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

Author

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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
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    Information Systems, University of Siegen, 57072 Siegen, Germany)

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.

Suggested Citation

  • Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:408-:d:548187
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    References listed on IDEAS

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    Cited by:

    1. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan, 2021. "Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning," Agriculture, MDPI, vol. 11(12), pages 1-12, December.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    3. Saeed Nosratabadi & Roya Khayer Zahed & Vadim Vitalievich Ponkratov & Evgeniy Vyacheslavovich Kostyrin, 2022. "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Papers 2209.07335, arXiv.org.
    4. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.
    5. Nosratabadi Saeed & Zahed Roya Khayer & Ponkratov Vadim Vitalievich & Kostyrin Evgeniy Vyacheslavovich, 2022. "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Organizacija, Sciendo, vol. 55(3), pages 181-198, August.

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