Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images

Species identification is critical for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms; however, naming, identifying, and differentiating between species is more complex than it may seem. Traditional methods, relying on dichotomous keys and manual observation, are time-consuming and error-prone. Precise species identification is crucial for all taxonomic investigations and biological procedures. Numerous experts are currently engaged in the task of identifying a solitary species. To address these challenges, we present a robust artificial intelligence framework for species identification using deep learning techniques, specifically leveraging the ResNet-50 Convolutional Neural Network (CNN). Our approach utilizes a ResNet-50-based CNN to accurately classify 15 species, including humans, plants, and animals, from images taken at unique locations and angles. The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. The proposed model achieved an overall accuracy of 96.5%, with class-specific accuracies of 98.25% for humans, 97.81% for animals, and 96.90% for plants. These results surpass those of existing models such as GoogleNet, VGG, SegNet, and DeepLab v3+, highlighting the efficacy of our approach. Performance was evaluated using metrics such as sensitivity, specificity, and error rate, further validating its reliability. Our findings suggest that the ResNet-50-based CNN model is highly effective for automatic species identification, offering significant improvements in accuracy and computational efficiency.