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An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction.

Anuradha Yenkikar1,2, Ved Prakash Mishra1, Manish Bali1

  • 1School of Engineering, Amity University Dubai Campus, Dubai, 25314, UAE.

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|July 4, 2025
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Summary
This summary is machine-generated.

This study introduces a hybrid AI model with Explainable Artificial Intelligence (XAI) for accurate crop yield prediction in India. The enhanced transparency aids farmers and policymakers in making informed decisions for sustainable agriculture.

Keywords:
Explainable AIExplainable AI (XAI) based Hybrid ML modelHybrid modelLocal interpretable model-agnostic explanationsLong short-term memoryRandom forestSHapley Additive explanationsXGBoost

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Agriculture is vital to India's economy and food security, necessitating accurate crop yield predictions.
  • Machine learning (ML) models improve yield forecasting accuracy but often lack interpretability, hindering adoption by stakeholders.
  • Explainable Artificial Intelligence (XAI) offers transparency in ML models, fostering trust and informed decision-making.

Purpose of the Study:

  • To develop and validate a hybrid AI model integrated with XAI techniques for enhanced crop yield prediction.
  • To improve the interpretability of AI-driven agricultural forecasts for policymakers and farmers.
  • To provide actionable insights for sustainable agricultural practices through a user-friendly interface.

Main Methods:

  • A hybrid AI model combining Random Forest (RF), Long Short-Term Memory (LSTM), and XGBoost algorithms was implemented.
  • Explainable Artificial Intelligence (XAI) methods, including SHAP, LIME, and Counterfactual Analysis, were integrated for model interpretability.
  • A comprehensive dataset of over 246,000 agricultural records from India, spanning multiple years, states, crops, seasons, and climatic factors, was utilized.

Main Results:

  • The hybrid AI model achieved high prediction accuracy, with R² values of 0.9827 for overall crop yield and 0.9721 for rice yield.
  • The integration of XAI techniques successfully enhanced the transparency of the prediction model, revealing nuanced feature interactions.
  • The developed 'E-Kisan' web interface provides actionable insights derived from the model's predictions.

Conclusions:

  • The research demonstrates the efficacy of integrating XAI with hybrid AI models for accurate and interpretable crop yield prediction.
  • The findings support the adoption of transparent AI tools to improve agricultural planning and sustainability in India.
  • The 'E-Kisan' platform offers a practical solution for disseminating AI-driven insights to agricultural stakeholders.