Prediction of body mass in dairy cows using LiDAR technology
¹Federal University of Lavras, School of Engineering, Department of Agricultural Engineering, University Campus, PO Box 3037 - CEP 37200-000 Lavras, Minas Gerais, Brazil
²Fluminense Federal University, Department of Agricultural and Environmental Engineering, Rua Passo da Pátria, 156, PO Box, 21065-230, Niterói, Brazil
³Florida International University, Knight Foundation School of Computing and Information Sciences, Miami, Florida, USA
⁴University of Florence, Department of Agriculture, Food, Environment and Forestry, IT50145 Florence, Italy
*Correspondence: patricia.ponciano@ufla.br
Abstract:
Precision livestock farming has driven the search for technological solutions that enable remote and non-invasive monitoring of animal health and zootechnical performance. This study aimed to investigate the use of computer vision for predicting body mass (BM) in a herd of Holstein Friesian dairy cows using three-dimensional images acquired via LiDAR (Light Detection and Ranging). From these images, the individual body volume (BV) of each animal was digitally estimated. Subsequently, the correlation between LiDAR-derived BV and body mass measured using the conventional weighing method was evaluated. Based on this relationship, statistical regression models were fitted to predict BM from BV. The results showed that BV measured using LiDAR achieved a coefficient of determination (R²) of 0.7861 and a mean absolute percentage error (MAPE) of 4.43%. These findings demonstrate the feasibility of LiDAR technology as an effective, non-invasive alternative tool for estimating bovine body mass. The use of LiDAR enabled detailed recording of morphological characteristics and measurements, allowing continuous monitoring of body development without the need for direct animal handling. In conclusion, the incorporation of computer vision systems into dairy cattle management can optimize operational efficiency, reduce labor requirements, and promote a more sustainable production system, while enhancing animal welfare through automated monitoring.
Key words:
computer vision, dairy farming, regression model