Automated Drip Irrigation System Using Neural Network

Abstract

Agriculture requires more than 85% of available water on earth, and the need exceeds our expectations shortly. Drip irrigation is one of the compelling methods to reduce water consumption, but this method is not successfully installed and tested for paddy crops because of the vast requirement of water. In this proposed model, the water utilization for paddy crop is minimized by detecting the optimum environmental conditions using a neural network. The values from soil moisture, humidity, and light intensity sensor are fed as inputs to the neural network kit and processed using regression analysis until it attains its desired output. The node MCU microcontroller provides all data to the farmers using the Internet of things to monitor the complete irrigation system in real-time. Henceforth, this IOT based smart drip irrigation reduces 45–50% of water consumption compared with the manual flood irrigation method.