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Erschienen in:
2017 | OriginalPaper | Buchkapitel
4. Machine Learning for Plant Leaf Analysis
verfasst von : Paolo Remagnino, Simon Mayo, Paul Wilkin, James Cope, Don Kirkup
Erschienen in: Computational Botany
Verlag: Springer Berlin Heidelberg
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Abstract
One of the key challenges for automated analysis of plant leaves lies in the range of variation presented by a species and even by a single plant (Fig. 2.1). As well as the natural variation to be expected from any organic object, the variation of a leaf can arise from a number of sources, for example, its age and developmental stage. Shape varies during development, with early growth phases occurring primarily length-wise and increase in width coming later. In some species with lobed leaves, the leaf lobes are not apparent until after a certain stage in development. In others, like many Eucalyptus taxa, the leaves of young shoots are morphologically very distinct from those of mature ones. Margin characteristics such as teeth may not develop until the leaf has reached full size, often appearing first near the apex and then gradually extending further back towards the insertion point. Pigmentation often changes as the leaf develops.