Recommendation and Real-Time Monitoring System for Precision Agriculture

Agriculture is a highly income-generating segment for the world's economy, as it requires millions of people to carry out the business. The paper states a comprehensive Crop Recommendation and Fertilizer Suggestion System that would help farmers in deciding the appropriate crops and fertilizers to be planted at any point in time with live environmental and soil data. In this regard, the research uses IoT sensors to gather the following soil parameters of nitrogen (N), phosphorus (P), potassium (K), moisture, temperature, humidity, and rainfall. The system uses machine learning models to give the best recommendation on crops and fertilizers. A crop recommendation model is established using the XGBoost algorithm, and a fertilizer suggestion model utilizes Random Forest, thus giving data-driven insights to farmers for improving the efficiency and sustainability of farming. Besides, the portal has continuous monitoring, alerts soil deficiencies, and uses predictive analytics to aid the farmer. This goes to create informed decisions that would eventually boost crop productivity and profitability.