Fruit ripeness classification:A survey

作     者：Matteo Rizzo Matteo Marcuzzo Alessandro Zangari Andrea Gasparetto Andrea Albarelli 

作者机构：Ca’Foscari Department of Environmental SciencesInformatics and StatisticsVia Torino 155Mestre(VE)30172Italy Ca’Foscari Department of ManagementCannaregio 873Fondamenta San GiobbeVenice 30121Italy 

出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)

年 卷 期：2023年第7卷第1期

页      面：44-57页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

基　　金：This paper was funded by Veneto Agricoltura within the scope of the project "Guaranteeing the continuity of the agri-food chain: the digitization of wholesale markets" 

主　　题：Fruit ripeness classification Machine learning Deep learning 

摘      要：Fruit is a key crop in worldwide agriculture feeding millions of *** standard supply chain of fruit products involves quality checks to guarantee freshness,taste,and,most of all,*** important factor that determines fruit quality is its stage of *** is usually manually classified by field experts,making it a labor-intensive and error-prone ***,there is an arising need for automation in fruit ripeness *** automatic methods have been proposed that employ a variety of feature descriptors for the food item to be *** learning and deep learning techniques dominate the top-performing ***,deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features,which are often *** this survey,we review the latest methods proposed in the literature to automatize fruit ripeness classification,highlighting the most common feature descriptors they operate on.