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. As inherent trait, leaf definitely contains important information for plant species identification despite its complexity. The objective of this research is to identify the effectiveness of three moment invariant methods, namely Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI) and Tchebichef Moment Invariant (TMI) to extract features from plant leaf images. Then, the resulting set of features representing the leaf images are classified using General Regression Neural Network (GRNN) for recognition purposes. 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 second stage. The second stage involves classification of leaf images based on the derived feature gained in the previous stage. It is found that GRNN classifier produces 100 percent classification rate with average computational time of 0.47 seconds.