Rice variety detection based on near-infrared spectroscopy anddeep learning data augmentation

It is widely recognized that the value of rice in the market is highly depends on its variety, with some varieties commanding significantly higher prices than others. Unfortunately, this has led to a situation where unscrupulous vendors may attempt to pass off inferior or lower-value rice varieties as more valuable ones. This urgently requires a reliable and efficient methods for accurately detecting and identifying different rice varieties. The use of near-infrared spectroscopy and deep learning for rice variety detection is an important research direction in this field. The establishment of an accurate detection model depends on a large-scale sample data, however, the process of collecting and preprocessing samples is very time-consuming, which limits the improvement of accuracy. To address the aforementioned needs and limitations, this paper proposes a rice variety detection method based on near-infrared spectroscopy combined with an improved deep convolutional generative adversarial network (DCGAN) for data augmentation. Based on the classical deep convolution generative adversarial neural network, the network increases the number of convolution layers and adjusts the network structure. Subsequent experiments showed that the network has better generation ability. In this study, we selected four rice varieties, namely Wuchang, Xiangshui, Yinshui, and Yueguang, and collected 80 samples for each variety, amounting to a total of 320 rice powder samples as experimental samples. Firstly, we collected near-infrared spectra of the samples and preprocessed the raw spectral data by applying detrend correction (DC) and uninformative variable elimination (UVE) algorithms to eliminate irrelevant spectral features. Then, we established an improved DCGAN model to augment the preprocessed spectral data. Three traditional data enhancement methods, random left and right translation, superimposed Gaussian noise and translation noise, were used for comparison. Comparative experiments demonstrated that the spectral data generated by the improved DCGAN showed significant superiority in terms of structural similarity compared to traditional data augmentation methods. Finally, we investigated different data augmentation methods combined with different classification methods to establish a rice variety classification model. We employed five classifiers, namely partial least squares discriminant analysis (PLS-DA), k-nearest neighbors (KNN), random forest (RF), decision tree (DT), and one-dimensional convolutional neural network (1D-CNN), and combined them with four data augmentation methods to build the rice variety classification model. The results showed that the model built using DCGAN-augmented data achieved the highest accuracy on the test set, the accuracy rate was 98.21%. Through quantitative analysis of the models built using DCGAN combined with the five classifiers, it was found that the variety detection model established by DCGAN-1D-CNN method performed the best on the test set, with a coefficient of determination R² of 0.9922, mean absolute error (MAE) of 0.0089, and root mean square error (RMSE) of 0.0505, indicating its optimal performance. Based on the comprehensive experimental results, the model established by combining the improved DCGAN data augmentation with the 1D-CNN classification algorithm exhibited the best performance in terms of accuracy on the test set, providing a new approach for convenient and accurate rice variety detection applications. By utilizing near-infrared spectroscopy and deep learning methods, combined with the improved DCGAN for data augmentation, we can enhance the accuracy and reliability of rice variety detection, thus meeting the market demand and consumer expectations.