Image processing and artificial intelligence for apple detection and localization: A comprehensive review
文献类型： 外文期刊
作者： Azizi, Afshin 1 ; Zhang, Zhao 1 ; Hua, Wanjia 3 ; Li, Meiwei 1 ; Igathinathane, C. 4 ; Yang, Liling 5 ; Ampatzidis, Yiannis 6 ; Ghasemi-Varnamkhasti, Mahdi 7 ; Radi 8 ; Zhang, Man 1 ; Li, Han 1 ;
作者机构： 1.Minist Educ, Key Lab Smart Agr Syst Integrat, Beijing 100083, Peoples R China
2.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing, Peoples R China
3.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China
4.North Dakota State Univ, Dept Agr & Biosyst Engn, Fargo, ND 58102 USA
5.Xinjiang Acad Agr Sci, Agr Mechanizat Inst, Urumqi 830091, Peoples R China
6.Univ Florida, Southwest Florida Res & Educ Ctr, Agr & Biol Engn Dept, IFAS, 2685 SR 29 North, Immokalee, FL 34142 USA
7.Shahrekord Univ, Dept Mech Engn Biosyst, Shahrekord, Iran
8.Univ Gadjah Mada, Fac Agr Technol, Dept Agr & Biosyst Engn, Yogyakarta, Indonesia
关键词： Apple; Detection; Localization; Image processing; Artificial intelligence; Robotics; Fruit harvesting
期刊名称：COMPUTER SCIENCE REVIEW （ 影响因子：12.7； 五年影响因子：16.0 ）
ISSN： 1574-0137
年卷期： 2024 年 54 卷
页码：
收录情况： SCI
摘要： This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection and localization such as occlusions, varying illumination conditions, and clustered apples are highlighted, as well as the impact of environmental factors such as light changes on the performance of these algorithms. Potential future research perspectives are identified through a comprehensive literature analysis. These include combining cutting-edge deep learning and multi-vision and multi-modal sensors to potentially apply them in real-time for apple harvesting robots. Additionally, utilizing 3D vision for a thorough analysis of complex and dynamic orchard environments, and precise determination of fruit locations using point cloud data and depth information are presented. The outcome of this review paper will assist researchers and engineers in the development of advanced detection and localization mechanisms for infield apples. The anticipated result is the facilitation of progress toward commercial apple harvest robots.
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