A Machine Learning-Based Dynamic Model for Crop Suitability Using Rainfall and Soil Parameters

This project proposes a machine learning-based crop recommendation system that uses environmental factors like soil pH, nutrient levels (NPK), temperature, humidity, and rainfall to assist farmers in selecting the most suitable crop. Leveraging classification algorithms such as Logistic Regression and Decision Trees, the model provides accurate and localized crop suggestions to improve agricultural productivity. The system is designed to support precision farming and sustainability by minimizing input costs, reducing crop failure, and enhancing yield through data-driven decisions. It is scalable, adaptable to various regions, and can be integrated with IoT and mobile platforms for real-time, smart agriculture applications.