Monkey Species Identification Using CNN Model

Abstract

The identification of monkey species using automated systems plays a crucial role in biodiversity research, wildlife conservation, and ecological studies. This study explores the application of Convolutional Neural Networks (CNNs) for classifying images of 10 monkey species collected from diverse sources, including the internet, zoos, and animal houses. A mixed dataset was developed, and six state-of-the-art CNN architectures—EfficientNet B7, DenseNet 201, VGG19, InceptionV3, MobileNet, and Xception—were evaluated for their performance. Among these, DenseNet 201 achieved the highest accuracy of 90.13% with a loss of 1.093, outperforming MobileNet, which attained 84.94% accuracy with 1.430 loss. EfficientNet B7 exhibited the lowest performance, with an accuracy of 64.28% and a loss of 3.76. To demonstrate the practical utility of the best-performing model, a web interface was developed using Python Flask API, enabling seamless image classification. This research provides valuable insights into the comparative performance of CNN architectures for species identification and highlights the potential of machine learning in advancing wildlife monitoring technologies.Using the Python Flask API, a web interface was created to show the best-performing model's usefulness in real-world scenarios by facilitating smooth picture categorization. This study demonstrates the potential of machine learning to advance wildlife monitoring technology and offers insightful information on the relative efficacy of CNN architectures for species identification.