Berry thinning is essential for producing high-quality grape varieties such as Shine Muscat because it directly impacts fruit size and quality. To address the labor-intensive nature of this task, this study presents an autonomous robotic arm system that integrates depth sensing with a learning-based transformation method and is implemented using a ResNet-18 convolutional neural network to predict berry coordinates and execute cutting actions. Its performance was compared with a geometric transformation method based on Robot Operating System 2 (ROS2) coordinate transformations in both indoor and outdoor environments. In indoor trials, the learning-based transformation approach achieved an approach accuracy of 96.8% and cutting accuracy of 78.5%, outperforming the geometric transformation approach, which achieved 94.6% for approach and 69.6% for cutting. On outdoor slopes, environmental challenges degraded the performance of both the approaches; however, the learning-based transformation method maintained higher accuracies, achieving 75.6% for approach and 60.3% for cutting, compared with the geometric transformation approach, which achieved 63.1% approach accuracy and 44.1% cutting accuracy. The complete thinning cycle required an average of 3.67 min to process 10 berries, confirming its feasibility for practical use. Limitations in the curved scissor end-effector reduced cutting effectiveness, highlighting the need for improved blade design. This study demonstrates the potential of combining geometric and learning-based transformation methods for artificial intelligence-driven robotic thinning to achieve efficient vineyard management.
Tan et al. (Sun,) studied this question.