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.
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