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.2026-07-07http://hdl.handle.net/123456789/457476Cattle 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.Editors: Joaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi. ICEIS 2026.openAccessEstrutura a partir do movimentoReconstrução 3DPecuária de precisãoEstimativa de pesoStructure-from-Motion3D ReconstructionPrecision livestockGado de CorteUnmanned aerial vehiclesCattleArtigo em anais e proceedings