文献类型： 外文期刊

作者： Zhang, Z. ¹ ; Igathinathane, C. ¹ ; Li, J. ² ; Cen, H. ³ ; Lu, Y. ⁴ ; Flores, P. ¹ ;

作者机构： 1.North Dakota State Univ, Dept Agr & Biosyst Engn, Fargo, ND 58102 USA

2.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

4.North Carolina State Univ, Dept Biol & Agr Engn, Raleigh, NC 27695 USA

关键词： Apple harvest; Shake-and-catch; Infield sorting; Harvest-assist platform; Mechanization; Robots

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2020 年 175 卷

页码：

收录情况： SCI

摘要： This article reviews the research and development progress of mechanical harvest technologies for fresh market apples over the past decades with a focus on the predominant technologies of shake-and-catch, robots, and harvest-assist platform methods. In addition, based on the review it points out the bottlenecks and future trends of these three technology categories. Major progress in the shake-and-catch method is related to theoretical studies on the effective removal of apples and catching mechanisms to minimize bruising. The unacceptable bruising conditions hinder the shake-and-catch method from commercial application. Two startups of apple harvesting robots are in the stage of commercializing their products based on vacuum and three-finger end-effectors, respectively. Economic benefits, as well as technology reliability and robustness of both robots, are pending for validation before they are on the market. In addition, a key obstacle faced by both robots before commercial use is to find a solution to pick apples grown in clusters. Harvest-assist platforms are gradually adopted by apple growers, but at a very low rate due to their doubts on economic benefits. Validation of harvest-assist platforms' economic benefits and incorporation with more functions (e.g., sorting) would enhance their adoption. With the rapid development of sensing and automation technologies, such as novel sensors, embedded systems, and machine learning algorithms, and the progress in new tree canopy structures that are friendlier for fruit visibility and accessibility, it is believed the robots for fresh market apple harvest would be realized and commercialized in the near future. Currently, more efforts should be invested in analyzing and validating the economic benefits of harvest-assist platforms, as well as adding more functions to the harvest-assist platforms, to increase their application rate for the benefit of the apple industry.