Detection of Rice Leaf Diseases Based on Improved YOLOv8n Algorithm

DAI Linhua; LI Yuansong; SHI Rui

doi:10.13501/j.cnki.42-1908/n.2024.09.008

Abstract：To address the issues of low detection accuracy,missed detection,and false detection in rice disease identification,an improved method based on you only look once version 8 normal(YOLOv8n)for rice leaf disease recognition and detection was proposed.Original YOLOv8n algorithm was utilized as the baseline model,with a convolutional block for down-sampling(ADown)module employed in the backbone network to reduce the loss of feature information.Additionally,a squeeze-and-excitation(SE)attention mechanism module was introduced between the backbone and neck networks to enhance the network′s feature fusion capability.A shared parameter detection head was also designed to increase the receptive field for detection tasks.Furthermore,the weighted interpolation of sequential evidence for the intersection over union(WIoU)loss function was used to improve the network′s detection performance further.Extensive experiments were conducted on a rice disease dataset.The results indicated that,compared to the original YOLOv8n algorithm,the improved YOLOv8n algorithm achieved a 6.59%increase in precision and a 6.86%increase in mean average precision.The improved YOLOv8n model satisfied the requirements for the speed and accuracy of rice leaf disease identification,while also enhancing the detection capability for small and dense targets,thereby reducing the incidence of missed and false detection.The improved YOLOv8n algorithm demonstrated certain advantages in detection speed and accuracy over current mainstream models,and the detection speed was 3.61 times that of YOLO version 7(YOLOv7)algorithm,achieving an 8.63%increase in mean average precision.This study is expected to have significant reference value in intelligent rice cultivation management.