Revolutionizing Lion Conservation with EfficientNet B7: Real-Time Detection and Health Classification

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts.