Soil Productivity Estimation for Environment Health Management Using Machine Learning and IOT

The fast rise of the world’s population makes it harder to keep food safe, hence smart farming methods need to be used. This research introduces an all-encompassing smart farming prediction model that utilizes machine learning and real-time sensor data to assess soil production and suggest the most suitable crops. A set of IoT sensors, including soil moisture, pH, salinity, and temperature, is used to collect and process data using advanced algorithms including Deep Neural Networks (DNN), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) networks. The DNN model was better than previous algorithms at estimating crop yield, with a prediction accuracy of 94.2% and an RMSE of 0.45. The system gives useful information on nutrient deficiencies, irrigation needs, and crop appropriateness. This shows that it has a lot of potential to improve the sustainability of farming, the efficiency of resources, and decision-making in precision farming.