Machine vision system for on-line wholesomeness inspection of poultry carcasses

Abstract

A line-scan machine vision system and multispectral inspection algorithm was developed and evaluated for differentiation of wholesome and systemically diseased chickens on a high-speed processing line. The inspection system acquires line-scan images of chicken carcasses on a 140 bird/min processing line and is able to automatically detect individual birds entering and exiting the field of view of the camera, locate a specified region of interest for spectral image analysis, and produce a decision output for each bird. The same spectral line-scan imaging system was used for hyperspectral data acquisition-analysis to develop the multispectral detection and differentiation algorithm and for multispectral implementation of the algorithm for real-time on-line inspection on the processing line. Results showed that effective multispectral inspection could be achieved by analysis of a selected region of interest across the breast area from images at the 580- and 620-nm wavebands. Overall system performance was evaluated during two 8-h shifts in which the system inspected over 100,000 chickens, with system results compared with Food Safety and Inspection Service inspector tallies of wholesome and systemically diseased birds for that same time period. During system verification, the system accurately classified wholesome and systemically diseased chickens that were observed by a veterinarian posted beside the system to perform real-time identifications of the same birds. The high accuracy of the results demonstrated that the spectra line-scan imaging system and multispectral detection and differentiation algorithm can be effectively used for on-line high-speed presorting applications for young broiler chickens.