文献类型： 会议论文
第一作者： Jun Liu
作者： Jun Liu 1 ; Yifeng Zheng 2 ; Jie Pi 3 ; Chenggang Zhou 4 ; Jie Kong 4 ;
作者机构： 1.College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China##Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210018, China
2.College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
3.Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210018, China
4.College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
关键词： Tomato harvesting system;Human-like mechanical hand;Visual recognition;Pose estimation;Harvesting strategy
会议名称： [ "IEEE International Conference on Computing, Control and Industrial Engineering" , "International conference on computing, control and industrial engineering"]
主办单位：
页码： 301-319
摘要： Addressing the challenges of tomato harvesting, such as fruit susceptibility to damage, incomplete positional information, and prolonged harvesting time, this study explores human-like artificial tomato picking behavior. It involves designing a mechanical hand that mimics human hand-picking methods, developing a tomato pose recognition system, proposing an efficient harvesting strategy, and conducting systematic integration and experimental verification. Results indicate that the mechanical hand can grasp a mass exceeding 1.4 kg, achieving an average tomato coverage of 3.5-6.5%, and enabling stable, damage-free grasping of tomatoes with diameters ranging from 60 to 105 mm. The success rate of stem recognition by the vision system reaches 85.23%, with an overall success rate of 82.42%. Absolute error between camera positioning and actual distance remains below 16mm, with relative error below 3.2%. After incorporating tomato pose information into the harvesting system, the lateral gripping method outperforms the bottom gripping method in terms of speed, interference resistance, and success rate. The average single-operation time ranges from 9.52 to 10.43 s, with success rates of 86.8-93.1%, achieving the objective of damage-free, precise, and efficient tomato harvesting.
分类号： tp3
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