A Health Management System for Forest Musk Deer Based on YOLOv5
Meiqi Zhao, Haiyan Wang, Yinuo Li, Chi Zhang · 2023
Forest musk deer is a national key protected species, and its population resources are mainly in captivity. However, parasitic diseases have become a major challenge in the captive breeding of these musk deer and are one of the main causes of mortality in these musk deer. To address the difficulties associated with screening and diagnosis, a parasite egg recognition model based on YOLOv5 is proposed, which use the existing computer vision techniques to detect parasites. This model aims to achieve accurate recognition of parasite eggs in forest musk deer. Specifically, the data source-based network architecture is described. The experimental results show that the mean average accuracy of the model is 0.949 in the test set. To enable practical deployment of the model, a forest musk deer health management system based on the WeChat applet is presented. The system includes several key functions such as parasite egg identification, archive management of diseased forest musk deer, and breeding knowledge base. the potential effects of model and health management system on the conservation of forest musk deer resources were discussed, and their application prospects were emphasized. In summary, this work offers a novel and effective approach to the health management of captive forest musk deer, which is critical to ensuring their survival and protecting this important resource.