Soil quality monitoring and evaluation system using machine learning and lorawan wireless communication

Abstract

This study demonstrates the successful deployment and operation of LoRaWAN technology, yielding significant positive outcomes. The system effectively collects critical soil parameters, including moisture, temperature, electrical conductivity (EC), pH, and NPK levels, thereby fulfilling the requirements for agricultural soil monitoring and management. Leveraging the LoRaWAN protocol, the system ensures reliable, long-range data transmission with minimal energy consumption, making it highly suitable for remote farmlands where traditional connectivity is limited. Moreover, its modular architecture and scalability provide flexibility for large-scale deployment across diverse agricultural regions. In addition, the collected data can be integrated with machine learning models to analyze soil dynamics, predict future trends, and optimize resource allocation, such as fertilizer and irrigation scheduling. Ultimately, the system contributes to sustainable farming practices by recommending suitable crops and improving overall agricultural productivity.