Species‐level image classification with convolutional neural network enables insect identification from habitus images

Abstract Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground‐dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here...

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Autores principales:	Oskar L. P. Hansen, Jens‐Christian Svenning, Kent Olsen, Steen Dupont, Beulah H. Garner, Alexandros Iosifidis, Benjamin W. Price, Toke T. Høye
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2020
Materias:	arthropod sampling automatic species identification camera trap entomological collection image classification image database Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/c01d4d02cbea445f9f5264d13dbac9e5
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spelling	oai:doaj.org-article:c01d4d02cbea445f9f5264d13dbac9e52021-11-04T13:06:09ZSpecies‐level image classification with convolutional neural network enables insect identification from habitus images2045-775810.1002/ece3.5921https://doaj.org/article/c01d4d02cbea445f9f5264d13dbac9e52020-01-01T00:00:00Zhttps://doi.org/10.1002/ece3.5921https://doaj.org/toc/2045-7758Abstract Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground‐dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine‐tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade‐off between classification accuracy, precision, and recall and taxonomic resolution. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. Fine‐tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species. Together, species‐level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.Oskar L. P. HansenJens‐Christian SvenningKent OlsenSteen DupontBeulah H. GarnerAlexandros IosifidisBenjamin W. PriceToke T. HøyeWileyarticlearthropod samplingautomatic species identificationcamera trapentomological collectionimage classificationimage databaseEcologyQH540-549.5ENEcology and Evolution, Vol 10, Iss 2, Pp 737-747 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	arthropod sampling automatic species identification camera trap entomological collection image classification image database Ecology QH540-549.5
spellingShingle	arthropod sampling automatic species identification camera trap entomological collection image classification image database Ecology QH540-549.5 Oskar L. P. Hansen Jens‐Christian Svenning Kent Olsen Steen Dupont Beulah H. Garner Alexandros Iosifidis Benjamin W. Price Toke T. Høye Species‐level image classification with convolutional neural network enables insect identification from habitus images
description	Abstract Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground‐dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine‐tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade‐off between classification accuracy, precision, and recall and taxonomic resolution. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. Fine‐tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species. Together, species‐level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.
format	article
author	Oskar L. P. Hansen Jens‐Christian Svenning Kent Olsen Steen Dupont Beulah H. Garner Alexandros Iosifidis Benjamin W. Price Toke T. Høye
author_facet	Oskar L. P. Hansen Jens‐Christian Svenning Kent Olsen Steen Dupont Beulah H. Garner Alexandros Iosifidis Benjamin W. Price Toke T. Høye
author_sort	Oskar L. P. Hansen
title	Species‐level image classification with convolutional neural network enables insect identification from habitus images
title_short	Species‐level image classification with convolutional neural network enables insect identification from habitus images
title_full	Species‐level image classification with convolutional neural network enables insect identification from habitus images
title_fullStr	Species‐level image classification with convolutional neural network enables insect identification from habitus images
title_full_unstemmed	Species‐level image classification with convolutional neural network enables insect identification from habitus images
title_sort	species‐level image classification with convolutional neural network enables insect identification from habitus images
publisher	Wiley
publishDate	2020
url	https://doaj.org/article/c01d4d02cbea445f9f5264d13dbac9e5
work_keys_str_mv	AT oskarlphansen specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT jenschristiansvenning specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT kentolsen specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT steendupont specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT beulahhgarner specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT alexandrosiosifidis specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT benjaminwprice specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages AT tokethøye specieslevelimageclassificationwithconvolutionalneuralnetworkenablesinsectidentificationfromhabitusimages
_version_	1718444926219321344

Species‐level image classification with convolutional neural network enables insect identification from habitus images

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