Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder. Issue 3 (8th April 2021) - Zhang, Yu‐Dong; Satapathy, Suresh Chandra; Wang, Shui‐Hua
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.