On-Line Automated Inspection of Poultry Carcasses by Machine Vision
Development of an automated poultry inspection system that is low-cost, operates 
with minimum human intervention, and is able to maintain its accuracy is important to 
the U.S. Food Safety and Inspection Service (FSIS) and the poultry industry. Such 
a system, placed strategically in the processing plants, would help improve the 
inspection speed, minimize problems of human error and variability, improve the 
effectiveness of the federal inspection program, and increase the slaughter plants’ 
productivity. This paper describes an automated poultry inspection system that 
utilizes machine vision technology. The system was tested on-line at a poultry plant. 
The results showed that the classification accuracy for wholesome and unwholesome 
carcasses were 94 and 87%, respectively.
On-line inspection of poultry carcasses by a dual-camera system
Abstract The Instrumentation and Sensing Laboratory (ISL) has developed a multi-spectral imaging system for on-line inspection of poultry carcasses. The ISL design is based on two principles: (1) wholesome and unwholesome birds have different chemical compositions of tissues and may have different skin color, and (2) unwholesome carcasses may have physical abnormalities which can be detected by computerized imaging. On-line trials of the multi-spectral chicken carcass inspection system were conducted during a 14-day period in a poultry-processing plant in New Holland, Pennsylvania, where spectral images of 13,132 wholesome and 1459 unwholesome chicken carcasses were measured. For off-line model development, the accuracies for classification of wholesome and unwholesome carcasses were 95% and 88%. On-line testing of the neural network classification models with combination of the filter information was performed. The inspection system gave accuracies of 94% and 87% for wholesome and unwholesome carcasses, respectively. This accuracy was consistent with the results obtained previously on laboratory studies. Thus, the inspection system shows promise for separation of unwholesome chicken carcasses from wholesome carcasses in poultry processing lines.
Ultrasonic image analysis for beef tenderness
Objective measurement of meat tenderness has been a topic of concern for palatability evaluation. In this study, a real-time ultrasonic B-mode imaging method was used for measuring beef palatability attributes such as juiciness, muscle fiber tenderness, connective tissue amount, overall tenderness, flavor intensity, and percent total collagen noninvasively. A temporal averaging image enhancement method was used for image analysis. Ultrasonic image intensity, fractal dimension, attenuation, and statistical gray-tone spatial-dependence matrix image texture measurement were analyzed. The contrast of the textural feature was the most correlated parameter with palatability attributes. The longitudinal scanning method was better for juiciness, muscle fiber tenderness, flavor intensity, and percent soluble collagen, whereas, the cross-sectional method was better for connective tissue, overall tenderness. The multivariate linear regression models were developed as a function of textural features and image intensity parameters. The determinant coefficients of regression models were for juiciness (R2 equals .97), for percent total collagen (R2 equals .88), for flavor intensity (R2 equals .75), for muscle fiber tenderness (R2 equals .55), and for overall tenderness (R2 equals .49), respectively.