Method of Plant Growth Modeling Based on Genetic Algorithm and RBF Network
Zhenjiang Cai, Yihua Hu, Sun Yumei, Shunbin Hu · 2007
The plant growth model is very difficult to be set up. Because the relations between the growth parameters and surround envelopment parameters are very complex. A new method that using artificial neural network for plant growth modeling is presented. For improving the algorithm convergence rate, the radial basis function (RBF) network is adopted. As an example of this method, the prediction of tomato stem daily growth and its surround relation is also presented. The experiment results show that the method is effective for plant growth modeling.