Animal Footprint Classification using Machine Learning and Advance Deep Learning Methods - Varanasi Anil Kumar; Kavila Moni Sushma Deep; I.Swathi (Utilitas Mathematica, 2025)
Categorization of animal footprints is a significant aspect of ecological research, conservation, and wildlife monitoring. Here in our research, we introduce a novel method to identify and classify animal footprints through the application of state-of-the-art deep learning and machine learning methods. An ensemble model combining "Xception and NasNetMobile" is primarily discussed, but "ResNet50, InceptionResNetV2, DenseNet, MobileNet, Xception, and NasNetMobile" are also used in the classification task. Contemporary algorithms like "YOLOV5x6, YOLOV5s6, YOLOV8n, and YOLOV9n" are utilized for footprint identification. The proposed ensemble model performs the highest classification accuracy by combining Xception and NasNetMobile. In the case of footprint identification, YOLOV5x6 was the superior method since it resulted in astounding outcomes with excellent precision. The efficacy of the models could be measured through calculating measures such as "recall, precision, and F1 score" evaluations. Results reveal that such cutting-edge deep machine learning algorithms find application in environment and wildlife research by simplifying and enhancing identification and categorization of animal footprints.