info:eu-repo/semantics/article
Body condition estimation on cows from depth images using Convolutional Neural Networks
Fecha
2018-12Registro en:
Rodríguez Alvarez, Juan Maximiliano; Arroqui, Mauricio; Mangudo, Pablo; Toloza, Juan Manuel; Jatip, Daniel Esteban; et al.; Body condition estimation on cows from depth images using Convolutional Neural Networks; Elsevier; Computers and Eletronics in Agriculture; 155; 12-2018; 12-22
0168-1699
CONICET Digital
CONICET
Autor
Rodríguez Alvarez, Juan Maximiliano
Arroqui, Mauricio
Mangudo, Pablo
Toloza, Juan Manuel
Jatip, Daniel Esteban
Rodriguez, Juan Manuel
Teyseyre, Alfredo Raul
Sanz, Carlos
Zunino Suarez, Alejandro Octavio
Machado, Claudio
Mateos Diaz, Cristian Maximiliano
Resumen
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.