Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning
Abstract In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing ca...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85372-w |
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| author | Nanding Li Naoshi Kondo Yuichi Ogawa Keiichiro Shiraga Mizuki Shibasaki Daniele Pinna Moriyuki Fukushima Shinichi Nagaoka Tateshi Fujiura Xuehong De Tetsuhito Suzuki |
| author_facet | Nanding Li Naoshi Kondo Yuichi Ogawa Keiichiro Shiraga Mizuki Shibasaki Daniele Pinna Moriyuki Fukushima Shinichi Nagaoka Tateshi Fujiura Xuehong De Tetsuhito Suzuki |
| author_sort | Nanding Li |
| collection | DOAJ |
| description | Abstract In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry. |
| format | Article |
| id | doaj-art-7195d0b291df40e3aa9adbd2c1859ce8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-7195d0b291df40e3aa9adbd2c1859ce82025-02-09T12:28:24ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-85372-wFundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learningNanding Li0Naoshi Kondo1Yuichi Ogawa2Keiichiro Shiraga3Mizuki Shibasaki4Daniele Pinna5Moriyuki Fukushima6Shinichi Nagaoka7Tateshi Fujiura8Xuehong De9Tetsuhito Suzuki10School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityGraduate School of Agriculture, Kyoto UniversityGraduate School of Agriculture, Kyoto UniversityGraduate School of Agriculture, Kyoto UniversityGraduate School of Agriculture, Kyoto UniversityDepartment of Agricultural Sciences, University of SassariGraduate School of Agriculture, Kyoto UniversityGraduate School of Agriculture, Kyoto UniversityGraduate School of Agriculture, Kyoto UniversitySchool of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityGraduate School of Bioresources, Mie UniversityAbstract In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry.https://doi.org/10.1038/s41598-025-85372-wFundus imagingDeep learningVitamin A estimationJapanese black cattlePrecision Livestock Farming |
| spellingShingle | Nanding Li Naoshi Kondo Yuichi Ogawa Keiichiro Shiraga Mizuki Shibasaki Daniele Pinna Moriyuki Fukushima Shinichi Nagaoka Tateshi Fujiura Xuehong De Tetsuhito Suzuki Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning Scientific Reports Fundus imaging Deep learning Vitamin A estimation Japanese black cattle Precision Livestock Farming |
| title | Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning |
| title_full | Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning |
| title_fullStr | Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning |
| title_full_unstemmed | Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning |
| title_short | Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning |
| title_sort | fundus camera based precision monitoring of blood vitamin a level for wagyu cattle using deep learning |
| topic | Fundus imaging Deep learning Vitamin A estimation Japanese black cattle Precision Livestock Farming |
| url | https://doi.org/10.1038/s41598-025-85372-w |
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