Classification of plant leaves using Morphological features and Zernike moments

Plants are an integral part of our ecosystems. Their identification and classification has always been a matter of interest for the botanists as well as for the laymen. With around 3 million recognized plant species, only a tiny part of the plants is known. The leaves of the plant carry a lot of information about the plant species. These features are extracted and are used as a basis for an automated identification and classification. The advancement in image processing has made this a quick and easy process. In the proposed work, Morphological features and Zernike moments are used to identify and classify the plant leaves. The features extracted are independent of leaf growth and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images. Then, a testing set of images is used for verifying the classification algorithms. The output displays the leaf classified into its particular class.