Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements

蚜虫 光谱辐射计 麦长管蚜 高光谱成像 均方误差 偏最小二乘回归 数学 光谱密度 相关系数 线性回归 遥感 园艺 植物 统计 生物系统 农学 蚜虫科 生物 反射率 物理 同翅目 光学 地质学 有害生物分析
作者
Juhua Luo,Wenjiang Huang,Jinling Zhao,Jingcheng Zhang,Chunjiang Zhao,Ronghua Ma
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:6 (2): 690-698 被引量:46
标识
DOI:10.1109/jstars.2013.2248345
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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