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Detection of peanut leaf spots disease using canopy hyperspectral reflectance

文献类型： 外文期刊

作者： Chen, Tingting 1 ; Zhang, Jialei 2 ; Chen, Yong 1 ; Wan, Shubo 2 ; Zhang, Lei 1 ;

作者机构： 1.South China Agr Univ, Coll Agr, Guangzhou 510642, Guangdong, Peoples R China

2.Shandong Acad Agr Sci, Biotech Res Ctr, Jinan 250100, Peoples R China

关键词： Arachis hypogaea L.; Disease index; Canopy hyperspectral reflectance; Vegetation index

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2019 年 156 卷

页码：

收录情况： SCI

摘要： Leaf spot is one of the most destructive diseases, which has a significant impact on the peanut production. Detecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease. In this study, we identified sensitive bands and derived hyperspectral vegetation index specific to leaf spot detection. Hyperspectral canopy reflectance spectra of peanut cultivars susceptibilities to leaf spot were measured at two experimental sites in 2017. The normalized difference spectral index (NDSI) was derived based on their correlation with disease index (DI) in the leaf spectrum between 325 nm and 1075 nm. The results showed that canopy spectral reflectance decreased significantly in the near-infrared regions (NIR) as DI increased (r < -0.90). The spectral index for detecting leaf spot in peanut were LSI: (NDSI (R-938, R-761)) with R-2 values of up to 0.68 for the regression model. The high fit between the observed and estimated values indicates that the DI detecting model based on the index could be used in peanut leaf spot detection in the absence of other stresses causing unhealthy symptoms. The results of this study show that it will provide a reliable, effective and accurate method for detecting leaf spot diseases in peanut through the analysis of hyperspectral data in the future.

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[1]Detection of peanut leaf spots disease using canopy hyperspectral reflectance. Chen, Tingting,Chen, Yong,Zhang, Lei,Zhang, Jialei,Wan, Shubo. 2019

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