Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method

WANG Jing; FAN Xiaofei; SHI Nan; ZHAO Zhihui; SUN Lei; SUO Xuesong

doi:10.23919/cje.2021.00.149

Volume 32 Issue 3

May 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(3): 655-662

WANG Jing, FAN Xiaofei, SHI Nan, et al., “Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 655-662, 2023, doi: 10.23919/cje.2021.00.149

Citation:

WANG Jing, FAN Xiaofei, SHI Nan, et al., “Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 655-662, 2023, doi: 10.23919/cje.2021.00.149

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Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method

doi: 10.23919/cje.2021.00.149

WANG Jing^1
,,
FAN Xiaofei^1
,,
SHI Nan^3
,,
ZHAO Zhihui^2
,,
SUN Lei^1
,,
SUO Xuesong^1
,

1.
College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China
2.
Research Center of Chinese Jujube, Hebei Agricultural University, Baoding 071001, China
3.
Key Laboratory of Microbial Diversity Research and Application of Hebei Province, College of Life Sciences, Hebei University, Baoding 071002, China

Funds: This work was supported by Hebei Talent Support Foundation (E2019100006), Key Research and Development Program of Hebei Province (20327403D), the National Natural Science Foundation of China (32072572), Talent Recruiting Program of Hebei Agricultural University (YJ201847), and University Science and Technology Research Project of Hebei Province (QN2020444)

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Author Bio:
Jing WANG received the B.S. degree from College of Modern Science and Technology, Agricultural University of Hebei, Baoding, China, in 2019. She is currently pursuing the master’s degree in mechanical and electrical engineering at Hebei Agricultural University, Baoding, China. Her current research interests covers image processing and measurement and control devices. (Email: w18730282320@163.com)

Xiaofei FAN received the Ph.D. degree from Department of Biomedical Engineering, University of Missouri, Missouri, USA, in 2009. He is currently a Professor of mechanical and electrical engineering, Hebei Agricultural University, Baoding, China. He is one of the third level talents introduced by Taihang Scholars and one of the “Hundred Talents Plan” for introducing high-level overseas talents in Hebei Province. His research interest is agricultural artificial intelligence. (Email: leopardfxf@163.com)

Nan SHI received the Ph.D. degree from Agricultural University of Hebei, Baoding, China, in 2019. She is currently an Associate Professor with the College of Life Sciences, Hebei University, Baoding, China. Her current research interests are microbial systematics, resource microbial selection, and active products. (Email: eshishi@126.com)

Zhihui ZHAO received the Ph.D. degree from Hebei Agricultural University, China, in 2006. She is currently a Professor at Research Center of Chinese Jujube, Hebei Agricultural University. Her current research interests include germplasm resources and molecular assisted breeding of Chinese jujube, and quality and safety of horticultural products. (Email: lyzhihuizhao@126.com)

Lei SUN received the Ph.D. degree from College of Modern Science and Technology, Agricultural University of Hebei, Baoding, China, in 2021. He is currently a Master’s Supervisor with the Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China. His current research interests include electrical and electronic technology and modern control system. (Email: slslsl0811@126.com)

Xuesong SUO (corresponding author) received the M.S. degree from College of Modern Science and Technology, Agricultural University of Hebei, Baoding, China, in 2003. He is currently a Professor with the Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China. His current research interests include intelligent detection, automatic control, and image processing. (Email: 13903120861@163.com)
Received Date: 2021-05-06
Accepted Date: 2022-03-13

Available Online: 2022-04-24

Publish Date: 2023-05-05

Abstract

Abstract

Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The acquisition of the soluble sugar content of jujube mainly relies on manual chemical measurement which is time-consuming and labor-intensive. In this study, the feasibility of multispectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model, and convolutional neural networks (CNNs) model were established by multispectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits.
- Jujube,
- Multi-spectral imaging,
- Convolutional neural networks

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References(27)

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