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An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers.

Hui Yu^1,2, Jian Deng², Ran Nathan³

¹Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, Australia.

Movement Ecology

|March 31, 2021

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Machine learning models like Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost can classify animal behavior from accelerometry (ACC) data in real-time. This enables long-term, fine-scale wildlife movement and behavior research.

Area of Science:

Movement ecology
Bio-logging
Machine learning applications

Background:

Accelerometry (ACC) is crucial for wildlife movement studies, but data limitations hinder long-term research.
Current ACC methods face challenges with data continuity and tracker retrieval for long-term studies.

Purpose of the Study:

To evaluate machine learning methods for continuous, on-board classification of ACC data.
To enable long-term, fine-scale behavioral research using accelerometry.

Main Methods:

Tested six supervised machine learning methods: LDA, DT, SVM, ANN, RF, and XGBoost.
Classified ACC data from five species: white stork, griffon vulture, common crane, dairy cow, and roe deer.

Main Results:

Keywords:

ANN Accelerometer Behaviour classification On-board processing Random forest XGBoost

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SVM, ANN, RF, and XGBoost demonstrated strong performance in behavior classification from ACC data.

ANN, RF, and XGBoost showed suitability for on-board classification with reduced features, meeting runtime and storage requirements.

Conclusions:

Feature reduction combined with ANN, RF, and XGBoost offers a viable solution for on-board ACC data classification.
This approach has significant potential for advancing movement ecology, wildlife conservation, and livestock management.