Cattle Breed Classification Using Deep Learning and EfficientNetB0

Abstract

Cattle breed classification plays a significant role in livestock management, agricultural pro-ductivity, and disease monitoring. Traditional identification methods rely on manual inspec-tion, which is time-consuming, error-prone, and requires expert knowledge. This research proposes a deep learning-based approach for automatic cattle breed classification using Effi-cientNetB0 with transfer learning. The dataset consists of labeled cattle images categorized into different breeds. Data preprocessing techniques such as resizing, normalization, and augmentation are applied to enhance model performance. The model is trained, fine-tuned, and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. A graphical user interface (GUI) is also developed to allow users to upload images and obtain real-time predictions. Experimental results show that the proposed system achieves high accuracy and generalization capability, making it suitable for real-world applications.