REAL-TIME DUAL-WAVELENGTH IMAGE PROCESSING FOR POULTRY SAFETY INSPECTION

Abstract

Real-time multispectral image processing algorithms were developed for online poultry carcass inspection. Neural network models with different learning rules (delta and hyperbolic tangent) and transfer functions (sigmoid and normcum-sigmoid) were examined using features extracted from spectral images at 540 nm and 700 nm. The classification accuracy using dual wavelength spectral images was much higher than single wavelength spectral images in identifying unwholesome poultry carcasses. The spectral image features at 700 nm were useful to identify wholesome carcasses, while the combination of spectral image features at 540 nm, 700 nm, and their ratio improved the classification accuracy of unwholesome carcasses. The optimum neural classifier utilized delta learning rule and hyperbolic tangent transfer function. The classification accuracy was 91.1 % for wholesome and 83.3 % for unwholesome carcasses when the spectral images of all 540 nm, 700 nm, and their ratio were used as inputs to the neural network model.