Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder. Issue 3 (8th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Yu‐Dong; Satapathy, Suresh Chandra; Wang, Shui‐Hua
• Other Names: Gupta Deepak guestEditor.; Kose Utku guestEditor.; Castillo Oscar guestEditor.; Al‐Turjman Fadi guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-08
• Subjects: autoencoder -- deep learning -- fractional Fourier entropy -- rotational angle vector grid; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12701
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Aim: Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth. Methods: This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five‐layer stacked sparse autoencoder was used as the classifier. Results: Ten runs on the test set showed our method achieved a micro‐averaged F1 score of 95.08% for an 18‐category fruit dataset. Conclusion: Our method gives better micro‐averaged F1 score than 10 state‐of‐the‐art approaches.