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Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes

Georges F. Backoulou, Norman C. Elliott, Kristopher L. Giles, Mustapha Mirik
Computers and electronics in agriculture 2015 v.115 pp. 34-39
Schizaphis graminum, Triticum aestivum, agronomic traits, computer software, discriminant analysis, drought, insect pests, monitoring, multispectral imagery, pest management, plant stress, plant-insect relations, spatial data, winter wheat
The greenbug, Schizaphis graminum (Rondani) (Hemiptera:Aphididae) is an important pest of small grains such as winter wheat (Triticum aestivum). The objective of this study was to determine the potential for multispectral imagery analyzed using spatial pattern metrics subjected to discriminant function analysis to differentiate patches of wheat plants within wheat fields infested by greenbug from stressed patches caused by other factors. Multispectral images of wheat fields were acquired using a Duncantech MS3100-CIR multispectral camera. Stress observed to wheat plants in wheat fields was grouped into categories: greenbug, drought and agricultural conditions. ERDAS Imagine software was used to process and analyze images, and FRAGSTATS was used to quantify spatial pattern. A set of 10 spatial pattern metrics were computed at the patch level for each stress factor. The analysis of spatial pattern metrics by discriminant function analysis revealed that the three types of stress could be reliably differentiated. The combination of multispectral data and spatial pattern metrics made it possible to differentiate patches in wheat fields infested by greenbug from patches caused by drought and agronomic conditions. The detection and differentiation of stressed patches may help in mapping stress within fields for the purpose of site-specific pest management and for monitoring systems to identify greenbug infestations at individual field and regional scales.