Modeling and Prediction of Environmental Parameters for Implementation of Smart Agriculture Using LoRa

Precision agriculture, driven by IoT(Internet Of Things) technologies, has revolutionized the farming industry by enabling real-time monitoring and data-driven decision-making. This study proposes an enhanced data transmission system for LoRa- (Long Range)based precision agriculture, leveraging the capabilities of dual LoRa devices. The system aims to overcome communication challenges in large agricultural areas and ensure reliable and extended-range connectivity between sensor nodes and the central gateway. The proposed system architecture includes IoT sensor nodes deployed throughout the farm, equipped with various environmental sensors to monitor crucial parameters such as soil moisture, temperature, humidity, light intensity, and pressure. Two LoRa devices are introduced within the system. A local LoRa device is placed in proximity to the sensor nodes, and a main LoRa gateway serves as the central hub for data aggregation. The local LoRa device acts as a relay point, receiving sensor data from the nearby nodes and transmitting it wirelessly to the main LoRa gateway. This dual LoRa device setup extends the communication range and enhances signal strength, enabling seamless and reliable transmission of sensor data over long distances, even in challenging terrains. The main LoRa gateway collects the transmitted data from the local LoRa device and forwards it to the network server for further analysis and decision-making. This data-driven approach empowers farmers to gain real-time insights into the farm's conditions, optimize resource allocation, and improve agricultural practices. In our project, we transfer the collected sensor data using LoRa and we implement it into Machine Learning for rainfall prediction.