Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones
Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5135 |
| _version_ | 1797596788232814592 |
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| author | Shufeng Zhang Yuxi Chen Weizhen Liu Bang Liu Xiang Zhou |
| author_facet | Shufeng Zhang Yuxi Chen Weizhen Liu Bang Liu Xiang Zhou |
| author_sort | Shufeng Zhang |
| collection | DOAJ |
| description | Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry. |
| first_indexed | 2024-03-11T02:57:58Z |
| format | Article |
| id | doaj.art-bed31c32776a441881ba1eae8342dc48 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bed31c32776a441881ba1eae8342dc482023-11-18T08:33:01ZengMDPI AGSensors1424-82202023-05-012311513510.3390/s23115135Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from SmartphonesShufeng Zhang0Yuxi Chen1Weizhen Liu2Bang Liu3Xiang Zhou4School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, ChinaKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, ChinaKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, ChinaMarbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry.https://www.mdpi.com/1424-8220/23/11/5135pork marblingpork quality evaluationimage segmentationpatch-based trainingcontext encoder network |
| spellingShingle | Shufeng Zhang Yuxi Chen Weizhen Liu Bang Liu Xiang Zhou Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones Sensors pork marbling pork quality evaluation image segmentation patch-based training context encoder network |
| title | Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones |
| title_full | Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones |
| title_fullStr | Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones |
| title_full_unstemmed | Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones |
| title_short | Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones |
| title_sort | marbling net a novel intelligent framework for pork marbling segmentation using images from smartphones |
| topic | pork marbling pork quality evaluation image segmentation patch-based training context encoder network |
| url | https://www.mdpi.com/1424-8220/23/11/5135 |
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