An Edge-computing flow meter reading recognition algorithm optimized for agricultural IoT network

Groundwater resources in Nebraska, U.S. are closely monitored by 23 Natural Resources Districts (NRDs) located across the state. Growers who use groundwater for irrigation are required to have flow meters installed at wells to monitor their water usage. However, many of these flow meters are still being read and recorded through in-person visits, which can be time-consuming and costly. Although some flow meters in Nebraska are monitored remotely by telemetry-enabled camera systems, yearly telemetry costs are high and making long-term operation financially burdensome. Using less expensive network protocol, such as Internet of Things (IoT), to transmit flow meter readings could enable new monitoring opportunities. However, there are challenges in directly transmitting flow meter images via IoT due to limited bandwidth. Therefore, in this study, we developed an algorithm using object detection deep learning techniques, i.e. You Only Look Once (YOLO) that can be programmed at an IoT node which can recognize readings from images of flow meters onsite before transmitting. The developed algorithm could significantly reduce data size and is essential for flow meter monitoring in an IoT network setting. The developed algorithm achieved 95.35% accuracy when recognizing 1,248 real-world flow meter images obtained at the courtesy of North Platte Natural Resources District (NPNRD) in western Nebraska. The framework and algorithm were also tested in a real-world scenario on a flow meter installed on a linear-move sprinkler irrigation system and showed promising results. By leveraging IoT and deep learning techniques, this research has the potential to revolutionize flow meter monitoring, reducing costs and improving efficiency in the management of groundwater resources in Nebraska, and potentially in other regions as well.