Abstract

The phenotypic parameters of breeder chickens are the external expression of their genetic genes. Accurate and efficient acquisition of chicken phenotypic parameters is a key technology in poultry breeding. However, the traditional manual measurement method is time-consuming and laborious, and the measurement error is large, which seriously affects the effect and efficiency of poultry breeding. Therefore, in this paper, Xinghua chicken as the research object, combining computer vision technology and deep learning algorithm, proposed the shank length and circumference measurement algorithm (SLCM) of breeder chickens. The algorithm firstly detected and extracted the region of chicken claw and shank bottom base by YOLOV5 algorithm. And then extracted the key points of shank length and circumference measurement based on the pixel distribution features of row direction and column direction in this region, and finally calculated the values of shank length and circumference. The test results of SLCM algorithm show that compared with the traditional manual measurement method, the Pearson correlation coefficient of shank length and circumference measured by algorithm is 0.8722 and 0.8869. Besides, this algorithm not only has smaller standard deviation (standard deviation of shank length is 1.35 mm, and shank circumference is 0.25 mm) and coefficient of variation (the mean coefficient of variation of shank length is 0.02, maximum is 0.06; and the mean coefficient of variation of shank circumference is 0.01, maximum is 0.03), but also has higher measurement efficiency. This research provided reference and basis for the accurate measurement of phenotypic parameters of breeder chickens and had important significance for poultry breeding.

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