Next generation insect taxonomic classification by comparing different deep learning algorithms
Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently,...
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| Format: | Article |
| Language: | English English |
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Plos One
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/35440/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/35440/2/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/35440/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279094 https://doi.org/10.1371/journal.pone.0279094 https://doi.org/10.1371/journal.pone.0279094 |
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https://eprints.ums.edu.my/id/eprint/35440/1/ABSTRACT.pdfhttps://eprints.ums.edu.my/id/eprint/35440/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/35440/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279094
https://doi.org/10.1371/journal.pone.0279094
https://doi.org/10.1371/journal.pone.0279094