Andmebaasi logo
 

Evaluation of chest circumference in 3D lateral images of dairy cattle farming for body mass prediction

Lühendatud kirje lehekülg
dc.contributor.authorOliveira, F.M.
dc.contributor.authorFerraz, P.F.P.
dc.contributor.authorFerraz, G.A.S.
dc.contributor.authorCecchin, D.
dc.contributor.authorStopatto, A.F.S.
dc.contributor.authorBecciolini, V.
dc.contributor.authorBarbari, M.
dc.date.accessioned2025-06-03T06:02:18Z
dc.date.available2025-06-03T06:02:18Z
dc.date.issued2025
dc.descriptionReceived: January 31st, 2025 ; Accepted: April 23rd, 2025 ; Published: May 5th, 2025 ; Correspondence: patrícia.ponciano@ufla.breng
dc.description.abstractThe advancement of precision livestock farming has underscored the importance of developing innovative and non-invasive methods for monitoring animal health and productivity. In this context, this study evaluated the application of computer vision to estimate the body mass (BM) of Holstein-Friesian dairy cows using 3D images captured laterally with the Intel RealSense D435i depth camera. The methodology involved correlating chest circumference (CC) measurements obtained in the field with those derived from lateral 3D images. A total of 250 animals were analyzed, with BM ranging from 420 to 855 kg, and the relationship between CC and BM was modeled using regression techniques. The results indicated a coefficient of determination (R² = 0.88) and a mean absolute percentage error (MAPE) of 3.94% for CC measured in the field. For CC derived from 3D images, R² was 0.847, with an MAPE of 5.29%. Although the 3D image-based method showed a slight reduction in accuracy, it demonstrated significant potential as a non-invasive and efficient alternative for estimating BM in dairy cows. Furthermore, the study highlights the role of 3D imaging technologies in acquiring detailed morphological data, enabling a more comprehensive understanding of body composition dynamics over time. These findings reinforce the potential of integrating digital technologies into dairy farming, promoting sustainable, precise, and labor-efficient management practices.eng
dc.identifier.citationOliveira, F. M., Ferraz, P. F. P., Ferraz, G. A. S., Cecchin, D., Stopatto, A. F. S., Becciolini, V., & Barbari, M. (2025). Evaluation of chest circumference in 3D lateral images of dairy cattle farming for body mass prediction. https://doi.org/10.15159/AR.25.035en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2025, vol. 23, Special Issue 1, pp. 164–179eng
dc.identifier.urihttp://hdl.handle.net/10492/9900
dc.identifier.urihttps://doi.org/10.15159/ar.25.035
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject3D imageseng
dc.subjectcomputer visioneng
dc.subjectdairy cattle farmingeng
dc.subjectnon-invasive body mass estimationeng
dc.subjectarticleseng
dc.titleEvaluation of chest circumference in 3D lateral images of dairy cattle farming for body mass predictioneng
dc.typeArticleeng

Failid

Originaal pakett

Nüüd näidatakse 1 - 1 1
Pisipilt
Nimi:
164-179-Oliveira1.pdf#abstract-10673.pdf
Suurus:
1.09 MB
Formaat:
Adobe Portable Document Format
Kirjeldus:
Article
Lae alla

Kollektsioonid

2025, Vol. 23, Special Issue 1