Identification of mould varieties infecting maize kernels based on Raman hyperspectral imaging technique combined with multi-channel residual module convolutional neural network

Abstract

Maize kernels are easily infected with different kinds of moulds in nature which has terrible negative effects on kernels. In this research, a series of multi-channel residual modules (MCRMs) were introduced to convolutional neural network (CNN) to identify the mould varieties combined with Raman hyperspectral imaging technique. Specifically, Raman hyperspectral images of maize kernels infected with different moulds were acquired and their characteristics were summarized and prepared for further analysis. Traditional machine learning (TML) models and deep learning (DL) models were both established and compared. For TML, three kinds of modeling methods combined with four kinds of variables selection methods were applied to establish the mould varieties identification model. For DL, five different residual units were explored to obtain the optimal MCRM-CNN architecture. The results showed that MCRM-CNN was more capable of mining hidden information compared with TML methods. Finally, MCRM-CNN-SVM provided the optimal model to identify the moulds varieties with accuracy in the testing set of 100% by taking the variables extracted by MCRM-CNN as the inputs of SVM. The residual unit was composed of three stacking residual blocks with different kernels of 3, 5, 7 without the shortcut connection (MNR) and the recall rates of A. niger, A. flavus, A. glaucus, A. parasiticus and control set were all 100%, respectively. Consequently, the proposed method expanded the nondestructive online detection for identifying mould varieties infecting maize kernels by combining RHSI and DL.