Hyperspectral imaging combined with dual-channel deep learning feature fusion model for fast and non-destructive recognition of brew wheat varieties

In this study, a dual-channel deep learning feature fusion model (DLFM) was developed to process hyperspectral imaging data for the rapid and nondestructive identification of brewing wheat varieties. The DLFM model extracts spectral features using a one-dimensional convolution module and spatial image features from the RGB image using a two-dimensional convolution module. These features are then fused using a feature adaptive fusion module within the DLFM and input into the fully connected layer for variety recognition. Support vector machine (SVM), one-dimensional convolutional neural network (1DCNN), two-dimensional convolutional neural network (2DCNN), and DLFM were built, respectively. Among them, DLFM had the highest recognition accuracy, which was 99.18%, 97.30%, and 93.18% for the three-variety, four-variety, and five-variety wheat combinations, respectively. The average accuracies of all combinations were improved by 11.93%, 6.84%, 12.54%, and 2.39% for 1DCNN, 2DCNN, and 1DCNN of fused data, respectively, compared to SVM. The results show that hyperspectral imaging (HSI) combined with DLFM can realize fast and nondestructive identification of different brewing wheat varieties, providing a new method for variety identification of cereals.