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Research on Rice Yield Prediction Model Based on Deep Learning.

Xiao Han1, Fangbiao Liu1, Xiaoliang He2

  • 1College of Agriculture, Jilin Agricultural University, Changchun 130000, Jilin, China.

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|May 6, 2022
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Summary
This summary is machine-generated.

This study explores deep learning and remote sensing for simulating rice yield factors. It analyzes regression models to predict actual rice yield, offering insights for agricultural applications.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Remote Sensing

Background:

  • Rising global population necessitates advanced food production monitoring.
  • Deep learning excels in image recognition, offering new analytical tools.
  • Remote sensing combined with AI shows promise for agricultural insights.

Purpose of the Study:

  • To simulate key factors influencing rice yield using advanced computational methods.
  • To evaluate the effectiveness of various regression models for yield prediction.
  • To explore the integration of deep learning with remote sensing data for agricultural applications.

Main Methods:

  • Utilized deep learning for image processing and analysis of remote sensing data.
  • Developed and tested various linear and nonlinear regression models.
  • Analyzed independent variables to identify key predictors of rice yield.

Main Results:

  • Identified optimal regression models for simulating rice yield factors.
  • Assessed the predictive performance and statistical significance of the models.
  • Demonstrated the potential of combining remote sensing and deep learning for yield estimation.

Conclusions:

  • Regression models effectively simulate key rice yield factors.
  • Deep learning and remote sensing offer a powerful approach for agricultural yield prediction.
  • Further research can refine estimation of actual rice yield using these integrated methods.