Automating Grape Thinning: Predicting Robotic Arm End-effector Positions Using Depth Sensing Technology and Neural Networks

Grape thinning is a labor-intensive task in viticulture that involves selectively removing grape clusters to improve overall fruit quality. The integration of robotics and artificial intelligence (AI) techniques has shown great potential in automating this process, reducing labor costs and increasing efficiency. We propose a novel approach for automating grape thinning using depth sensing and neural networks. The approach was evaluated in both indoor and real-world field environments, achieving a success rate of 97% and 90% in accurately approaching the target berries, respectively.