Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adapti...Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest(multi-ROI).First,the image was segmented into crop and soil and divided into several horizontally labeled strips.Feature points were located in the first image strip and initial ROI was determined.Then,the ROI window was shifted upward.For the next image strip,the operations for the previous strip were repeated until multiple ROIs were obtained.Finally,the least square method was carried out to extract navigation lines and detection lines in multi-ROI.The detection accuracy of the method was 95.3%.The average computation time was 240.8 ms.The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.展开更多
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,weedin...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.展开更多
基金The authors acknowledge that the research was financially supported by the National Key Research and Development Program of China(Grant No.2017YFD0700902)the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2020-011).
文摘Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest(multi-ROI).First,the image was segmented into crop and soil and divided into several horizontally labeled strips.Feature points were located in the first image strip and initial ROI was determined.Then,the ROI window was shifted upward.For the next image strip,the operations for the previous strip were repeated until multiple ROIs were obtained.Finally,the least square method was carried out to extract navigation lines and detection lines in multi-ROI.The detection accuracy of the method was 95.3%.The average computation time was 240.8 ms.The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.
基金National Key R&D Program of China(Grant No.2021YFB3901302)Shandong Province,China(Grant No.2021YFB3901300).
文摘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.
