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Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements

文献类型： 外文期刊

作者： Luo, Juhua 1 ; Huang, Wenjiang 2 ; Zhao, Jinling 3 ; Zhang, Jingcheng 3 ; Zhao, Chunjiang 3 ; Ma, Ronghua 1 ;

作者机构： 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing, Jiangsu, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Aphid density;partial least square regression (PLSR);spectral feature (SF);winter wheat

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：3.784； 五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2013 年 6 卷 2 期

页码：

收录情况： SCI

摘要： Wheat aphid, Sitobion avenae F. is one of the most destructive pests that emerge in northwest China almost every year, impacting on the production of winter wheat. Hyperspectral remote sensing has been demonstrated to be superior to a traditional method in detecting diseases and pests. In this study, spectral features (SFs) were examined by four methods to detect aphid density of wheat leaf and model was established to estimate aphid density using partial least square regression (PLSR). A total of 60 wheat leaves with different aphid densities were selected. Aphid density of the leaves was first visually estimated, and then the reflectance of leaves was measured in the spectral range of 350-2500 nm using a spectroradiometer coupling with a leaf clip. A total of 48 spectral features were obtained and examined via correlation analysis, independent t-test by spectral derivative method, continuous removal method, continuous wavelet analysis (CWA) and commonly used vegetation indices for stress detection. Based on variable importance in projection (VIP), five spectral features (VIP >= 1) were selected from 17 spectral features due to their strong correlation with aphid density (R-2 >= 0.5) to establish the model for estimating aphid density by PLSR. The result showed that the model had a great potential in detecting aphid density with a relative root mean square error (RMSE) of 15 and a coefficient of determination (R-2) of 0.77.

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