Enhancing Crop Yield Projections through Machine Learning Models

Our research aims to revolutionize crop yield prediction by combining machine learning (ML) techniques with government datasets and detailed soil information. Traditional prediction methods frequently overlook soil health, leading to inaccuracies. To address this, we incorporate soil data into our predictive model. Using the Random Forest Classifier and extensive datasets that include crop-specific and soil characteristics, our approach achieves high accuracy in forecasting crop yields. Our results highlight the power of data-driven methods in guiding agricultural decision-making and stress the importance of interdisciplinary collaboration to address modern agricultural challenges.