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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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.
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