Enhanced Rice Crop Yield Prediction Through Fuzzy Logic Modeling

Abstract

Predicting rice yield accurately plays a pivotal role in agricultural planning, resource allocation, and food security strategies. This paper proposes a comprehensive rice yield prediction model leveraging machine learning techniques, with a focus on Fuzzy Logic. The model integrates diverse datasets, including historical yield records, meteorological data, soil characteristics, and crop management practices. Through rigorous data preprocessing, feature engineering, and selection, relevant features are extracted to capture the complex relationships influencing rice yield. The machine learning model, utilizing Fuzzy Logic, is trained and validated to ensure robust performance and generalization capability. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are employed to assess model accuracy. The proposed model provides accurate predictions of rice yield, empowering stakeholders with valuable insights for informed decision-making in agriculture. This research contributes to the advancement of predictive modeling techniques in agriculture, facilitating sustainable crop production and food security.