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