Research on Rice Yield Prediction Model Based on Deep Learning.

Journal: Computational intelligence and neuroscience
Published Date:

Abstract

Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people's health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.

Authors

  • Xiao Han
    College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
  • Fangbiao Liu
    College of Agriculture, Jilin Agricultural University, Changchun 130000, Jilin, China.
  • Xiaoliang He
    Jilin Danong Seed Co, Ltd, Changchun 130000, Jilin, China.
  • Fenglou Ling
    College of Agriculture, Jilin Agricultural University, Changchun 130000, Jilin, China.