Na\"ive Bayes and Random Forest for Crop Yield Prediction

TL;DR

This paper compares machine learning models, especially Na"ive Bayes and Random Forest, for crop yield prediction in India, showing their high effectiveness and potential to improve agricultural forecasting accuracy.

Contribution

It introduces the application of Na"ive Bayes and Random Forest models to crop yield prediction, demonstrating their superior performance in agricultural data analysis.

Findings

Na"ive Bayes and Random Forest achieved high prediction accuracy.

Model integration improved reliability of crop yield forecasts.

The study provides valuable insights for agricultural data science.

Abstract

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.