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Article

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

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Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
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Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
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Institute of Economic Sciences, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
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Institute of Information Society, University of Public Service, 1083 Budapest, Hungary
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Faculty of Civil Engineering, Technische Universitat Dresden, 01069 Dresden, Germany
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John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
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Information Systems, University of Siegen, 57072 Siegen, Germany
*
Authors to whom correspondence should be addressed.
Academic Editors: Selma Boumerdassi, Eric Renault and Christopher Robin Bryant
Received: 2 February 2021 / Revised: 26 April 2021 / Accepted: 29 April 2021 / Published: 2 May 2021
(This article belongs to the Special Issue Latest Advances for Smart and Sustainable Agriculture)
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. View Full-Text
Keywords: food production; machine learning; agricultural production; prediction; big data; data science; deep learning; forecasting; data-driven decision making; food demand; artificial intelligence food production; machine learning; agricultural production; prediction; big data; data science; deep learning; forecasting; data-driven decision making; food demand; artificial intelligence
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MDPI and ACS Style

Nosratabadi, S.; Ardabili, S.; Lakner, Z.; Mako, C.; Mosavi, A. Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS. Agriculture 2021, 11, 408. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050408

AMA Style

Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi A. Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS. Agriculture. 2021; 11(5):408. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050408

Chicago/Turabian Style

Nosratabadi, Saeed, Sina Ardabili, Zoltan Lakner, Csaba Mako, and Amir Mosavi. 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS" Agriculture 11, no. 5: 408. https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050408

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