| Summary: | 碩士 === 國立屏東科技大學 === 生物機電工程系所 === 101 === To ensure the safety of chicken meal, the BAPHIQ (Bureau of Animal and Plant Health Inspection and Quarantine) deploys more than 400 veterinarians in slaughter houses to inspect each chicken carcass in Taiwan. The inspection job is a tedious and time consuming which needs to be improved with automation technique. This study applied a home-make spectral imaging system to detect the carcasses and to identify their lesions along with to classify their wholesomeness. Line spectra were obtained from breast portion and back portion. Nine ROIs of each portion were analyzed by PCA (principle component analysis) scores and loadings. Results show classification error is 9.0% and 9.3% for training and testing data set, respectively, when 40 scores were used in model. More score used in model deceased the classification error. Nine ROIs suggested that the middle portion of ROI region (label 2, 5, and 8) have the lowest classification error. Test results also indicates that when the ratio of the first to third loading of PCA were used in model along with differential treatments on spectrum, LS-SVM approach could have error on lesion classification of 5.13, 3.85, 9.07 and 7.58% on four major lesions: abnormal, ascites, inflammatory extrudes, and the others. Loading analysis also suggested important bands for identification locating around 450 nm and 800 nm. These findings might assist on further development for automatic machine.
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