Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite ImageReportar como inadecuado




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1

Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

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National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

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Key Laboratory for Information Technologies in Agriculture, the Ministry of Agriculture, Beijing 100097, China

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Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China

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Department of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, OK 74078, USA





*

Author to whom correspondence should be addressed.



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. View Full-Text

Keywords: powdery mildew; winter wheat; SPOT-6; maximum likelihood classifier; mahalanobis distance; artificial neural network powdery mildew; winter wheat; SPOT-6; maximum likelihood classifier; mahalanobis distance; artificial neural network





Autor: Lin Yuan 1,4, Jingcheng Zhang 1,2,3,4,* , Yeyin Shi 5, Chenwei Nie 1, Liguang Wei 1 and Jihua Wang 1,2,3,4

Fuente: http://mdpi.com/



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