Combining the WGAN and ResNeXt Networks to Achieve Data Augmentation and Classification of the FT-IR Spectra of Strawberries.

It is essential to use deep learning algorithms for big data to implement a new generation of artificial intelligence. The effective use of deep learning methods depends largely on the number of samples. This work proposes a method combining the Wasserstein generative adversarial network (WGAN) with the specific deep learning model (ResNeXt) network to achieve data enhancement and classification of the Fourier transform infrared (FT-IR) spectra of strawberries. In this method, the data are first preprocessed using convolution, the FT-IR spectral data are augmented by WGAN, and the data are finally classified using the ResNeXt network. For the experimental investigation, 10 types of dimensionalityreduction algorithms combined with nine types of classification algorithms were used for comparing and arranging the 90 groups. The results obtained from these experiments prove that our method of using a combination of WGAN and ResNeXt is highly suitable for the classification of the IR spectra of strawberries and provides a data augmentation idea as a foundation for future research. [ABSTRACT FROM AUTHOR]