| Summary: | 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.
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