Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

• Main Authors: Gemma A. Miller, James J. Hyslop, David Barclay, Andrew Edwards, William Thomson, Carol-Anne Duthie
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-05-01
• Series: Frontiers in Sustainable Food Systems
• Subjects: finishing beef cattle; 3D imaging; carcass characteristics; machine learning; precision livestock farming
• Online Access: https://www.frontiersin.org/article/10.3389/fsufs.2019.00030/full

Selection of finishing beef cattle for slaughter and evaluation of performance is currently achieved through visual assessment and/or by weighing through a crush. Consequently, large numbers of cattle are not meeting target specification at the abattoir. Video imaging analysis (VIA) is increasingly used in abattoirs to grade carcasses with high accuracy. There is potential for three-dimensional (3D) imaging to be used on farm to predict carcass characteristics of live animals and to optimise slaughter selections. The objectives of this study were to predict liveweight (LW) and carcass characteristics of live animals using 3D imaging technology and machine learning algorithms (artificial neural networks). Three dimensional images and LW's were passively collected from finishing steer and heifer beef cattle of a variety of breeds pre-slaughter (either on farm or after entry to the abattoir lairage) using an automated camera system. Sixty potential predictor variables were automatically extracted from the live animal 3D images using bespoke algorithms; these variables included lengths, heights, widths, areas, volumes, and ratios and were used to develop predictive models for liveweight and carcass characteristics. Cold carcass weights (CCW) for each animal were provided by the abattoir. Saleable meat yield (SMY) and EUROP fat and conformation grades were also determined for each individual by VIA of half of the carcass. Performance of prediction models was assessed using R2 and RMSE parameters following regression of predicted and actual variables for LW (R2 = 0.7, RMSE = 42), CCW (R2 = 0.88, RMSE = 14) and SMY (R2 = 0.72, RMSE = 14). The models predicted EUROP fat and conformation grades with 54 and 55% accuracy (R2), respectively. This study demonstrated that 3D imaging coupled with machine learning analytics can be used to predict LW, SMY and traditional carcass characteristics of live animals. This system presents an opportunity to reduce a considerable inefficiency in beef production enterprises through autonomous monitoring of finishing cattle on the farm and marketing of animals at the optimal time.