A Robust LoRa-Enabled Multi-Sensor Platform for Smart Agricultural Monitoring
DOI:
https://doi.org/10.62643/Abstract
For unique irrigation, crop protection, and yield optimisation selections, area-degree knowledge of soil, air, and mild conditions have to be maintained constantly. The constrained variety of short-distance wireless technology like Wi-Fi, Bluetooth, and LoRa, as well as the excessive infrastructure price of cellular-primarily based IoT connectivity, make it difficult for traditional tracking setups to in my view track soil moisture, temperature, humidity, and ambient mild in far off or scattered agricultural fields. Agricultural monitoring made easy with this LoRa-enabled multi-sensor platform. At the sphere transmitter node, you will locate an Arduino microcontroller with embedded software, a DHT11 humidity and temperature sensor, a soil moisture sensor, and a mild-dependent resistor (LDR) based totally mild sensor. Data gathered from these sensors is sent to a faraway receiver node thru an extended-range LoRa wireless hyperlink, in which it is able to be displayed and alarms can be set up. In order to offer real-time visibility and indicators on the field, the transmitter module constantly gathers information on temperature, humidity, soil moisture, and ambient light from the 3 sensor modules. It then translates this data the use of the Arduino software and powers a neighborhood LCD and buzzer. Upon transmission, the processed information and alert repute are sent to the receiver station through a LoRa-Rx module. At the reception station, a 2nd Arduino, LCD, and buzzer display the received readings and warn the farmer or discipline supervisor. The generation works nicely in big or geographically scattered agricultural fields in which conventional briefvariety wireless connections can not efficiently hyperlink the sensor node to the tracking station due to LoRas extended conversation variety and occasional power intake. A practical use of incorporated LoRa wireless generation for smart, related agricultural discipline monitoring, the suggested platform is simple to implement, resilient against interference, and price-powerful. It can scale to many area nodes.
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