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An expert botanical feature extraction technique based on phenetic features for identifying plant species

Kolivand, H, Mei Fern, B, Mohd Rahim, MS, Sulong, G, Baker, T and Tully, D (2018) An expert botanical feature extraction technique based on phenetic features for identifying plant species. PLoS ONE. ISSN 1932-6203

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Abstract

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

Item Type: Article
Additional Information: Kolivand H, Fern BM, Rahim MSM, Sulong G, Baker T, Tully D (2018) An expert botanical feature extraction technique based on phenetic features for identifying plant species. PLoS ONE 13(2): e0191447. https://doi.org/ 10.1371/journal.pone.0191447
Uncontrolled Keywords: MD Multidisciplinary
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QK Botany
Divisions: Computer Science
Publisher: Public Library of Science (PLoS)
Date Deposited: 15 Feb 2018 10:18
Last Modified: 15 Feb 2018 10:18
DOI or Identification number: 10.1371/journal.pone.0191447
URI: http://researchonline.ljmu.ac.uk/id/eprint/8034

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