Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder

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.