Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617369
Title: Computational methods for the classification of plants
Author: Cope, James S.
ISNI:       0000 0004 5350 3971
Awarding Body: Kingston University
Current Institution: Kingston University
Date of Award: 2014
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.617369  DOI: Not available
Keywords: Biological sciences ; Computer science and informatics
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