An Integrated System of Bulk Tea Harvesting Robot With Profiling Logic - Wenyu Yi; Pan Wang; Zhi Xu; Sicheng Dong; Guangshuai Liu (IEEE Access, 2024)
There is a growing demand for tea harvesting robots due to the harsh harvesting environment and rising in labor costs. Despite considerable efforts by the research community, the robustness to hilly field environment and the harvesting efficiency of the device in mechanical harvesting of bulk tea remain unimproved. To lay the foundation for automated tea harvesting, this paper proposes an integrated system of bulk tea harvesting robots with autonomous profiling logic based on computer vision. A saliency detection algorithm is applied to detect tender leaves, and a target localization system is designed with it. Moreover, a depth cue-based profiling logic and the corresponding pose adjustment strategy for cutting tool are detailed. And an actuator driven by a combination of motors is developed to provide a precise and flexible motion control to the cutting tool. Additionally, the standard position of the RGB-D camera, accuracy and timeliness of the profiling operation are confirmed by the field experiment. The results of field experiment show that the average harvesting accuracy is 87.7%, and failure rate is controlled within 15%. An analysis of failure causes reveals that damage and cutting failure are the primary reasons for the unsuccessful harvesting.