Improved Peanut Quality Detection Method of YOLOv8n

Peanut quality screening is of great significance in agricultural production and food safety. In order to solve the problem of low efficiency of traditional peanut quality screening methods, a lightweight peanut quality detection model LE-YOLO (lightweight and efficient) with improved YOLOv8n algorithm is proposed. A grouped shuffling bottleneck (GSBottleneck) module is proposed, which increases the nonlinear fitting ability of the model and reduces the model inference time. A residual group shuffling block (ResGSBlock) is designed, and a lightweight neck (LW-Neck) is constructed by combining GSConv (grouped shuffle convolution), which reduces the cost of model calculation and improves the inference speed of the model. An adaptive feature optimization block (AFOB) is proposed to enhance the information interaction and model characterization capabilities between channels. Experimental verification on the DW peanut dataset shows that compared with the YOLOv8n algorithm, the computational cost of LE-YOLO is reduced by 1 GFlops, the FPS is increased by 25%, and the average accuracy reaches 98% mAP@0.5, which verifies the good performance of the algorithm in detection accuracy and speed, and provides an effective method for peanut quality screening. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.