Assessing the potential for deep learning and computer vision to identify bumble bee species from images

Abstract Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this...

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Autores principales:	Brian J. Spiesman, Claudio Gratton, Richard G. Hatfield, William H. Hsu, Sarina Jepsen, Brian McCornack, Krushi Patel, Guanghui Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b66770b97f5443319c585fe634311ce8
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spelling	oai:doaj.org-article:b66770b97f5443319c585fe634311ce82021-12-02T18:15:09ZAssessing the potential for deep learning and computer vision to identify bumble bee species from images10.1038/s41598-021-87210-12045-2322https://doaj.org/article/b66770b97f5443319c585fe634311ce82021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87210-1https://doaj.org/toc/2045-2322Abstract Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.Brian J. SpiesmanClaudio GrattonRichard G. HatfieldWilliam H. HsuSarina JepsenBrian McCornackKrushi PatelGuanghui WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brian J. Spiesman Claudio Gratton Richard G. Hatfield William H. Hsu Sarina Jepsen Brian McCornack Krushi Patel Guanghui Wang Assessing the potential for deep learning and computer vision to identify bumble bee species from images
description	Abstract Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification. We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). Species-level error rates were generally smaller for species represented by more training images. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.
format	article
author	Brian J. Spiesman Claudio Gratton Richard G. Hatfield William H. Hsu Sarina Jepsen Brian McCornack Krushi Patel Guanghui Wang
author_facet	Brian J. Spiesman Claudio Gratton Richard G. Hatfield William H. Hsu Sarina Jepsen Brian McCornack Krushi Patel Guanghui Wang
author_sort	Brian J. Spiesman
title	Assessing the potential for deep learning and computer vision to identify bumble bee species from images
title_short	Assessing the potential for deep learning and computer vision to identify bumble bee species from images
title_full	Assessing the potential for deep learning and computer vision to identify bumble bee species from images
title_fullStr	Assessing the potential for deep learning and computer vision to identify bumble bee species from images
title_full_unstemmed	Assessing the potential for deep learning and computer vision to identify bumble bee species from images
title_sort	assessing the potential for deep learning and computer vision to identify bumble bee species from images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b66770b97f5443319c585fe634311ce8
work_keys_str_mv	AT brianjspiesman assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT claudiogratton assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT richardghatfield assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT williamhhsu assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT sarinajepsen assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT brianmccornack assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT krushipatel assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages AT guanghuiwang assessingthepotentialfordeeplearningandcomputervisiontoidentifybumblebeespeciesfromimages
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Assessing the potential for deep learning and computer vision to identify bumble bee species from images

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