Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks

Abstract

Sprouted wheat kernels have a great impact on the quality of flour, bread and other wheat products, but they are hard to be identified by human eyes, especially that sprouted slightly. This study utilized hyperspectral imaging technology, combined with deep learning algorithms to classify slightly sprouted and sound wheat kernels. Hyperspectral images collected from wheat kernels placed randomly will be pre-processed by a series of commonly used pre-processing methods. And then four deep convolutional neural network (CNN) models, 1D, 2D, 3D and mixed CNNs, would be established and used as classifiers to identify the two types of wheat kernels through the spectral or hyperspectral data. Data augmentation method was also used on the training set, and the proposed 2D, 3D and mixed CNN models were trained again, and the performances on the validation set were compared to the results got before data augmentation. The results showed that data augmentation did beneficial to the performance of the proposed 2D, 3D and mixed CNN models, and the four proposed models performed well when classifying slightly sprouted wheat kernels and sound wheat kernels, getting accuracy rate of 96.81% (1D CNN model), 96.02% (2D CNN model), 98.40% (3D CNN model), and 98.12% (mixed CNN model) on testing set. The results indicated that models proposed in this paper, especially the 3D CNN model with the highest accuracy rate and the mixed CNN model with the least trainable parameters, have a good prospect of being used as classifiers to detect sprouted and sound wheat kernels.