Journal of Dairy, Veterinary & Animal Research Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review Submit Manuscript | http://medcraveonline.com Volume 5 Issue 4 - 2017 1 Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Argentina 2 ISISTAN Research Institute - UNCPBA-CONICET, Argentina 3 CIVETAN Research Institute - FCV UNCPBA-CONICET-CIC, Argentina *Corresponding author: Juan Rodríguez Alvarez, CIVETAN Research Institute, FCV-UNCPBA-CONICET-CIC, Campus Universitario, Tandil, Buenos Aires, Argentina, Email: Received: June 02, 2017 | Published: June 26, 2017 Mini Review J Dairy Vet Anim Res 2017, 5(4): 00149 Abstract 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. Keywords: Precision livestock; Body condition score; Automatic detection; Image analysis [2,3]. Extreme values of BCS are related with health risk, low productivity level and impaired pregnancy rate [4-7]. The subjectivity in the judgment of raters can lead to different scores for the same cow under consideration, or inconsistent scores of the same expert, which requires regular repeatability assessments [8]. As a result of the increasing availability of wide range of information and communication technology (ICT), more and higher-quality information to be available is expected in support of daily decision-making [9]. Consequently, there are multiple opportunities for automation and digitalization of livestock farming tasks, and different studies have particularly focused on automation of BCS. This brief review selects the most relevant and recent studies on the topic. Discussion Different authors have studied the feasibility of utilizing digital images to determine BCS. In this mini review relevant works later than 2007 and based on cow images from a top view were considered. In the Table 1 main characteristics and results from the selected papers are shown. Developed methods have two stages: Abbreviations: BCS: Body Condition Score; ICT: Information and Communication Technology; 3D: Three-Dimensional Introduction The BCS system is a means of accurately determining body condition of cows, independent of body weight and frame size [1], using a 5-point scale with 0.25-point increments (with 1 representing emaciated cows and 5 representing obese cows) Table 1: General characteristics and results of BCS estimation systems. Work Camera Cow Breed Dataset Size (# of Images) Automation level Real Time Results Bewley et al. [10] 2D Digital Holstein- Fresian 834 (US-BCS), 767 (UK- BCS) Low NO 92.79% within 0.25, 100% within 0.5 Krukowski [11] 3D, ToF SRB 351 (training), 120 (test) Medium NO Test Set: 20% within 0.25, 46% within 0.5 Anglart [20] 3D, ToF SRB 1329 (10% training, 90% test) Medium N/A R=0.84. 69% within 0.25, 95% within 0.50 Azzaro et al. [12] 2D Digital Holstein- Fresian 286 Low NO Error LOOCV =0.31 Halachmi et al. [17] Termal Holstein 172 High YES R=0.94 Bercovich et al. [13] 2D Digital Holstein 87 (training), 64 (test) Medium NO Test set: R 2 =0.64. Around 50% within 0.25, around 100% within 0.75