Satellite Remote Sensing of Wheat Infected byWheat streak mosaic virus

Abstract

The prevalence of wheat streak mosaic, caused by Wheat streak mosaic virus, was assessed using Landsat 5 Thematic Mapper (TM) images in two counties of the Texas Panhandle during the 2005–2006 and 2007–2008 crop years. In both crop years, wheat streak mosaic was widely distributed in the counties studied. Healthy and diseased wheat were separated on the images using the maximum likelihood classifier. The overall classification accuracies were between 89.47 and 99.07% for disease detection when compared to “ground truth” field observations. Omission errors (i.e., pixels incorrectly excluded from a particular class and assigned to other classes) varied between 0 and 12.50%. Commission errors (i.e., pixels incorrectly assigned to a particular class that actually belong to other classes) ranged from 0 to 23.81%. There were substantial differences between planted wheat acreage reported by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) and that detected by image analyses. However, harvested wheat acreage reported by USDA-NASS and that detected by image classifications were closely matched. These results indicate that the TM image can be used to accurately detect and quantify incidence of wheat streak mosaic over large areas. This method appears to be one of the best currently available for identification and mapping disease incidence over large and remote areas by offering a repeatable, inexpensive, and synoptic strategy during the course of a growing season.

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

“…The visible region is defined as pigment-related absorption wavelengths that are primarily governed by the presence of chlorophylls a and b and carotenoids [28,29,71]. This indicates that actively growing musk thistle, tansy mustard, brome grass, wheat, Johnsongrass and Russian thistle had higher chlorophylls and carotenoids than senescent grass.…”

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

“…Even though slightly symptomatic plants were not included in our study as diseased, this omission was not considered problematic because the goal was to quantify moderate to severe symptoms of WSM that ultimately would result in reduced forage or grain yields. WSM is a progressive disease, and late infections, which result in greatly reduced symptom expression, cause significantly less damage to the crop [10,13,41]. Late infections that were not quantified in this study in mid-April or afterward likely had no negative effect on crop yield.…”

“…Reflectance in the shortwave infrared (SWIR: 1200 -2400 nm) is affected due to absorptions by water, proteins, and other carbon constituents in the vegetation [38,40]. Reduction in green leaf area due to growth limiting factors (pathogens, insect feeding, nutrient deficiencies), leaf senescence, and defoliation causes high reflectance in the visible spectrum due to chlorophyll degradation, low reflectance in NIR due to reduced green leaf area and senescence, and high reflectance in SWIR due to modified tissue chemistry [38,40,41]. There are numerous successful applications of stress and disease detection and quantification in wheat and other vegetation canopies using a variety of sensor systems including aerial photographs, airborne and satellite multispectral and hyperspectral sensors, ground-based instruments and other spatial information technologies [42][43][44][45][46][47].…”

“…Therefore, continuously collected data and availability of Landsat real time images are appealing as a research method for WSM. This research is a continuation of a previous study done by [41] with the following objectives: 1) To explore the utility of Landsat 5 TM imagery for continuous monitoring of WSM progression over time and space for its site-specific management; and 2) To evaluate Constrained Energy Minimization (CEM) sub-pixel classifier to gain improved understanding of the spatial distribution and remote detection of WSM 2. …”

Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

“…Thus, wheat ear is an important factor to reduce disease diagnosis accuracy and should be more concerned in disease monitoring by optical remote sensing. At present, the field-scale crop disease diagnosis using the images acquired from space-borne and airborne are limited by the 1-30 m spatial resolution (Franke et al, 2008;Mewes et al, 2011;Mirik et al, 2011). Such remote sensing mainly focuses on large-scale monitoring and forecasting of crop diseases and it does not consider the impact of wheat ears on disease assessment.…”