Abstract
Cow’s body condition scoring (BCS) based on neural network algorithms is necessary to monitor the health and productivity of the animals. To develop an algorithm for assessing the physiological fatness of dairy cows according to the parameters of the fermur heads, the sacrum and the hunger hollow, the authors studied eighty animals on a farm of the state unitary enterprise “Grigorievskoe”, which were divided into fatness groups (from 1 to 5). The studies were carried out in November 2021 during the morning milking. Data were collected using a 3D ToF camera O3D303. Previously, the installation of a three-dimensional camera on the farm was sim ulated, and an algorithm was developed that takes into account the cow height and the distance from the highest point of the spine to the three-dimensional camera. An algorithm for assessing the physiological fatness of dairy cows (BCS) has been developed to take into account the condition of the fermur heads, the sacrum, the hunger hollow, which determines the highest point of the withers, the proportions between the body length and width, as well as the depth of the hunger hollows and the severity of the caudal ligament. Software has been developed to register the unique number of a cow and determine the BCS, as well as show the dynamics of changes in the animal’s fatness. Data were processed in accordance with the developed algorithm. The image was processed using the regression method. Comparison results of the BCS of cows, obtained according to the developed algorithm, and the experts’ assessment showed that the algorithm error in the fatness range of 2…4 points averaged 10%. When determining the BCS of cows with borderline and limit fatness state (1 and 5 points), the measurement error by the proposed algorithm increased to 25%. Based on the results obtained, the authors recommend pre-setting a neural network for further research; determine the correction factor for fatness points 1 and 5; fi nalize the software, develop customized software and an automatic system for body condition scoring, and conduct tests.
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