Research on branch reconstruction method based on branch growth characteristics for apple picking

During the operation of apple picking robots, it is necessary to accurately identify branches to prevent collisions with them. However, obtaining branch images in complex environments is often fragmented, making it difficult to reconstruct the entire tree. To solve this problem, this study obtained branch images by removing non-branch areas, and extract tree skeletons and endpoints based on 8-connection and thin line methods. This study investigates the growth characteristics of fruit tree branches and the distribution of endpoints between branches at the same level, using this as a constraint to connect broken branches. Finally, the reconstruction of the entire tree is achieved. The experimental results show that the algorithm has a branch recognition rate of 92.1% on sunny days and 84.2% on cloudy days. Therefore, the algorithm proposed in this study can provide accurate branch information for apple picking robots.