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

作　　者：WANG Jing FAN Xiaofei SHI Nan ZHAO Zhihui SUN Lei SUO Xuesong 

机构地区：[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

出　　处：《Chinese Journal of Electronics》2023年第3期655-662,共8页电子学报（英文版）

基　　金：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);University Science and Technology Research Project of Hebei Province(QN2020444).

摘　　要：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.

关 键 词：JUJUBE Multi-spectral imaging Convolutional neural networks. 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] S665.1[农业科学—果树学]