Research Area:  Machine Learning

Abstract:

Plant leaf identification is a significant challenge in the fields of computer vision and pattern recognition. This article presents a new approach to plant leaf identification, one that integrates shape and texture characteristics. First, we introduce the shape and texture features used by the proposed plant leaf recognition method. The proposed multiscale triangle descriptor (MTD) is employed to characterize the shape information of a plant leaf, and the local binary pattern histogram Fourier (LBP-HF) is used as the texture feature. Then, the shape and texture features of a leaf image are combined by weighted distance measurement, where distance and chi-square distance are used for shape and texture features, respectively. The proposed approach provides a robust descriptor for the task of plant leaf recognition by combining the complementary MTD and LBP-HF features. The proposed approach has been thoroughly evaluated on three benchmark leaf datasets, including the Flavia, Swedish and MEW2012 leaf datasets. Our method achieves 77.6%, 85.7%, and 67.5% retrieval accuracy on the Flavia, Swedish and MEW2012 leaf datasets, respectively, while the corresponding classification accuracy is 99.1%, 98.4%, 95.6%. The recognition performance of our method is better or comparable to prior state-of-the-art plant leaf recognition method.

Keywords:  
Plant leaf recognition
shape
texture features
multiscale triangle descriptor
computer vision
pattern recognition
Machine Learning

Author(s) Name:  Chengzhuan Yang

Publisher name:  Elsevier

DOI:  10.1016/j.patcog.2020.107809

Volume Information:  Volume 112, April 2021, 107809

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0031320320306129