Published Paper Details:

Crop prediction plays a crucial role in agricultural planning and decision-making. In this research paper, we propose an automated crop prediction system that utilizes machine learning algorithms to forecast crop yields. The system leverages historical climate data, soil characteristics, and crop-specific features as input variables to train and develop accurate predictive models. To build our crop prediction system, we employ a comprehensive dataset comprising years of historical agricultural data, including weather patterns, soil properties, and crop yields. Feature engineering techniques are applied to extract meaningful features from the dataset, while ensuring data quality and consistency. Several machine learning algorithms, such as random forests, support vector machines, and gradient boosting, are employed to develop predictive models. These algorithms are trained and validated using cross-validation techniques to optimize their performance and generalization capabilities.