Plants Identification Using Feature Fusion Technique and Bagging Classifier

In this paper, a plant identification approach using 2D digital images of leaves is proposed. This approach will be used to develop an expert system to identify plant species by processing colored images of its leaf. The approach made use of feature fusion technique and the Bagging classifier. Feature fusion technique is used to combine color, shape, and texture features. Color moments, invariant moments, and Scale Invariant Feature Transform (SIFT) are used to extract the color, shape, and texture features, respectively. Linear Discriminant Analysis (LDA) is used to reduce the number of features and Bagging ensemble is used to match the unknown image and the training or labeled images. The proposed approach was tested using Flavia dataset which consists of 1907 colored images of leaves. The experimental results showed that the accuracy of feature fusion approach was much better than all other single features. Moreover, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.