Abstract: To improve the accuracy of the automatic plant identification system， this paper proposed a novel methodology of characterizing and recognizing plant leaves using shape and texture features with neural network ensemble. Shape features of the leaves were captured using invariant moments together with geometric parameter of leaves. Texture of the leaf was modeled using gray level co\|occurrence matrix （GLCM）. And an artificial neural network ensemble in which the base classifier was composed by the union of a binary classifier and a multiclass classifier was used for the resolution of multi\|class problems. Since some of these features were in general sensitive to the orientation of the leaf images， a pre\|processing stage prior to feature extraction was applied to make corrections for varying rotation. After the pre\|processing stage， the neural network ensemble was used to train and classify the plant leaf samples. Experimentations to demonstrate the efficacy of the proposed approach were performed on a dataset of 600 images divided into 20 classes with 30 images per class， collected from Flavia, and the accuracy was 91%. In contrast with the other plant leaf recognition methods， the results showed that this method could significantly improve the accuracy of the system.

Key words: plant leaf recognition, shape feature, texture feature, artificial neural network ensemble