A gentle introduction to computer vision-based specimen classification in ecological datasets.

Area of Science:

• Ecology
• Computer Science
• Machine Learning

Background:

• Manual specimen classification is time-consuming and costly, hindering ecological research and conservation efforts.
• Computer vision (CV) offers automated, rapid, and accurate image classification for ecological data.
• Existing CV tools require ecologists to understand fundamental principles for effective model selection and training.

Purpose of the Study:

• To provide a foundational guide for ecologists on using computer vision for species classification.
• To equip researchers with the knowledge to select appropriate CV models and workflows for their specific ecological datasets and goals.
• To address unique challenges in ecological data, such as class imbalance and species similarity, within CV applications.

Main Methods:

• Demonstration of data preparation techniques for ecological image datasets.
• Explanation of basic machine learning model training procedures tailored for species identification.
• Guidance on model evaluation and selection, considering factors like data domains and feature extractors.

Main Results:

• A practical framework for ecologists to implement and adapt CV for species classification.
• Strategies for handling common ecological dataset quirks, including 'unknown' species and long-tail distributions.
• Improved understanding of how data characteristics influence CV model performance in ecological contexts.

Conclusions:

• Ecologists can leverage computer vision to enhance the efficiency and scale of biodiversity monitoring and conservation research.
• Understanding CV principles empowers ecologists to overcome data-specific challenges and optimize model performance.
• This resource facilitates the adoption of machine learning for visual classification tasks in ecology, advancing scientific discovery.