Development and Application of Cell-Phone-Based Internet of Things (IoT) Systems for Soil Moisture Monitoring

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2021-05-20 13:27:37

Active soil moisture monitoring is an important consideration in irrigation water management. A permanent and readily accessible record of changes in soil moisture can be used to improve future water management decision-making. Similarly, accessing stored soil moisture data in near-real-time is also essential for making timely farming and management decisions, such as where, when, and how much irrigation to apply. Access to reliable communication systems and delivery of real-time data can be affected by its availability near production fields. Therefore, soil moisture monitoring systems with real-time data functionality that can meet the needs of farmers at an affordable cost are currently needed. The objective of the study was to develop and fieldtest affordable cell-phone-based Internet of things (IoT) systems for soil moisture monitoring. These IoT systems were designed using low-cost hardware components and open-source software to transmit soil moisture data from the Watermark 200SS or ECH2O EC-5 sensors. These monitoring systems utilized either Particle Electron or Particle Proton Arduino-compatible devices for data communication. The IoT soil moisture monitoring systems have been deployed and operated successfully over the last three years in South Carolina.

Soil moisture is an essential component of the hydrological, agricultural, ecological, and environmental cycles. It affects land-atmosphere interactions, the exchange of water and energy fluxes, rainfall-runoff processes, net ecosystem exchange, and food security [1]. It also plays a significant role in affecting critical physical processes in numerical weather prediction, climate modeling, agricultural crop growth modeling, and flood forecasting [2]. Soil moisture is an essential component of the hydrologic cycle and a critical variable for optimizing efficient irrigation water management in agriculture [3]. Therefore, obtaining accurate real-time estimates of spatial and temporal variations in soil moisture are critical [4]. Significant effort has been devoted to soil moisture measurement using various techniques, including manual in-situ estimation, automated in-situ sensing networks, numerical modeling, and remote sensing applications [1].

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