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Estimating the severity of apple mosaic disease with hyperspectral images

文献类型： 外文期刊

作者： Ban, Songtao 1 ; Tian, Minglu 2 ; Chang, Qingrui 1 ;

作者机构： 1.Northwest A&F Univ, Coll Nat Resource & Environm, Yangling 712100, Shaanxi, Peoples R China

2.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China

3.Shanghai Engn Res Ctr Digital Agr, Shanghai 201403, Peoples R China

关键词： hyperspectral image; apple leaf; mosaic disease; SPAD

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2019 年 12 卷 4 期

页码：

收录情况： SCI

摘要： Soil Plant Analysis Development (SPAD) Chlorophyll Meter reading is used to effectively characterize chlorophyll content, which is an important indicator of the health status of plant leaves. In this study, the hyperspectral images of apple leaves infected by apple mosaic virus (ApMV) were captured, and their SPAD values were measured. The spectral reflectance of leaves with varying degree infection of disease is significantly different. In particular, the reflectance in visible wavebands of leaves with a more serious infection was higher than that of leaves with a less severe infection. Several hyperspectral vegetation indices were highly correlated with the SPAD values of apple leaves (correlation coefficient > 0.9). Models were established to estimate apple foliar SPAD values based on these vegetation indices. Among the models, the multivariate regression model with partial least square regression (PLSR) method achieved the highest accuracy. The SPAD value of a whole apple leaf was calculated from its SPAD distribution image and used as a quantitative index to represent the health status of an apple leaf. Furthermore, the SPAD value of a whole apple leaf could also be estimated rapidly and accurately by extracting the spectral average value of the whole leaf using a simple model. It can be used as a rapid detection method of SPAD values of apple leaves to monitor and describe the health conditions of apple leaves quantitatively.

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