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Multi-temporal wheat disease detection by multi-spectral remote sensing

Content Provider	Springer Nature Link
Author	Franke, Jonas Menz, Gunter
Copyright Year	2007
Abstract	For the implementation of site-specific fungicide applications, the spatio-temporal dynamics of crop diseases must be well known. Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites. However, the identification of fungal infections at an early growth stage is essential. This study examines the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases. Within an experimental field, a 6 ha plot of winter wheat was grown, containing all possible infective stages of the powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita) pathogens. Three high-resolution remote sensing images were used to execute a spatio-temporal analysis of the infection dynamics. A decision tree, using mixture tuned matched filtering (MTMF) results and the Normalized Difference Vegetation Index (NDVI), was applied to classify the data into areas showing different levels of disease severity. Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8%, whereas the scenes from May 28$^{th}$ and June 20$^{th}$ achieved considerably higher accuracies of 65.9% and 88.6% respectively. The results showed that high-resolution multi-spectral data are generally suitable to detect in-field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections.
Starting Page	161
Ending Page	172
Page Count	12
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	8
Issue Number	3
e-ISSN	15731618
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2007-06-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Remote sensing Stress detection Crop diseases Spectral mixture analysis Mixture Tuned Matched Filtering Wheat Remote Sensing/Photogrammetry Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Meteorology/Climatology Agriculture Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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