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.
Multispectral imaging for detecting contamination in poultry carcasses
A multispectral imaging system with selected optical filters of 542 and 700-nm wavelength was shown feasible for detecting contaminated poultry carcasses with high accuracy. The analysis of textural features based on co-occurrence matrix (COM) was conducted to determine the performance of multispectral image analyses in discriminating unwholesome poultry carcasses from wholesome carcasses. The variance, sum average, sum variance, and sum entropy of COM were the most significant texture features (P less than 0.0005) to identify unwholesome poultry carcasses. The feature values of angular second moment, variance, sum average, sum variance, and sum entropy did not vary with the distances and directions of COM for the spectral images. When a direction was equal to 0 degrees, the contrast was lower and the inverse difference moment and difference variance were higher (P less than 0.01) than any other direction in the visible spectral images. The characteristics of variance and sum variance texture feature of spectral images varied with the wavelength of spectral images and unwholesomeness of poultry carcasses as well. The sum variance of wholesome was higher (P less than 0.005) than unwholesome carcasses at the spectral image of 542-nm. The linear discriminant model was able to identify wholesome carcasses with classification accuracy of 83.9 percent and the unwholesome carcasses could be identified by quadratic model with 97.1 percent accuracy when textural features of spectral image at 700-nm wavelength were used as input data for models.
A Color Vision System for Peach Grading
ABSTRACT Acolor computer vision system was developed to inspect and grade fresh market peaches. System components included a color solid-state television camera, frame grabber, interface circuitry, and microcomputer. Digital color images of peaches were acquired as the fruit moved on a belt conveyor through an illumination chamber. Image analysis algorithms were developed to compare peach ground color to reference peach maturity colors, and to estimate the amount of blushed surface area. Ground color maturity classification with the computer vision system agreed with manual maturity classification in 54% of the test samples, and was within one color standard in 88% of the samples. Correlation coefficient between machine and manual estimates of blush surface area was 0.92.