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dc.creatorPapageorgiou, E. I.en
dc.creatorMarkinos, A. T.en
dc.creatorGemtos, T. A.en
dc.date.accessioned2015-11-23T10:43:25Z
dc.date.available2015-11-23T10:43:25Z
dc.date.issued2011
dc.identifier10.1016/j.asoc.2011.01.036
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/11615/31769
dc.description.abstractThis work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps. Fuzzy cognitive map (FCM) is a fusion of fuzzy logic and cognitive map theories, and is used for modeling and representing experts' knowledge. It is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. It is a challenging approach for decision making especially in complex processing environments. The FCM approach presented here was chosen to be utilized in agriculture because of the nature of the application. The prediction of yield in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. Throughout this proposed method, FCMs designed and developed to represent experts' knowledge for cotton (Gossypium hirsutum L.) yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main factors affecting cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton yield. The investigated methodology was evaluated for 360 cases measured during the time of six subsequent years (2001-2006) in a 5 ha experimental cotton field, in predicting the yield class between two possible categories ("low" and "high"). The results obtained reveal its comparative advantage over the benchmarking machine learning algorithms tested for the same data set for the years mentioned by providing decisions that match better with the real measured ones. The main advantage of this approach is its simple structure and flexibility, representing knowledge visually and more descriptively. Hence, it might be a convenient tool in predicting cotton yield and improving crop management. (C) 2011 Elsevier B.V. All rights reserved.en
dc.source.uri<Go to ISI>://WOS:000289508000034
dc.subjectFuzzy cognitive mapsen
dc.subjectModelingen
dc.subjectKnowledge representationen
dc.subjectFuzzy setsen
dc.subjectDecision makingen
dc.subjectCottonen
dc.subjectYielden
dc.subjectNEURAL-NETWORKSen
dc.subjectMODELen
dc.subjectMANAGEMENTen
dc.subjectKNOWLEDGEen
dc.subjectCORNen
dc.subjectENTERPRISESen
dc.subjectDYNAMICSen
dc.subjectTOOLen
dc.subjectComputer Science, Artificial Intelligenceen
dc.subjectComputer Science,en
dc.subjectInterdisciplinary Applicationsen
dc.titleFuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture applicationen
dc.typejournalArticleen


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