Determining Body Condition of Dairy Cows for Early Diagnosis of Physiological Diseases

Abstract

The paper points out the problem with automated diagnostics of body condition in dairy cattle, including ketosis. The conducted research is aimed at determining the possibility for non-contact automated diagnostics of the cattle physiological state on a daily basis. (Research purpose) To develop an algorithm for complex operational diagnostics of the physiological state of dairy cows by their live weight and body condition. (Materials and methods) Field data were collected in 2021-2022 on the FSUE Grigoryevskoye (Yaroslavl Region), Istra Cheese Factory and Lenin Dairy State Farm (Moscow Region). A commercial 3D ToF (Time-of-Flight) camera  O3D303 was used. The 3D camera is capable of calculating and displaying the Point Cloud space as a multidimensional array. The program received 144 images, 136 images passed the filtering stage, 6 images did not detect the areas of interest, because of the high level of image noise, and the sacrum was not detected. 62 cows were subject to research. (Results and discussion) The sample and the dependence are proved to be representative as the Pearson correlation coefficient equals R=0.849, which shows a strong linear relationship between the body condition score and live weight. It was determined that in 24 percent of cases the body condition score is less than the least normal one. An algorithm was developed to help veterinarians to detect the animals that need additional examination. (Conclusions) It was found that the developed algorithm helps to quickly detect ketosis in dairy cows and automatically diagnose physiological diseases at an early stage, without additional labor and monetary costs.