Research on Rice Yield Prediction Model Based on Deep Learning

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 populatio...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-04, Vol.2022, p.1922561-9
Main Authors: Han, Xiao, Liu, Fangbiao, He, Xiaoliang, Ling, Fenglou
Format: Article
Language:English
Subjects:
Agricultural production
Agriculture
Algorithms
Analysis
Crop yield
Crop yields
Deep Learning
Food security
Forecasts and trends
Grain
Humans
Image processing
Image Processing, Computer-Assisted
Independent variables
Industrial development
Neural networks
Neural Networks, Computer
Object recognition
Oryza
Prediction models
Regression analysis
Regression models
Remote sensing
Rice
Satellites
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/1922561