Authors :
Francis Lowu; Hudson Nandere
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3ZqVbSa
DOI :
https://doi.org/10.5281/zenodo.7780070
Abstract :
The decline in global biodiversity is a
pressing concern due to human activities, leading to
millions of species at risk of extinction. East Africa is
especially affected by habitat destruction, poaching,
and climate change, resulting in significant losses in
wildlife populations. Machine learning (ML) has
demonstrated potential in identifying species, especially
in camera trap images, acoustic recordings, and genetic
data. However, there is a need to further explore the
use of ML in identifying wildlife species in East Africa.
To address this need, we developed ML classification
models to identify wildlife species in East Africa. Our
dataset included taxonomic features and characteristics
of wildlife species from East African countries between
2018 and 2021. We used the random forest algorithm,
which is suitable for complex datasets with multiple
features. Our evaluation achieved an accuracy of
63.4% and a baseline score of 8.02%, showing the
potential of our models in identifying wildlife species in
East Africa. Our study could contribute to wildlife
conservation by detecting and preventing illegal wildlife
trade activities, monitoring population trends, assessing
the impact of human activities on different species in
East Africa, and preserving biodiversity
Keywords :
Biodiversity Conservation, Wildlife, Machine learning, Habitat, Poaching, Climate Change Introduction
The decline in global biodiversity is a
pressing concern due to human activities, leading to
millions of species at risk of extinction. East Africa is
especially affected by habitat destruction, poaching,
and climate change, resulting in significant losses in
wildlife populations. Machine learning (ML) has
demonstrated potential in identifying species, especially
in camera trap images, acoustic recordings, and genetic
data. However, there is a need to further explore the
use of ML in identifying wildlife species in East Africa.
To address this need, we developed ML classification
models to identify wildlife species in East Africa. Our
dataset included taxonomic features and characteristics
of wildlife species from East African countries between
2018 and 2021. We used the random forest algorithm,
which is suitable for complex datasets with multiple
features. Our evaluation achieved an accuracy of
63.4% and a baseline score of 8.02%, showing the
potential of our models in identifying wildlife species in
East Africa. Our study could contribute to wildlife
conservation by detecting and preventing illegal wildlife
trade activities, monitoring population trends, assessing
the impact of human activities on different species in
East Africa, and preserving biodiversity
Keywords :
Biodiversity Conservation, Wildlife, Machine learning, Habitat, Poaching, Climate Change Introduction