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.
Highlights
One of the imperative steps to preserve and conserve the biological diversity is to automatically recognize, understand, and identify them
This section carries out the detection of external leaf structure, and includes the detection of local maxima, local minima, leaf boundary, apex, base, margin, and venation
The characteristic of each plant species are described based on the information of the well-established Electronic Data information Source (EDIS) that operates since 2003
Summary
One of the imperative steps to preserve and conserve the biological diversity is to automatically recognize, understand, and identify them. Plants are classified and catalogued based on the plant taxonomy method in a manual manner using a human operator. This method relies heavily on a professional botanist, which is time consuming, tedious, cumbersome, high cost, and a potential error prone task. The sharply development in computer technology in recent decades provide a potential opportunity to digitize and computerize the plant identification methodology.
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