Title: Neuro-fuzzy-based smart DSS for crop specific irrigation control and SMS notification generation for precision agriculture
Authors: Ambarish G. Mohapatra; Saroj Kumar Lenka
Addresses: Department of Applied Electronics and Instrumentation, Silicon Institute of Technology, Bhubaneswar, 751024, Odisha, India ' Department of Information Technology, Mody University, Lakshmangarh, 332311, Rajasthan, India
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, green-house 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.
Keywords: feedforward neural networks; linear regression; LabVIEW; decision support systems; smart DSS; fuzzy logic; SMS notification; GSM modem interface; crop specific irrigation; irrigation control; precision agriculture; irrigation management; soil moisture content; India; evapotranspiration; short message service.
International Journal of Convergence Computing, 2016 Vol.2 No.1, pp.3 - 22
Accepted: 16 Jun 2016
Published online: 17 Nov 2016 *