Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

Frontiers in Plant Science

Bo Zhao¹  Juntao Xiong²  Zhaoguo Zhang³  Peng He⁴  Suchun Liu⁵  Chenglin Wang⁵  Yawei Wang⁵  Guichao Lin⁶  Lufeng Luo⁷ 

[1] Chinese Academy of Agricultural Mechanization Sciences, Beijing, China;College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China;Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China;School of Electronic and Information Engineering, Taizhou University, Taizhou, China;School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China;School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China;School of Mechatronic Engineering and Automation, Foshan University, Foshan, China;

关键词: computer vision;    deep learning;    convolutional neural network;    fruit detection;    fruit production;   

DOI  :  10.3389/fpls.2022.868745

来源: DOAJ

【 摘 要 】

As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

【 授权许可】

Unknown