Location: Crop Production Systems ResearchTitle: Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects Author
Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2013
Publication Date: 1/1/2014
Citation: Yuan, L., Huang, Y., Loraamm, R., Nie, C., Wang, J., Zhang, J. 2014. Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crops Research. 156:100-207. Interpretive Summary: The infestations of diseases and insects, such as yellow rust, powdery mildew, and wheat aphid, have caused reduced yield and grain quality of winter wheat. Accurate discrimination of the crop stress caused by the infestations is critical for selection of pesticides to provide a proper management of pests. The scientists from National Engineering Research Center for Information Technology in Agriculture (NERCITA), China, USDA-ARS Crop Production Systems Research Unit, and University of South Florida have collaboratively developed hyperspectral sensor systems in discriminating these infestations in winter wheat leaves. The results indicated that the infestations could be discriminated well with the spectral information extracted from hyperspectral measurement. Furthermore, the infestation intensity could be estimated well with a regression model. This study illustrated that use of hyperspectral information has a potential in discriminating and estimating the intensities of yellow rust, powdery mildew and wheat aphid infestation in winter wheat.
Technical Abstract: Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and pesticides) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.