Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

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 monitoring and mapping over time and space during the course of a growing season.

Hyper Spectral Remote Sensing for Damage Detection and Classification Models in Agriculture - A Review

“…They indicated that spectral reflectance contrasts and canopy characteristics between harm from disease contamination and harm from insect infestation can be measured by remote sensing in crop canopies. Mirik et al . ( 2013) (Mirik et al 2013) concluded that explicit Wheat Streak Mosaic Ailment infection at the site can be reliably classify and quantify by using the land sat 5 TM image.…”

Precision Agriculture: Irrigation

“…Imagery was acquired biweekly, beginning in early March, and diseased wheat was separated from healthy wheat using a subpixel classifier with 491% accuracy (Mirik et al, 2013). A study was conducted to evaluate Landsat 5 TM imagery for detection, quantification, and mapping of WSM in irrigated wheat fields.…”

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

“…Based on the acceptable spatial and temporal resolutions, multispectral satellite technique becomes a feasible method for crop diseases and pests monitoring [8,14,15]. For instance, based on Landsat-5 Thematic Mapper (TM) data, Mirik et al [16] successfully assessed the infection and progression of wheat streak mosaic. Navrozidis et al [17] demonstrated that field spectroscopy and wide area remote sensing (i.e., Landsat-8) can be used to create sufficiently accurate quantification models of crop disease severity.…”