AI-Powered Animal Species Predictor

Abstract: Wildlife conservation, biodiversity monitoring, and ecological research require accurate and efficient identification of animal species. Traditional methods of species identification depend on domain experts and manual examination, which are time-consuming, expensive, and impractical at scale. This paper presents an AI-Powered Animal Species Predictor, a deep learning-based web application that automatically identifies animal species from images using Convolutional Neural Networks (CNN) and Transfer Learning. The proposed system employs Efficient Net [1]. B3 as the backbone feature extractor, fine-tuned on a curated dataset of animal images spanning 10 major species. The platform accepts image input through a web interface, performs preprocessing and feature extraction, and outputs the predicted species name along with its confidence score, habitat information, diet, and conservation status. The system is developed using Python with TensorFlow and Keras for the deep learning model, Flask for the web backend, and React.js for the frontend interface. The model achieves an overall classification accuracy of 93.7%, precision of 92.5%, recall of 91.9%, and F1-score of 92.2% on the test dataset. Experimental results demonstrate that the proposed system outperforms existing baseline models such as VGG16 [3], ResNet50 [2], InceptionV3 [4], and MobileNetV2 [5]. The system is designed to support wildlife conservationists, researchers, educators, and nature enthusiasts by providing instant, reliable, and informative species identification.

Keywords: Animal Species Prediction, Deep Learning, Convolutional Neural Networks, Transfer Learning, EfficientNet [1]B3, Image Classification, Wildlife Conservation, TensorFlow, Flask, React.js, Biodiversity Monitoring.