Enhancing Species Identification with Fine-Tuned ResNet50: Performance Analysis and Classification of Diverse Species

This work categorizes various species, including animals, birds, insects, and fish, using a polished ResNet50 architecture. This work is significant since automated species identification is increasingly required in many different fields, including ecological studies, environmental monitoring, and conservation of biodiversity, therefore promoting sustainable ecosystems. Conventional approaches of species identification limit scalability by requiring specialist knowledge and time-consuming nature of operations. This work intends to solve these constraints and offer a scalable, efficient solution for species classification across 29 classes by using deep learning, therefore supporting life on land and life under water. The working dataset consists of 19,141 images; 3,922 for validation and 15,219 for training. Transfer learning helped the ResNet50 model to be tuned and consequently fit the specific classification problem. With this approach, the model achieved an accuracy of 88% indicating its ability in handling several and complex image data. The results indicate that, in automated species identification, the proposed model may be a valuable tool with considerable prospects for uses in wildlife conservation and biodiversity studies, therefore complementing the aims of climate action.