Crop Recommendation System using Machine Learning

Machine learning has built this crop yield predictability by complex analysis of advanced agriculture algorithms on multiple features like Temperature, Humidity, PH of soil, Types of crops, N, P, K, cultivation practices and Rainfall. Using historical data, they can optimize and allow crop production to be predicted better through resource allocation and planning. Predictive models help farmers in optimizing the use of water, fertilizers and pesticides and reduce wastage as well as effects of climate variability. Machine learning allows one to discover patterns in large datasets in real-time to make timely insights into better decision-making and policy formulation, stabilization of food markets, and ultimately, support for sustainable agricultural practices to meet this ever- growing global demand for food.