Fog-IoPM: Fog computing for Internet of Plants data management

Traditional irrigation methods often rely on static schedules, which limits adaptability to dynamic growing conditions. Current Internet of Things (IoT) and fog based irrigation systems encounter challenges, such as network interruptions, high latency, data loss, and inaccurate water allocation due to limited precision in calculating irrigation requirements. Addressing these issues in precision irrigation requires a flexible and resilient architecture that combines advanced technologies for improved accuracy. This study introduces Fog-IoPM, a fog-based system, employing Fog computing, LoRaWAN, and a Microservices Architecture (MSA) to enhance scalability, availability, and resource efficiency in precision irrigation. The Fog-IoPM architecture mitigates data loss during network outages by locally storing data, which it transmits to the cloud upon reconnection, thus ensuring a complete dataset for decision-making and reducing water consumption. Experiments were conducted across two outdoor areas and an indoor prototype cultivated with Moringa oleifera Lam, comparing data collected before and after implementing the system. Results show a significant improvement in data availability, increasing from 65.10% to 93.86%, and a reduction in packet loss to 7%. Additionally, water usage decreased by 72.72% due to more precise, data-driven irrigation scheduling. These findings demonstrate the potential of Fog-IoPM to enhance irrigation accuracy, optimize resource use, and provide scalable solutions for the Internet of Plants (IoP) in agriculture.