An Enhanced Animal Species Classification and Prediction Engine using CNN

Animal species classification is a fundamental task in wildlife conservation, animal behavior studies, and biodiversity research. Convolutional Neural Networks (CNNs) have become a potent technique for automatic classification tasks in recent times. This abstract presents an overview of the use of CNNs for animal species classification. The proposed approach involves pre-processing of the input images, followed by feature extraction and classification using CNN architecture. The pre-processing step involves image resizing, normalization, and augmentation to enhance the resilience of the model. The feature extraction is performed by convolutional layers, followed by max-pooling layers, and fully connected layers for classification. Transfer learning is also utilized to leverage the pre-trained CNN models and fine-tune them for specific animal species classification tasks. The proposed approach achieves high accuracy of 98% and can be extended to various animal species classification tasks. Overall, CNNs provide an effective means for automated animal species classification, enabling more efficient and accurate animal behavior studies, and wildlife conservation efforts.