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Living plant identification based on images of leaf is a very challenging task in the field of pattern recognition and computer vision. However, leaf classification is an important component of computerized living plant recognition. The leaf contains important information for plant species identification despite its complexity. The objective of this study is to compare the effectiveness of Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI) and Tchebichef Moment Invariant (TMI) features in extracting features from leaf images. Then, the features extracted from the most effective moment invariant technique are classified using the General Regression Neural Network (GRNN). There are two main stages involved in plant leaf identification. The first stage is known as feature extraction process where moment invariant methods are applied. The output of this process is a set of a global vector feature that represents …