Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models
Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or vir...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media SA
2023
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/108341/1/108341.pdf |
| _version_ | 1817927552388300800 |
|---|---|
| author | Bakar, Mohd Anif A. A. Ker, Pin Jern Tang, Shirley G. H. Baharuddin, Mohd Zafri Lee, Hui Jing Omar, Abdul Rahman |
| author_facet | Bakar, Mohd Anif A. A. Ker, Pin Jern Tang, Shirley G. H. Baharuddin, Mohd Zafri Lee, Hui Jing Omar, Abdul Rahman |
| author_sort | Bakar, Mohd Anif A. A. |
| collection | UPM |
| description | Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chickens comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95 accuracy, followed by SVM-RBF kernel, and KNN with 93 accuracy, Decision Tree with 90 accuracy, and lastly, SVM-Sigmoidal kernel with 83 accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100 sensitivity and 95 accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95 accuracy) have performed exceptionally well, compared to other reported results (99.469 accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications. |
| first_indexed | 2024-12-09T02:20:15Z |
| format | Article |
| id | upm.eprints-108341 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-12-09T02:20:15Z |
| publishDate | 2023 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | upm.eprints-1083412024-10-15T01:55:57Z http://psasir.upm.edu.my/id/eprint/108341/ Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models Bakar, Mohd Anif A. A. Ker, Pin Jern Tang, Shirley G. H. Baharuddin, Mohd Zafri Lee, Hui Jing Omar, Abdul Rahman Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chickens comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95 accuracy, followed by SVM-RBF kernel, and KNN with 93 accuracy, Decision Tree with 90 accuracy, and lastly, SVM-Sigmoidal kernel with 83 accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100 sensitivity and 95 accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95 accuracy) have performed exceptionally well, compared to other reported results (99.469 accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications. Frontiers Media SA 2023 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/108341/1/108341.pdf Bakar, Mohd Anif A. A. and Ker, Pin Jern and Tang, Shirley G. H. and Baharuddin, Mohd Zafri and Lee, Hui Jing and Omar, Abdul Rahman (2023) Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models. Frontiers in Veterinary Science, 10. pp. 1-14. ISSN 2297-1769 https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2023.1174700/full 10.3389/fvets.2023.1174700 |
| spellingShingle | Bakar, Mohd Anif A. A. Ker, Pin Jern Tang, Shirley G. H. Baharuddin, Mohd Zafri Lee, Hui Jing Omar, Abdul Rahman Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title | Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title_full | Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title_fullStr | Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title_full_unstemmed | Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title_short | Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models |
| title_sort | translating conventional wisdom on chicken comb color into automated monitoring of disease infected chicken using chromaticity based machine learning models |
| url | http://psasir.upm.edu.my/id/eprint/108341/1/108341.pdf |
| work_keys_str_mv | AT bakarmohdanifaa translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels AT kerpinjern translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels AT tangshirleygh translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels AT baharuddinmohdzafri translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels AT leehuijing translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels AT omarabdulrahman translatingconventionalwisdomonchickencombcolorintoautomatedmonitoringofdiseaseinfectedchickenusingchromaticitybasedmachinelearningmodels |