Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

Abstract The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP‐LCNet module respectively to reduce the parameters (Params) and giga floating‐point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization‐based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real‐time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots.