文献类型： 外文期刊

作者： Hua, Wanjia ¹ ; Zhang, Zhao ² ; Zhang, Wenqiang ¹ ; Liu, Xiaohang ² ; Hu, Can ⁴ ; He, Yichuan ⁴ ; Mhamed, Mustafa ² ; Li, Xiaolong ⁵ ; Dong, Haoxuan ⁶ ; Saha, Chayan Kumer ⁷ ; Khan, Wali Ullah ² ; Abid, Fazeel ⁸ ; Abdelhamid, Mahmoud A. ⁹ ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

2.China Agr Univ, Key Lab Smart Agr Syst Integrat, Minist Educ, Beijing 100083, Peoples R China

3.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China

4.Tarim Univ, Coll Mech & Elect Engn, Alar 843300, Peoples R China

5.Ningxia Acad Agr & Forestry Sci, Inst Hort, Ningxia 750002, Peoples R China

6.North China Elect Power Univ, Sch Energy Power & Mech Engn, Beijing 102206, Peoples R China

7.Bangladesh Agr Univ, Dept Farm Power & Machinery, Mymensingh 2202, Bangladesh

8.Univ Lahore, Dept Comp Sci & Informat Technol, Lahore, Pakistan

9.Ain Shams Univ, Fac Agr, Dept Agr Engn, Cairo 11241, Egypt

关键词： AI; Robotics; Apple harvesting; Developments; Mechanization; Machine vision

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 235 卷

页码：

收录情况： SCI

摘要： Harvesting robots offer a promising solution for the automated collection of fresh apples, significantly enhancing efficiency and productivity in the agricultural sector. However, their effectiveness in orchards is influenced by various factors, including orchard production models, environmental conditions, and sensor technologies. This study aims to enhance the intelligence and commercial viability of harvesting robots by summarizing, analyzing, and forecasting critical technologies that influence robot performance in areas such as fruit identification, localization, arm motion planning, end-effectors design, and fruit detachment mechanisms. A comprehensive review of apple recognition methods is presented, which introduces various sensor localization technologies, examines robotic arms and motion planning algorithms for harvesting, and analyzes end-effectors and their corresponding detachment methods. An overview of the current state of apple-harvesting robots is provided, highlighting those that are poised for commercialization. Significant challenges in the field are addressed, including lengthy harvesting cycles, poor success rates, severe fruit damage, and high operational costs. The paper also outlines promising future directions for advancing commercial applications in this domain. These include innovations aimed at simplifying orchard environmental complexities, optimizing recognition and motion planning algorithms, integrating diverse sensor technologies, enabling autonomous multi-arm collaboration, developing vacuum-based end-effectors, and implementing secondary picking strategies. Collectively, these advancements are geared towards the creation of market-ready apple harvesting robots, ultimately fostering growth within the apple industry.