Automated chick gender determination using optical coherence tomography and deep learning
Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by human expertise, labor intensity, and insufficient resolution. This study introduces a novel approach that integrates Optical Coherence Tomography (O...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S003257912500272X |
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| author | Jadsada Saetiew Papawit Nongkhunsan Jiraporn Saenjae Rapeephat Yodsungnoen Amonrat Molee Sirichok Jungthawan Ittipon Fongkaew Panomsak Meemon |
| author_facet | Jadsada Saetiew Papawit Nongkhunsan Jiraporn Saenjae Rapeephat Yodsungnoen Amonrat Molee Sirichok Jungthawan Ittipon Fongkaew Panomsak Meemon |
| author_sort | Jadsada Saetiew |
| collection | DOAJ |
| description | Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by human expertise, labor intensity, and insufficient resolution. This study introduces a novel approach that integrates Optical Coherence Tomography (OCT) and deep learning to enable high-resolution, non-invasive chick sexing. Unlike conventional imaging techniques, OCT provides micrometer-scale visualization of cloacal structures, allowing precise differentiation between male and female chicks based on internal anatomical markers. We developed a custom convolutional neural network (CNN) optimized for OCT data, incorporating asymmetric image resizing and enhanced feature extraction to improve classification accuracy. Our model achieved 79 % accuracy, outperforming conventional architectures such as Inception (63 %) and VGG-16 (74 %), highlighting the importance of a tailored, domain-specific model. This is the first study to integrate OCT with deep learning for automated chick sexing, demonstrating a scalable, real-time alternative to expert-dependent vent sexing. With further advancements in imaging and machine learning, our approach has the potential to transform chick sexing in commercial hatcheries, reducing reliance on skilled labor while enhancing classification efficiency and precision. |
| format | Article |
| id | doaj-art-1ba6e2a51c76455f97a4b610b1fdf548 |
| institution | OA Journals |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-1ba6e2a51c76455f97a4b610b1fdf5482025-08-20T01:51:44ZengElsevierPoultry Science0032-57912025-05-01104510503310.1016/j.psj.2025.105033Automated chick gender determination using optical coherence tomography and deep learningJadsada Saetiew0Papawit Nongkhunsan1Jiraporn Saenjae2Rapeephat Yodsungnoen3Amonrat Molee4Sirichok Jungthawan5Ittipon Fongkaew6Panomsak Meemon7School of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Integrated Science and Innovation, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Animal Technology and Innovation, Institute of Agricultural, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, ThailandSchool of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, Thailand; Corresponding author.Chick gender classification is crucial for optimizing poultry production, yet traditional methods such as vent sexing and ultrasound remain limited by human expertise, labor intensity, and insufficient resolution. This study introduces a novel approach that integrates Optical Coherence Tomography (OCT) and deep learning to enable high-resolution, non-invasive chick sexing. Unlike conventional imaging techniques, OCT provides micrometer-scale visualization of cloacal structures, allowing precise differentiation between male and female chicks based on internal anatomical markers. We developed a custom convolutional neural network (CNN) optimized for OCT data, incorporating asymmetric image resizing and enhanced feature extraction to improve classification accuracy. Our model achieved 79 % accuracy, outperforming conventional architectures such as Inception (63 %) and VGG-16 (74 %), highlighting the importance of a tailored, domain-specific model. This is the first study to integrate OCT with deep learning for automated chick sexing, demonstrating a scalable, real-time alternative to expert-dependent vent sexing. With further advancements in imaging and machine learning, our approach has the potential to transform chick sexing in commercial hatcheries, reducing reliance on skilled labor while enhancing classification efficiency and precision.http://www.sciencedirect.com/science/article/pii/S003257912500272XChick gender classificationVent sexingOptical coherence tomographyDeep learningMachine learning |
| spellingShingle | Jadsada Saetiew Papawit Nongkhunsan Jiraporn Saenjae Rapeephat Yodsungnoen Amonrat Molee Sirichok Jungthawan Ittipon Fongkaew Panomsak Meemon Automated chick gender determination using optical coherence tomography and deep learning Poultry Science Chick gender classification Vent sexing Optical coherence tomography Deep learning Machine learning |
| title | Automated chick gender determination using optical coherence tomography and deep learning |
| title_full | Automated chick gender determination using optical coherence tomography and deep learning |
| title_fullStr | Automated chick gender determination using optical coherence tomography and deep learning |
| title_full_unstemmed | Automated chick gender determination using optical coherence tomography and deep learning |
| title_short | Automated chick gender determination using optical coherence tomography and deep learning |
| title_sort | automated chick gender determination using optical coherence tomography and deep learning |
| topic | Chick gender classification Vent sexing Optical coherence tomography Deep learning Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S003257912500272X |
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