Crop Recommendation System using Machine Learning and IoT

In contemporary agriculture, optimizing crop yield while minimizing resource usage is paramount. This project addresses these challenges by presenting a Crop Recommendation System that utilizes both machine learning algorithms and the Internet of Things (IoT). Soil parameters such as nitrogen, phosphorus, potassium, pH value, and rainfall data are provided by the user, and other real-time climate data from IoT sensors including temperature and humidity, are integrated into the system. The problem at hand is the need for a personalized and data-driven approach to farmers for optimal crop selection that is best suited to their land according to soil characteristics and climate conditions. Traditional methods often fail to adapt to changing environmental conditions resulting in suboptimal crop recommendations. By training a machine learning model on a comprehensive dataset and incorporating the data provided by the user and IoT sensors, the system provides accurate and timely suggestions for optimizing crop productivity. The integration of machine learning models and IoT ensures the system's adaptability to varying environmental conditions. By considering both soil properties and climatic factors, the system outperforms traditional methods of farming, offering farmers a valuable tool for informed decision-making in crop selection. In conclusion, this project offers farmers a user-friendly crop recommendation system that holds promise for sustainable and resource-efficient agricultural production.