Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1187153
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dc.contributor.authorCASTANHEIRO, L. F.
dc.contributor.authorTETILA, E. C.
dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorSILVA, J. P. da
dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorROMANI, L. A. S.
dc.contributor.authorBOLFE, E. L.
dc.date.accessioned2026-06-02T16:48:57Z-
dc.date.available2026-06-02T16:48:57Z-
dc.date.created2026-05-27
dc.date.issued2026
dc.identifier.citationIn: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 28., 2026, Benidorm. Proceedings [...]. Setúbal: SCITEPRESS - Science and Technology Publications, 2026. v. 2, p. 1133-1140.
dc.identifier.isbn978-989-758-834-1
dc.identifier.issn2184-4992
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187153-
dc.descriptionCattle 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEstrutura a partir do movimento
dc.subjectReconstrução 3D
dc.subjectPecuária de precisão
dc.subjectEstimativa de peso
dc.subjectStructure-from-Motion
dc.subject3D Reconstruction
dc.subjectPrecision livestock
dc.titleCattle weight estimation from dense point clouds.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroGado de Corte
dc.subject.nalthesaurusUnmanned aerial vehicles
dc.subject.nalthesaurusCattle
dc.description.notesEditors: Joaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi. ICEIS 2026.
riaa.ainfo.id1187153
riaa.ainfo.lastupdate2026-06-02
dc.identifier.doi10.5220/0014925500004018
dc.contributor.institutionLETÍCIA 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.
Appears in Collections:Artigo em anais de congresso (CNPTIA)

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