Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review - Wang, Chenglin; Liu, Suchun; Wang, Yawei; Xiong, Juntao; Zhang, Zhaoguo; Zhao, Bo; Luo, Lufeng; Lin, Guichao; He, Peng (Frontiers in Plant Science, 2022)
As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantages 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 socio-economic 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 the 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 the 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 the 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 areas exploration, and multiple tasks execution of fresh fruit production in the future.