Paper Title
Crop Yield Prediction using a Bagging Regressor Model

Abstract
This paper explores the critical domain of Crop Yield Prediction (CYP) and its pivotal role in precision agriculture, particularly in the face of climate uncertainties impacting global crop production. Traditional methods, relying on empirical knowledge, have proven insufficient in this dynamic landscape, necessitating the adoption of Machine Learning (ML) solutions. The study introduces a Bagging Regressor model, showcasing its high accuracy in predicting crop yields and providing valuable insights for precise agricultural planning. The broader discourse emphasizes ML’s transformative role in sustainable farming practices and optimized resource allocation. Despite acknowledging trade-offs, the research aims to minimize farmers’ efforts, contributing to sustainable practices and enhanced food security. The presented work advocates for the integration of ML techniques in agriculture marking a paradigm shift in addressing contemporary challenges. Keywords - Bagging Regressor, Crop Yield Prediction, Machine Learning