Analyzing the pregnancy status of giant pandas with hierarchical behavioral information

As giant pandas (Ailuropoda melanoleuca) are difficult to conceive and prone to abortion, humans have turned to artificial captive breeding to increase their population. Therefore, it is crucial for their reproduction to analyze their pregnancy status accurately and promptly in artificial captive breeding. To determine whether a giant panda is pregnant, with the current methods, experts must keep a close eye on them and frequently collect their urine or blood, which requires significant resources with a high misdiagnosis rate, and will adversely affect giant pandas’ daily lives. Consequently, it is essential to rapidly advance the development of automated, precise methods that will not disrupt the pandas’ lives to analyze giant pandas’ behaviors and determine whether or not they are pregnant. In this paper, we propose an end-to-end intelligent system for predicting the pregnancy status of giant pandas and their Expected Date of Delivery (EDD). We first introduce expert knowledge to machine learning methods to solve this problem, which can significantly improve the accuracy of prediction. Experimental results show that this system achieves an accuracy of 91.5% for the pregnancy diagnosis and 0.579 days of mean average error for EDD prediction when the observation period is 5 days. Our automated system significantly reduces the need for human intervention, thus minimizing disruptions to the pandas’ daily lives. It has the potential to contribute to the health and genetic diversity of the giant pandas, as well as aid in the panda's artificial reproduction and population growth.