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Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Automatic species identification from images for Aotearoa.

Hongyu Wang¹, Paul Schlumbom², Eibe Frank¹

¹School of Computing and Mathematical Sciences, University of Waikato, Hamilton, New Zealand.

Journal of the Royal Society of New Zealand

|August 4, 2025

Summary

We developed machine learning models for automatic species identification in New Zealand. These AI tools achieve over 76% accuracy, aiding conservation and education efforts.

Keywords:

Image classification computer vision convolutional neural networks finetuning species identification transfer learning

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Last Updated: Sep 13, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Area of Science:

Ecology
Computer Science
Bioinformatics

Background:

Accurate species identification is crucial for conservation and education.
New Zealand's unique biodiversity presents an opportunity for automated identification systems.
Machine learning offers a powerful approach for image classification tasks.

Purpose of the Study:

To develop and evaluate neural network-based image classification models for New Zealand species.
To enable accurate, offline species identification on mobile devices.
To provide open-source tools for biodiversity research and public engagement.

Main Methods:

Utilized a dataset of 14,991 species from iNaturalist, covering Animalia, Plantae, Fungi, and other kingdoms.
Trained neural network models for image classification.
Calibrated model confidence using temperature scaling and employed input attribution for interpretability.

Main Results:

Achieved over 76% classification accuracy across all species.
Generated calibrated class probability estimates for confidence assessment.
Demonstrated the utility of input attribution for understanding model predictions.

Conclusions:

The developed models provide accurate and reliable species identification for New Zealand organisms.
The open-source, offline-capable applications support conservation, education, and research.
This work facilitates biodiversity monitoring and public engagement with Aotearoa's unique species.