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Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat

文献类型： 外文期刊

作者： Yuan, Lin 1 ; Zhang, Jing-Cheng 1 ; Wang, Ke 1 ; Loraamm, Rebecca-W. 3 ; Huang, Wen-Jiang 4 ; Wang, Ji-Hua 1 ; Zhao, J 1 ;

作者机构： 1.Zhejiang Univ, Inst Remote Sensing & Informat Applicat, Hangzhou 310058, Zhejiang, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Univ S Florida, Dept Geog Environm & Planning, Tampa, FL 33620 USA

4.Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing 100094, Peoples R China

关键词： Hyperspectral;Yellow rust;Leaf orientation Spectral features (SFs);Partial least square regression analysis (PLSR)

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： Disease detection by means of hyperspectral reflectance is inevitably influenced by the spectral difference between foreside (adaxial surface) and backside (abaxial surface) of a leaf. Taking yellow rust disease in winter wheat as an example, the spectral differences between the foreside and backside of healthy and diseased wheat leaves at both jointing stage and grain filling stage were investigated based on spectral measurements with a large sample size. The spectral difference between leaf orientations was found to be confused with disease signals to some extent. Firstly, the original bands and spectral features (SFs) that were sensitive to the disease were identified through a correlation analysis. Then, to eliminate the influence of leaf orientation, a pairwise t test was used to screen for the orientation insensitive bands and SFs. By conducting an overlapping procedure, the bands/SFs that were sensitive to the disease yet insensitive to the leaf orientations were selected and tested for disease detection. The results suggested that the Ref(525-745 nm), Ref(1060-1068 nm), DEP920-1120, DEP1070-1320, AREA1070-1320, SR and NDVI at the jointing stage, and the Ref(606-697) (nm), Ref(740-752 nm), WID550-770, SR, NDVI, GNDVI, RDVI, GI and MCARI at the grain filling stage were capable of eliminating the influence of leaf orientation, and were retained for disease detection. Given these features, models based on the partial least square regression analysis showed a better performance at the grain filling stage, with the R (2) of 0.854 and RMSE of 0.104. This result indicated that reliable estimation of disease severity can be made until the grain filling stage. In the future, more attention should be given to leaf orientation when detecting disease at the canopy level.

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