IoT Applications in Wildlife Conservation: Tracking and Protecting Endangered Species

Human activity degrades natural environments, and wildlife protection is crucial. Recent technological advances have improved animal monitoring and preservation activities. This research provides a real-time wildlife monitoring and analysis system using the IoT (IoT) and machine learning, specifically the Random Forest algorithm. The proposed method uses a network of IoT devices, including motion sensors and cameras. In real-time, these sensors generate massive volumes of diverse data regarding animal movement, behavior, and environmental variables. The Random Forest technique manages this data flow and gets significant insights since it can handle high-dimensional datasets and missing values. A large dataset of animal activity, environmental, and habitat characteristics is used to train the machine learning model. The Random Forest model outperforms existing classification algorithms in a rigorous assessment procedure, allowing exact species identification. Wildlife conservationists and academics may utilize the system's user-friendly interface to engage with data and see real-time analytics. This interface helps animal population protection by supporting decision-making. It shows the usefulness of the suggested IoT-based wildlife monitoring system and Random Forest algorithm in real-time species recognition and behavior analysis. Technology and advanced machine learning may boost conservation efforts and ensure a sustainable coexistence between people and animals in their natural environments.