A Two-Stage Method to Detect the Sex Ratio of Hemp Ducks Based on Object Detection and Classification Networks

Simple Summary The sex ratio of hemp ducks is considered to be a major animal welfare issue in the commercial hemp duck farming industry, but currently, it still relies on an inefficient and inaccurate manual counting method. In order to obtain an efficient and accurate way of calculating the sex ratio of ducks to solve this problem, we established the world's first manually marked sex classification dataset for hemp ducks and used multiple deep neural network models for the target detection and sex classification of ducks, with an average accuracy of 98.68%. The evaluation of the algorithm's performance indicates that the automation method proposed in this paper is feasible for the sex classification of ducks in the farming environment and is thus a feasible tool for sex ratio estimation. The sex ratio is an important factor affecting the economic benefits of duck groups in the process of hemp duck breeding. However, the current manual counting method is inefficient, and the results are not always accurate. On the one hand, ducks are in constant motion, and on the other hand, the manual counting method relies on manpower; thus, it is difficult to avoid repeated and missed counts. In response to these problems, there is an urgent need for an efficient and accurate way of calculating the sex ratio of ducks to promote the farming industry. Detecting the sex ratio of ducks requires accurate counting of male ducks and female ducks. We established the world's first manually marked sex classification dataset for hemp ducks, including 1663 images of duck groups; 17,090 images of whole, individual duck bodies; and 15,797 images of individual duck heads, which were manually captured and had sex information markers. Additionally, we used multiple deep neural network models for the target detection and sex classification of ducks. The average accuracy reached 98.68%, and with the combination of Yolov5 and VovNet_27slim, we achieved 99.29% accuracy, 98.60% F1 score, and 269.68 fps. The evaluation of the algorithm's performance indicates that the automation method proposed in this paper is feasible for the sex classification of ducks in the farm environment, and is thus a feasible tool for sex ratio estimation.

Automated summary

This study established the world's first manually marked sex classification dataset for hemp ducks and used deep neural network models to accurately detect and classify the sex of ducks. The evaluation of the algorithm's performance suggests that this automated method is feasible for sex classification of ducks in the farming environment, serving as a reliable tool for sex ratio estimation.