A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard

Abstract Reliable and robust vision algorithms to detect the cutting points on peduncles of overlapping grape clusters in the unstructured vineyard are essential for efficient use of a harvesting robot. In this study, we designed an approach to detect these cutting points in three main steps. First, the areas of pixels representing grape clusters in vineyard images were obtained using a segmentation algorithm based on k-means clustering and an effective color component. Next, the edge images of grape clusters were extracted, and then a geometric model was used to obtain the contour intersection points of double overlapping grape clusters. Profile analysis was used to separate the regional pixels of double grape clusters by a line connecting double intersection points. Finally, the region of interest of the peduncle for each grape clusters was determined based on the geometric information of each pixel region, and a computational method was used to determine the appropriate cutting point on the peduncle of each grape cluster by use of a geometric constraint method. Thirty vineyard images that were captured from different perspectives were tested to validate the performance of the presented approach in a complex environment. The average recognition accuracy was 88.33%, and the success rate of visual detection of the cutting point on the peduncle of double overlapping grape clusters was 81.66%. The demonstrated performance of this developed method indicated that it could be used by harvesting robots.