Predicting meat quality measures from ultrasound images of beef cattle

Harron, Wilson
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University of Guelph

This thesis is an investigation of image processing and machine learning methods to predict meat quality measures in beef cattle from ultrasound images. A method of determining two different measures of meat quality is presented, the marbling grade and the percentage of intramuscular fat. Textural features are extracted from ultrasound images of the living animals and the subsequent feature set is reduced using different methods of feature selection and the resulting feature set is used as inputs to a number of prediction algorithms. The results fro each method of feature selection and prediction methods are compared and discussed. The results of the system indicate that each of the measures can be estimated accurately; the marbling grade can be estimated to within one grade of accuracy and the percentage intramuscular fat can be estimated to within 20% of its value. Finally, the system design is discussed with suggestions of improvements to enhance the accuracy of the estimation.

image processing, machine learning, prediction, meat quality, measures, beef cattle, ultrasound images, marbling grade, intramuscular fat