Development of Fuzzy Logic Based Differentiation Algorithm and Fast Line-scan Imaging System for Chicken Inspection

A fuzzy logic-based algorithm was developed for a hyperspectral line-scan imaging system for differentiation of wholesome and systemically diseased fresh chickens. The hyperspectral imaging system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera and an imaging spectrograph. The imaging system acquired line-scan images of chicken carcasses as they passed through the pixel-wide vertical linear field of view. The chickens were hung on a closed-loop laboratory processing line moving at a speed of 70 birds per minute. The use of light-emitting-diode (LED) line lights was selected following comparative evaluation of LED and quartz–tungsten–halogen (QTH) line lights. From analysis of wholesome and systemically diseased chicken spectra, four key wavelengths for differentiating between wholesome and systemically diseased chickens were selected: 413, 472, 515, and 546 nm; a reference wavelength at 626 nm was also selected. The ratio of relative reflectance between each key wavelength and the reference wavelength was calculated for use as input image features. A fuzzy logic-based algorithm utilising the image features was developed to identify individual pixels on the chicken surface exhibiting symptoms of systemic disease. Two differentiation methods utilising the fuzzy logic-based algorithm were tested using two separate image sets, the first containing 65 wholesome and 74 systemically diseased chickens, and the second containing 48 wholesome and 42 systemically diseased chickens. The first method achieved 100% accuracy in identifying chickens in both image sets. The second method achieved 96% and 100% accuracy in identifying chickens in the first and second image sets, respectively.