Grapevine structure estimating system using RGB-D cameras

This study proposes a novel system for estimating the 3D structure of grapevines as part of a robotic pruning system. The system aims to accurately and efficiently estimate grapevine structures. Utilizing two RGB-D cameras based on Time-of-Flight (ToF) technology, depth images were captured from a wide field of view. This paper employs a minimum spanning tree (MST) to estimate the grapevine skeleton using a cost function that considers node distance, gravitropism, and connection smoothness. Notably, we developed a new component classification method that accurately classifies structural parts—cordons, shoots, and buds—using only skeletal information. The results demonstrated that the system could effectively distinguish between different parts of the grapevine using just the 3D skeletal structure. The system was evaluated on 10 grapevines in real-world vineyard environments. The proposed method achieved high alignment accuracy with manually constructed ground-truth skeletons, even under occlusion. For bud estimation, statistical analysis based on field data facilitated effective estimation. The proposed method outperformed existing approaches in processing speed, with an average processing time of 619 ms per grapevine. These results indicate the potential for real-time application in robotic pruning, enabling efficient structure estimation with high accuracy. Future work will focus on integration with other subsystems integrated within a robotic pruning system, which is expected to produce synergistic effects and enhance overall system performance.