IoT based Soil Testing System with Recommendation of Organic Fertilizer

The research is about the proposed IoT-based soil testing system that focuses on improving soil quality by providing custom organic fertilizer recommendations and crop suggestions based on real-time soil analysis. The system makes use of a network of sensors, including moisture sensors and temperature and humidity sensors, to measure critical soil parameters. These readings are collected and processed using a Node MCU, which forms the basis of the data acquisition process. The data collected are analyzed with a machine learning approach. Initially, sensor readings are processed using the Random Forest algorithm to predict soil nutrient composition in terms of nitrogen (N), phosphorus (P), and potassium (K). Then, these values of nutrients are input into the Euclidean distance algorithm, which calculates the similarity that exists between the soil's current condition and a defined dataset of ideal soil profiles. The results on the suggested organic fertilizer and available crops are rendered on an easily accessible webpage that is user-friendly for farmers to use.