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 realtime 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 irrigation system. Field experiments were conducted in a cotton field at the Clemson University Edisto Research and Education Center (South Carolina, USA) across three growing seasons (2020-2022). In 2021 and 2022, four irrigation treatments were compared, including a control dryland treatment and irrigation triggered when the average SWP reached one of three SWP thresholds . 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.