Biological Systems Engineering, Department of
Document Type
Article
Date of this Version
4-25-2014
Citation
Remote Sens. 2014, 6, 3611-3623; doi:10.3390/rs6053611
Abstract
Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.
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Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons
Comments
This article is available at DigitalCommons@University of Nebraska - Lincoln: http://digitalcommons.unl.edu/biosysengfacpub/577