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Title: Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors

Author
item BACKOULOU, GEORGES - Oklahoma State University
item Elliott, Norman - Norm
item GILES, KRISTOPHER - Oklahoma State University
item PHOOFOLA, MPHO - Oklahoma State University
item CATANA, VASILE - Oklahoma State University
item MIRIK, MUSTAFA - Texas A&M University
item MICHELS, JERRY - Texas A&M University

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/15/2011
Publication Date: 9/1/2011
Citation: Backoulou, G., Elliott, N.C., Giles, K., Phoofola, M., Catana, V., Mirik, M., Michels, J. 2011. Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors. Computers and Electronics in Agriculture. 78:123-129.

Interpretive Summary: The Russian wheat aphid (RWA) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. Multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors, which can vary spatially in presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can affect individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images, in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A multivariate statistical technique called discriminant function analysis was applied to 15 variables. A set of 13 variables were found to be important in discriminating stress caused by RWA from stress caused by other factors. These 13 variables were retained in the disciminant function model. Overall, 97% of patches of stressed wheat were correctly categorized. We conclude that it is possible to discriminate stress induced by RWA from other stress causing factors using our method. The importance of this research rests in developing a method by which the cause of stress detected in multispectral imagery can be reliably determined, which has previously been difficult to accomplish.

Technical Abstract: The Russian wheat aphid (RWA) Diuraphis noxia (Mordvilko) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. Multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors, which can vary spatially in presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can affect individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images, in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A discriminant function analysis was applied to 15 discriminating variables. A set of 13 variables were retained in the model. Overall, 97% of original patches of stress were correctly categorized. Stressed patches caused by RWA were 96.8% correctly classified, patches caused by drought were 95.8% correctly classified, and patches caused by cultural issues were 99% correctly classified. We conclude that it is possible to discriminate stress induced by RWA from other stress causing factors.