3D imaging for on-farm estimation of live cattle traits and carcass weight prediction.

Alen Alempijevic, Teresa Vidal-Calleja, Raphael Falque, Brad Walmsley, Malcolm McPhee
Author Information
  1. Alen Alempijevic: Robotics Institute, University of Technology Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. Electronic address: alen.alempijevic@uts.edu.au.
  2. Teresa Vidal-Calleja: Robotics Institute, University of Technology Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. Electronic address: teresa.vidalcalleja@uts.edu.au.
  3. Raphael Falque: Robotics Institute, University of Technology Sydney, P.O. Box 123, Broadway, NSW 2007, Australia. Electronic address: raphael.falque@uts.edu.au.
  4. Brad Walmsley: NSW Department of Primary Industries and Regional Development, Animal Genetics and Breeding Unit (AGBU: AGBU is a Joint Venture of NSW Department of Primary Industries and University of New England), Armidale, NSW 2351, Australia. Electronic address: brad.walmsley@dpi.nsw.gov.au.
  5. Malcolm McPhee: NSW Department of Primary Industries and Regional Development, Livestock Industries Centre, Armidale, NSW 2351, Australia. Electronic address: malcolm.mcphee@dpi.nsw.gov.au.

Abstract

This study presents a 3-dimensional (3D) imaging system, operating at processing speed, deployed at a commercial feedlot, that assesses hip height (cm), subcutaneous fat thickness at the P8 site (mm), and hot standard carcass weight (HSCW, kg) from the shape of individual live cattle. A two-part study was conducted: Study 1 evaluated measured hip height (cm) on 247 steers and ultrasound scanned P8 fat (mm) on 219 steers versus projections from 3D images; and Study 2 evaluated abattoir HSCW on 32 Angus steers versus predictions from 3D images. Hip height was directly estimated from the 3D images, while P8 fat and HSCW were predicted using a model based on features extracted from these images through supervised learning with Gaussian Processes. The models were evaluated using cross-validation. The measured hip height versus live estimates from 3D imaging resulted in a RMSE = 3.07 cm, and R = 0.69. The ultrasound scanned P8 fat versus live predictions from 3D imaging resulted in a RMSE = 2.38 mm, and R = 0.78; and the abattoir HSCW versus live predictions from 3D imaging resulted in a RMSE = 8.15 kg, and R = 0.79. The design of the 3D imaging system, with multiple cameras, was installed into a traditional race for processing cattle and effectively operates with variation in length and breeds of cattle. The 3D imaging system demonstrates the feasibility of adoption by the beef industry that creates value through the integration of 3D imaging and BeefSpecs into a technology called CattleAssess3D.

Keywords

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Created with Highcharts 10.0.03DimagingheightfatP8liveversusHSCWcattleimagessystemhipcarcassweightevaluatedsteerspredictionsresultedR = 0studyprocessingcmmmStudymeasuredultrasoundscannedabattoirHipusingcameraspresents3-dimensionaloperatingspeeddeployedcommercialfeedlotassessessubcutaneousthicknesssitehotstandardkgshapeindividualtwo-partconducted:1247219projections232AngusdirectlyestimatedpredictedmodelbasedfeaturesextractedsupervisedlearningGaussianProcessesmodelscross-validationestimatesRMSE = 307 cm69RMSE = 238 mm78RMSE = 815 kg79designmultipleinstalledtraditionalraceeffectivelyoperatesvariationlengthbreedsdemonstratesfeasibilityadoptionbeefindustrycreatesvalueintegrationBeefSpecstechnologycalledCattleAssess3Don-farmestimationtraitspredictionBeefComputervisionHotRGB-D

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