Crop Recommendation and Irrigation System Using Machine Learning with Integrated IoT Devices

In agriculture, timely and efficient irrigation is crucial to achieving maximum crop yield. However, traditional irrigation methods are often inefficient and wasteful, leading to water scarcity and abbreviated crop productivity. To solve these problems, we suggest an intelligent irrigation system that combines IoT devices and machine learning techniques to suggest the best crop for a particular area and deliver the best irrigation based on real-time weather data and soil moisture levels. The system accumulates data on soil nutrients, temperature, and sultriness utilizing IoT sensors and uses XGBoost, a popular machine learning algorithm, to recommend the most lucrative crop predicated on historical data. The system additionally incorporates authentic-time weather data from APIs and water level sensors to provide customized irrigation for each crop. Our system aims to improve crop productivity, minimize water waste, and avail farmers to make data-driven decisions to maximize their profits