Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

Saeed Nosratabadi, Sina Ardabili, Zoltan Lakner, Csaba Mako and Amir Mosavi
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Saeed Nosratabadi: Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
Sina Ardabili: Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
Zoltan Lakner: Institute of Economic Sciences, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
Csaba Mako: Institute of Information Society, University of Public Service, 1083 Budapest, Hungary
Amir Mosavi: Faculty of Civil Engineering, Technische Universitat Dresden, 01069 Dresden, Germany

Agriculture, 2021, vol. 11, issue 5, 1-13

Abstract: Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

Keywords: food production; machine learning; agricultural production; prediction; big data; data science; deep learning; forecasting; data-driven decision making; food demand; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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