A novel power aware smart agriculture management system based on RNN-LSTM

In the realm of economics, agriculture holds supreme importance. The Internet of Things (IoT) is now pivotal in agriculture, aiding farmers in monitoring crop yield. Smart meters and control methods streamline agricultural operations, managing intelligent equipment, bidirectional communication, and user interaction. Data from sensors capturing soil and environmental parameters like moisture, humidity and temperature are integrated into Neural Networks for predictive analysis. Water scarcity, irrigation, and electrical power utilization creates impact on global crop growth and quality. This paper introduces an IoT-enabled product for coconut farming, enabling real-time monitoring and control of irrigation, energy usage, and power quality. The Smart Agriculture Irrigation Management System (AIMS) monitors valves, pumps, water levels, soil, and environmental conditions autonomously. Users can implement automated or manual decision-making processes. Additionally, a Smart Agriculture Energy Management System with integrated Smart Agriculture Energy Meter monitors power consumption, Power Quality, anomalies, and disturbances, notifying farmers via cloud services with predicted values. Implemented in a coconut farm in Sirumalai, Tamil Nadu, India, the system aims to reduce manual stress, enhancing productivity, yield, and water saving by over 30%. Predicted energy consumption patterns and tariffs help farmers avoid excessive costs, resulting in around 40% energy savings, facilitated by the superior performance of RNN-LSTM model over traditional methods.