Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

作     者：Xiao Ya Ma Jin Tong Fei Jiang Min Xu Li Mei Sun Qiu Yan Chen 

作者机构：Department of Logistics Management and EngineeringNanning Normal UniversityNanning530023China Guangxi Key Lab of Human-Machine Interaction and Intelligent DecisionNanning Normal UniversityNanning530023China School of Management and MarketingFaculty of Business and LawTaylor’s UniversityKuala Lumpur47500Malaysia Graduate DepartmentYunnan Normal UniversityKunming650500China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第3期

页      面：6145-6159页

核心收录：

学科分类：120301[管理学-农业经济管理] 12[管理学] 1203[管理学-农林经济管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：This work was supported by the 2021‘Cultivation plan for thousands of young andmiddle-aged backbone teachers in Guangxi Colleges and universities’by the Project of Humanities and Social Sciences in‘Research on Collaborative Integration of Logistics Service Supply Chain under High-QualityDevelopmentGoals’(2021QGRW044) In addition,the studywas supported by the 2019 National Social Science Project in‘Research on the Integration of Transnational Supply Chains under the Belt and Road Initiative(19BJY184)’ This paper was also supported by Guangxi Philosophy and Social Science Planning Office Project:Research on the DynamicMechanism and Model Innovation of the Cross-border Integration Growth of Guangxi Logistics Enterprises(18BGL010) 

主　　题：Internet of things intelligent agricultural supply chain deep learning production prediction 

摘      要：Production prediction is an important factor influencing the realization of an intelligent agricultural supply *** an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply *** an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative *** root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,*** these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,*** results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more *** prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.