数字农科院2.0

Research on kiwifruit harvesting robot worldwide: A solution for sustainable development of kiwifruit industry

文献类型： 外文期刊

作者： Zhiwei Tian;Xiangyu Guo;Wei Ma;Xinyu Xue

作者机构：

关键词： Deep learning;End effector;Harvesting robotics;Kiwifruit;Robotic arm path planning

期刊名称： Smart Agricultural Technology

ISSN： 2772-3755

年卷期： 2025 年 10 卷

页码：

收录情况： ESCI（2025版）

摘要： Kiwifruit harvesting is labor-intensive, and social issues like an aging population and a declining agricultural workforce have significantly increased costs, presenting unprecedented challenges to the industry. Automatic harvesting systems utilizing multi-sensor fusion, AI, and automation technologies show great potential for replacing manual labor in kiwi harvesting. This paper reviews over 140 research articles related to kiwi fruit harvesting robots, summarizing existing progress in two key areas: target fruit recognition and positioning systems, and fruit picking and collection systems. We compare the pros and cons of various methods, including traditional image recognition and deep learning, active and passive localization techniques, diverse end-effector design structure and driving mechanisms, robotic arm path planning, and harvesting systems. The results show that challenges remain in the commercialization of kiwi harvesting robots. The absence of a unified evaluation standard for robot performance makes the latest research achievements hard to be inherited, leading to slow advancements. Current algorithms are often not lightweight enough for low-cost embedded systems. Additionally, the reliance on manual labeling of dense targets and the accumulation of system error compromise the robustness of target recognition and spatial positioning in open environments. The existing studies tend to focus on local improvements rather than the entire harvesting system. So addressing these issues should be a priority for future research. This paper can provide a reference for researchers and assist industry professionals in understanding the trends in harvesting robot development.

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