Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel

Abstract

Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.

Keywords: Data augmentation; Deep convolutional generative adversarial network; Fake data; Hyperspectral imaging technology; Maize kernel; Oil content.