A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images

TL;DR

This paper introduces CTSAR-CNN, a novel self-attention residual CNN model that improves the accuracy and speed of classifying cow teat health from images, aiding dairy farm assessments.

Contribution

The paper presents a new self-attention residual CNN architecture specifically designed for cow teat health classification, addressing environmental and positional challenges.

Findings

Enhanced accuracy with residual and self-attention mechanisms

Faster and more adaptable health assessment tool

Potential to assist veterinarians in dairy industry

Abstract

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.