A Study of Selection Criteria for Constructing Identification Keys

Payne, R. W.; Dixon, TD

doi:10.1007/978-3-642-51883-6_20

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…A common approach to identify species relies on dichotomous keys (Pankhurst, 1975;Payne & Preece, 1980). Extensive computer applications and theory have been developed to generate keys (Osbourne, 1963;Dallwitz, 1974;Payne & Preece, 1980;Pankhurst, 1986;Payne & Thompson, 1989;Reynolds & al., 2003;Delta and Intkey: Dallwitz & al. 2000; Navikey: http://www.…”

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

“…The quality of the trait characterization provided by an ANN can also be incorporated into multiple-entry keys. Much work has focused on the ability to include information about quality of traits, polymorphic traits, error in trait characterization, and prior probabilities of species labeling (Dallwitz, 1974;Payne & Preece, 1980;Pankhurst, 1986;Payne & Thompson, 1989;Reynolds & al., 2003), but these considerations were not included in the derivation of equation [7], which relates accuracy to number of characters used. Errors in trait assessment and identification using keys can be quite large in practical situations (Fermanian & al., 1989), and when character states are assessed incorrectly in the field or misrepresented in the database, the identification will fail no matter how many characters are considered.…”

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

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Shape analysis for the automated identification of plants from images of leaves

Hearn

2009

TAXON

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Species identification is a necessary component of most studies of biological diversity, and computational approaches are beginning to automate it. In particular, leaves of plants provide taxon‐specific information that has successfully been applied to plant identification. Prior studies have not investigated the number of leaves or the resolution of the digitized leaf image required to represent a species’ shape. Moreover, the relationship between accuracy and the size of the leaf shape database, and methods to integrate automated approaches with more traditional dichotomous keys have yet to be explored. Here, I use a database of 2,420 leaves from 151 species to address these issues. Using distance metrics derived from Fourier and Procrustes analyses, it is found that a minimum of 10 leaves of each species, 100 margin points, and 10 Fourier harmonics are required to accurately represent leaf shape of a species. These results are used to assess the success of species identification from images of leaves: 72% for all 151 species. The tight relationship between database size and accuracy is then used in conjunction with results from probability theory to predict accuracy of species identification when dichotomous multiple‐entry keys and combined Fourier and Procrustes analysis are used together. Combining these two approaches to identification can greatly improve identification accuracy. Open‐source software is available to implement the automated distance‐based approach.

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Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

Section: Integration With Multiple-entry Dichotomous Keysmentioning

confidence: 99%

Shape analysis for the automated identification of plants from images of leaves

Hearn

2009

TAXON

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Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification

Reynolds

Dicks

Roberts

et al. 2003

Lecture Notes in Computer Science

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Algorithms for the automated creation of low cost identification keys are described and theoretical and empirical justifications are provided. The algorithms are shown to handle differing test costs, prior probabilities for each potential diagnosis and tests that produce uncertain results. The approach is then extended to cover situations where more than one measure of cost is of importance, by allowing tests to be performed in batches. Experiments are performed on a real-world case study involving the identification of yeasts

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Identification Keys, Diagnostic Tables and Expert Systems

Payne

1988

Compstat

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A Study of Selection Criteria for Constructing Identification Keys

Cited by 5 publications

References 6 publications

Shape analysis for the automated identification of plants from images of leaves

Shape analysis for the automated identification of plants from images of leaves

Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification

Identification Keys, Diagnostic Tables and Expert Systems

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