| 不同基因型小麦冻害无人机遥感高通量表型 |
| |
| 引用本文： | 刘易雪,蔚睿,吴建辉,韩德俊,苏宝峰.不同基因型小麦冻害无人机遥感高通量表型[J].农业工程学报,2023,39(5):128-136. |
| |
| 作者姓名： | 刘易雪 蔚睿 吴建辉 韩德俊 苏宝峰 |
| |
| 作者单位： | 1. 西北农林科技大学机械与电子工程学院，杨凌 712100; 2. 农业农村部农业物联网重点实验室，杨凌 712100; 3. 陕西省农业信息感知与智能服务重点实验室;;4. 西北农林科技大学农学院，杨凌 712100; 5. 西北农林科技大学旱区作物逆境生物学国家重点实验室，杨凌 712100; |
| |
| 基金项目： | 国家重点研发计划（2021YFD1200600）；杨凌种业创新中心重点研发项目（YLzy-xm-01） |
| |
| 摘 要： | 为了实现田间条件下小麦抗冻性状相关的数量性状基因座（quantitative trait locus, QTL）分析，该研究针对4个试验地491份小麦核心种质资源的抗冻性状，基于无人机多光谱遥感提出了一种高通量表型方法。首先通过光谱植被指数对小麦抗冻性状进行评估，基于机器学习分类算法使用16个光谱植被指数特征构建了小麦冻害评价模型，并完成了光谱特征相关性分析及对评价模型的贡献率分析。对比随机森林（random forests,RF）、分布式梯度增强（extreme gradient boosting,XGBoost）、梯度提升决策树（gradient boosting decision tree,GBDT）及支持向量机（support vector machine,SVM）算法建立的小麦冻害等级评价模型，结果表明，使用XGBoost建立的评价模型准确率最高，达67.94%;16个光谱特征相关性及其对评价模型的贡献率分析表明，简化冠层叶绿素含量指数（simplified canopy chlorophyii content index, SCCCI）对小麦抗冻表型鉴定的贡献率最大。其次，...
|
| 关 键 词： | 无人机 遥感 小麦冻害 多光谱 关联分析 机器学习 |
| 收稿时间： | 2022/6/27 0:00:00 |
| 修稿时间： | 2023/2/22 0:00:00 |
High-throughput phenotyping for different genotype wheat frost using UAV-based remote sensing |
| |
| LIU Yixue,YU Rui,WU Jianhui,HAN Dejun,SU Baofeng.High-throughput phenotyping for different genotype wheat frost using UAV-based remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(5):128-136. |
| |
| Authors: | LIU Yixue YU Rui WU Jianhui HAN Dejun SU Baofeng |
| |
| Institution: | 1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China; 2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China; 3. Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service, Yangling 712100, China;;4. College of Agronomy, Northwest A&F University, Yangling 712100, China; 5. State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling 712100, China; |
| |
| Abstract: | Wheat (triticum aestivum l.) breeding technology can face a great challenge on the long cycle, low efficiency, and narrow genetic background. An important breakthrough can be combining the high-throughput phenotyping of in-field wheat and genome-wide association, thereby revealing the genetic variation in dynamic response to environmental stress. Fortunately, the unmanned aerial vehicle (UAV) remote sensing and machine learning can be expected to bridge the genotype-phenotype gap of the wheat in the breeding process. Among them, frost tolerance is an important phenotype target, particularly with the winter survival of wheat in various environments. It is a high demand for the rapid and cost-effective assessment of frost tolerance from the UAV multi-spectral imagery using machine learning. In this study, a genome-wide association study (GWAS) was assessed for the quantitative genomic analysis of wheat frost tolerance. A bi-parental wheat population consisting of 491 doubled haploid lines was also used in four study sites. 491 wheat core materials with a relatively consistent growth stage were selected to obtain their high-density genotype data with the 660 kb single nucleotide polymorphism (SNP). The UAV-based multi-spectral imagery of the wheat canopy was collected at the overwintering stage at four experimental sites. At the same time, the wheat in-field phenotypes of frost tolerance were investigated by the wheat breeding experts at the same time. The image pre-processing was performed on the features generation of 16 spectral vegetation indices, including image mosaic, geometric correction, radiometric correction and index calculation. Image segmentation was utilized to obtain the features of the wheat canopy using unsupervised clustering. The features correlation analysis and importance analysis were implemented to compare with the in-field investigation, in order to identify quantitative trait loci (QTL) underlying frost tolerance. A comparison was then made on the evaluation models of wheat freezing injury established by random forests (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and support vector machine (SVM). The results showed that significantly high accuracy was achieved up to 67.94% of the classifier in the XGBoost, compared with the in-field investigation. The correlation and importance of features were also analyzed during this time. The importance of 22 spectral features to the prediction performance of the classifier was evaluated using the information gain brought by the feature, when the sub node of the classifier split. The results showed that there was the most important for the prediction performance of the classifier in the simplified Canopy Chlorophyll content index (SCCCI) among the 16 spectral features of the wheat canopy. Three QTLs were also closely related to the frost resistance detected by the genome-wide association analysis. The three loci of 2B, 3A, and 5A on chromosome 21 of wheat presented a significant SNP, even exceeding the threshold (-lgP=4). The SNPs were continuously distributed. Therefore, the spectral features using UAV remote sensing can be expected to serve as the wheat frost resistance QTL. The UAV-enabled phenotyping can be an effective, high-throughput, and cost-effective approach to understanding the genetic basis of wheat frost tolerance in genetic studies and practical breeding. This finding can also provide a fast way for the high-throughput phenotyping of wheat frost tolerance for wheat winter survival in the field. |
| |
| Keywords: | UAV remote sensing wheat frost multispectral GWAS machine learning |
|
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
