Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass
A machine vision system was used to collect 948 three-dimensional images of chicken carcasses on the broiler slaughter line. This study aimed to develop a rapid method for the identification of primary dermatitis in chicken carcasses. The acquired images were preprocessed and segmented into 128 × 12...
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| Format: | Article |
| Language: | English |
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China Food Publishing Company
2023-10-01
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| Series: | Shipin Kexue |
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| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-20-041.pdf |
| _version_ | 1797435897730301952 |
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| author | WU Jiangchun, WANG Huhu, XU Xinglian |
| author_facet | WU Jiangchun, WANG Huhu, XU Xinglian |
| author_sort | WU Jiangchun, WANG Huhu, XU Xinglian |
| collection | DOAJ |
| description | A machine vision system was used to collect 948 three-dimensional images of chicken carcasses on the broiler slaughter line. This study aimed to develop a rapid method for the identification of primary dermatitis in chicken carcasses. The acquired images were preprocessed and segmented into 128 × 128 pixel pictures with grids. A total of 762 pictures of dermatitic skin and 775 pictures of normal skin were selected. A total of 24 feature values were extracted including third-order color moments, mean and variance of gray-level co-occurrence matrix features, Tamura texture features from the 1 537 pictures and the segmentation threshold and area of dermatitis region. Based on dimensionality reduction by principal component analysis (PCA), linear discriminant analysis model, quadratic discriminant analysis model, support vector machine, random forest, back propagation neural network (BPNN) and GoogLeNet models were established, and their classification performances were compared. Among these models, the GoogLeNet model was the most effective in classifying dermatitic skin samples with an overall accuracy of 90.5% and an average detection speed of 122.65 sheets per second. The prediction accuracy of the model for chicken carcasses with dermatitis was 100%, while that for qualified chicken carcasses was 90%. |
| first_indexed | 2024-03-09T10:54:54Z |
| format | Article |
| id | doaj.art-fe806fd7e7ea4324b4c1f2e811e89103 |
| institution | Directory Open Access Journal |
| issn | 1002-6630 |
| language | English |
| last_indexed | 2024-03-09T10:54:54Z |
| publishDate | 2023-10-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj.art-fe806fd7e7ea4324b4c1f2e811e891032023-12-01T03:19:24ZengChina Food Publishing CompanyShipin Kexue1002-66302023-10-01442035035610.7506/spkx1002-6630-20221010-084Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler CarcassWU Jiangchun, WANG Huhu, XU Xinglian0(State Key Laboratory of Meat Quality Control and Cultured Meat Development, College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China)A machine vision system was used to collect 948 three-dimensional images of chicken carcasses on the broiler slaughter line. This study aimed to develop a rapid method for the identification of primary dermatitis in chicken carcasses. The acquired images were preprocessed and segmented into 128 × 128 pixel pictures with grids. A total of 762 pictures of dermatitic skin and 775 pictures of normal skin were selected. A total of 24 feature values were extracted including third-order color moments, mean and variance of gray-level co-occurrence matrix features, Tamura texture features from the 1 537 pictures and the segmentation threshold and area of dermatitis region. Based on dimensionality reduction by principal component analysis (PCA), linear discriminant analysis model, quadratic discriminant analysis model, support vector machine, random forest, back propagation neural network (BPNN) and GoogLeNet models were established, and their classification performances were compared. Among these models, the GoogLeNet model was the most effective in classifying dermatitic skin samples with an overall accuracy of 90.5% and an average detection speed of 122.65 sheets per second. The prediction accuracy of the model for chicken carcasses with dermatitis was 100%, while that for qualified chicken carcasses was 90%.https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-20-041.pdfmachine vision; primary dermatitis of chicken carcass; machine learning; defect detection |
| spellingShingle | WU Jiangchun, WANG Huhu, XU Xinglian Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass Shipin Kexue machine vision; primary dermatitis of chicken carcass; machine learning; defect detection |
| title | Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass |
| title_full | Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass |
| title_fullStr | Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass |
| title_full_unstemmed | Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass |
| title_short | Rapid Machine Vision Method for Detection of Primary Dermatitis in Broiler Carcass |
| title_sort | rapid machine vision method for detection of primary dermatitis in broiler carcass |
| topic | machine vision; primary dermatitis of chicken carcass; machine learning; defect detection |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2023-44-20-041.pdf |
| work_keys_str_mv | AT wujiangchunwanghuhuxuxinglian rapidmachinevisionmethodfordetectionofprimarydermatitisinbroilercarcass |