Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard
Tantan Jin,
Xiongzhe Han (),
Pingan Wang,
Yang Lyu,
Eunha Chang,
Haetnim Jeong and
Lirong Xiang
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Tantan Jin: Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Xiongzhe Han: Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Pingan Wang: Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Yang Lyu: Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Eunha Chang: Horticultural Research Division, Gangwon Agricultural Research & Extension Services, Chuncheon 24203, Republic of Korea
Haetnim Jeong: Horticultural Research Division, Gangwon Agricultural Research & Extension Services, Chuncheon 24203, Republic of Korea
Lirong Xiang: Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27607, USA
Agriculture, 2025, vol. 15, issue 15, 1-25
Abstract:
Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies.
Keywords: apple harvesting robot; vision-based detection; kinematic planning; soft gripper pick; field testing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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