Research Article
Prediction Analysis of Soluble Solids Content in Apples Based on Wavelet Packet Analysis and BP Neural Network
@INPROCEEDINGS{10.1007/978-3-030-51103-6_31, author={Xingwei Yan and Shuhui Bi and Tao Shen and Liyao Ma}, title={Prediction Analysis of Soluble Solids Content in Apples Based on Wavelet Packet Analysis and BP Neural Network}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Apple Wavelet packet analysis Near infrared spectrum BP neural network}, doi={10.1007/978-3-030-51103-6_31} }- Xingwei Yan
Shuhui Bi
Tao Shen
Liyao Ma
Year: 2020
Prediction Analysis of Soluble Solids Content in Apples Based on Wavelet Packet Analysis and BP Neural Network
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_31
Abstract
Considering Fuji apple, the relationship between the near infrared spectrum and the soluble solids content (SSC), which is one of the important indexes to measure the internal quality of apple, is studied in this paper. In order to reduce the computational complexity and to improve the accuracy of modeling, this paper adopts the wavelet packet threshold denoising method for spectral spectrum processing, and uses the method of wavelet packet analysis (WPA) to filter the characteristic wavelength of the spectrum. Moreover, a prediction model of SSC is proposed based on BP neural network due to its characteristics of anti-noise, anti-interference, strong nonlinear conversion ability and the good capacity in handling nonlinear measured data with uncertain causality. Finally, the simulation results show that wavelet packet analysis can not only reduce the calculation of modeling variables, but also Improve modeling accuracy of the BP neural network model. The proposed method can make a better prediction of the SSC of apple.
