Computer vision species identification of lichens and bryophytes from biocrusts in Australian drylands.

ABSTRACT:

Premise

Due to their small size and lack of easily visible macroscopic characters, the identification of cryptogam species has always been challenging. Here, the use of a machine learning computer vision method is explored for the identification of species of lichens and bryophytes from Australian biocrusts.

Methods

Three models were trained using mostly images from herbarium specimens. The models were then evaluated based on statistics produced by Microsoft Azure Custom Vision and a bench-test with the CSIRO Horama ID mobile app.

Results

Despite the small size and reduced habit of lichens and bryophytes, the Cryptogam (lichens and bryophytes) model performance value is just slightly lower than the performance of a vascular plant model of similar scope (64% accuracy f

SUBMITTER: Lawler C 

PROVIDER: S-EPMC12747547 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature