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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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Detalles Bibliográficos
Autores principales: Ong, Song-Quan, Hamid, Suhaila Ab.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803097/
https://www.ncbi.nlm.nih.gov/pubmed/36584101
http://dx.doi.org/10.1371/journal.pone.0279094
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author Ong, Song-Quan
Hamid, Suhaila Ab.
author_facet Ong, Song-Quan
Hamid, Suhaila Ab.
author_sort Ong, Song-Quan
collection PubMed
description 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, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects—order, family, and genus—and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.
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spelling pubmed-98030972022-12-31 Next generation insect taxonomic classification by comparing different deep learning algorithms Ong, Song-Quan Hamid, Suhaila Ab. PLoS One Research Article 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, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects—order, family, and genus—and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification. Public Library of Science 2022-12-30 /pmc/articles/PMC9803097/ /pubmed/36584101 http://dx.doi.org/10.1371/journal.pone.0279094 Text en © 2022 Ong, Hamid https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ong, Song-Quan
Hamid, Suhaila Ab.
Next generation insect taxonomic classification by comparing different deep learning algorithms
title Next generation insect taxonomic classification by comparing different deep learning algorithms
title_full Next generation insect taxonomic classification by comparing different deep learning algorithms
title_fullStr Next generation insect taxonomic classification by comparing different deep learning algorithms
title_full_unstemmed Next generation insect taxonomic classification by comparing different deep learning algorithms
title_short Next generation insect taxonomic classification by comparing different deep learning algorithms
title_sort next generation insect taxonomic classification by comparing different deep learning algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803097/
https://www.ncbi.nlm.nih.gov/pubmed/36584101
http://dx.doi.org/10.1371/journal.pone.0279094
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