Despite the growing prevalence of robotics in agriculture, there is still a limited amount of research specifically targeting the automation of grapevine management. Due to the complexity, the pruning task during the dormant season demands skilled laborers, who are increasingly scarce during the winter months. For the potential prospect, CANOPIES, a H2020 European Project, seeks to pioneer a new collaborative approach between humans and robots in precision agriculture, specifically targeting permanent crops such as table-grape vineyards. Its goal is to enable farm workers to seamlessly collaborate with robot teams to carry out harvesting and pruning more efficiently and effectively. Aiming to fulfill the need for winter pruning task for the CANOPIES project, this master’s thesis presents a novel design of perception in the pruning procedure. The algorithm is mainly developed based on deep learning, providing selective solutions for different application scenarios for the tasks such as plant reconstruction, vineyards organ segmentation and potential pruning point identification. The developed system highly satisfies the need of CANOPIES project for winter pruning. In plant reconstruction section, several models with satisfactory performance are obtained whose best mIoU is 88.72%, and shortest computational cost is 0.1124 seconds. While in the part of vine organ segmentation, the best network gets mPA50 of 60.43% also with a high speed. Finally, a node graph is generated to show the topological structure of the relationship between different organs along with pruning point localization.
