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Watershed Planning within a Quantitative Scenario Analysis Framework
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Analysis and evaluate of agricultural resources using data analytic methods.

Min Tang1

  • 1School of Marxism, Xi'an Jiaotong University, Xi'an 710049, China.

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Summary
This summary is machine-generated.

This study introduces an Enhanced Gravitational Search Optimized based Gated Recurrent Unit (EGSO-GRU) for accurate crop yield prediction. The novel EGSO-GRU model significantly improves agricultural forecasting, aiding farmers and policymakers.

Keywords:
agriculture researchcrop estimationdata analytic methodenhanced gravitational search optimized based gated recurrent unit (EGSO-GRU)enhanced independent component analyses

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Area of Science:

  • Agricultural Science
  • Data Science
  • Machine Learning

Background:

  • Farmers face complex decisions influenced by numerous variables.
  • Accurate crop yield predictions are crucial for investment and policy.
  • Incomplete data and diverse environmental factors complicate agricultural assessments.

Purpose of the Study:

  • To introduce a novel approach for calculating crop production.
  • To enhance the accuracy of crop yield predictions.
  • To address challenges posed by data limitations and environmental variability.

Main Methods:

  • Dataset collection and pre-processing using normalization.
  • Feature extraction via Enhanced Independent Component Analysis (EICA).
  • Development and application of the Enhanced Gravitational Search Optimized based Gated Recurrent Unit (EGSO-GRU) model.

Main Results:

  • The EGSO-GRU model achieved high accuracy (95.89%).
  • Demonstrated strong performance with specificity (92.4%), MSE (0.071), RMSE (0.210), and MAE (0.199).
  • Outperformed existing models in crop prediction accuracy.

Conclusions:

  • The EGSO-GRU model offers a significant advancement in crop production forecasting.
  • This technological progress is vital for optimizing agricultural resources.
  • The approach fosters enhanced productivity and long-term sustainability in the farming industry.