Milk is a highly important consumer for Americans and the health of the cows' teats directly affects the quality of the milk. Traditionally, veterinarians manually assessed teat health by visually inspecting teat-end hyperkeratosis during the milking process which is limited in time, usually only tens of seconds, and weakens the accuracy of the health assessment of cows' teats. Convolutional neural networks (CNNs) have been used for cows' teat-end health assessment. However, there are challenges in using CNNs for cows' teat-end health assessment, such as complex environments, changing positions and postures of cows' teats, and difficulty in identifying cows' teats from images. To address these challenges, this paper proposes a cows' teats self-attention residual convolutional neural network (CTSAR-CNN) model that combines residual connectivity and self-attention mechanisms to assist commercial farms in the health assessment of cows' teats by classifying the magnitude of teat-end hyperkeratosis using digital images. The results showed that upon integrating residual connectivity and self-attention mechanisms, the accuracy of CTSAR-CNN has been improved. This research illustrates that CTSAR-CNN can be more adaptable and speedy to assist veterinarians in assessing the health of cows' teats and ultimately benefit the dairy industry.

本研究解决了传统兽医在牛乳头健康评估中所面临的时间限制和准确性不足的问题。提出了一种结合残差连接和自注意机制的牛乳头自注意残差卷积神经网络（CTSAR-CNN）模型，以改善对乳头末端角质增生程度的分类。研究结果表明，CTSAR-CNN 的准确性得到了提升，能更快速适应兽医的需求，从而促进乳业的发展。

用于牛乳头图像健康状况分类的自注意残差卷积神经网络