ABSTRACT
This paper examines the growing role of deep learning in analyzing animal datasets. It provides a thorough review of current literature on deep learning and machine learning techniques pertinent to animal analysis, encompassing data collection, preprocessing, and innovative analytical methods. Experimental findings, integrating both machine learning and deep learning approaches, are detailed and assessed. A comparative analysis highlights the consistent superiority of DL models over traditional ML methods, notably in species classification tasks, with Support Vector Machines (SVMs) and Random Forests outperforming k-Nearest Neighbors (k-NN). Additionally, a novel approach merging fine-tuning with data augmentation is introduced, demonstrating promising results and potential for improving the accuracy and reliability of animal data analysis. The study underscores deep learning's crucial role in advancing conservation efforts and fostering harmony between humans and wildlife. It concludes by outlining directions for future research, elucidating the ongoing refinement of deep learning methodologies for analyzing animal datasets.
