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Kim Bjerge

Deep learning and computer vision will transform entomology

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Standard

Deep learning and computer vision will transform entomology. / Høye, Toke Thomas; Ärje, Johanna; Bjerge, Kim; Hansen, Oskar Liset Pryds; Iosifidis, Alexandros; Leese, Florian; Mann, Hjalte Mads Rosenstand; Meissner, Kristian ; Melvad, Claus; Raitoharju, Jenni.

I: Proceedings of the National Academy of Sciences of the United States of America, Bind 118, Nr. 2, e2002545117, 01.2021.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Høye, TT, Ärje, J, Bjerge, K, Hansen, OLP, Iosifidis, A, Leese, F, Mann, HMR, Meissner, K, Melvad, C & Raitoharju, J 2021, 'Deep learning and computer vision will transform entomology', Proceedings of the National Academy of Sciences of the United States of America, bind 118, nr. 2, e2002545117. https://doi.org/10.1073/pnas.2002545117

APA

Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C., & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America, 118(2), [e2002545117]. https://doi.org/10.1073/pnas.2002545117

CBE

Høye TT, Ärje J, Bjerge K, Hansen OLP, Iosifidis A, Leese F, Mann HMR, Meissner K, Melvad C, Raitoharju J. 2021. Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America. 118(2):Article e2002545117. https://doi.org/10.1073/pnas.2002545117

MLA

Høye, Toke Thomas o.a.. "Deep learning and computer vision will transform entomology". Proceedings of the National Academy of Sciences of the United States of America. 2021. 118(2). https://doi.org/10.1073/pnas.2002545117

Vancouver

Høye TT, Ärje J, Bjerge K, Hansen OLP, Iosifidis A, Leese F o.a. Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America. 2021 jan;118(2). e2002545117. https://doi.org/10.1073/pnas.2002545117

Author

Høye, Toke Thomas ; Ärje, Johanna ; Bjerge, Kim ; Hansen, Oskar Liset Pryds ; Iosifidis, Alexandros ; Leese, Florian ; Mann, Hjalte Mads Rosenstand ; Meissner, Kristian ; Melvad, Claus ; Raitoharju, Jenni. / Deep learning and computer vision will transform entomology. I: Proceedings of the National Academy of Sciences of the United States of America. 2021 ; Bind 118, Nr. 2.

Bibtex

@article{894f935b7f4b4b1984984107cab9aff4,
title = "Deep learning and computer vision will transform entomology",
abstract = "Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.",
author = "H{\o}ye, {Toke Thomas} and Johanna {\"A}rje and Kim Bjerge and Hansen, {Oskar Liset Pryds} and Alexandros Iosifidis and Florian Leese and Mann, {Hjalte Mads Rosenstand} and Kristian Meissner and Claus Melvad and Jenni Raitoharju",
year = "2021",
month = jan,
doi = "10.1073/pnas.2002545117",
language = "English",
volume = "118",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "The National Academy of Sciences of the United States of America",
number = "2",

}

RIS

TY - JOUR

T1 - Deep learning and computer vision will transform entomology

AU - Høye, Toke Thomas

AU - Ärje, Johanna

AU - Bjerge, Kim

AU - Hansen, Oskar Liset Pryds

AU - Iosifidis, Alexandros

AU - Leese, Florian

AU - Mann, Hjalte Mads Rosenstand

AU - Meissner, Kristian

AU - Melvad, Claus

AU - Raitoharju, Jenni

PY - 2021/1

Y1 - 2021/1

N2 - Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

AB - Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

U2 - 10.1073/pnas.2002545117

DO - 10.1073/pnas.2002545117

M3 - Journal article

C2 - 33431561

VL - 118

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 2

M1 - e2002545117

ER -