Detection of fungal infection in wheat with high-resolution multispectral data

Jonas Franke; Gunter Menz

doi:10.1117/12.680913

27 September 2006 Detection of fungal infection in wheat with high-resolution multispectral data

Jonas Franke, Gunter Menz

Proceedings Volume 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III; 62980C (2006) https://doi.org/10.1117/12.680913
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

The exact knowledge of the spatiotemporal dynamics of crop diseases for an implementation of a site-specific fungicide application is fundamental. Remote sensing is an appropriate tool to monitor the heterogeneity of fungal diseases within agricultural sites. However, the identification of an infection at an early growth stage is essential. This study assesses the potential of multispectral remote sensing for multitemporal analyses of crop diseases. Within an experimental test site near Bonn (Germany) a 6-ha sized plot with winter wheat was created, containing crops with each possible infection stage of three different pathogens. Two multispectral QuickBird images (04/22/2005 and 06/20/2005) and a spectrally resampled HyMap image (05/28/2005) were used to analyse the spatiotemporal dynamic of infection. The data preprocessing comprised a radiometric and a precise geometric correction by using DGPS-measurements that is an important requirement for Precision Agriculture applications. Ground truth data, in particular infection severity, growth stage/height, and spectroradiometer measurements were collected. A decision tree, using mixture tuned matched filtering results and a vegetation index was applied to classify the data (infected and non-infected areas). Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8% whereas the scene of 28 May (65.9%) and the scene of 20 June (88.6%) achieved considerably higher accuracies. The results showed that high-resolution multispectral data are generally suitable to detect in-field heterogeneities of vegetation vitality though they are only moderately suitable for early detection of stress factors.

Citation Download Citation

Jonas Franke and Gunter Menz "Detection of fungal infection in wheat with high-resolution multispectral data", Proc. SPIE 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III, 62980C (27 September 2006); https://doi.org/10.1117/12.680913

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available