A local binary pattern based texture descriptors for classification of tea leaves

• An effective method of tea leaves classification is crucial for the real-time control of tea production lines.
• The proposed method for tea leaves classification combines a Non-Overlap Window LBP and GLCM, which has better discrimination ability and classification accuracy, compared with other traditional approaches.
• The proposed texture descriptor requires much lower computation load than that of classic LBP and GLCM, thanks to the power of the Non-Overlap Window LBP.

For tea processing production lines, different fresh tea leaves require different processing parameters for the control systems of tea machines. Hence, an effective algorithm for classification of tea leaves will be important for automatic tea processing. However, most of tea classification researches were focused on gross tea, instead of fresh tea leaves. In this paper, a texture extraction method combing a non-overlap window local binary pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) has been proposed for green tea leaves classification. By taking advantages of both LBP and GLCM for texture extraction, this method is able to effectively extract texture of tea leaves for classification at low computational cost to meet automatic tea production line requirements. The experiments have been conducted to prove the effectiveness of the proposed method.