TY - JOUR
T1 - Detecting Powdery Mildew of Winter Wheat Using Leaf Level Hyperspectral Measurements
AU - Zhang, Jing-Cheng
AU - Pu, Ruiliang
AU - Wang, Jihua
AU - Huang, Wenjiang
AU - Yuan, Lin
AU - Luo, Juhua
PY - 2012/7/1
Y1 - 2012/7/1
N2 - Powdery mildew ( Blumeria graminis ) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t -test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination ( R 2 ) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer’s and user’s accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew.
AB - Powdery mildew ( Blumeria graminis ) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t -test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination ( R 2 ) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer’s and user’s accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew.
KW - powdery mildew
KW - spectral feature
KW - partial least square regression (PLSR)
KW - fisher linear discriminate analysis (FLDA)
KW - cross validation
UR - https://digitalcommons.usf.edu/geo_facpub/357
UR - https://doi.org/10.1016/j.compag.2012.03.006
U2 - 10.1016/j.compag.2012.03.006
DO - 10.1016/j.compag.2012.03.006
M3 - Article
VL - 85
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
ER -