Buffalo Identification in Mixed-Species Environments: A Comparative Deep Learning Approach Using ResNet50 and EfficientNetB3

Background: Buffaloes are integral to agricultural economies, particularly in regions that depend on them for milk production, labor, and income. However, their accurate visual identification in mixed-species environments, especially when co-existing with animals like elephants and rhinos, remains a technological challenge. Methods: This study explores deep learning-based image classification for species-specific buffalo detection using two convolutional neural network architectures: ResNet50 and EfficientNetB3. A balanced image dataset comprising four classes (buffalo, elephant, rhino, zebra) was curated, with training (80%) and validation (20%) splits. The models were fine-tuned using transfer learning, with custom dense layers added atop frozen base layers. EfficientNetB3 used higher-resolution inputs (300x300) and extensive augmentation, while ResNet50 operated on 300x300 images. Performance was evaluated using confusion matrices and key metrics, including validation accuracy, precision, recall, and F1-score, primarily focusing on buffalo classification. Results: ResNet50 achieved a validation accuracy of 47%, and EfficientNetB3 achieved 42%. However, ResNet50 misclassified buffaloes heavily, resulting in a buffalo recall of only 0.07 and an F1-score of 0.11. In contrast, EfficientNetB3 correctly classified 72 out of 200 buffalo images, achieving a buffalo recall of 0.36 and an F1-score of 0.32. These numerical results highlight EfficientNetB3’s superior ability to identify buffaloes accurately in complex visual contexts. Conclusion: EfficientNetB3 is more effective than ResNet50 for buffalo-focused image recognition tasks, offering higher sensitivity and precision in buffalo classification. This study supports the development of AI-powered species-specific monitoring tools, aiding in health tracking, ecological studies, and smart agricultural systems.