Grapevine structure estimating system using RGB-D cameras

Abstract

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.

Plant phenotyping relevance

RGB-D画像からブドウ樹の3D構造、器官分類、芽数を推定する手法を開発・評価しており、ロボット剪定への応用を超えて植物形態フェノタイピングが中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.