利用无人机高通量表型和机器学习技术评价小麦种质抗旱性

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Xiaojing Zhu , Xin Liu , Qian Wu , Mengshi Liu , Xueli Hu , Hui Deng , Yun Zhang , Yunfeng Qu , Baoqi Wang , Xiaoman Gou , Qiongge Li , Changsheng Han , Junhao Tu , Xiaolong Qiu , Ge Hu , Jian Zhang , Lin Hu , Yun Zhou , Zhen Zhang
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引用次数: 0

摘要

小麦是一种主粮作物,在干旱条件下,尤其是在关键的繁殖阶段,产量会大幅下降。评估小麦抗旱性的传统方法往往是破坏性的,劳动密集型的,并且未能捕捉到抗旱性的多面性。植被指数是一种有效的非破坏性生理生化指标。然而,利用高通量光谱指标定量小麦抗旱性状的潜力尚未得到充分的研究。在本研究中,我们利用无人机(UAV)平台结合机器学习,对水分充足和干旱条件下不同生育期52个小麦基因型的206个光谱指标进行了评估。我们还评估了11个传统性状,以检查它们与无人机性状的相关性。我们的研究确定了127个光谱指标作为干旱相关性状,并揭示了传统性状与无人机性状之间的显著相关性。我们从RGB图像中发现了3个新的干旱相关性状——植被颜色指数(CIVE)、红绿蓝指数(RGBI)和过量绿减过量红指数(ExG_ExR),它们与叶绿素含量相关,显示出与籽粒相关性状的强相关性。此外,我们开发了一个先进的干旱条件下产量稳定性预测模型,该模型使用了通过机器学习选择的17个光谱指数。17个指标综合评价值(D)鉴定出1个高度抗旱基因型和13个抗旱基因型,并通过田间试验进一步验证。本研究不仅证实了无人机性状在抗旱性方面的有效性,也为抗旱性小麦的遗传改良提供了宝贵的种质资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing UAV-based high-throughput phenotyping and machine learning to evaluate drought resistance in wheat germplasm
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 UAV-based traits in indicating drought tolerance but also provides valuable germplasm for the genetic improvement of drought-resistant wheat.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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