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The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.<\/jats:p>","DOI":"10.1007\/s40747-020-00192-x","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T03:10:33Z","timestamp":1602472233000},"page":"2209-2219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["A hybrid deep learning-based fruit classification using attention model and convolution autoencoder"],"prefix":"10.1007","volume":"9","author":[{"given":"Gang","family":"Xue","sequence":"first","affiliation":[]},{"given":"Shifeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yicao","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"192_CR1","doi-asserted-by":"crossref","unstructured":"Pak M, Kim S (2017) A review of deep learning in image recognition. 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