Plant leaf identification based on the multi-feature fusion and deep belief networks method

Plant identification based on digital leaf image is a hot topic in research of automatic classification of plants. However, with the increase in the number of plant species, the leaf recognition rate is low due to the single trait extraction and simple structure classifier in the traditional classification methods. This study applied the combination of texture features and shape features for identification, using the deep belief networks (DBNs) as the classifier. Texture features are derived from local binary patterns, Gabor filters and gray level co-occurrence matrix while shape feature vector is modeled using Hu Moment invariants and Fourier descriptors. In order to avoid overfitting, we trained the DBNs with “dropout” method. The proposed algorithm was tested on the Flavia dataset, and the recognition rate was 99.37% for 32 species, while on the ICL dataset the recognition rate was 93.939% for 220 kinds of leaves. The experimental results illustrated that the proposed method has stronger robustness and higher recognition rate compared to the traditional methods.