Intelligent Multi-Modal Monitoring Framework for Birds in Forest Ecosystems and Zoo Animals through AI, CCTV, IR, and Drone Surveillance

Birds are indispensable bioindicators for evaluating environmental health, however, the continuous monitoring of specific species in forests is often hindered by low visibility, seasonal changes, and the activity of nocturnal animals. To overcome this, we suggest an AI-based solution that utilizes smart CCTV integrated with infrared (IR) imaging for remote observation 24/7. The system utilizes a dual-camera arrangement combined with motion triggers, behaviour-based positioning, and deep learning models that are trained for recognition under different light conditions. The monitoring during the day is done using CCTV, whereas the IR cameras are capturing the thermal signatures at night; these cameras are placed in strategic locations near nesting, drinking, and migration areas. Species recognition, population counting, and behavioural trend analysis are done through real-time data processing during the different seasons. Making an extension of this framework, we have a proposal for a drone-based system for monitoring zoo animals where drones will record only when there is motion detected to save storage, with videos uploaded to the cloud for further analysis. Using this system, accurate counts of animals, disease and distress detection, and monitoring of individuals hidden by plants or terrain can be achieved. Such AI-powered detection combined with smart surveillance and cloud management allows the system to be a scalable and low-cost solution for both biodiversity conservation in forests and welfare management in zoos, thereby creating a better protection framework for rare and endangered species.