Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 175, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105528
Keywords
CNN architectures; Fine-tuning; Sheep face dataset; Sheep face classification; Transfer learning
Funding
- National Natural Science Foundation of China, China [61671185]
- Robotics and Internet-of-Things Laboratory of Prince Sultan University, Saudi Arabia
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Digital image recognition has been used in the different aspects of life, mostly in object classification and detections. Monitoring of animal life with image recognition in natural habitats is essential for animal health and production. Currently, Sheep Pain Facial Expression Scale (SPFES) has become the focus of monitoring sheep from facial expression. In contrast, pain level estimation from facial expression is an efficient and reliable mark of animal life. However, the manual assessment is lack of accuracy, time-consuming, and monotonous. Hence, the recent advancement of deep learning in computer vision helps to classify facial expression as fast and accurate. In this paper, we proposed a sheep face dataset and framework that uses transfer learning with fine-tuning for automating the classification of normal (no pain) and abnormal (pain) sheep face images. Current state-of-the-art convolutional neural networks (CNN) based architectures are used to train the sheep face dataset. The data augmentation, L2 regularization, and fine-tuning has been used to prepare the models. The experimental results related to the sheep facial expression dataset achieved 100% training, 99.69% validation, and 100% testing accuracy using the VGG16 model. While employing other pre-trained models, we gained 93.10% to 98.4% accuracy. Thus, it shows that our proposed model is optimal for high-precision classification of normal and abnormal sheep faces and can check on a comprehensive dataset. It can also be used to assist other animal life with high accuracy, save time and expenses.
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