Crop Recommendation Using Weather and Soil Content

Abstract - In this growing area of Precision Agriculture, the project presents a Crop Recommendation System using machine learning techniques. The system requires important agro-climatic factors specific to growing crops (N, P, K, temperature, humidity, pH, rainfall) in order to predict the most appropriate crop based in the trained Random Forest model (deployed using a Flask API). The frontend is designed with ReactJS to create a friendly user interface (with dark/light toggle mode, loading spinner and user recent prediction history). The crop recommendation system is intended to support farmers, agronomists and researchers with data-driven crop recommendations to support improving productivity, better use of resources, and encourage sustainable agriculture. Key Words: Crop Recommendation, Precision Agriculture, Machine Learning, Random Forest Classifier, Soil Nutrients (NPK), Weather Parameters, React JS, Flask API, Smart Farming, Sustainable Agriculture, Crop Prediction System, Data-Driven Farming, AI in Agriculture.