Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image
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,...
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doaj-63fff890e81945769f060865c28ac63e2020-11-25T01:03:09ZengMDPI AGRemote Sensing2072-42922014-04-01653611362310.3390/rs6053611rs6053611Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite ImageLin Yuan0Jingcheng Zhang1Yeyin Shi2Chenwei Nie3Liguang Wei4Jihua Wang5Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaDepartment of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, OK 74078, USABeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaPowdery 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.http://www.mdpi.com/2072-4292/6/5/3611powdery mildewwinter wheatSPOT-6maximum likelihood classifiermahalanobis distanceartificial neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lin Yuan Jingcheng Zhang Yeyin Shi Chenwei Nie Liguang Wei Jihua Wang |
spellingShingle |
Lin Yuan Jingcheng Zhang Yeyin Shi Chenwei Nie Liguang Wei Jihua Wang Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image Remote Sensing powdery mildew winter wheat SPOT-6 maximum likelihood classifier mahalanobis distance artificial neural network |
author_facet |
Lin Yuan Jingcheng Zhang Yeyin Shi Chenwei Nie Liguang Wei Jihua Wang |
author_sort |
Lin Yuan |
title |
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image |
title_short |
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image |
title_full |
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image |
title_fullStr |
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image |
title_full_unstemmed |
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image |
title_sort |
damage mapping of powdery mildew in winter wheat with high-resolution satellite image |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2014-04-01 |
description |
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. |
topic |
powdery mildew winter wheat SPOT-6 maximum likelihood classifier mahalanobis distance artificial neural network |
url |
http://www.mdpi.com/2072-4292/6/5/3611 |
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