Neuro-fuzzy-based smart DSS for crop specific irrigation control and SMS notification generation for precision agriculture

Abstract

A feed forward neural network and fuzzy logic-based hybrid smart decision support system (DSS) for crop specific irrigation notification and control in precision agriculture (PA) is proposed in this paper. This proposed neuro-fuzzy smart DSS can be implemented in any farm land, greenhouse and poly-house for efficient irrigation management and control for PA. A feed forward neural network is trained and linear regression is performed to predict soil moisture content (MC) in hourly basis. The predicted soil MC is utilised by fuzzy logic-based smart DSS model to produce SMS notification to the farmer. The proposed DSS model can work on real-time mode using National Instruments LabVIEW. This hybrid smart DSS prediction algorithm is implemented using data group of 24 cases measured in the farming land located in Bhubaneswar, the southern part of India. Crop wise evapotranspiration is also calculated using Blaney-Criddle method to notify the farmers via SMS service. He has a number of national and international conference as well as journal papers in the field of engineering and technology. His research interests are data mining, wireless sensor network, and cloud computing. He lectures cloud computing, data mining technology, distributed computing and BigData at Mody University of Science and Technology, Lakshmangarh-Rajasthan, India. This paper is a revised and expanded version of a paper entitled 'Hybrid decision model for weather dependent farm irrigation using resilient backpropagation based neural network pattern classification and fuzzy logic' presented at