Computers and Electronics in Agriculture
Selection of the most efficient wavelength bands for discriminating weeds from crop
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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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. The utilization of 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 that can vary spatially in their presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can occur 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 to develop a model to differentiate the three types of stress. Overall, 97 percent of patches of stress used to validate the model were correctly categorized. Stressed patches caused by RWA were 98 percent correctly classified, patches caused by drought were 94 percent correctly classified, and patches caused by agronomic conditions were 99 correctly classified. It is possible to discriminate stress induced by RWA from other stress causing factors in multispectral data when spatial attributes of the stress causing factors are incorporated in the analysis.