Título:	Cattle weight estimation from dense point clouds.
Autoria:	CASTANHEIRO, L. F. TETILA, E. C. FURUYA, D. E. G. SILVA, J. P. da BARBEDO, J. G. A. ROMANI, L. A. S. BOLFE, E. L.
Afiliação:	LETÍCIA FERRARI CASTANHEIRO; EVERTON CASTELÃO TETILA, UNIVERSIDADE FEDERAL DA GRANDE DOURADOS; DANIELLE ELIS GARCIA FURUYA; JOÃO PAULO DA SILVA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; EDSON LUIS BOLFE, CNPTIA.
Ano de publicação:	2026
Referência:	In: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 28., 2026, Benidorm. Proceedings [...]. Setúbal: SCITEPRESS - Science and Technology Publications, 2026. v. 2, p. 1133-1140.
Conteúdo:	Cattle weight is essential for decision-making in precision livestock farming, directly supporting nutrition management, animal welfare, and production efficiency. Existing methods rely on close-range measurements or manual intervention, limiting scalability. This work proposes an workflow for cattle weight estimation based on point clouds derived from aerial images. RGB images acquired at low altitude were processed using Structure from Motion (SfM) techniques to generate dense point clouds. Individual animals were automatically segmented from the reconstructed 3D scene, and voxel-based volumetric features were extracted for each animal. Body weight was then estimated through linear regression models calibrated with ground truth measurements obtained from individual weighing. The proposed approach was evaluated on Nellore cattle in a feedlot environment and achieved a root mean square error (RMSE) of 8.35 kg, corresponding to an average relative error of approximately 2.29%. The results highlight the potential of UAV-based photogrammetry as a cost-effective decision support tool for digital and sustainable livestock management.
Thesagro:	Gado de Corte
NAL Thesaurus:	Unmanned aerial vehicles Cattle
Palavras-chave:	Estrutura a partir do movimento Reconstrução 3D Pecuária de precisão Estimativa de peso Structure-from-Motion 3D Reconstruction Precision livestock
ISBN:	978-989-758-834-1
ISSN:	2184-4992
Digital Object Identifier:	10.5220/0014925500004018
Notas:	Editors: Joaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi. ICEIS 2026.
Tipo do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPTIA)