Yield prediction in apples using Fuzzy Cognitive Map learning approach

dc.contributor.author	Παπαγεωργίου, Ελπινίκη Ι.	el
dc.contributor.author	Αγγελοπούλου, Κατερίνα Δ.	el
dc.contributor.author	Γέμτος, Θεοφάνης Α.	el
dc.contributor.author	Νάνος, Γιώργος Δ.	el
dc.date.accessioned	2015-05-21T19:13:25Z
dc.date.available	2015-05-21T19:13:25Z
dc.date.issued	2015-05-21
dc.identifier.uri	http://hdl.handle.net/11400/10854
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0168169912002657#	el
dc.subject	Modeling
dc.subject	Knowledge representation
dc.subject	Fuzzy sets
dc.subject	Decision making
dc.subject	Malus domestica
dc.subject	Fuzzy Cognitive Maps
dc.subject	Μοντελοποίηση
dc.subject	Αναπαράσταση γνώσης
dc.subject	Ασαφή σύνολα
dc.subject	Λήψη αποφάσης
dc.title	Yield prediction in apples using Fuzzy Cognitive Map learning approach	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Computer science
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/childrensSubjects/sj96005954
heal.keywordURI	http://zbw.eu/stw/descriptor/29233-4
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85052627
heal.keywordURI	http://skos.um.es/unescothes/C00984
heal.keywordURI	http://lod.nal.usda.gov/11312
heal.identifier.secondary	doi:10.1016/j.compag.2012.11.008
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2013-02
heal.bibliographicCitation	Papageorgiou, E. I., Aggelopoulou, K. D., Gemtos, T. A. and Nanos, G. D. (2013). Yield prediction in apples using Fuzzy Cognitive Map learning approach. "Computers and Electronics in Agriculture", vol. 91, February 2013. pp. 19–29. Available from: http://www.sciencedirect.com/science/article/pii/S0168169912002657#. [Accessed 22/12/2012]	en
heal.abstract	This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and easiness of use. In general, they model the behavior of a complex system, have inference capabilities and can be used to predict new knowledge. In this work, a data driven non-linear FCM learning approach was chosen to categorize yield in apples, where very few decision making techniques were investigated. Through the proposed methodology, FCMs were designed and developed to represent experts’ knowledge for yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main soil factors affecting yield, [such as soil texture (clay and sand content), soil electrical conductivity (EC), potassium (K), phosphorus (P), organic matter (OM), calcium (Ca) and zinc (Zn) contents], and the directed edges show the cause-effect (weighted) relationships between the soil properties and yield. The main purpose of this study was to classify apple yield using an efficient FCM learning algorithm, the non-linear Hebbian learning, and to compare it with the conventional FCM tool and benchmark machine learning algorithms. All algorithms have been implemented in the same data set of 56 cases measured in 2005 in an apple orchard located in central Greece. The analysis showed the superiority of the FCM learning approach in yield prediction.	en
heal.journalName	Computers and Electronics in Agriculture	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true