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

作     者：Jian Wei Mengfan Zhang Caicong Wu Qin Ma Weitao Wang Chuanfeng Wan 

作者机构：College of Information and Electrical EngineeringChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data ApplicationsMinistry of Agriculture and Rural AffairsBeijing 100083China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2024年第17卷第1期

页      面：189-198,I0001页

核心收录：

学科分类：0828[工学-农业工程] 09[农学] 0901[农学-作物学] 

基　　金：National Key R&D Program of China(Grant No.2021YFB3901302) Shandong Province,China(Grant No.2021YFB3901300) 

主　　题：computer vision crop row detection precision agriculture semantic segmentation 

摘      要：Accurate extraction of crop row is very important for automation of agricultural *** rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and *** this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural *** 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 *** proposed system was experimentally determined to overcome the interference of different complex *** it can be applied to crop rows of different numbers,straight lines and *** analyses were performed to check the robustness of the *** this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 *** accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/*** Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row *** provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.