A spraying path planning algorithm based on colour-depth fusion segmentation in peach orchards

Abstract

Automatic and precise spraying in orchards is currently an active area in precision agriculture research. Research on this topic mainly focuses on the recognition of fruit trees and the planning of driving paths. The dense canopy and complex background of peach tree orchards present great challenges in fruit tree recognition. In this study, an algorithm based on colour-depth vision was developed to solve the path planning problem for precise spraying in peach orchards. First, a colour-depth binocular sensor was used to acquire video images in peach orchards. Then, a colour-depth fusion segmentation method (CDFS) based on the leaf wall area (LWA) of the colour-depth images was proposed. This method combines the result of Otsu segmentation for the green layer of the colour image and the K-means layering result of the depth image to accurately detect the LWA of peach trees. Then, image erosion was used to delimit the two largest LWAs as the region of interest (ROI). Finally, the spraying path was planned by detecting the midpoint of ROI spacing as the end of the spraying path. A comparative analysis of five image segmentation algorithms showed that the proposed CDFS algorithm produced the closest LWA to that obtained by manual segmentation and thus achieved the best segmentation effect. Comparing this automatic path plan method with the drivable areas showed that the overall accuracy rate of the path planning was 97.5%. Therefore, the proposed algorithm planned paths within the drivable area and can thus be used to accurately and automatically plan spraying paths in peach orchards, except when there were gaps between rows in the fruit orchard.