IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i2p264-d748382.html
   My bibliography  Save this article

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Author

Listed:
  • Hiwa Golpira

    (Department of Biosystems Engineering, University of Kurdistan, 66177-15175 Sanandaj, Iran)

  • Rafael R. Sola-Guirado

    (Department of Mechanics, University of Cordoba, 14071 Cordoba, Spain)

Abstract

Conventional redesign methodologies applied on the grain harvester headers for the mechanical harvesting of chickpeas cause its progress to not be as rapid and technological. This paper presents a hybrid modeling-optimization methodology to design harvester reels for efficient chickpea harvesting. The five fabricated headers were tested in both real and virtual modeling environments to optimize the operational parameters of the reel for minimum losses. Harvesting losses data gathered from chickpea fields over ten years of trials were fed into a fuzzy logic model, which in turn was merged with simulated annealing to develop a simulator. To this end, simulated annealing was used to produce combinations of reel diameter and number of bats, to be fed into the fuzzy model until achieving a minimum harvesting loss. The proposed model predicts the reel structure measured in-field evaluation, which fits well with the previously established mathematical model. A significant improvement in harvesting performance, 71% pod harvesting, validates the benefits of the proposed fuzzy-simulated annealing approach to optimize the design of grain harvester headers.

Suggested Citation

  • Hiwa Golpira & Rafael R. Sola-Guirado, 2022. "Data-Driven Simulator: Redesign of Chickpea Harvester Reels," Agriculture, MDPI, vol. 12(2), pages 1-11, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:264-:d:748382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/2/264/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/2/264/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    2. L. Ingber, 1993. "Simulated annealing: Practice versus theory," Lester Ingber Papers 93sa, Lester Ingber.
    3. L. Ingber, 2012. "Adaptive simulated annealing," Lester Ingber Papers 12as, Lester Ingber.
    4. H.A. Oliveira, Jr. & A. Petraglia & L. Ingber & M.A.S. Machado & M.R. Petraglia, . "Stochastic global optimization and its applications with fuzzy adaptive simulated annealing," Lester Ingber Books, Lester Ingber, number 12a2, December-.
    5. Mohamed Rafik N. Qureshi & Ram Karan Singh & Mohd. Abul Hasan, 2018. "Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 641-659, April.
    6. Hongguang Yang & Mingzhu Cao & Bing Wang & Zhichao Hu & Hongbo Xu & Shenying Wang & Zhaoyang Yu, 2022. "Design and Test of a Tangential-Axial Flow Picking Device for Peanut Combine Harvesting," Agriculture, MDPI, vol. 12(2), pages 1-18, January.
    7. Andrzej Osuch & Ewa Osuch & Piotr Rybacki & Przemysław Przygodziński & Radosław Kozłowski & Andrzej Przybylak, 2020. "A Decision Support Method for Choosing an Agricultural Machinery Service Workshop Based on Fuzzy Logic," Agriculture, MDPI, vol. 10(3), pages 1-11, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chang-Yong Lee & Dongju Lee, 2014. "Determination of initial temperature in fast simulated annealing," Computational Optimization and Applications, Springer, vol. 58(2), pages 503-522, June.
    2. Sha Lin & Xin-Jiang He, 2022. "Analytically Pricing European Options under a New Two-Factor Heston Model with Regime Switching," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1069-1085, March.
    3. L. Ingber, 2018. "Model of Models (MOM)," Lester Ingber Papers 18mo, Lester Ingber.
    4. Dimitrios Karpouzos & Konstantinos Katsifarakis, 2013. "A Set of New Benchmark Optimization Problems for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3333-3348, July.
    5. Hime Aguiar e Oliveira, 2022. "Deterministic sampling from uniform distributions with Sierpiński space-filling curves," Computational Statistics, Springer, vol. 37(1), pages 535-549, March.
    6. Mohamed Abdel-Basset & Reda Mohamed & Nazeeruddin Mohammad & Karam Sallam & Nour Moustafa, 2021. "An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems," Mathematics, MDPI, vol. 9(10), pages 1-27, May.
    7. Ricardo Silva & Mauricio Resende & Panos Pardalos, 2014. "Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm," Journal of Global Optimization, Springer, vol. 60(2), pages 289-306, October.
    8. Xin‐Jiang He & Wenting Chen, 2021. "A semianalytical formula for European options under a hybrid Heston–Cox–Ingersoll–Ross model with regime switching," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 343-352, January.
    9. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & B. Gharehpetian, G., 2017. "Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 116-129.
    10. L. Ingber, 1993. "ASA-README included with ASA code," Lester Ingber Papers 93as, Lester Ingber.
    11. L. Ingber, 2017. "Quantum Path-Integral qPATHINT Algorithm," Lester Ingber Papers 17qa, Lester Ingber.
    12. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    13. Asma Khalil Alkhamis & Manar Hosny, 2023. "A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza," Sustainability, MDPI, vol. 15(21), pages 1-37, October.
    14. Vo Le & Kent Matthews & David Meenagh & Patrick Minford & Zhiguo Xiao, 2014. "Banking and the Macroeconomy in China: A Banking Crisis Deferred?," Open Economies Review, Springer, vol. 25(1), pages 123-161, February.
    15. Liu, Chunping & Minford, Patrick, 2014. "Comparing behavioural and rational expectations for the US post-war economy," Economic Modelling, Elsevier, vol. 43(C), pages 407-415.
    16. Bergey, Paul K. & Ragsdale, Cliff, 2005. "Modified differential evolution: a greedy random strategy for genetic recombination," Omega, Elsevier, vol. 33(3), pages 255-265, June.
    17. Felipe, Ángel & Ortuño, M. Teresa & Righini, Giovanni & Tirado, Gregorio, 2014. "A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 111-128.
    18. Zhuang Zhao & Xiaoning He & Shuqi Shang & Jialin Hou & Hao Zhu & Haiqing Wang & Yuetao Wang & Dongjie Li & Zengcun Chang & Chao Xia & Dongwei Wang, 2022. "Design and Testing of Discrete Element-Based Counter-Rotating Excavation Device for Cyperus esculentus," Agriculture, MDPI, vol. 12(10), pages 1-24, October.
    19. Sakata, Shinichi & White, Halbert, 2001. "S-estimation of nonlinear regression models with dependent and heterogeneous observations," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 5-72, July.
    20. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2020. "Establishing optimal forest harvesting regulation with continuous approximation," Operations Research Perspectives, Elsevier, vol. 7(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:264-:d:748382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.