Using spectral response properties to identify and characterize infestations of Dectes texanus in soybean

The soybean stem borer, Dectes texanus Leconte (Coleoptera: Cerambycidae) has become an important pest in Kansas soybean. Currently, farmers rely on field level inspections to identify adult D. texanus presence and/or severity of an infestation, which can be time-consuming or inaccurate especially when there is no established sampling plan. In order to combat this problem the use of remote sensing platforms, particularly aerial imagery, has shown promise in its ability to track and monitor plant health via detectable changes in spectral responses, vegetation phenology curves, and associated phenology metrics (VPM). The objective of this study was to investigate the VPM's of soybean infested with different densities of D. texanus (stem feeders) and pests with different feeding habits (leaf chewers). During 2013 and 2014, 50 whole-plant exclusion cages were placed in a soybean production field at the Ashland Bottoms Research Farm. Plants were in a randomized complete block design (n = 10) with five treatments (increasing numbers of adult D. texanus). A modified Cannon S100 camera was used to capture near-infrared images of each cage from June through September. Using AgPixel software, the normalized difference vegetative index (NDVI) and green normalized difference vegetative index (GNDVI) were calculated from the near-infrared images to create vegetation phenology curves. Using the VPM's we anticipate quantifying the physiological impact of D. texanus when feeding on soybean and at what infestation level such feeding is detectable. This leads to better understanding of D. texanus biology, which will aid in developing and implementing site-specific management strategies