Novel Lora-Based Smart Water Meter For Water Consumption Forecasting In Smart Agriculture

Water scarcity and the efficient utilization of water resources are critical challenges faced by the agricultural sector. Smart water meters have emerged as a promising technology to address these issues by providing real-time monitoring and precise measurement of water consumption. By leveraging the capabilities of smart water meters, agricultural stakeholders can significantly enhance their water management practices, and implement effective measures to optimize water consumption and reduce wastage. Smart water meters enable farmers to monitor and optimize irrigation practices by providing accurate and real-time data about water usage. By tracking water consumption on a per-field or per-crop basis, farmers can adjust irrigation schedules and volumes based on the specific water requirements of different crops. This optimized approach minimizes water wastage, reduces overwatering, and improves crop yield. Thus, the integration of smart water meters in the agricultural sector offers immense potential to upgrade the industry and pave the way toward sustainable water resource management. This paper introduces a system that used TinyML technology for water consumption forecasting and LoRa to send data. The system operates by examining the energy consumption data of customers in the meter itself, and if any anomalies or suspicious behavior are detected, alerts and notifications are sent to both the farmers and network operators. The performance of the proposed system is assessed by analyzing data obtained from the developed smart meter. The system's performance for water consumption forecasting based on this data is deemed satisfactory, as it achieved an f1-Score of 0.76.

Novel Lora-Based Smart Water Meter For Water Consumption Forecasting In Smart Agriculture Keywords:

Smart Water meter , Smart Agriculture , Machine learning , Consumption Forecasting , LoRa Networks