EXPERIMENTS OF ZERNIKE MOMENTS FOR LEAF IDENTIFICATION

So far, plant identification has challenges for several researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build foliage plant identification systems. In this case, Zernike moments were combined with other features: geometric features, color moments and gray-level co-occurrence matrix (GLCM). To implement the identifications systems, two approaches has been investigated. First approach used a distance measure and the second used Probabilistic Neural Networks (PNN). The results show that Zernike Moments have a prospect as features in leaf identification systems when they are combined with other features