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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22695

Title: Hybrid learning of fuzzy cognitive maps for sugarcane yield classification
Authors: Natarajan, Rajathi
Subramanian, Jayashree
Papageorgiou, Elpiniki
Issue Date: 2016
Publisher: ELSEVIER SCI LTD
Citation: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 127, p. 147-157
Abstract: Sugarcane is one of India's most important renewable commercial crops. The sugarcane cultivation and sugar industry plays a vital role towards socio-economic development in the rural areas by creating higher income and employment opportunities. Early detection and management of problems associated with sugarcane yield indicators enables the decision makers and planners to decide import or export policies. In this work, a hybrid approach using fuzzy cognitive map (FCM) learning algorithms for sugarcane yield classification is proposed, combining the key aspects of Data Driven Nonlinear Hebbian Learning (DDNHL) algorithm and Genetic Algorithm (GA) called FCM-DDNHL-GA. The FCM model developed for the proposed study includes various soil and climate parameters which influence the precision agriculture application of sugarcane yield prediction. The classification accuracies and inference capabilities of the hybrid learning algorithm of FCMs are analyzed and compared with some well-known machine learning algorithms for sugarcane yield monitoring application. Experimental results show the superiority of the hybrid learning approach by providing significantly higher classification accuracy. (C) 2016 Elsevier B.V. All rights reserved.
Notes: [Natarajan, Rajathi] Kumaraguru Coll Technol, Dept Informat Technol, Coimbatore, Tamil Nadu, India. [Subramanian, Jayashree] PSG Coll Technol, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India. [Papageorgiou, Elpiniki I.] Technol Educ Inst Cent Greece, Dept Comp Engn, Lamia, Greece. [Papageorgiou, Elpiniki I.] Hasselt Univ, Fac Business Econ, Agoralaan Gebouw D, BE-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/22695
DOI: 10.1016/j.compag.2016.05.016
ISI #: 000383527100016
ISSN: 0168-1699
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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