Chicken Heart Disease Characterization by Multi-spectral Imaging

Abstract

Optical spectral reflectance and multi-spectral image analysis techniques were investigated to characterize chicken hearts for real-time disease detection. Spectral signatures of five categories of chicken hearts (airsacculitis, ascites, normal, cadaver, and septicemia) were obtained from optical reflectance measurements taken with a visible/near-infrared spectroscopic system in the range 473 to 974 nm. Multivariate statistical analysis was applied to select the most significant wavelengths from the chicken heart reflectance spectra. By optimizing the selection of wavelengths of interest for different poultry diseases, four wavelengths were selected (495, 535, 585, and 605 nm). The multi-spectral imaging system utilizes four narrow-band filters to provide four spectrally discrete images on a single CCD focal-plane. Using the filters at the wavelengths selected from the reflectance spectra, it was possible to easily implement multi-spectral arithmetic operations for disease detection. Based on analysis (t-test) of spectral image data, the multi-spectral imaging method could potentially differentiate individual diseases in chicken hearts in real time. All conditions except cadaver were shown to be separable (92-100%) by discriminant algorithms involving differences of average image intensities.