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An optimal control methodology for plant growth—Case study of a water supply problem of sunflower

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  • Wu, Lin
  • Le Dimet, François-Xavier
  • de Reffye, Philippe
  • Hu, Bao-Gang
  • Cournède, Paul-Henry
  • Kang, Meng-Zhen

Abstract

An optimal control methodology is proposed for plant growth. This methodology is demonstrated by solving a water supply problem for optimal sunflower fruit filling. The functional–structural sunflower growth is described by a dynamical system given soil water conditions. Numerical solutions are obtained through an iterative optimization procedure, in which the gradients of the objective function, i.e. the sunflower fruit weight, are calculated efficiently either with adjoint modeling or by differentiation algorithms. Further improvements in sunflower yield have been found compared to those obtained using genetic algorithms in our previous studies. The optimal water supplies adapt to the fruit filling. For instance, during the mid-season growth, the supply frequency condenses and the supply amplitude peaks. By contrast, much less supplies are needed during the early and ending growth stages. The supply frequency is a determining factor, whereas the sunflower growth is less sensitive to the time and amount of one specific irrigation. These optimization results agree with common qualitative agronomic practices. Moreover they provide more precise quantitative control for sunflower growth.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:82:y:2012:i:5:p:909-923
    DOI: 10.1016/j.matcom.2011.12.007
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    References listed on IDEAS

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    1. Mailhol, Jean Claude & Olufayo, Ayorinde A. & Ruelle, Pierre, 1997. "Sorghum and sunflower evapotranspiration and yield from simulated leaf area index," Agricultural Water Management, Elsevier, vol. 35(1-2), pages 167-182, December.
    2. Dayan, E. & Presnov, E. & Fuchs, M., 2004. "Prediction and calculation of morphological characteristics and distribution of assimilates in the ROSGRO model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 101-116.
    3. Kang, M.Z. & Cournède, P.H. & de Reffye, P. & Auclair, D. & Hu, B.G., 2008. "Analytical study of a stochastic plant growth model: Application to the GreenLab model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(1), pages 57-75.
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    Cited by:

    1. 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.
    2. 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.

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