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Pregled bibliografske jedinice broj: 1268595

ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application)


Hackenberger Kutuzović, Davorka; Hackenberger Kutuzović, Branimir; Hackenberger Kutuzović, Domagoj; Đerđ, Tamara
ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application) // Global soil biodiversity conference - book of abstracts
Dublin, Irska, 2023. 695, 1 (poster, međunarodna recenzija, sažetak, znanstveni)


CROSBI ID: 1268595 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application)

Autori
Hackenberger Kutuzović, Davorka ; Hackenberger Kutuzović, Branimir ; Hackenberger Kutuzović, Domagoj ; Đerđ, Tamara

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Global soil biodiversity conference - book of abstracts / - , 2023

Skup
3rd Global Soil Biodiversity Conference

Mjesto i datum
Dublin, Irska, 13.03.2023. - 15.03.2023

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
earthworm ; species identification ; machine learning ; application

Sažetak
Aim: Taxonomic species identification is an essential part of biodiversity research. Despite advances in computer technology, there is still a lack of user-friendly software tools for earthworm species identification and simultaneous storage of data on the location of findings. This research focused on developing a machine learning-based earthworm identification key and GIS platform (ErIK2), an upgraded derivative of the software-based identification key (ErIK) previously developed by our team. Method: The ErIK2 platform comprises earthworm species identification keys (tabular and dichotomous key) and a GIS component. The machine learning-driven tabular key allows species determination based on morphological and anatomical features of the individual, while the dichotomous key considers user choices from a series of couplets. The GIS component allows input and display of data stored in a dedicated geospatial database. Sampling time and location of various earthworm species can be entered following species identification within the ErIK2 platform or independently from the species identification process. Results: The ErIK2 platform allows identification of 58 earthworm species distributed throughout Europe and ten endemic species being present exclusively in Croatia. The functionalities of the platform have been tested and confirmed on different operating systems, web browsers and smartphones. The ErIK2 web application is hosted on http://earthworms.eu/erik2, while the ErIK2 Application can be downloaded from Google Play. Conclusions: ErIK2 is an easy-to-use software platform that has the potential to become a central tool in mapping earthworm fauna distribution across Europe both in scientific research and citizen science projects.

Izvorni jezik
Engleski

Znanstvena područja
Biologija



POVEZANOST RADA


Projekti:
VLASTITA-SREDSTVA-3105-19 - Utvrđivanje osobitosti faune gujavica (O)ligochaeta:Lumbricidae (Hrvatska) (Hackenberger Kutuzović, Davorka, VLASTITA-SREDSTVA ) ( CroRIS)

Ustanove:
Sveučilište u Osijeku - Odjel za biologiju


Citiraj ovu publikaciju:

Hackenberger Kutuzović, Davorka; Hackenberger Kutuzović, Branimir; Hackenberger Kutuzović, Domagoj; Đerđ, Tamara
ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application) // Global soil biodiversity conference - book of abstracts
Dublin, Irska, 2023. 695, 1 (poster, međunarodna recenzija, sažetak, znanstveni)
Hackenberger Kutuzović, D., Hackenberger Kutuzović, B., Hackenberger Kutuzović, D. & Đerđ, T. (2023) ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application). U: Global soil biodiversity conference - book of abstracts.
@article{article, author = {Hackenberger Kutuzovi\'{c}, Davorka and Hackenberger Kutuzovi\'{c}, Branimir and Hackenberger Kutuzovi\'{c}, Domagoj and \DJer\dj, Tamara}, year = {2023}, pages = {1}, chapter = {695}, keywords = {earthworm, species identification, machine learning, application}, title = {ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application)}, keyword = {earthworm, species identification, machine learning, application}, publisherplace = {Dublin, Irska}, chapternumber = {695} }
@article{article, author = {Hackenberger Kutuzovi\'{c}, Davorka and Hackenberger Kutuzovi\'{c}, Branimir and Hackenberger Kutuzovi\'{c}, Domagoj and \DJer\dj, Tamara}, year = {2023}, pages = {1}, chapter = {695}, keywords = {earthworm, species identification, machine learning, application}, title = {ErIK2 - Machine Learning-Based Earthworm Identification Key and GIS Platform (Web and Mobile Application)}, keyword = {earthworm, species identification, machine learning, application}, publisherplace = {Dublin, Irska}, chapternumber = {695} }




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