AGRIFOG: Fog-Assisted IOT Enabled Agriculture Monitoring System

In response to the evolving landscape of agriculture, this report introduces a Fog-enabled IoT agriculture monitoring system aimed at transforming precision farming. The system’s architecture integrates an ESP32 microcontroller interfacing with soil moisture and DHT11 sensors, establishing a resilient network for real-time data acquisition and transmission. Leveraging MQTT communication protocols, the ESP32 seamlessly interacts with a Raspberry Pi Fog node, enabling local real-time analysis and control actions. The integration with the Google Drive API ensures secure cloud storage and periodic synchronization, facilitating historical analysis and visualization. The system’s scalability and affordability are demonstrated through ESP32 firmware development using the Arduino IDE, presenting a practical solution for widespread adoption. The assessment of the system’s performance involved evaluating latency and traffic metrics. With a recorded latency of 0.393 milliseconds, the system exhibits remarkable responsiveness to requests, ensuring efficient interactions with the Google Drive API. Simultaneously, the traffic rate of 0.131 requests per second indicates a moderate level of activity between the application and the cloud platform, underscoring the system’s efficiency in handling data interactions. This Fog-enabled IoT agriculture monitoring system addresses inherent flaws in traditional IoT applications. By incorporating Fog computing, the system overcomes latency challenges, ensuring real-time responsiveness crucial for precision farming. Field tests have substantiated the system’s efficacy in providing timely and reliable data for informed decision-making in agricultural practices. Future directions for system enhancement include the incorporation of advanced machine learning techniques and additional sensors such as $\mathbf{p H}$, light intensity, NPK, and airflow sensors. Thes additions aim to broaden the range of monitored parameters offering nuanced insights for precision agriculture.