Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect

The behavior of animals can reflect animal health status and physiological stages. Automatic recognition of animal behavior can provide a powerful tool for improving the breeding management level and ensuring animal welfare. Although the image-based deep learning algorithms can be used to recognize animal behavior automatically, there has been no unified and clear conclusive definition of the characteristics and amount of training data of the deep learning model. To address this issue, this paper proposes a deep learning model based on the YOLO v5 network for sheep behavior recognition. The proposed model is trained using various types of datasets divided into two categories based on whether the training data have high similarity data characteristics with the test data. The model training included several rounds with different training data amounts. The experimental results show that if the training and testing data have the same characteristics, only 1,125 images per behavior type are required to achieve the recognition precision of 0.967 and recall of 0.965. However, when training and test data have different characteristics, it is challenging to achieve such high precision and recall values, even when using many datasets. These results demonstrate that in a structured scenario, when training data and data generated in the practical application have consistent characteristics, there is no need to use a large amount of training data. As a result, deep learning deployment efficiency in practical applications can be improved.

Automated summary

Animal behavior reflects health status and physiological stages. Automatic recognition of animal behavior improves breeding management and animal welfare. A deep learning model based on YOLO v5 network for sheep behavior recognition was proposed, showing that in structured scenarios with consistent training and test data characteristics, large amounts of training data are unnecessary.