Carnivorous Animal Detection Using AI & ML

Carnivorous Animal Detection Project explores the application of deep Learning models, Specifically ResNet-50, for detection and classification of carnivorous animals in diverse ecological for the detection and classification of carnivorous animals in diverse ecological environments. Utilizing advanced computer vision techniques, the system automates the identification of species and their dietary habits, offering an efficient, scalable solution for wildlife monitoring and conservation. By leveraging large datasets of animal images, the model is trained to accurately distinguish between carnivorous and noncarnivorous species, enhancing the understanding of predator-prey dynamics. The system's real-time detection capability provides immediate insights, supporting conservation efforts, ecological research, and mitigating human-wildlife conflicts. Future improvements aim to expand the model's scope to include more species, refine detection accuracy, and integrate additional data modalities such as acoustic and thermal imaging. This project highlights the potential of AI-driven solutions to advance wildlife conservation, offering a robust tool for researchers and environmental organizations.