Next generation insect taxonomic classification by comparing different deep learning algorithms.

ABSTRACT: 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, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, whi

SUBMITTER: Ong SQ 

PROVIDER: S-EPMC9803097 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature