  One of the challenging problems in biology is to classify plants based on
their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so
interesting, because its genetic structure has some similarities with that of
human beings. Biologists classify the type of this plant to mutated and not
mutated (wild) types. Phenotypic analysis of these types is a time-consuming
and costly effort by individuals. In this paper, we propose a modified feature
extraction step by using velocity and acceleration of root growth. In the
second step, for plant classification, we employed different Support Vector
Machine (SVM) kernels and two hybrid systems of neural networks. Gated Negative
Correlation Learning (GNCL) and Mixture of Negatively Correlated Experts (MNCE)
are two ensemble methods based on complementary feature of classical
classifiers; Mixture of Expert (ME) and Negative Correlation Learning (NCL).
The hybrid systems conserve of advantages and decrease the effects of
disadvantages of NCL and ME. Our Experimental shows that MNCE and GNCL improve
the efficiency of classical classifiers, however, some SVM kernels function has
better performance than classifiers based on neural network ensemble method.
Moreover, kernels consume less time to obtain a classification rate.
