Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

Asmaa Mourhir, Elpiniki I. Papageorgiou, Konstantinos Kokkinos and Tajjeeddine Rachidi
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Asmaa Mourhir: Computer Science Department, School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco
Elpiniki I. Papageorgiou: Computer Engineering Department, Technological Education Institute (TEI) of Sterea Ellada, Lamia 35100, Greece
Konstantinos Kokkinos: Computer Science Department, University of Thessaly, Lamia 35131, Greece
Tajjeeddine Rachidi: Computer Science Department, School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco

Sustainability, 2017, vol. 9, issue 7, 1-23

Abstract: One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%.

Keywords: precision agriculture; fuzzy cognitive maps; fuzzy inference systems; cotton yield; scenario simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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