Automated Intelligent Wireless Drip Irrigation Using ANN Techniques

Abstract

The aim of this work is to address the water scarcity prevalent in our country through smart irrigation practices. In this work we take certain environmental factors such as soil moisture content into consideration for creating a sustainable and smart irrigation system. The models proposed here for the day to day estimation of evapotranspiration are derived by using the daily data parameters such as temperature, solar radiation, wind speed and humidity for a period of 4 years (2009–2013) from Karunya University’s meteorological station, at Coimbatore, Tamil Nadu, India. An Artificial Neural Network approach is adopted to run the software part using the environmental parameters and the output obtained from the ANN method with the least RMSE error is taken into account for the ETo value. The reliability of the computational models used are done based on the results achieved through two prominent empirical methods. These include Penman-Monteith equation and Hargreaves equation and comparing their respective Mean Square Errors (MSE) and also the Root Mean Square Errors (RMSE). Hargreaves method is suitable with the least RMSE error. In the hardware approach Hargreaves method has been implemented using Raspberry PI controller. The real-time data from the field controller is relayed to a hardware setup at the local base station. This is done through a wireless ZigBee protocol which eventually transmits the necessary data via a GPRS link to the remote station. The output volumetric water content was calculated using Crop coefficient ETC. Solenoid valves are remotely controlled to release a calculated value of water based on the data acquired at the local base station. This method of automated irrigation will mitigate the problems usually associated with farming and will finally result in generating greater yields of crop production.