Hybrid Intelligence for Field-Scale Soil Analysis and Crop Advisory Using Embedded Sensors and Machine Learning

This work provides the design and development of a portable soil testing system utilising artificial intelligence (AI) and machine learning (ML) capabilities for real-time soil analysis and crop suggestion. It uses a series of sensors to measure the essential soil parameters of pH, moisture, temperature, electrical conductivity, and macronutrients-nitrogen (N), phosphorus (P), and potassium (K). These sensor data are processed in a microcontroller-based system and analyzed by applying ML algorithms in order to assess the fertility and categorize the soil. A rule-based system integrated with a Random Forest Regressor provides dynamic, explainable crop recommendation using soil condition, environmental conditions, and past data trends. Users can view real-time sensor data in a web-based visualization, monitor history, and receive AI-driven crop advisory support through a web-based dashboard. This field-friendly device is rugged, low in cost, and oriented towards the requirements of smallholder producers. Field experiments using 500 soil samples also exhibit high conformity to a commercial soil tester, as the respective RMSEs are 2.26 mg/kg for nitrogen, 1.90 mg/kg for phosphorus, 1.97 mg/kg for potassium, and 0.60 for pH. The deployed MLmodel had an R² value of 0.978 in test data. These findings validate the system’s accuracy, dependability, and potential to facilitate sustainable, data-based decision-making in precision agriculture.