Improved deep belief networks and multi-feature fusion for leaf identification

Plant identification based on digital leaf images is a hot topic in the automatic classification of plants. However, due to the increase in the number of plant species, the leaf recognition rate is low because the traditional classification methods extract the few characteristics or use the classifiers with simple structures. This paper applied a combination of texture features and shape features for identification. Texture features include local binary patterns, Gabor filters and gray level co-occurrence matrices, while the shape feature vector is modeled using Hu moment invariants and Fourier descriptors. Improved deep belief networks (DBNs) with dropout, which use proportion integration differentiation control (PID) to decrease the reconstruction error in the process of pre-training, are used as the classifiers. The proposed algorithm was tested on the ICL dataset, and the average recognition rate is 93.9% for 220 types of leaves. The experimental results show that the proposed method has a higher recognition rate and is more robust than the traditional methods, and the training process is completed in a shorter time.