caa a22 4500 606160051 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1864-5 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1864-5 Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India's Malabar region [Elektronische Daten] [L. Jayashree, Nidhil Palakkal, Elpiniki Papageorgiou, Konstantinos Papageorgiou] Coconut is one of the major perennial food crops that has a long development phase of 44months. The climatic and seasonal variations affect all stages of coconut's long development cycle. Besides, the soil composition also plays a vital role in deciding the coconut yield behavior. The present study is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM. Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala state's Malabar region were used for training and evaluation of the FCM. The present work demonstrates the classification and prediction capabilities of FCM for the described precision agriculture application, with the two most known and efficient FCM learning approaches, viz., nonlinear Hebbian (NHL) and data-driven nonlinear Hebbian (DDNHL). The DDNHL-FCM offers an overall classification accuracy of 96%. The various case studies furnished in the paper demonstrate the power of NHL-FCM in effectively reasoning new knowledge pertaining to the presented precision agriculture application. The Natural Computing Applications Forum, 2015 Fuzzy cognitive map nationallicence Precision agriculture nationallicence Prediction nationallicence Hebbian learning nationallicence Machine learning nationallicence Jayashree L. Computer Science and Engineering Department, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India aut Palakkal Nidhil Computer Science and Engineering Department, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India aut Papageorgiou Elpiniki Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece aut Papageorgiou Konstantinos Ministry of Education and Religious Affairs, Athens, Greece aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1963-1978 0941-0643 26:8<1963 2015 26 521 https://doi.org/10.1007/s00521-015-1864-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1864-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jayashree L. Computer Science and Engineering Department, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India aut NATIONALLICENCE 700 1- Palakkal Nidhil Computer Science and Engineering Department, RVS College of Engineering and Technology, Coimbatore, Tamil Nadu, India aut NATIONALLICENCE 700 1- Papageorgiou Elpiniki Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece aut NATIONALLICENCE 700 1- Papageorgiou Konstantinos Ministry of Education and Religious Affairs, Athens, Greece aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1963-1978 0941-0643 26:8<1963 2015 26 521