文献类型： 外文期刊
作者： Bai, Xiulin 1 ; Fang, Hui 2 ; He, Yong 1 ; Zhang, Jinnuo 1 ; Tao, Mingzhu 1 ; Wu, Qingguan 1 ; Yang, Guofeng 1 ; Wei, Yuzhen 3 ; Tang, Yu 4 ; Tang, Lie 5 ; Lou, Binggan 6 ; Deng, Shuiguang 7 ; Yang, Yong 8 ; Feng, Xuping 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
2.Huzhou Inst Zhejiang Univ, Huzhou 313000, Peoples R China
3.Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China
4.Guangdong Polytech Normal Univ, Acad Interdisciplinary Studies, Guangzhou 510665, Peoples R China
5.Iowa State Univ, Dept Agr & Biosyst Engn, Ames, IA 50011 USA
6.Zhejiang Univ, Coll Agr & Biotechnol, Hangzhou 310058, Peoples R China
7.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China
8.Zhejiang Acad Agr Sci, Inst Virol & Biotechnol,,Zhejiang Prov Key Lab Bio, State Key Lab Managing Biot & Chem Treats Qual & S, Key Lab Biotechnol Plant Protect,Minist Agr & Rura, Hangzhou 31002, Peoples R China
期刊名称：PLANT PHENOMICS （ 影响因子：6.5； 五年影响因子：7.5 ）
ISSN： 2643-6515
年卷期： 2023 年 5 卷
页码：
收录情况： SCI
摘要： Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an Rp2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.
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