IoT-Based Automated Farmland Contamination Monitoring Systems

Farmland contamination not only hinders crop growth but also poses significant risks to consumer health, a concern that has become increasingly apparent in recent years. Effective mitigation of pollution relies on the prerequisite of monitoring farmland contamination. This paper implements recent advancements in wireless sensors, aerial networks, and Internet of Things (IoT) systems to tackle the challenge of monitoring farmland contamination. Our methodology involves deploying wireless sensor networks to monitor areas of farmland. Drones, IoT gateways, IoT servers, and data storage are utilized to seamlessly collect, transmit, and analyze data. Major steps involved in the entire data acquisition and analysis process are discussed. In addition, the paper proposes system models to determine the minimum number of sensors required for efficient farmland monitoring. Having these system models not only provides guidance on achieving an sufficient monitoring performance but also factors in IoT resource usage to facilitate sustainable agriculture. Our simulations demonstrate the applicability of using these system models in deploying IoT sensors.