Application of Internet of Things and Cloud Platform in Smart Farms in Sierra Leone

Agriculture is central to Sierra Leone's economy and food security, yet the sector faces persistent challenges including climate variability, soil degradation, and reliance on traditional practices, compounded by limited access to affordable monitoring technologies. This research investigates Internet of Things (IoT) and cloud-based technologies to develop a low-cost, multi-sensor farm monitoring system tailored to Sierra Leone's agricultural environment. The system integrates an ESP32 microcontroller with soil moisture, temperature, humidity, CO₂, and PIR motion sensors for continuous farm monitoring. Sensor data are processed and securely transmitted via Wi-Fi to the ThingSpeak cloud platform using HTTPS, providing time-series storage, real-time visualization, and threshold-based alerts delivered through mobile channels. A design-science engineering methodology guided system development emphasizing affordability, energy efficiency, and usability for resource-constrained environments. Experimental evaluation demonstrated reliable real-time monitoring with sensor accuracy exceeding acceptable thresholds, data transmission latency below three seconds, and stable cloud connectivity under typical rural network conditions. Findings indicate that integrating low-cost IoT hardware with lightweight cloud platforms reduces manual monitoring, improves situational awareness, and supports data-driven decision-making regarding soil conditions, microclimate, air quality, and farm security. The modular design enables adaptation for other developing regions with similar infrastructural constraints. This study demonstrates that an ESP32-based IoT framework with cloud analytics offers a practical, scalable approach to smart agriculture in Sierra Leone, contributing a validated low-cost model bridging advanced technologies with smallholder farmer needs while laying foundations for predictive analytics and large-scale deployment.