Plant leaf identification using moment invariants & General Regression Neural Network

Abstract

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 the shape of the leaf images. It is shown that TMI can extract vector feature with Percentage of Absolute Error (PAE) less than 10.38 percent. Therefore, TMI vector feature will be the input to the second stage. The second stage involves classification of leaf images based on the derived feature gained in the previous stage. It is found that the feature vectors enabled the GRNN classifier to achieve 100 percent classification rate. Thus, the finding from this study can provide useful information for developing automated plant classification tools.