Quantitative Regression Modeling of Cocoa Bean Content Based on Gated Dilated Convolution Network(1).pdf (702.4 kB)
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Quantitative Regression Modeling of Cocoa Bean Content Based on Gated Dilated Convolution Network

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conference contribution
posted on 15.03.2021, 10:33 by Y Chen, W Zhou, M Fei, H Wang, X Han, Huiyu Zhou
By analyzing the near-infrared spectrum, we can determine the quantitative relationship model between the spectral data of different cocoa beans and the target components. This paper proposes a predictive regression model based on 1D-CNN. Based on the traditional convolutional neural network, gating mechanisms and dilated convolutions are combined. The particle swarm optimization method is used to optimize the hyper-parameters of one-dimensional convolution. The end-to-end near-infrared predictive regression model does not require wavelength selection. It is convenient to use and has a strong promotional value. Taking the public cocoa beans near-infrared data set as an example, the method can predict the water and fat content in cocoa beans, and the effectiveness of the method is verified. Comparing the improved one-dimensional convolution with traditional one-dimensional convolution results and partial least squares regression, it shows better prediction accuracy and robustness.

Funding

This research is financially supported by Natural Science Foundation of China (61877065), the National Key Research and Development Program of China (No. 2019YFB1405500) and Key Project of Science and Technology Commission of Shanghai Municipality under Grant (No. 16010500300).

History

Citation

LSMS 2020, ICSEE 2020: Recent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops pp 456-468

Author affiliation

School of Informatics

Source

International Conference on Intelligent Computing for Sustainable Energy and Environment, 2020

Version

AM (Accepted Manuscript)

Published in

International Conference on Life System Modeling and Simulation

Pagination

456 - 468

Publisher

Springer Singapore

issn

1865-0929

eissn

1865-0937

isbn

9789813363779

Copyright date

2021

Available date

12/01/2021

Language

en