Intelligent Classifier for Identifying and Managing Sheep and Goat Faces Using Deep Learning

Abstract

Computer vision, particularly in artificial intelligence (AI), is increasingly being applied in various industries, including livestock farming. Identifying and managing livestock through machine learning is essential to improve efficiency and animal welfare. The aim of this work is to automatically identify individual sheep or goats based on their physical characteristics including muzzle pattern, coat pattern, or ear pattern. The proposed intelligent classifier was built on the Roboflow platform using the YOLOv8 model, trained with 35,204 images. Initially, a Convolutional Neural Network (CNN) model was developed, but its performance was not optimal. The pre-trained VGG16 model was then adapted, and additional fine-tuning was performed using data augmentation techniques. The dataset was split into training (88%), validation (8%), and test (4%) sets. The performance of the classifier was evaluated using precision, recall, and F1-Score metrics, with comparisons against other pre-trained models such as EfficientNet. The YOLOv8 classifier achieved 95.8% accuracy in distinguishing between goat and sheep images. Compared to the CNN and VGG16 models, the YOLOv8-based classifier showed superior performance in terms of both accuracy and computational efficiency. The results confirm that deep learning models, particularly YOLOv8, significantly enhance the accuracy and efficiency of livestock identification and management. Future research could extend this technology to other livestock species and explore real-time monitoring through IoT integration.