Next generation insect taxonomic classification by comparing different deep learning algorithms

• Main Authors: Song-Quan Ong, Suhaila Ab. Hamid
• Format: Article
• Language: English; English
• Published: Plos One 2022
• Subjects: Q1-295 General; QL461-599.82 Insects
• Online Access: https://eprints.ums.edu.my/id/eprint/35440/1/ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/35440/2/FULL%20TEXT.pdf; https://eprints.ums.edu.my/id/eprint/35440/; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279094; https://doi.org/10.1371/journal.pone.0279094; https://doi.org/10.1371/journal.pone.0279094

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,...