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Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

文献类型： 外文期刊

作者： Tao, Mingzhu 1 ; He, Yong 1 ; Bai, Xiulin 1 ; Chen, Xiaoyun 2 ; Wei, Yuzhen 3 ; Peng, Cheng 2 ; Feng, Xuping 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou, Peoples R China

2.Zhejiang Acad Agr Sci, Key Lab Traceabil Agr Genet Modified Organisms, Minist Agr & Rural Affairs, Hangzhou, Peoples R China

3.Huzhou Univ, Sch Informat Engn, Huzhou, Peoples R China

关键词： decision model; glyphosate resistance; hyperspectral imaging; source domain updating; support vector machine; transfer component analysis

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： 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.

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