Bornean orangutan nest identification using computer vision and deep learning models to improve conservation strategies

The results show that out of four DL models, Inception V3 has the best model performance with more than 99% accuracy and precision, while VGG19 has the lowest performance. In addition, gradient-weighted class activation maps were used to interpret the results, allowing visualization of the regions used by InceptionV3 and VGG19 for classification. This study shows the potential of integrating DL into orangutan conservation, particularly in monitoring the orangutan population in the protected environment. Future research should focus on the automatic detection of nests to improve UAV-based monitoring of orangutans.