  Biodiversity plays a crucial role in maintaining the balance of the
ecosystem. However, poaching and unintentional human activities contribute to
the decline in the population of many species. Hence, active monitoring is
required to preserve these endangered species. Current human-led monitoring
techniques are prone to errors and are labor-intensive. Therefore, we study the
application of deep learning methods like Convolutional Neural Networks (CNNs)
and transfer learning, which can aid in automating the process of monitoring
endangered species. For this, we create our custom dataset utilizing
trustworthy online databases like iNaturalist and ZooChat. To choose the best
model for our use case, we compare the performance of different architectures
like DenseNet, ResNet, VGGNet, and YOLOv8 on the custom wildlife dataset.
Transfer learning reduces training time by freezing the pre-trained weights and
replacing only the output layer with custom, fully connected layers designed
for our dataset. Our results indicate that YOLOv8 performs better, achieving a
training accuracy of 97.39 % and an F1 score of 96.50 %, surpassing other
models. Our findings suggest that integrating YOLOv8 into conservation efforts
could revolutionize wildlife monitoring with its high accuracy and efficiency,
potentially transforming how endangered species are monitored and protected
worldwide.
