Automated plant classification system using a hybrid of shape and color features of the leaf
Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. The last few decades have witnessed various approaches to implement plant classification systems. Several approaches h...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/67103/1/FK%202016%20128%20IR.pdf |
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| Summary: | Automated plant leaf classification is a computerized approach that employs
computer vision and machine learning algorithms to identify a plant based on
the features of its leaf. The last few decades have witnessed various approaches
to implement plant classification systems. Several approaches have been
proposed using different features and classifiers. However, the majority of the
existing methods either rely on large numbers of training samples or select
certain leaves within a dataset to achieve high accuracy rates. The disadvantage
of such practices is that the results may not reflect the actual expressiveness of
the features to tackle the high interclass similarity among different species.
Furthermore, most of the existing systems rely on human intervention to select
certain points of the leaf to help the system align the leaf or to select the best
result among a few candidates after the classification is done.
An Automated Plant Classification System (APCS) is introduced in this thesis to
overcome the aforementioned limitations by proposing an automated alignment
algorithm to eliminate the need for human intervention to align the leaf. A new
set of Quartile Features (QF) is also proposed to express the partial shape of the
leaf. Furthermore, optimizing the performance is also targeted in this research
by integrating the proposed Quartile Features with the most discriminant shape
and color features in the literature, in order to select the optimal feature vector
for the proposed system. The proposed automated alignment algorithm is based
on a similarity measure between the vertical and horizontal halves of the leaf.
Once the leaf is aligned, the image is sliced into horizontal and vertical quartiles,
and the area of each quartile is calculated to extract the proposed Quartile
Features.
To optimize the performance and select the final features for the proposed
system, Quartile Features and the other categories of shape and color features
investigated in this research have been tested and evaluated individually and in combinations. The most discriminant features in each category are then
combined to form the final feature vector input to the classifier. A Nearest
Neighbor classifier (1-NN) is used to compute the similarity of a query leaf
image with all the samples in the database by calculating the distance between
their respective feature vectors.
The experiments in this research have been conducted using two leaf datasets.
The first is Flavia dataset which has been used as a benchmark by several
researchers in the field of plant recognition. The second dataset is collected by
the author, from Putrajaya and Perdana Botanical gardens, containing a total of
396 leaves from 17 species endemic to Malaysia and Tropical Asia. The
experimental results and comparisons indicate the efficiency of the proposed
automated alignment algorithm and the proposed Quartile Features. The results
of using the final selected features have shown an impressive performance,
achieving an average accuracy rate of 98.32% for Flavia dataset and 91.29% for
Leaves dataset, using k-fold cross-validation. |
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