Progress 10/01/09 to 09/30/10
Outputs
Progress Report Objectives (from AD-416) Demonstrate that an image analysis approach based on multi-spectral imagery combined with spatial pattern analysis can work to identify Russian wheat aphid-infested wheat fields. Approach (from AD-416) We will identify study fields infested with Russian wheat aphids and fields not infested from which to acquire multi-spectral imagery using a Terraverde multi-spectral imaging system. Ground-based sampling will be accomplished to ground-truth fields as infested or non-infested and to characterize the spatial pattern of the infestation in each field. Fields will be imaged from an altitude of 5000 ft above ground level using from a Cessna 172 aircraft. We will evaluate reflectance patterns in multi-spectral imagery using image analysis software. Previous research has shown that a strong, repeatable relationship between Russian wheat aphid density and/or plant injury can be detected. The analytical approach will involve imagery using a filtering operation whereby GIS operations are used to adjust the image for variation in reflectance expected based on topography and soil type (and perhaps other factors) which affect the growth and vigor of wheat plants, and thus affect light reflectance patterns from the plant canopy. Once an acceptable statistical model is developed it will be tested by categorizing numerous wheat fields with respect to overall level of infestation by Russian wheat aphids. Testing will be accomplished by imaging many wheat fields and simultaneously sampling each field for infestation. The value of the model will be assessed by its ability to correctly classify fields as infested versus not infested (or lightly infested). During FY2010, multispectral images of ca. 50 wheat fields were acquired using an MS3100-CIR multispectral camera. Unfortunately none of the fields were infested with Russian wheat aphids, and we could not find any location where Russian wheat aphids were present in greater than negligible numbers. The low Russian wheat aphid numbers were probably caused by exceptionally poor conditions for overwintering survival. In any event, we were unable to achieve one of our objectives, which was to add to our database imagery of 50 fields with and without Russian wheat aphid infestations. However, data from previous years was analyzed and modeled during FY 2010 to determine if Russian wheat aphid-infested fields can be differentiated from non-infested fields using spectral information combined with information on the spatial pattern of the plant stress resulting from the characteristic non-random distribution of the aphid population within the field. Based on imagery from 50 fields (ca. 25) infested with Russian wheat aphids and a similar number stressed by other factors, we found that we could differentiate infested from non- infested fields with over 95% accuracy. ERDAS Imagine software was used to process and analyze images, and FRAGSTATS was used to quantify spatial pattern. The analysis of landscape metrics using discriminant function analysis quantitatively differentiated among fields with Russian wheat aphid-induced stress from fields with other types of stress. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site-specific pesticide application and for monitoring systems to identify Russian wheat aphid infestations. The ADODR monitored activities via periodic progress meetings directly with the cooperator and maintains regular contact via e-mail and phone.
Impacts
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Progress 07/01/08 to 08/31/10
Outputs
Progress Report Objectives (from AD-416) Demonstrate that an image analysis approach based on multi-spectral imagery combined with spatial pattern analysis can work to identify Russian wheat aphid-infested wheat fields. Approach (from AD-416) We will identify study fields infested with Russian wheat aphids and fields not infested from which to acquire multi-spectral imagery using a Terraverde multi-spectral imaging system. Ground-based sampling will be accomplished to ground-truth fields as infested or non-infested and to characterize the spatial pattern of the infestation in each field. Fields will be imaged from an altitude of 5000 ft above ground level using from a Cessna 172 aircraft. We will evaluate reflectance patterns in multi-spectral imagery using image analysis software. Previous research has shown that a strong, repeatable relationship between Russian wheat aphid density and/or plant injury can be detected. The analytical approach will involve imagery using a filtering operation whereby GIS operations are used to adjust the image for variation in reflectance expected based on topography and soil type (and perhaps other factors) which affect the growth and vigor of wheat plants, and thus affect light reflectance patterns from the plant canopy. Once an acceptable statistical model is developed it will be tested by categorizing numerous wheat fields with respect to overall level of infestation by Russian wheat aphids. Testing will be accomplished by imaging many wheat fields and simultaneously sampling each field for infestation. The value of the model will be assessed by its ability to correctly classify fields as infested versus not infested (or lightly infested). Project officially terminated on 08/31/2010; no additional progress was made in FY11.
Impacts
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Progress 10/01/08 to 09/30/09
Outputs
Progress Report Objectives (from AD-416) Demonstrate that an image analysis approach based on multi-spectral imagery combined with spatial pattern analysis can work to identify Russian wheat aphid-infested wheat fields. Approach (from AD-416) We will identify study fields infested with Russian wheat aphids and fields not infested from which to acquire multi-spectral imagery using a Terraverde multi-spectral imaging system. Ground-based sampling will be accomplished to ground-truth fields as infested or non-infested and to characterize the spatial pattern of the infestation in each field. Fields will be imaged from an altitude of 5000 ft above ground level using from a Cessna 172 aircraft. We will evaluate reflectance patterns in multi-spectral imagery using image analysis software. Previous research has shown that a strong, repeatable relationship between Russian wheat aphid density and/or plant injury can be detected. The analytical approach will involve imagery using a filtering operation whereby GIS operations are used to adjust the image for variation in reflectance expected based on topography and soil type (and perhaps other factors) which affect the growth and vigor of wheat plants, and thus affect light reflectance patterns from the plant canopy. Once an acceptable statistical model is developed it will be tested by categorizing numerous wheat fields with respect to overall level of infestation by Russian wheat aphids. Testing will be accomplished by imaging many wheat fields and simultaneously sampling each field for infestation. The value of the model will be assessed by its ability to correctly classify fields as infested versus not infested (or lightly infested). Significant Activities that Support Special Target Populations During FY2009, multispectral images were acquired using an MS3100-CIR multispectral camera. Stress observed in wheat in fields was grouped into categories based on the cause of the stress: Russian wheat aphid, drought, and cultural issues, which encompassed the major types of stress present in the fields. ERDAS Imagine software was used to process and analyze images, and FRAGSTATS was used to quantify spatial pattern. Seven landscape metrics were computed at the patch level for each stress factor. The analysis of landscape metrics quantitatively differentiated the three types of stress. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site- specific pesticide application and for monitoring systems to identify Russian wheat aphid infestations.
Impacts
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