Identification and Recognition of Diverse Species Using Deep Learning

This paper presents a method for neural network-based identification and recognition of an extensive variety of flora and fauna. The system that has been proposed was designed to assist field naturalists, people who take special attention to and identify patterns that exist in the wild identify various species. Utilizes a Digital Naturalist dataset, which was created by gathering photos from numerous internet sources and camera-captured images. Our solution revolves around a Convolutional Neural Network (CNN) which employs a deep learning model built on the InceptionV3 architecture. The model is trained using a large set of photos from several subclasses, such as birds, flowers, and animals. Using the Flask framework, the trained model provides the basis for the creation of a simple web application. This application makes predictions in real time based on input photographs, making species detection more convenient for field naturalists. The web application enhances the accuracy and thoroughness of information by directing users to the relevant Wikipedia page, once they submit an image and click the "predict" button. This holistic strategy creates a greater interest in conservation by enabling researchers, field naturalists, and visitors to communicate with the natural world in an effortless way.