction. The ImplicitHead detection head was introduced into the neck network to replace the original structure, and the loss function was replaced with WIoU, further optimizing the detection performance of the model.The results showed that the precision, recall, mean average precision (mAP), number of parameters, and computational cost of the CIW-YOLOv8n model were 92.6%, 82.4%, 91.7%, 2.761 MB, and 7.4×10⁹, respectively. This model achieved higher detection accuracy while maintaining low complexity, effectively balancing model performance and efficiency.

Key words: CIW-YOLOv8n model, YOLOv8 model, cotton leaf disease, detection, recognition

CLC Number:

TP391.4

LI Jia-jun, DONG Hui, YU Lin, LIU Jin-tao, LI Shuang, YANG Yi. A CIW-YOLOv8n-based method for cotton leaf disease detection and recognition[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(10): 207-212.

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