Autonomous detection of crop rows based on adaptive multi-ROI in maize fields
Yang Zhou, Yang Yang, Boli Zhang, Xing Ping Wen, Xuan Yue, Liqing Chen
Abstract
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. Keywords: machine vision, crop rows detection, navigation, multi-ROI DOI: 10.25165/j.ijabe.20211404.6315 Citation: Zhou Y, Yang Y, Zhang B L, Wen X, Yue X, Chen L Q. Autonomous detection of crop rows based on adaptive multi-ROI in maize fields. Int J Agric & Biol Eng, 2021; 14(4): 217–225.