Image data set based on the age of giant pandas

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information which is mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.

Methods
The image data set of captive giant pandas used in this study are all from the Chengdu Research Base of Giant Panda Breeding and its partner units, such as: Yunnan Wildlife Park, Suzhou Wildlife Park, Shenzhen Wildlife Park, etc. By using a Panasonic dvx200 video camera and three digital cameras (Canon 1DXmarkII, Canon 5DmarkIII, and a Panasonic Lumix DMC-GH4), the image data of 218 captive pandas was collected. All data were cleaned and annotated to establish a larger giant panda age image database.