An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification
This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600...
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| Format: | Article |
| Language: | English |
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Taiwan Association of Engineering and Technology Innovation
2023-04-01
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| Series: | Proceedings of Engineering and Technology Innovation |
| Subjects: | |
| Online Access: | https://ojs.imeti.org/index.php/PETI/article/view/11361 |
| _version_ | 1797808538651721728 |
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| author | Thavamani Subramani Vijayakumar Jeganathan Sruthi Kunkuma Balasubramanian |
| author_facet | Thavamani Subramani Vijayakumar Jeganathan Sruthi Kunkuma Balasubramanian |
| author_sort | Thavamani Subramani |
| collection | DOAJ |
| description |
This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7.
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| first_indexed | 2024-03-13T06:39:01Z |
| format | Article |
| id | doaj.art-58b640e341e04e32af442eec38390a08 |
| institution | Directory Open Access Journal |
| issn | 2413-7146 2518-833X |
| language | English |
| last_indexed | 2024-03-13T06:39:01Z |
| publishDate | 2023-04-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | Proceedings of Engineering and Technology Innovation |
| spelling | doaj.art-58b640e341e04e32af442eec38390a082023-06-08T18:28:31ZengTaiwan Association of Engineering and Technology InnovationProceedings of Engineering and Technology Innovation2413-71462518-833X2023-04-012410.46604/peti.2023.11361An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed ClassificationThavamani Subramani0Vijayakumar Jeganathan1Sruthi Kunkuma Balasubramanian2Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, IndiaDepartment of Electronics and Instrumentation, Bharathiar University, Coimbatore, IndiaDepartment of Electronics and Instrumentation, Bharathiar University, Coimbatore, India This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7. https://ojs.imeti.org/index.php/PETI/article/view/11361Native chicken breed classification, Gender classification, GLCM, PCA, Machine learning algorithms |
| spellingShingle | Thavamani Subramani Vijayakumar Jeganathan Sruthi Kunkuma Balasubramanian An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification Proceedings of Engineering and Technology Innovation Native chicken breed classification, Gender classification, GLCM, PCA, Machine learning algorithms |
| title | An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification |
| title_full | An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification |
| title_fullStr | An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification |
| title_full_unstemmed | An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification |
| title_short | An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification |
| title_sort | effective supervised machine learning approach for indian native chicken s gender and breed classification |
| topic | Native chicken breed classification, Gender classification, GLCM, PCA, Machine learning algorithms |
| url | https://ojs.imeti.org/index.php/PETI/article/view/11361 |
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