Individual identification of dairy cows based on Gramian Angular Field and Migrating Convolutional Neural Networks

Abstract

The individual identification of dairy cows is of great significance to the development of modern intelligent animal husbandry. It is of great help in remotely monitoring the individual health status of dairy cows and promoting the field of live dairy cattle leasing. Traditional methods of individual identification of dairy cows rely on manual identification, or artificial feature extraction of cow activity data so the accuracy of individual identification of dairy cows cannot be guaranteed. Aiming at this problem, this paper proposes a classification method based on Gramian Angle Field and Migrating Convolutional Neural Networks. By transforming the activity data of 20 cows for 56 days into the Gramian Angle Field and converting it into a three-dimensional image, the time dependence and correlation of the cow activity data are preserved. Combined with the idea of migration learning, a model called MCNN based on VGG16 is proposed. The MCNN model of the generated cow images is classified. The experimental results show that the classification accuracy of this method is about 99.3%, and the classification time is short, which can effectively realize the individual identification of dairy cows.