Near infrared spectroscopy detection of the content of wheat based on improved deep belief network

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Li, Wenwen^1 ; Lin, Min^1 ; Huang, Yongmei^1 ; Liu, Huijun^1 ; Zhou, Xinqi^2

College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, China^1

Focused Photonics (Hangzhou) Inc., Hangzhou

310052, China^2

关键词: BP neural networks;    Correlation coefficient;    Deep belief network (DBN);    Deep belief networks;    Network modeling;    Quantitative analysis model;    Sparse network;    Standard error of prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012046/pdf
DOI  :  10.1088/1742-6596/887/1/012046

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In order to solve the complicated problem of traditional detection of the content of Wheat, a method for predicting the content of wheat components by near infrared spectroscopy based on improved deep belief network is proposed. In this paper, wavelet transform is used to preprocess near infrared spectroscopy of wheat, and then a quantitative analysis model of wheat's moisture, protein and ash content is established by using deep belief network. And combined with the random hidden algorithm, the network model is sparse processed, and the sparse network is obtained. So as to improve the accuracy and stability of the network. The experimental results show that using the improved deep of belief network to establish the quantitative analysis model on the content of wheat, and the correlation coefficient of moisture, protein and ash content were 0.9978, 0.9928, 0.9920, standard error of prediction were 0.0069, 0.0628,0.0535. Compared with the traditional deep belief network (DBN) and the traditional shallow learning BP neural network algorithm, the prediction results have been significantly improved.