Neural Network and Fuzzy Logic Based Smart DSS Model for Irrigation Notification and Control in Precision Agriculture

The efficiency and uniformity of irrigation can be maintained from the complex and diverse information based systems by considering weather, soil, water, and crop data. The model is created by suitable decision support system (DSS) algorithm. The DSS model acquires real-time soil and environmental data using our wireless sensor network mechanism. A radial basis function type of neural network is performed to predict hourly soil moisture content (MC) requirement as well as required soil evapotranspiration using Blaney-Criddle method. In this work, a fuzzy logic based weather dependent irrigation control mechanism is also developed and it is integrated with the DSS to generate adequate SMS notifications by interfacing a GSM modem. The soil MC prediction algorithm is implemented by collecting field data from a test land in Bhubaneswar located in the eastern region of India. The comparative analysis is also performed by calculating prediction RMSE, RSE, MSE, RPD and algorithm running time. The proposed smart DSS model is used to compensate the amount of water loss through evapotranspiration by considering weather, soil, water, and crop data.