Stereo-vision-based multi-crop harvesting edge detection for precise automatic steering of combine harvester

With the development of the intelligent combine harvesters, the whole-field automatic harvesting has become feasible and includes driving straight along the unharvested crop edge and steering in the crop headland. Detecting the unharvested crop edge and the end of the cultivation crop, is the foundation of whole-field automatic harvesting. A rapid and robust detection method for detecting the harvesting edge for multiple crops based on stereo vision was developed. In order to improve the processing speed and keep the crop harvesting edge within the target region, a dynamic ROI (region of interest) extraction algorithm based on HSV (hue, saturation, and value) space scanning was developed. A coordinate transformation method was applied to generate a crop elevation image, and the unharvested crop area was obtained by the Ostu algorithm. Simultaneous detection of unharvested crop edge and crop end edge was realised by the method based on the horizontal length feature of the unharvested area contour. This enabled automatic driving along the unharvested crop edge, and automatic steering into the next harvest path according to the crop end edge. Finally, field tests with crops of paddy, oil-seed rape, and maize crops were carried out to test the system performance, and the accuracy of the detection was found to be higher than 98% for paddy and rape, and higher than 94% for maize with an average processing speed of 49 ms frame-1. The experiments showed that the proposed method exhibited high accuracy and efficiency and was suitable for various complex field environments. (C)& nbsp;2021 Published by Elsevier Ltd on behalf of IAgrE.