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Andleeb S, Abbasi WA, Ghulam Mustafa R, Islam GU, Naseer A, Shafique I, Parween A, Shaheen B, Shafiq M, Altaf M, Ali Abbas S. ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy. PLoS One 2021; 16:e0255674. [PMID: 34529673 PMCID: PMC8445633 DOI: 10.1371/journal.pone.0255674] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/21/2021] [Indexed: 11/19/2022] Open
Abstract
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.
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Affiliation(s)
- Saiqa Andleeb
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Wajid Arshad Abbasi
- Computaional Biology and Data Analysis Laboratory, Department of Computer Sciences & Information Technology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Rozina Ghulam Mustafa
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Ghafoor ul Islam
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Anum Naseer
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Irsa Shafique
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Asma Parween
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Bushra Shaheen
- Biotechnology Laboratory, Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Muhamad Shafiq
- Environmental Protection Agency (AJK-EPA), Government of Azad Jammu and Kashmir, Muzaffarabad, AJ&K, Pakistan
| | - Muhammad Altaf
- Department of Forestry Range and Wildlife Management, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Syed Ali Abbas
- Computaional Biology and Data Analysis Laboratory, Department of Computer Sciences & Information Technology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, Pakistan
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