Evaluation of chest circumference in 3D lateral images of dairy cattle farming for body mass prediction

The advancement of precision livestock farming has underscored the importance of developing innovative and non-invasive methods for monitoring animal health and productivity. In this context, this study evaluated the application of computer vision to estimate the body mass (BM) of Holstein-Friesian dairy cows using 3D images captured laterally with the Intel RealSense D435i depth camera. The methodology involved correlating chest circumference (CC) measurements obtained in the field with those derived from lateral 3D images. A total of 250 animals were analyzed, with BM ranging from 420 to 855 kg, and the relationship between CC and BM was modeled using regression techniques. The results indicated a coefficient of determination (R² = 0.88) and a mean absolute percentage error (MAPE) of 3.94% for CC measured in the field. For CC derived from 3D images, R² was 0.847, with an MAPE of 5.29%. Although the 3D image-based method showed a slight reduction in accuracy, it demonstrated significant potential as a non-invasive and efficient alternative for estimating BM in dairy cows. Furthermore, the study highlights the role of 3D imaging technologies in acquiring detailed morphological data, enabling a more comprehensive understanding of body composition dynamics over time. These findings reinforce the potential of integrating digital technologies into dairy farming, promoting sustainable, precise, and labor-efficient management practices.