Animal Species Classification using Convolutional Neural Network
Mohammad Hairil Anuar, Habibah Ismail, Ismail Ahmedy · 2025
The research on Animal Species Classification using Convolutional Neural Networks (CNNs) addressed challenges such as manual identification methods, limited dataset sizes, image occlusions, and variations in lighting conditions. The study aimed to investigate the CNN algorithm for Animal Species Classification, develop a CNN based classification system, and evaluate the system's accuracy in classifying animal species. The methodology included collecting an image dataset, preprocessing the images through standardization and data augmentation, and partitioning the data into training, testing, and validation sets. A pre-trained model, MobileNet, served as a base architecture for feature extraction. The CNN architecture was meticulously designed, considering layers, activation functions, and model complexity. Additionally, a user-friendly graphical interface was created to enhance user interaction. The final results indicate a training accuracy of approximately 0.9068 with a training loss of 0.2818. The validation accuracy and loss were 0.9350 and 0.2043, respectively, while the testing accuracy was 0.91. The implications of this research are significant, potentially impacting biodiversity conservation, ecological studies, educational tools, and wildlife monitoring. Future improvements could involve expanding the dataset, fine-tuning the model to increase accuracy, and exploring applications in protecting endangered species and monitoring habitats.