IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v312y2015icp363-373.html
   My bibliography  Save this article

A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth

Author

Listed:
  • Fan, Xing-Rong
  • Kang, Meng-Zhen
  • Heuvelink, Ep
  • de Reffye, Philippe
  • Hu, Bao-Gang

Abstract

This paper proposes a novel knowledge-and-data-driven modeling (KDDM) approach for simulating plant growth that consists of two submodels. One submodel is derived from all available domain knowledge, including all known relationships from physically based or mechanistic models; the other is constructed solely from data without using any domain knowledge. In this work, a GreenLab model was adopted as the knowledge-driven (KD) submodel and the radial basis function network (RBFN) as the data-driven (DD) submodel. A tomato crop was taken as a case study on plant growth modeling. Tomato growth data sets from twelve greenhouse experiments over five years were used to calibrate and test the model. In comparison with the existing knowledge-driven model (KDM, BIC=1215.67) and data-driven model (DDM, BIC=1150.86), the proposed KDDM approach (BIC=1144.36) presented several benefits in predicting tomato yields. In particular, the KDDM approach is able to provide strong predictions of yields from different types of organs, including leaves, stems, and fruits, even when observational data on the organs are unavailable. The case study confirms that the KDDM approach inherits advantages from both the KDM and DDM approaches. Two cases of superposition and composition coupling operators in the KDDM approach are also discussed.

Suggested Citation

  • Fan, Xing-Rong & Kang, Meng-Zhen & Heuvelink, Ep & de Reffye, Philippe & Hu, Bao-Gang, 2015. "A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth," Ecological Modelling, Elsevier, vol. 312(C), pages 363-373.
  • Handle: RePEc:eee:ecomod:v:312:y:2015:i:c:p:363-373
    DOI: 10.1016/j.ecolmodel.2015.06.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380015002550
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2015.06.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wu, Lin & Le Dimet, François-Xavier & de Reffye, Philippe & Hu, Bao-Gang & Cournède, Paul-Henry & Kang, Meng-Zhen, 2012. "An optimal control methodology for plant growth—Case study of a water supply problem of sunflower," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 909-923.
    2. Atanasova, Nataša & Todorovski, Ljupčo & Džeroski, Sašo & Kompare, Boris, 2008. "Application of automated model discovery from data and expert knowledge to a real-world domain: Lake Glumsø," Ecological Modelling, Elsevier, vol. 212(1), pages 92-98.
    3. Gutiérrez-Estrada, Juan C. & Pulido-Calvo, Inmaculada & Bilton, David T., 2013. "Consistency of fuzzy rules in an ecological context," Ecological Modelling, Elsevier, vol. 251(C), pages 187-198.
    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. Verwaeren, Jan & Van der Weeën, Pieter & De Baets, Bernard, 2015. "A search grid for parameter optimization as a byproduct of model sensitivity analysis," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 8-27.
    2. Čerepnalkoski, Darko & Taškova, Katerina & Todorovski, Ljupčo & Atanasova, Nataša & Džeroski, Sašo, 2012. "The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems," Ecological Modelling, Elsevier, vol. 245(C), pages 136-165.

    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:eee:ecomod:v:312:y:2015:i:c:p:363-373. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.