Prediction and Analysis of Water Requirement in Automated Irrigation System using Artificial Neural Network(ANN) and Lora Technology

Abstract

Agriculture is our primary source of food and other raw materials, so it is regarded as a basic human need.70% of farmers and general people depend on agriculture. Regrettably, many farmers continue to use antiquated farming methods. The irrigation system has been practiced in India and other Asian countries from the early times. Due to the scarcity of water in today’s world, smart irrigation methods are becoming increasingly important. An Automated Irrigation system is developed using IoT to overcome the above issue. This project makes use of artificial neural networks (ANN) to optimize water usage in agriculture. Temperature and moisture sensors are used to read the temperature and moisture level of soil in the system. Lora transmitter transmits the sensor data. The data transmitted by the LoRa transmitter is received by the LoRa receiver, which then passes it on to the controller unit. The data is processed and compared to a predetermined threshold value by the controller unit. If the value exceeds the threshold, the motor is activated; otherwise, the motor is turned off. The GSM module is used to convey the motor status to the registered user through SMS. The readings of the sensors and the quantity of water required to reach the threshold value are broadcast to the web once the motor is turned on/off (Thingspeak). Thingspeak is being used to record all sensor data and water consumption for a specific temperature/moisture. ANN analyses the data and determines the amount of water that will be needed in the next few days.