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.
Intensified Multispectral Imaging System for Poultry Carcass Inspection
A multispectral imaging system possessing six optical filters at 542, 570, 641, 700, 720, and 847 nm was demonstrated feasible for discriminating normal poultry carcasses from abnormal carcasses. Gray level image intensities of selected wavelengths were used to classify normal carcasses from abnormal carcasses. Visible wavelengths, 542, 570, and 641 nm, were useful for separation of normal carcasses from septicemic and cadaver carcasses. The accuracy was 89.3% for separation of normal carcasses from abnormal carcasses. The accuracy for separation of the normal carcasses from the septicemic was 89.3%, and from cadaver, 96.5%. Among the abnormal carcasses, the accuracy of classification of the septicemic carcasses was 90.0% and of the cadaver carcasses, 96.6%. The accuracies of classification for poultry carcasses into three classes using a neural networks classifier were 92.9% for normal, 83.4% for septicemic, and 96.6% for cadaver carcasses.
Effect of Soybean Cyst Nematode (Heterodera glycines) on Yield of Resistant and Susceptible Soybean Cultivars Grown in Ohio.
Soybean (Glycine max) producers in Ohio rarely use soybean cyst nematode (Heterodera glycines, SCN)-resistant cultivars because of concerns over limited yield potential and lack of resistance to Phytophthora sojae. A two-year study was initiated to determine grain yield and nematode population increase on soybean cyst nematode-resistant cultivars in maturity groups II and III in production fields. Sites differed in soil texture, nematode densities, and P. sojae infestation at a number of locations in Ohio. Soil was assayed for nematode densities before planting and at harvest. Yields of resistant cultivars averaged 0% to 18% higher than those of susceptible cultivars in fine-textured soils with average preplant populations ranging from 463 to 14,330 SCN eggs/100 cm(3) soil. In coarse-textured soils, yields of susceptible cultivars were 21% to 56% less than the resistant cultivars with average preplant densities ranging from 1,661 to 15,558 SCN eggs/100 cm(3) soil. The reproductive index ranged from 0.1 to 5.5 for resistant cultivars and 0.4 to 112 for susceptible cultivars. In 1993, yield of P. sojae-susceptible, nematode-resistant 'Asgrow A 3431' was as high as yield of the P. sojae-resistant, nematode-susceptible cultivar 'Resnik' in a Phytophthora-infested field. The nematode-resistant cultivars Madison Experimental 131527 and Asgrow A3431 had higher yields than AgVenture AV1341 and susceptible cultivars Resnik and Kenwood when compared over five nematode-infested sites. Nematode-resistant cultivars were found to be excellent alternatives to currently grown susceptible cultivars for managing SCN where group III cultivars are used. However, better cultivar alternatives may be needed for sites with combined Phytophthora root rot and cyst nematode problems.