文章基本信息

标题：The Prediction Research of Population Density Based on Deep Learning in Grain Stored Insects
本地全文：下载
作者：Wu Jian-Jun ; Dang Hao ; Li Miao 等
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：10
页码：251-258
出版社：SERSC
摘要：Precision of pests, in stored grain insect population density, has been a hot and difficult research in pest detection and control system. The accuracy of prediction of pest density will directly affect to warehouse grain temperature and the food quality etc. In order to improve the accuracy, the paper which using the depth study method, established aninsects density prediction mode with the depth of the belief network as the core. The model is applied to the algorithm of deep learning predictive control. According to the temperature and humidity of the grain obtained from the actual measurement and the initial density of the pest, we predicted the pest density. Simulation results show that the root mean square error is small between the predictive value and actual value, high prediction accuracy. The deep learning algorithm is applied to the population density of pests is effective
关键词：deep;learning;population;density;deep;confidence;network;predictive and ;control Introduction