TITLE:
Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat
AUTHORS:
Mustafa Mirik, R. James Ansley, Jacob A. Price, Fekede Workneh, Charles M. Rush
KEYWORDS:
Wheat Streak Mosaic Virus; Viral Disease; Remote Sensing of Disease; Disease Mapping; Wheat Disease; Site-Specific Disease Management
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.1,
March
25,
2013
ABSTRACT: Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease mo- nitoring and mapping over time and space during the course of a growing season.