Visual models of plants interacting with their environment
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Recipes for adjoint code construction
ACM Transactions on Mathematical Software (TOMS)
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Plant models faithful to botanical structure and development
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Mathematics and Computers in Simulation - Special issue: Selected papers of the IMACS/IFAC fourth international symposium on mathematical modelling and simulation in agricultural and bio-industries
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Analytical study of a stochastic plant growth model: Application to the GreenLab model
Mathematics and Computers in Simulation
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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.