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Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

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Engineering for Sustainable Future (INTER-ACADEMIA 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 101))

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Abstract

Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.

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Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

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Authors and Affiliations

  1. Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Tarahom Mesri Gundoshmian

  2. Institute of Advanced Studies Koszeg, Koszeg, Hungary

    Sina Ardabili

  3. Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary

    Amir Mosavi & Annamária R. Várkonyi-Kóczy

  4. School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK

    Amir Mosavi

  5. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamária R. Várkonyi-Kóczy

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  1. Tarahom Mesri Gundoshmian
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  2. Sina Ardabili
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  3. Amir Mosavi
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  4. Annamária R. Várkonyi-Kóczy
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Correspondence to Amir Mosavi .

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  1. Obuda University, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

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Gundoshmian, T.M., Ardabili, S., Mosavi, A., Várkonyi-Kóczy, A.R. (2020). Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology. In: Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, vol 101. Springer, Cham. https://doi.org/10.1007/978-3-030-36841-8_34

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  • DOI: https://doi.org/10.1007/978-3-030-36841-8_34

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