Application of Convolutional Neural Network Combined with Hyperspectral Imaging in Chicken Quality Classification
WANG Jiuqing, XING Suxia*, WANG Xiaoyi, CAO Yu
Beijing Key Laboratory of Big Data Technology for Food Safety, College of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Abstract：Hyperspectral imaging is an important modern technique rnin food detection. This study presented the application of hyperspectral imaging to rapidly and nondestructively chicken quality. The hyperspectral data reflecting internal quality and external characteristics of chicken were extracted and they were preprocessed to establish a convolutional neural network (CNN) model based on the spectra and/or the color images. The results showed that the integrated CNN model based on both the spectra and the color images had the best performance for chicken quality classification with an accuracy and loss function of 93.58% and 0.30, respectively, demonstrating that the integrated use of internal quality and external characteristics of chicken can effectively improve the detection accuracy of chicken quality.
2018, Vol. 32 
