Jean-François Sigrist - Numerical Simulation, An Art of Prediction, Volume 2

COMMENT ON FIGURE 1.23.– The efficiency of the L-systems is such that researchers show that the simulations are able to produce a variability in the characteristics of synthetic plants close to that of real plants – otherwise the data used by the learning algorithms would not be of good quality. The researchers even demonstrate that the latter learn, with similar effectiveness, either from real data or from data produced by synthetic models [UBB 18] .

Let us conclude this chapter with the understanding that, in general, agricultural modeling addresses three scientific issues:

– understand and predict plant growth processes;

– assess the impact of agricultural practices in ecological and economic terms;

– informing the policies of decision makers and farmers’ choices.

They are thus becoming an essential tool for agricultural research, meeting the vital needs of humanity in the 21st Century: feeding populations and preserving their environment.

1 1 Available at: https://www6.inra.fr/record.

2 2 Data available at: http://www.fao.org/forestry.

3 3 Available at: http://www.theplantlist.org/.

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