Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning
文献类型： 外文期刊
作者： Zhang, Jinnuo 1 ; Feng, Xuping 1 ; Wu, Qingguan 1 ; Yang, Guofeng 1 ; Tao, Mingzhu 1 ; Yang, Yong 2 ; He, Yong 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Key Lab Spect, Minist Agr & Rural Affairs, Hangzhou 310058, Peoples R China
2.Zhejiang Acad Agr Sci, Inst Virol & Biotechnol, Zhejiang Prov Key Lab Biotechnol Plant Protect,St, Minist Agr & Rural Affairs,Key Lab Biotechnol Pla, Hangzhou 310021, Peoples R China
关键词： Plant disease; Visible; near-infrared spectroscopy; Attention mechanism; Deep learning; Rice bacterial blight resistance
期刊名称：PLANT METHODS （ 影响因子：5.827； 五年影响因子：5.904 ）
ISSN：
年卷期： 2022 年 18 卷 1 期
页码：
收录情况： SCI
摘要： Background Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
- 相关文献
作者其他论文 更多>>
-
Developing fluorescence hyperspectral imaging methods for non-invasive detection of herbicide safeners action mechanism and effectiveness
作者：Chu, Hangjian;Zhao, Yiying;Zhang, Xiaobin;Chu, Hangjian;Gouda, Mostafa;He, Yong;Li, Xiaoli;Liu, Yufei;Gouda, Mostafa;Li, Yu
关键词：Visible/near-infrared hyperspectral imaging; Chlorophyll a fluorescence; Chemometric analysis; Machine learning models
-
A Novel Allelic Variant of OsAGPL2 Influences Rice Eating and Cooking Quality
作者：Dan, Yuqing;He, Yong;Peng, Ruixiao;Tian, Zhihong;Huang, Fudeng;Li, Chunshou;Song, Jiayu;Hao, Yuanyuan;Xu, Junfeng;Xu, Junfeng
关键词：rice quality;
OsAGPL2 ; white-core endosperm mutant; superior haplotype -
Whole-transcriptome sequencing reveals the global molecular responses and ceRNA regulatory network involved in programmed cell death of rice cultivars zyk639 and zyk-lm
作者：Zhang, Haipeng;Zheng, Han;Zhao, Hongyu;Wang, Chengyu;Zhang, Haipeng;Zheng, Han;Liang, Weifang;Zhao, Hongyu;Chen, Jianping;Yang, Yong;Zhang, Junjie;Li, Jianzhong;Zhou, Yujie;Xie, Liujie;Yu, Chulang;Chen, Jianping;Dai, Binfeng;Zhong, Liequan;Hou, Fan
关键词：PCD; Whole-transcriptome; LncRNA; WGCNA; ce-RNA
-
3D-based precise evaluation pipeline for maize ear rot using multi-view stereo reconstruction and point cloud semantic segmentation
作者：Yang, Rui;He, Yong;Lu, Xiangyu;Liu, Fei;Zhao, Yiying;Li, Yanmei;Yang, Yinhui;Kong, Wenwen
关键词：Maize ear rot; Multi-view stereo reconstruction; Point cloud; 3D semantic segmentation; Deep learning
-
Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision
作者：Xu, Shan;He, Yong;Feng, Xuping;Shen, Jia;Wei, Yuzhen;Li, Yu;Feng, Xuping;Hu, Hui
关键词：Plant phenotyping; Machine learning; Deep learning; Computer vision
-
Identification and Genome Sequencing of Novel Virulent Strains of Xanthomonas oryzae pv. oryzae Causing Rice Bacterial Blight in Zhejiang, China
作者：Liang, Weifang;Chen, Jianping;Liang, Weifang;Zhou, Yuhang;Chen, Xinyu;Chen, Jianping;Yang, Yong;Zhou, Yuhang;Xu, Zhongtian;Li, Yiyuan;Yu, Chulang;Chen, Jianping;Chen, Xinyu;Hou, Fan;Dai, Binfeng;Zhong, Liequan;Bi, Ji-An;Yan, Chengqi;Xie, Liujie
关键词：avirulence gene; genome sequencing; TAL effector;
Xanthomonas oryzae pv.oryzae -
Metabarcoding reveals response of rice rhizosphere bacterial community to rice bacterial leaf blight
作者：Jiang, Hubiao;Xu, Xinyan;Ogunyemi, Solabomi Olaitan;Ahmed, Temoor;Li, Bin;Fang, Yuan;Li, Xuqing;Yang, Yong;Yan, Chengqi;Chen, Jianping;Fang, Yuan;Chen, Jianping;Li, Bin
关键词：Bacterial leaf blight; Rice; 16S rRNA gene sequence; Rhizosphere; Microbial community
