Accurate crop row recognition of maize at the seedling stage using lightweight network

Accurate extraction of crop row is very important for automation of agricultural production. Crop rows are required for accurate machine guidance in agricultural production such as fertilization, plant protection, weeding and harvesting. In this study, an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed, which combined BiSeNet V2 with a spatial convolutional neural network. The proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows. A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light, shadows, presence of weeds, and irregular crop shape. The proposed system was experimentally determined to overcome the interference of different complex scenes. And it can be applied to crop rows of different numbers, straight lines and curves. Different analyses were performed to check the robustness of the algorithm. Comparing this algorithm with the Fully Convolutional Networks (FCN) algorithm, it exhibited superior performance and saved 84.85 ms. The accuracy rate reached 0.9811, and the detection speed reached 65.54 ms/frame. The Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row recognition. It provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.