Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique

文献类型： 外文期刊

第一作者： Bai, Xiulin

作者： Bai, Xiulin;Feng, Xuping;Tao, Mingzhu;Zhang, Jinnuo;Yang, Guofeng;Wu, Qingguan;He, Yong;Zhou, Yujie;Deng, Shuiguang;Lou, Binggan;Yu, Li;Yang, Yong

作者机构：

关键词： plant disease; hyperspectral imaging; spectral index; deep learning; attention mechanism

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Hyperspectral imaging technique combined with machine learning is a powerful tool for the evaluation of disease phenotype in rice disease-resistant breeding. However, the current studies are almost carried out in the lab environment, which is difficult to apply to the field environment. In this paper, we used visible/near-infrared hyperspectral images to analysis the severity of rice bacterial blight (BB) and proposed a novel disease index construction strategy (NDSCI) for field application. A designed long short-term memory network with attention mechanism could evaluate the BB severity robustly, and the attention block could filter important wavelengths. Best results were obtained based on the fusion of important wavelengths and color features with an accuracy of 0.94. Then, NSDCI was constructed based on the important wavelength and color feature related to BB severity. The correlation coefficient of NDSCI extended to the field data reached -0.84, showing good scalability. This work overcomes the limitations of environmental conditions and sheds new light on the rapid measurement of phenotype in disease-resistant breeding.

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