Utilizing UAV-based high-throughput phenotyping and machine learning to evaluate drought resistance in wheat germplasm

文献类型： 外文期刊

第一作者： Zhu, Xiaojing

作者： Zhu, Xiaojing;Liu, Xin;Wu, Qian;Liu, Mengshi;Hu, Xueli;Deng, Hui;Zhang, Yun;Qu, Yunfeng;Wang, Baoqi;Gou, Xiaoman;Li, Qiongge;Han, Changsheng;Tu, Junhao;Qiu, Xiaolong;Zhou, Yun;Zhang, Zhen;Hu, Ge;Hu, Lin;Zhang, Jian

作者机构：

关键词： Unmanned aerial vehicle (UAV); Wheat; Drought resistance; Machine learning; Germplasm identification

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 237 卷

页码：

收录情况： SCI

摘要： Wheat is a staple crop that suffers significant yield reductions under drought conditions, especially during the critical reproductive stages. Traditional methods for assessing drought resistance in wheat are often destructive, labor-intensive, and fail to capture the multi-faceted nature of drought tolerance. Vegetation indices serve as effective non-destructive indicators of physiological and biochemical traits. However, the potential of high-throughput spectral indices for quantifying drought resistance traits in wheat have not yet been thoroughly investigated. In this study, we employed an unmanned aerial vehicle (UAV) platform combined with machine learning to assess 206 spectral indices across 52 wheat genotypes at various growth stages under both well-watered and drought conditions. We also evaluated 11 traditional traits to examine their correlations with UAV-based traits. Our study identified 127 spectral indices as drought-related traits and revealed significant correlations between traditional and UAV-based traits. We identified three novel drought-related traits-the Color Index of Vegetation (CIVE), Red-Green-Blue Index (RGBI), and Excess Green Minus Excess Red Index (ExG_ExR)derived from RGB images and correlated with chlorophyll content, showing strong associations with kernel-related traits. Additionally, we developed an advanced prediction model for yield stability under drought conditions using 17 spectral indices selected through machine learning. A comprehensive evaluation value (D) based on these 17 indices enabled the identification of one highly drought-resistant genotype and 13 drought-resistant genotypes, further validated through field experiments. Our study not only confirms the effectiveness of UAVbased traits in indicating drought tolerance but also provides valuable germplasm for the genetic improvement of drought-resistant wheat.

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