Abstract
In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry.