Toward new tools for biodiversity studies: the use of portable near-infrared spectroscopy combined with machine learning to identify species of Decapoda
Fabrício Lopes Carvalho
A * , Wendel Resende Ramos Novais B , Ana Carla Soares-Silva C , Douglas William Menezes Flores D and Robson da Silva Magalhães E
+ Author Affiliations
- Author Affiliations
A Universidade Federal do Sul da Bahia (UFSB), Centro de Formação em Ciências Agroflorestais (CFCAf), Grupo de Pesquisa em Carcinologia e Biodiversidade Aquática (GPCBio), Cepec, Rodovia Ilhéus-Itabuna, km 22, Ilhéus, Bahia 45662-200, Brazil.
B Universidade Estadual de Santa Cruz (UESC), Programa de Pós-Graduação em Ecologia e Conservação da Biodiversidade (PPGECO), Campus Soane Nazaré de Andrade, Rodovia Ilhéus-Itabuna, km 16 – Salobrinho, Ilhéus, Bahia 45662-900, Brazil. Email: wrrnovais@uesc.br
C UESC, Programa de Pós-Graduação em Zoologia (PPGZOO), Campus Soane Nazaré de Andrade, Rodovia Ilhéus-Itabuna, km 16 – Salobrinho, Ilhéus, Bahia 45662-900, Brazil. Email: acssilva@uesc.br
D Spectral Solutions – Department of Research and Development, Astro34, Rua Belém, 106 – Jardim Vista Alegre, Embu das Artes, SP 06807-340, Brazil. Email: douglas@spectralsolutions.com.br
E UFSB, Centro de Formação em Técno-Ciências e Inovação (CF-TCI), Grupo de Inteligência Artificial e Aprendizagem Profunda – GIAAP, Cepec, Rodovia Ilhéus-Itabuna, km 22, Ilhéus, Bahia 45662-200, Brazil. Email: robsonmagalhaes@ufsb.edu.br
Marine and Freshwater Research 74(6) 511-521 https://doi.org/10.1071/MF22183
Submitted: 26 November 2021 Accepted: 9 February 2023 Published: 10 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Accuracy in species identification is a crucial factor for the quality of biodiversity studies and species management. Ensuring high accuracy is challenging for diverse taxonomic groups, including those with fishery importance such as Decapoda.
Aims: The objective of the present study was to use portable near-infrared spectroscopy combined with machine learning through a neural network (ANN) to identify species of Decapoda.
Methods: We propose an ANN application that rapidly and accurately emulates the results that would be obtained by a specialist. We used 124 specimens from seven marine Decapoda species as a dataset to fit the model.
Key results: The ANN was able to correctly learn (classify) all the patterns of the species (100% accuracy), with an overall mean probability of 0.97 ± 0.068.
Conclusions: The results obtained using portable near-infrared spectroscopy combined with machine learning (ANN) demonstrated that this method can be used with high accuracy to distinguish Decapoda species.
Implications: Studies aiming at comparisons among species may consider the use of this technique for the precise and inexpensive separation among species by non-specialists or for species that require the identification of a large number of individuals.
Keywords: artificial intelligence, artificial neural networks, benthos, biodiversity, crustaceans, diversity, invertebrates, marine, species identification.
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