BCS is a method to estimate body fat stores and accumulated energy balance of cows. This value influences productivity, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve a better animal response. In practice, this task is performed by expert scorers mainly visually, and could vary between scorers and be time-consuming. For this reason, several studies have tried to automate BCS by applying image analysis and machine learning techniques. An overview of selected studies is provided in this mini review. 74% within 0.25, 91% within 0.5 2D, two dimensional; 3D, three-dimensional; ToF, time-of-flight; SRB, swedish red breed; GLM, generalized linear model; US-BCS, united state body condition score; UK-BCS, united kingdom body condition score; R, correlation coefficient; R 2 , coefficient of determination; LOOCV, leave one out cross validation; RMSE, root mean square error ConclusionThe literature attempts to automate BCS assessment look promising as a tool for supporting cattle decision-making, in a context where ICT technology is becoming more efficient, productive, and cheaper. Acceptable accuracy within the range of human error has been reported, with room for improvement as more effective computing processing methods became available. AcknowledgementsNone. Conflict of interestAuthor declares that there is no conflict of interest.
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