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    Automated Classification System Based on YOLO Architecture for Body Condition Score in Dairy Cows
    (Mdpi, 2024) Dandil, Emre; Cevik, Kerim Kursat; Boga, Mustafa
    Simple Summary This study proposes an automatic classification system for determining body condition score in dairy cows using a deep learning architecture. An original dataset was created by categorizing images of different breeds from different farms into five body condition score classes: Emaciated, Poor, Good, Fat, and Obese. In the experimental analysis, the proposed deep learning model accurately classified 102 out of 126 cow images in the test set, achieving an average accuracy of 0.81 for all classes in Holstein and Simmental cows and an average area under the precision-recall curve of 0.87. The proposed body condition score classification system can help to accurately monitor rapid declines in body condition in dairy cows and serve as a tool for production decision-makers to reduce negative energy balance during early lactation.Abstract Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision-recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance.