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DOI | 10.1073/PNAS.2002545117 |
Deep learning and computer vision will transform entomology | |
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. | |
发表日期 | 2021 |
ISSN | 00278424 |
卷号 | 118期号:2 |
英文摘要 | 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. © 2021 National Academy of Sciences. All rights reserved. |
英文关键词 | Automated monitoring; Ecology; Image-based identification; Insects; Machine learning |
语种 | 英语 |
来源期刊 | Proceedings of the National Academy of Sciences of the United States of America |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/181108 |
作者单位 | Department of Bioscience, Aarhus University, Rønde, DK-8410, Denmark; Arctic Research Centre, Aarhus University, Rønde, DK-8410, Denmark; Unit of Computing Sciences, Tampere University, Tampere, FI-33720, Finland; School of Engineering, Aarhus University, Aarhus N, DK-8200, Denmark; Natural History Museum Aarhus, Aarhus C, DK-8000, Denmark; Department of Biology, Center for Biodiversity Dynamics in a Changing World, Aarhus University, Aarhus C, DK-8000, Denmark; Department of Biology-Ecoinformatics and Biodiversity, Aarhus University, Aarhus C, DK-8000, Denmark; Department of Engineering, Aarhus University, Aarhus, DK-8200, Denmark; Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, 45141, Germany; Programme for Environmental Information, Finnish Environment Institute, Jyväskylä, FI-40500, Finland |
推荐引用方式 GB/T 7714 | Høye T.T.,Ärje J.,Bjerge K.,et al. Deep learning and computer vision will transform entomology[J],2021,118(2). |
APA | Høye T.T..,Ärje J..,Bjerge K..,Hansen O.L.P..,Iosifidis A..,...&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). |
MLA | Høye T.T.,et al."Deep learning and computer vision will transform entomology".Proceedings of the National Academy of Sciences of the United States of America 118.2(2021). |
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