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Screening Verticillium wilt-resistant germplasm by monitoring the time-series chlorophyll content of cotton canopies via a UAV-based high-throughput platform

文献类型： 外文期刊

作者： Xu, Bowei 1 ; Yang, Jiajie 1 ; Chen, Deyong 2 ; Wang, Xuwen 4 ; Ai, Xiantao 5 ; Liu, Le 1 ; Zhao, Rumeng 1 ; Chen, Jieyin 2 ; Ma, Xiaomei 4 ; Li, Fuguang 1 ; Yang, Zuoren 1 ; Fan, Liqiang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Cotton Res, State Key Lab Cotton Biobreeding & Integrated Util, Anyang 455000, Peoples R China

2.Inst Western Agr CAAS, Xinjiang Key Lab Crop Gene Editing & Germplasm Inn, Changji 831100, Xinjiang, Peoples R China

3.Chinese Acad Agr Sci, State Key Lab Biol Plant Dis & Insect Pests, Inst Plant Protect, Beijing 100193, Peoples R China

4.Xinjiang Acad Agr & Reclamat Sci, Minist Agr, Northwest Inland Reg Key Lab Cotton Biol & Genet, Cotton Res Inst, Shihezi 832000, Peoples R China

5.Xinjiang Univ, Res Inst, Coll Smart Agr, Urumqi 830046, Peoples R China

关键词： Cotton; Verticillium wilt; UAV; Chlorophyll content; Deep learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 238 卷

页码：

收录情况： SCI

摘要： Verticillium wilt (VW) is a highly detrimental disease of cotton that causes significant reductions in yield and fiber quality. Efficient and accurate screening of VW-resistant varieties is essential for cotton breeding and production. However, traditional identification methods, such as manual observation, are inefficient and costly. Unmanned aerial vehicle (UAV) and remote sensing technologies have opened new insights into the screening of field crops for disease-resistant germplasm. This study utilized a UAV multispectral platform to collect data from five growth stages of 150 cotton varieties with different VW resistances. The normalized difference vegetation index (NDVI) was identified as a reliable predictor of chlorophyll levels through hierarchical segmentation analysis. We further compared four deep learning models for chlorophyll monitoring: 1D-CNN, CNN-BiLSTM, CNN-BiLSTM-Adaboost, and CNN-BiLSTM-Attention, with the CNN-BiLSTM-Attention model performing best (R2 = 0.92). The optimum model was then used to invert the extent of VW infection using single- and multiperiod chlorophyll, and the latter was found to have the best results with the highest R2 value of 0.96. Multidimensional clustering of chlorophyll content over multiple periods was used to screen different cotton VWresistant germplasm, and the ISODATA cluster method outperformed the other three methods (K-means, K means++, and GMM). This study highlights that combining a UAV multispectral platform with an accurate chlorophyll inversion model can enable high-throughput assessment of the cotton VW infection in the field, providing a powerful tool for screening cotton VW-resistant germplasm and thus supporting cotton breeding efforts.

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