Leveraging AI for the Identification of Aquatic Invertebrate Species
Document Type
Conference Proceeding
Publication Date
4-2025
Abstract
The identification of similar-looking aquatic invertebrate species is an especially difficult task due to their subtle morphological differences. To address this challenge, we developed a Roboflow model trained on a dataset that includes images from iNaturalist as well as images from our own collection. The model is designed to identify 15 distinct species of aquatic invertebrates native to the greater San Antonio region. Here, we evaluate the model’s performance, in terms of its accuracy in detecting and correctly labeling each of these species. Additionally, we are working towards deploying this model in a mobile format such that it is accessible for field use, allowing for the identification of species in real-time. Ultimately, this tool benefits both researchers and students by enabling the accurate, efficient, and rapid identification of aquatic invertebrates.
Recommended Citation
Mejia, Abigayle, "Leveraging AI for the Identification of Aquatic Invertebrate Species" (2025). Student Research Symposium 2025. 54.
https://digitalcommons.tamusa.edu/srs_2025/54
Comments
Poster Session 2
5:30-7:00 p.m.
BLH Lobby