Computational methods for the classification of plants

Cope, James S (2014) Computational methods for the classification of plants. (PhD thesis), Kingston University, .

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Abstract

Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The current availability of botanists is increasingly failing to meet the growing demands for their expertise. These demands range from amateurs desiring help in identifying plants, to agricultural applications such as automated weeding systems, and to the cataloguing of biodiversity for conservational purposes. This thesis aims to help fill this gap, by exploring computational techniques for the automated analysis and classification of plants from images of their leaves. The main objective is to provide novel techniques and the required frame¬work for a robust, automated plant identification system. This involves firstly the accurate extraction of different features of the leaf and the generation of appropriate descriptors. One of the biggest challenges involved in working with plants is the high amounts of variation that may occur within a species, and high similarity that exists between some species. Algorithms are introduced which aim to allow accurate classification in spite of this. With many features of the leaf being available for use in classification, a suitable framework is required for combining them. An efficient method is proposed which selects on a leaf-by-leaf basis which of the leaf features are most likely to be of use. This decreases computational costs whilst increasing accuracy, by ignoring unsuitable features. Finally a study is carried out looking at how professional botanists view leaf images. Much can be learnt from the behaviour of experts which can be applied to the task at hand. Eye-tracking technology is used to establish the difference between how botanists and non-botanists view leaf images, and preliminary work is performed towards utilizing this information in an automated system.

Item Type: Thesis (PhD)
Physical Location: This item is held in stock at Kingston University library.
Research Area: Biological sciences
Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
Depositing User: Niki Wilson
Date Deposited: 21 Jul 2014 08:23
Last Modified: 06 Nov 2018 11:59
URI: http://eprints.kingston.ac.uk/id/eprint/28759

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