Leaf recognition for accurate plant classification.
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Date
2017
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Abstract
Plants are the most important living organisms on our planet because they are
sources of energy and protect our planet against global warming. Botanists were
the first scientist to design techniques for plant species recognition using leaves. Although
many techniques for plant recognition using leaf images have been proposed
in the literature, the precision and the quality of feature descriptors for shape, texture,
and color remain the major challenges. This thesis investigates the precision
of geometric shape features extraction and improved the determination of the Minimum
Bounding Rectangle (MBR). The comparison of the proposed improved MBR
determination method to Chaudhuri's method is performed using Mean Absolute
Error (MAE) generated by each method on each edge point of the MBR. On the
top left point of the determined MBR, Chaudhuri's method has the MAE value of
26.37 and the proposed method has the MAE value of 8.14.
This thesis also investigates the use of the Convexity Measure of Polygons for the
characterization of the degree of convexity of a given leaf shape. Promising results
are obtained when using the Convexity Measure of Polygons combined with other
geometric features to characterize leave images, and a classification rate of 92% was
obtained with a Multilayer Perceptron Neural Network classifier. After observing
the limitations of the Convexity Measure of Polygons, a new shape feature called
Convexity Moments of Polygons is presented in this thesis. This new feature has
the invariant properties of the Convexity Measure of Polygons, but is more precise
because it uses more than one value to characterize the degree of convexity of a
given shape. Promising results are obtained when using the Convexity Moments
of Polygons combined with other geometric features to characterize the leaf images
and a classification rate of 95% was obtained with the Multilayer Perceptron Neural
Network classifier.
Leaf boundaries carry valuable information that can be used to distinguish between
plant species. In this thesis, a new boundary-based shape characterization
method called Sinuosity Coefficients is proposed. This method has been used in
many fields of science like Geography to describe rivers meandering. The Sinuosity
Coefficients is scale and translation invariant. Promising results are obtained when
using Sinuosity Coefficients combined with other geometric features to characterize
the leaf images, a classification rate of 80% was obtained with the Multilayer
Perceptron Neural Network classifier.
Finally, this thesis implements a model for plant classification using leaf images,
where an input leaf image is described using the Convexity Moments, the Sinuosity
Coefficients and the geometric features to generate a feature vector for the recognition
of plant species using a Radial Basis Neural Network. With the model designed
and implemented the overall classification rate of 97% was obtained.
Description
Doctor of Philosophy in Computer Science, University of KwaZulu-Natal, Durban 2017.
Keywords
Theses -- Computer science.