ZUNINO SUAREZ Alejandro Octavio
Body condition estimation on cows from depth images using Convolutional Neural Networks
RODRÍGUEZ ALVAREZ, JUAN; ARROQUI, MAURICIO; MANGUDO, PABLO; TOLOZA, JUAN; JATIP, DANIEL; RODRÍGUEZ, JUAN M.; TEYSEYRE, ALFREDO; SANZ, CARLOS; ZUNINO, ALEJANDRO; MACHADO, CLAUDIO; MATEOS, CRISTIAN
COMPUTERS AND ELETRONICS IN AGRICULTURE
ELSEVIER SCI LTD
Año: 2018 vol. 155 p. 12 - 12
BCS (?Body Condition Score?) is a method used to estimate body fat reserves and accumulated energy balance of cows. BCS heavily influences milk production, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve better animal response, but this is a time-consuming and subjective task performed visually by expert scorers. Several studies have tried to automate BCS of dairy cows by applying image analysis and machine learning techniques. This work analyzes these studies and proposes a system based on Convolutional Neural Networks (CNNs) to improve overall automatic BCS estimation, whose use might be extended beyond dairy production. The developed system has achieved good estimation results in comparison with other systems in the area. Overall accuracy of BCS estimations within 0.25 units of difference from true values was 78%, while overall accuracy within 0.50 units was 94%. Similarly, weighted precision and recall, which took into account imbalance BCS distribution in the built dataset, show similar values considering those error ranges.