PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging
Chinese cabbage (Brassica rapa L. ssp. pekinensis), a leafy vegetable, exhibits a range of leaf colors, with the dark green varieties being favored by consumers. Manual visual identification of Chinese cabbage leaf color phenotypes is subjective and it is difficult to distinguish between subtle diff...
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| Format: | Article |
| Language: | English |
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Maximum Academic Press
2023-01-01
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| Series: | Vegetable Research |
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| Online Access: | https://www.maxapress.com/article/doi/10.48130/VR-2023-0025 |
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| author | Ziwei Xie Jinghui Yan Hao Liang Xiaonan Yue Xiangjie Su Huixin Wei Yin Lu Xiaofei Fan Wei Ma Xiaomeng Zhang Xiaoxue Sun Dongfang Zhang Jingrui Li Jianjun Zhao Mengyang Liu |
| author_facet | Ziwei Xie Jinghui Yan Hao Liang Xiaonan Yue Xiangjie Su Huixin Wei Yin Lu Xiaofei Fan Wei Ma Xiaomeng Zhang Xiaoxue Sun Dongfang Zhang Jingrui Li Jianjun Zhao Mengyang Liu |
| author_sort | Ziwei Xie |
| collection | DOAJ |
| description | Chinese cabbage (Brassica rapa L. ssp. pekinensis), a leafy vegetable, exhibits a range of leaf colors, with the dark green varieties being favored by consumers. Manual visual identification of Chinese cabbage leaf color phenotypes is subjective and it is difficult to distinguish between subtle differences in leaf color, posing challenges for precision breeding. In this study, we constructed a partial least squares discriminant analysis (PLS-DA) leaf color identification model and compared four classification methods for leaf color, namely red, green, and blue (RGB) channels, hue, saturation, and lightness (HSL) color space, multi-spectrum and data-fusion. The PLS-DA supervised leaf color phenotype identification model based on data fusion can improve the recognition rate by 1%−13% compared to a single spectral model. To further validate the model, we conducted a bulked segregant analysis (BSA) of a mixed pool of a Chinese cabbage F2 population (F2-449) using whole-genome sequencing. The candidate locus related to dark green leaf color was reduced by 9.76 Mb compared to the manual visual inspection which provides convenience for the localization of candidate genes. Therefore, the development of a precise phenotypic identification system for Chinese cabbage that can distinguish subtle leaf color differences using high-throughput phenotype analysis technology is of great significance and agricultural practical value for the mining of high-throughput genomic data. |
| format | Article |
| id | doaj-art-82c73102ab8a4c46b38f96ff95c4df47 |
| institution | OA Journals |
| issn | 2769-0520 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Maximum Academic Press |
| record_format | Article |
| series | Vegetable Research |
| spelling | doaj-art-82c73102ab8a4c46b38f96ff95c4df472025-08-20T02:27:15ZengMaximum Academic PressVegetable Research2769-05202023-01-013111010.48130/VR-2023-0025VR-2023-0025PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imagingZiwei Xie0Jinghui Yan1Hao Liang2Xiaonan Yue3Xiangjie Su4Huixin Wei5Yin Lu6Xiaofei Fan7Wei Ma8Xiaomeng Zhang9Xiaoxue Sun10Dongfang Zhang11Jingrui Li12Jianjun Zhao13Mengyang Liu14State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, ChinaChinese cabbage (Brassica rapa L. ssp. pekinensis), a leafy vegetable, exhibits a range of leaf colors, with the dark green varieties being favored by consumers. Manual visual identification of Chinese cabbage leaf color phenotypes is subjective and it is difficult to distinguish between subtle differences in leaf color, posing challenges for precision breeding. In this study, we constructed a partial least squares discriminant analysis (PLS-DA) leaf color identification model and compared four classification methods for leaf color, namely red, green, and blue (RGB) channels, hue, saturation, and lightness (HSL) color space, multi-spectrum and data-fusion. The PLS-DA supervised leaf color phenotype identification model based on data fusion can improve the recognition rate by 1%−13% compared to a single spectral model. To further validate the model, we conducted a bulked segregant analysis (BSA) of a mixed pool of a Chinese cabbage F2 population (F2-449) using whole-genome sequencing. The candidate locus related to dark green leaf color was reduced by 9.76 Mb compared to the manual visual inspection which provides convenience for the localization of candidate genes. Therefore, the development of a precise phenotypic identification system for Chinese cabbage that can distinguish subtle leaf color differences using high-throughput phenotype analysis technology is of great significance and agricultural practical value for the mining of high-throughput genomic data.https://www.maxapress.com/article/doi/10.48130/VR-2023-0025chinese cabbageleaf colormulti-spectrumpls-da |
| spellingShingle | Ziwei Xie Jinghui Yan Hao Liang Xiaonan Yue Xiangjie Su Huixin Wei Yin Lu Xiaofei Fan Wei Ma Xiaomeng Zhang Xiaoxue Sun Dongfang Zhang Jingrui Li Jianjun Zhao Mengyang Liu PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging Vegetable Research chinese cabbage leaf color multi-spectrum pls-da |
| title | PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging |
| title_full | PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging |
| title_fullStr | PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging |
| title_full_unstemmed | PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging |
| title_short | PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging |
| title_sort | pls da model for accurate identification of chinese cabbage leaf color based on multispectral imaging |
| topic | chinese cabbage leaf color multi-spectrum pls-da |
| url | https://www.maxapress.com/article/doi/10.48130/VR-2023-0025 |
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