Gate to Computer Vision and Pattern Recognition (gtCVPR) – Volume 1 Number 1 – 2015

Abstract

Leaf identification is a challenging research. So far, many approaches have been proposed. In this paper, an approach that combines Fourier descriptors with other shape features was investigated to identify 100 hundred kinds of leaves. The result shows that the combination of Fourier descriptors and several other shape features can be used to identify leaves with the accuracy rate of 88%. This result indicates that this approach is a promising way for identifying leaves.

Abstract

Plants play one of the main roles in our ecosystem. Manual identification for the leaves sometimes leads to greater difference due to look-alike. Hence authentications of leaves are much required for medicinal purposes. The aim of this work is to form the database in order to classify and authenticate tulsi leaf for the purpose of herbal medicines. This paper aims in developing the database with 50 samples of tulsi under various conditions for classification. After forming the database the next step is to compare it with the test leaf to identify the closest match.

Abstract

In this research paper, comparison of edge detection methods is presented in leaf shape analysis for plant classification. Leaf is a very significant component of plant species that identify and classify the plants. Plant classification is the task performed by trained botanists and taxonomists. This task includes performing a set of operations. Because of this the task of classification of plants manually is time consuming. There are many biometric features of the leaves of the plants for classification. In this paper, Sobel and Canny edge detection methods are compared for their performance in leaf shape analysis operation for plant classification.