CROP RECOMMENDATION USING RANDOM FOREST ML ALGORITHM

Abstract

Crop prediction using machine learning techniques has garnered significant attention in
agricultural research due to its potential to revolutionize farming practices and improve crop
yield forecasts. This study proposes a novel approach to crop prediction by leveraging machine
learning algorithms on agricultural datasets. The primary objective is to develop accurate
predictive models that can forecast crop yields based on various environmental factors such as
weather conditions, soil quality, and historical crop data.The methodology involves several key
steps. Firstly, comprehensive agricultural datasets encompassing relevant variables are
collected from diverse sources, including meteorological stations, soil databases, and crop yield
records. Next, feature engineering techniques are applied to preprocess the data and extract
informative features for model training. Subsequently, different machine learning algorithms,
such as decision trees, random forests, support vector machines, and neural networks, are
employed to build predictive models.The performance of these models is evaluated using
metrics such as accuracy, precision, recall, and F1-score. Additionally, cross-validation
techniques are utilized to assess the generalization ability of the models and mitigate overfitting
issues. The results demonstrate the effectiveness of the proposed approach in accurately
predicting crop yields across different regions and crop types.