AWS Cloud Enabled Crop Recommendation using Machine Learning Algorithms

Agriculture plays a vital role in ensuring food security, yet farmers often face difficulties in selecting suitable crops due to varying soil and climatic conditions. This project introduces a cloud-enabled crop recommendation system that leverages machine learning techniques to provide reliable crop suggestions. The system processes key input parameters such as soil nutrients (Nitrogen, Phosphorus, Potassium, and pH) along with environmental factors (temperature, humidity, and rainfall) to determine the most suitable crops for cultivation. Two machine learning models, Random Forest and XGBoost, were developed and evaluated, with Random Forest demonstrating superior performance. To enhance accessibility and scalability, the bestperforming model was deployed on Amazon Web Services (AWS), enabling farmers and stakeholders to access recommendations anytime and anywhere. By integrating machine learning with cloud computing, this work highlights the potential of data-driven solutions to support sustainable and efficient agricultural practices.