Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification
Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.973745/full |
| _version_ | 1828292787234144256 |
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| author | Mingzhu Tao Yong He Xiulin Bai Xiaoyun Chen Yuzhen Wei Cheng Peng Xuping Feng |
| author_facet | Mingzhu Tao Yong He Xiulin Bai Xiaoyun Chen Yuzhen Wei Cheng Peng Xuping Feng |
| author_sort | Mingzhu Tao |
| collection | DOAJ |
| description | Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection. |
| first_indexed | 2024-04-13T11:13:45Z |
| format | Article |
| id | doaj.art-c58c83232db5485c9e21efbddca940cb |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T11:13:45Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-c58c83232db5485c9e21efbddca940cb2022-12-22T02:49:02ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-08-011310.3389/fpls.2022.973745973745Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identificationMingzhu Tao0Yong He1Xiulin Bai2Xiaoyun Chen3Yuzhen Wei4Cheng Peng5Xuping Feng6College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaKey Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, ChinaSchool of Information Engineering, Huzhou University, Huzhou, ChinaKey Laboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Zhejiang Academy of Agricultural Sciences, Hangzhou, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaGlyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.https://www.frontiersin.org/articles/10.3389/fpls.2022.973745/fulldecision modelglyphosate resistancehyperspectral imagingsource domain updatingsupport vector machinetransfer component analysis |
| spellingShingle | Mingzhu Tao Yong He Xiulin Bai Xiaoyun Chen Yuzhen Wei Cheng Peng Xuping Feng Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification Frontiers in Plant Science decision model glyphosate resistance hyperspectral imaging source domain updating support vector machine transfer component analysis |
| title | Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification |
| title_full | Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification |
| title_fullStr | Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification |
| title_full_unstemmed | Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification |
| title_short | Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification |
| title_sort | combination of spectral index and transfer learning strategy for glyphosate resistant cultivar identification |
| topic | decision model glyphosate resistance hyperspectral imaging source domain updating support vector machine transfer component analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.973745/full |
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