Advanced sensor-based system for soil nutrient analysis and AI-driven crop recommendation

Abstract

This research presents an automated soil analysis system integrating NPK sensors with machine learning for precision agriculture. The framework employs Arduino UNO microcontroller and ESP8266 Wi-Fi module to capture real-time nitrogen, phosphorus, and potassium levels from soil samples. Collected data transmits to cloud infrastructure where trained algorithms process nutrient concentrations and generate crop cultivation recommendations. An OLED interface provides immediate feedback to agricultural practitioners. Testing demonstrates reliable nutrient quantification and appropriate crop suggestions compared to traditional laboratory methods. The solution offers advantages including immediate results, reduced operational costs, and simplified operation. This technology supports sustainable farming through optimized resource allocation. Future developments will incorporate meteorological data and additional soil characteristics to enhance predictive capabilities and recommendation precision.