Automation of Irrigation System Using ANN based Controller: Modeling & Simulation approach

Irrigation systems are as old as man itself since agriculture is the foremost occupation of civilized humanity. To irrigate large areas of plants is an onerous job. In order to overcome this problem many irrigation scheduling techniques have been developed which are mainly based on monitoring the soil, crop and weather conditions. Irrigation scheduling engrosses when to irrigate and how much water to be applied. Currently most of the irrigation scheduling systems and their corresponding automated hardware are fixed rate. Variable rate irrigation is very essential not only for the improvement of irrigation system but also to reduce the irrigation cost and to increase crop yield. The heart of automatic irrigation system (fixed rate or variable rate) is its control unit: as it controls irrigation time and water flow. Intelligent control based irrigation is necessitated to maximize the efficiency and production. Existing technologies varies from water balance or check book method to sophisticated sensor-based systems [1].Most of the irrigation systems use ON/OFF controllers. These controllers can not give optimal results for varying time delays and varying system parameters. This paper presents Artificial Neural Network (ANN) based intelligent control system for effective irrigation scheduling. The proposed Artificial Neural Network (ANN) based controller is prototyped using MATLAB. The input parameters like air temperature, soil moisture, radiations and humidity are modeled. Then using appropriate method, ecological conditions, evapotranspiration and type of crop, the amount of water needed for irrigation is estimated and then associated results are simulated. Index Term-Artificial Neural Network, Automated hardware, Irrigation scheduling, Evapotranspiration.