Grass Leaf Identification Using dbN Wavelet and CILBP

Abstract

Grass is one of the most important resources in the ecosystem for the sustainable development of human beings. However, the studies focusing on grass identification, which were traditionally implemented by experts with low efficiency and precision, cannot meet the requirements of modern grassland management. In this study, we proposed cubic interpolation LBP (CILBP) and dbN wavelets for grass identification based on leaf images. A low-frequency component of leaf images decomposed by dbN wavelets was used as the input of CILBP for more subtle texture extraction. The novelty of the proposed method was that CILBP can better describe the texture features from the low-frequency subimage, as compared with the original bilinear LBP. The effectiveness in identification accuracy of the proposed method for grass leaf was demonstrated by the experimental results.