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.