ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species....

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Autores principales:	Saiqa Andleeb, Wajid Arshad Abbasi, Rozina Ghulam Mustafa, Ghafoor Ul Islam, Anum Naseer, Irsa Shafique, Asma Parween, Bushra Shaheen, Muhamad Shafiq, Muhammad Altaf, Syed Ali Abbas
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6c50d9ae423d43e0bedd7efe0f9d92ae
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id	oai:doaj.org-article:6c50d9ae423d43e0bedd7efe0f9d92ae
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spelling	oai:doaj.org-article:6c50d9ae423d43e0bedd7efe0f9d92ae2021-12-02T20:08:13ZESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.1932-620310.1371/journal.pone.0255674https://doaj.org/article/6c50d9ae423d43e0bedd7efe0f9d92ae2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255674https://doaj.org/toc/1932-6203Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.Saiqa AndleebWajid Arshad AbbasiRozina Ghulam MustafaGhafoor Ul IslamAnum NaseerIrsa ShafiqueAsma ParweenBushra ShaheenMuhamad ShafiqMuhammad AltafSyed Ali AbbasPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0255674 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saiqa Andleeb Wajid Arshad Abbasi Rozina Ghulam Mustafa Ghafoor Ul Islam Anum Naseer Irsa Shafique Asma Parween Bushra Shaheen Muhamad Shafiq Muhammad Altaf Syed Ali Abbas ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
description	Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.
format	article
author	Saiqa Andleeb Wajid Arshad Abbasi Rozina Ghulam Mustafa Ghafoor Ul Islam Anum Naseer Irsa Shafique Asma Parween Bushra Shaheen Muhamad Shafiq Muhammad Altaf Syed Ali Abbas
author_facet	Saiqa Andleeb Wajid Arshad Abbasi Rozina Ghulam Mustafa Ghafoor Ul Islam Anum Naseer Irsa Shafique Asma Parween Bushra Shaheen Muhamad Shafiq Muhammad Altaf Syed Ali Abbas
author_sort	Saiqa Andleeb
title	ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
title_short	ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
title_full	ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
title_fullStr	ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
title_full_unstemmed	ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.
title_sort	eside: a computationally intelligent method to identify earthworm species (e. fetida) from digital images: application in taxonomy.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6c50d9ae423d43e0bedd7efe0f9d92ae
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ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

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