000135845 001__ 135845
000135845 005__ 20240619140701.0
000135845 0247_ $$2doi$$a10.1093/jigpal/jzae059
000135845 0248_ $$2sideral$$a138864
000135845 037__ $$aART-2024-138864
000135845 041__ $$aeng
000135845 100__ $$aRiego del Castillo, Virginia
000135845 245__ $$aA non-stressful vision-based method for weighing live lambs
000135845 260__ $$c2024
000135845 5203_ $$aAccurate measurement of livestock weight is a primary indicator in the meat industry to increase the economic gain. In lambs, the weight of a live animal is still usually estimated manually using traditional scales, resulting in a tedious process for the experienced assessor and stressful for the animal. In this paper, we propose a solution to this problem using computer vision techniques; thus, the proposed procedure estimates the weight of a lamb by analysing its zenithal image without interacting with the animal, which speeds up the process and reduces weighing costs. It is based on a data-driven decision support system that uses RGB-D machine vision techniques and regression models. Unlike existing methods, it does not require walk-over-weighing platforms or special and expensive infrastructures. The proposed method includes a decision support system that automatically rejects those images that are not appropriate to estimate the lamb weight. After determining the body contour of the lamb, we compute several features that feed different regression models. Best results were achieved with Extra Tree Regression ($R^{2}$=91.94%), outperforming the existing techniques. Using only an image, the proposed approach can identify with a minimum error the optimal weight of a lamb to be slaughtered, so as to maximise the economic profit.
000135845 536__ $$9info:eu-repo/grantAgreement/ES/AEI/TED2021-132356B-I00$$9info:eu-repo/grantAgreement/ES/DGA/GCP-2017-2700
000135845 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000135845 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135845 700__ $$aSánchez-González, Lidia
000135845 700__ $$aFernández, Laura
000135845 700__ $$0(orcid)0000-0002-7891-609X$$aRebollar, Rubén$$uUniversidad de Zaragoza
000135845 700__ $$aSamperio, Enrique$$uUniversidad de Zaragoza
000135845 7102_ $$15002$$2720$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Proyectos de Ingeniería
000135845 773__ $$g(2024), [13 pp.]$$pLog. j. IGPL$$tLogic Journal of the IGPL$$x1367-0751
000135845 8564_ $$s970480$$uhttps://zaguan.unizar.es/record/135845/files/texto_completo.pdf$$yVersión publicada
000135845 8564_ $$s1692567$$uhttps://zaguan.unizar.es/record/135845/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135845 909CO $$ooai:zaguan.unizar.es:135845$$particulos$$pdriver
000135845 951__ $$a2024-06-19-13:22:57
000135845 980__ $$aARTICLE