Using a wireless sensor network and the Internet of Things (IoT) to automate a lateral move irrigation system based on real-time soil water potential data

• A wireless sensor network and IoT system were created to automate irrigation using a lateral move irrigation system. • The system was created using open-source electronics and IoT platform. • The automation system was field-tested during field experiments over three growing seasons. Automated irrigation systems driven by in-field sensing can improve water-use efficiency in row-crop production, yet their integration with lateral-move irrigation systems remains limited. In this study, we developed and field-tested a low-cost, open-source wireless sensor network to automate irrigation zone control based on real-time soil water potential (SWP). The system integrates LoRa-based soil moisture nodes, a position-tracking unit, and a relay-controlled valve array mounted on a lateral-move irrigator. Field experiments were conducted in cotton at the Clemson University Edisto Research and Education Center (South Carolina, USA) across three growing seasons (2020–2022). In 2021 and 2022, irrigation was triggered when the average SWP in each zone reached -30, -40, or -50 kPa, with a dryland treatment included for comparison. Sixteen independently controlled irrigation zones were created within a 1.5-ha field to evaluate sensor-driven water application. The automated system operated reliably, with minor issues related to wildlife interference and solar charging that were subsequently mitigated. Due to high and well-distributed rainfall during the study years, irrigation events were infrequent, and no significant yield differences were detected among treatments. Nevertheless, the system demonstrated accurate zone-level control, stable wireless communication, and consistent real-time decision-making. These results show that open-source IoT platforms can provide a flexible, scalable framework for sensor-guided irrigation automation, with potential benefits for water savings and greater precision in drier years and on spatially variable fields.