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Advanced Computational Methods for Agriculture Machinery Movement Optimization with Applications in Sugarcane Production

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Listed:
  • Martin Filip

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

  • Tomas Zoubek

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

  • Roman Bumbalek

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

  • Pavel Cerny

    (Faculty of Education, University of South Bohemia, Jeronymova 10, 371 15 Ceske Budejovice, Czech Republic)

  • Carlos E. Batista

    (Faculty of Engineering of Ilha Solteira (FEIS/UNESP), São Paulo State University, Passeio Monção 830, 15385-000 Ilha Solteira, Brazil)

  • Pavel Olsan

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

  • Petr Bartos

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic
    Faculty of Education, University of South Bohemia, Jeronymova 10, 371 15 Ceske Budejovice, Czech Republic)

  • Pavel Kriz

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic
    Faculty of Education, University of South Bohemia, Jeronymova 10, 371 15 Ceske Budejovice, Czech Republic)

  • Maohua Xiao

    (College of Engineering, Nanjing Agriculture University, Nanjing 210031, China)

  • Antonin Dolan

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

  • Pavol Findura

    (Faculty of Agriculture, University of South Bohemia, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic)

Abstract

This paper considers the evolution of processes applied in agriculture for field operations developed from non-organized handmade activities into very specialized and organized production processes. A set of new approaches based on the application of metaheuristic optimization methods and smart automatization known as Agriculture 4.0 has enabled a rapid increase in in-field operations’ productivity and offered unprecedented economic benefits. The aim of this paper is to review modern approaches to agriculture machinery movement optimization with applications in sugarcane production. Approaches based on algorithms for the division of spatial configuration, route planning or path planning, as well as approaches using cost parameters, e.g., energy, fuel and time consumption, are presented. The combination of algorithmic and economic methodologies including evaluation of the savings and investments and their cost/benefit relation is discussed.

Suggested Citation

  • Martin Filip & Tomas Zoubek & Roman Bumbalek & Pavel Cerny & Carlos E. Batista & Pavel Olsan & Petr Bartos & Pavel Kriz & Maohua Xiao & Antonin Dolan & Pavol Findura, 2020. "Advanced Computational Methods for Agriculture Machinery Movement Optimization with Applications in Sugarcane Production," Agriculture, MDPI, vol. 10(10), pages 1-20, September.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:434-:d:420187
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    References listed on IDEAS

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    1. Neungmatcha, Woraya, 2016. "Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operationsAuthor-Name: Sethanan, Kanchana," European Journal of Operational Research, Elsevier, vol. 252(3), pages 969-984.
    2. Goldemberg, José & Coelho, Suani Teixeira & Guardabassi, Patricia, 2008. "The sustainability of ethanol production from sugarcane," Energy Policy, Elsevier, vol. 36(6), pages 2086-2097, June.
    3. Qi He & Cheng Zha & Wei Song & Zengzhou Hao & Yanling Du & Antonio Liotta & Cristian Perra, 2020. "Improved Particle Swarm Optimization for Sea Surface Temperature Prediction," Energies, MDPI, vol. 13(6), pages 1-18, March.
    4. Alberto V. Donati & Jette Krause & Christian Thiel & Ben White & Nikolas Hill, 2020. "An Ant Colony Algorithm for Improving Energy Efficiency of Road Vehicles," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Ilkyeong Moon & Sanghyup Lee & Moonsoo Shin & Kwangyeol Ryu, 2016. "Evolutionary resource assignment for workload-based production scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 375-388, April.
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    Cited by:

    1. Saira Latif & Torbjörn Lindbäck & Magnus Karlberg & Johanna Wallsten, 2022. "Bale Collection Path Planning Using an Autonomous Vehicle with Neighborhood Collection Capabilities," Agriculture, MDPI, vol. 12(12), pages 1-20, November.

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