AUTHENTICATED VIRTUAL MONITORING OF ANIMAL SPECIES

Abstract: Species detection plays a crucial role in various fields, including wildlife conservation, biodiversity monitoring, and ecological research. With the rapid advancement of Internet of Things(IoT) technologies, it is now possible to develop intelligent systems capable of automatically identifying and classifying different species. This paper proposes a novel approach to species detection using IoT devices, specifically leveraging the power of Convolutional Neural Networks (CNN) and the You Only Look Once (YOLO) object detection framework. Our solution is designed to utilize Raspberry Pi 4 as the IoT component, along with an ultrasonic sensor, RFID RC522, and a webcam for enhanced data collection and processing capabilities. This approach offers several advantages over traditional species detection methods. The combination of CNN and YOLO guarantees high accuracy and efficiency in species identification. The integration of ultrasonic sensor and RFID technology provides valuable contextual information for species detection and tracking. This also enhances the accuracy and context of species detection.

Keywords: Convolution Neural Network, You Only Look Once, Raspberry Pi, Microwave Radar sensor