Time to automate identification

MacLeod, Norman; Benfield, Mark C.; Culverhouse, Phil F.

doi:10.1038/467154a

Cited by 232 publications

(188 citation statements)

References 6 publications

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“…Typically, palynologists examine by eye the sizes and shapes of attributes that are thought to have taxonomic significance, such as the sculptural elements on the sporomorph surface, and then use these attributes to qualitatively compare unknown specimens to classified reference material. Experiments designed to measure the consistency of classifications undertaken in this way raise doubts about the ability of human analysts to consistently repeat such classifications over the course of an entire study that may encompass tens of thousands of specimens (e.g., Culverhouse 2007;MacLeod et al 2010;Mander et al 2014). For example, seven human subjects classified 120 SEM images of 12 grass pollen species with 68%-82% accuracy (10 images per species), but the classification schemes of each subject were very different from one another, and consistency between subjects was just 28% (Mander et al 2013).…”

Section: Computational Image Analyses and Machine Learningmentioning

confidence: 99%

“…We suspect that expert palynologists would fare well in such a test and, by extension, that they generate consistent classifications at the genus or family level in Quaternary time. However, as testing suggests (Mander et al 2013(Mander et al , 2014, we have little doubt that palynologists struggle to generate species-level classifications of problematic groups that are both self-consistent and consistent between workers, and this view has led to calls for an algorithmic approach to classification that employs computational image analyses and machine learning to quantify small differences between morphologically similar species (MacLeod 2010;MacLeod et al 2010;Punyasena et al 2012;Mander et al 2013). …”

Section: Computational Image Analyses and Machine Learningmentioning

confidence: 99%

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On the Taxonomic Resolution of Pollen and Spore Records of Earth’s Vegetation

Mander

Punyasena

2014

International Journal of Plant Sciences

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Premise of research. Pollen and spores (sporomorphs) are a valuable record of plant life and have provided information on subjects ranging from the nature and timing of evolutionary events to the relationship between vegetation and climate. However, sporomorphs can be morphologically similar at the species, genus, or family level. Studies of extinct plant groups in pre-Quaternary time often include dispersed sporomorph taxa whose parent plant is known only to the class level. Consequently, sporomorph records of vegetation suffer from limited taxonomic resolution and typically record information about plant life at a taxonomic rank above species.Methodology. In this article, we review the causes of low taxonomic resolution, highlight examples where this has hampered the study of vegetation, and discuss the strategies researchers have developed to overcome the low taxonomic resolution of the sporomorph record. Based on this review, we offer our views on how greater taxonomic precision might be attained in future work.Pivotal results. Low taxonomic resolution results from a combination of several factors, including inadequate reference collections, the absence of sporomorphs in situ in fossilized reproductive structures, and damage following fossilization. A primary cause is the difficulty of accurately describing the very small morphological differences between species using descriptive terminology, which results in palynologists classifying sporomorphs conservatively at the genus or family level to ensure that classifications are reproducible between samples and between researchers.Conclusions. In our view, the most promising approach to the problem of low taxonomic resolution is a combination of high-resolution imaging and computational image analysis. In particular, we encourage palynologists to explore the utility of microscopy techniques that aim to recover morphological information from below the diffraction limit of light and to employ computational image analyses to consistently quantify small morphological differences between species.

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Section: Computational Image Analyses and Machine Learningmentioning

confidence: 99%

Section: Computational Image Analyses and Machine Learningmentioning

confidence: 99%

On the Taxonomic Resolution of Pollen and Spore Records of Earth’s Vegetation

Mander

Punyasena

2014

International Journal of Plant Sciences

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“…Rapid and precise identifications are important for society as a whole. Computer-based automated species recognition has therefore been suggested as a potential technology to aid in the rapid identification of species, particularly taxa that form part of routine investigations (MacLeod et al 2010).…”

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confidence: 99%

“…For others more experience is required, and in some cases inconsistent identification can be over 40% (MacLeod et al 2010). To reduce such errors we rely on expert opinion for the verification of a taxon's identity.…”

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Species differentiation of slipper orchids using color image analysis

2013

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abstraCt. A number of automated species recognition systems have been developed recently to aid nonprofessionals in the identification of taxa. These systems have primarily used geometric morphometric based techniques, however issues surround their wider applicability due to the need for homologous landmarks. Here we investigate the use of color to discriminate species using the two horticulturally important slipper orchid genera of Paphiopedilum and Phragmipedium as model systems. The ability to differentiate the various taxonomic groups varied, depending on the size of the group, diversity of colors within the group, and the background of the image. In this study the image analysis was conducted with images of single flowers of the species, however since flowers are ephemeral, flowering for a relatively short period of time, such analysis should be extended to vegetative parts, particularly as this is the form in which they are most often traded internationally.rEsumEn. Una gran cantidad de sistemas de reconocimiento automático de especies se han desarrollado en los últimos años, como ayuda a aquellas personas que no son especialistas en la identificación de especies. Estos sistemas han utilizado sistemas de reconocimiento automático basados en geometría morfométrica, sin embargo existen límites debido a la necesidad de encontrar puntos de georreferenciación en los diferentes organismos. En este artículo investigamos el uso de los colores para diferenciar especies en los géneros Paphiopedilum y Phragmipedium, ambos con gran importancia en la horticultura. La capacidad de discriminación varía entre los grupos taxonómicos, dependiendo del tamaño del taxón, la variedad de colores entre las especies y el fondo de las imágenes. En este estudio el análisis de imágenes se ha llevado a cabo con fotografías de flores individuales. No obstante dado que las flores son órganos efímeros, en el futuro esta investigación incluirá partes vegetativas, ya que es en estado vegetativo la forma en la que se suele comerciar internacionalmente más a menudo.

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Phenotypes in Insect Biodiversity Research

István

2018

Insect Biodiversity

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Time to automate identification

Cited by 232 publications

References 6 publications

On the Taxonomic Resolution of Pollen and Spore Records of Earth’s Vegetation

On the Taxonomic Resolution of Pollen and Spore Records of Earth’s Vegetation

Species differentiation of slipper orchids using color image analysis

Phenotypes in Insect Biodiversity Research

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