Cotton Weed-YOLO: A Lightweight and Highly Accurate Cotton Weed Identification Model for Precision Agriculture

Abstract

Precise weed recognition is an important step towards achieving intelligent agriculture. In this paper, a novel weed recognition model, Cotton Weed-YOLO, is proposed to improve the accuracy and efficiency of weed detection. CW-YOLO is based on YOLOv8 and introduces a dual-branch structure combining a Vision Transformer and a Convolutional Neural Network to address the problems of the small receptive field of the CNN and the high computational complexity of the transformer. The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. A Scale-Invariant Shared Convolutional Detection (SSCD) head is proposed to fully utilize the advantages of shared convolution and significantly reduce the number of parameters in the detection head. The experimental results show that the CW-YOLO model outperforms existing methods in terms of detection accuracy and speed. Compared with the original YOLOv8n, the detection accuracy, mAP value, and recall rate are improved by 1.45, 0.7, and 0.6%, respectively, the floating-point numbers are reduced by 2.5 G, and the number of parameters is reduced by 1.52 × 106 times. The proposed CW-YOLO model provides powerful technical support for smart agriculture and is expected to promote the development of agricultural production in the direction of intelligence and precision.

Keywords

• weed recognition
• YOLOv8
• deep learning
• cotton weeds
• precision agriculture