Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region

Coconut is one of the major perennial food crops that has a long development phase of 44 months. 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.