SIMPLE MULTISPECTRAL IMAGE ANALYSIS FOR SYSTEMICALLY DISEASED CHICKEN IDENTIFICATION.

A simple multispectral differentiation method for the identification of systemically diseased chickens was developed and demonstrated. Color differences between wholesome and systemically diseased chickens were used to select interference filters at 488, 540, 580, and 610 nm for the multispectral imaging system. Over a period of 6 months, 660 chicken images were collected in three batches. An image processing algorithm to locate the region of interest (ROI) was developed in antler to define four classification areas on each image: whole carcass (WC), region of interest (ROI), upper region (UR), and lower region (LR). Three feature types, average intensity (AI), average normalization (AN), and average difference normalization (ADN), were defined using several wavebands for a total of 12 classification features. A decision tree algorithm was used to determine threshold values for each of the 12 classification features in each of the four classification areas. The AI feature type was found to identify wholesome and systemically diseased chickens better than the AN and ADN features types. Classification by AI in the ROI area, using the 540 and 580 nm wavebands, achieved the best accuracies. AI540 achieved 96.3% and 97.1% classification accuracies for wholesome and systemically diseased chickens, respectively. AI580 achieved 96.3% and 98.6% classification accuracies for wholesome and systemically diseased chickens, respectively. This simple differentiation method shows potential for automated on--line chicken inspection. [ABSTRACT FROM AUTHOR]