Using spectral response properties to identify and characterize infestations of naturally occurring Dectes texanus infestations 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 D. texanus presence and/or severity of infestation, which can be time-consuming or inaccurate especially when there is no established sampling plan. Potential solutions to this problem include the use of remote sensing platforms, particularly aerial imagery, which are capable of tracking and monitoring plant health via detectable changes in spectral responses and associated vegetation phenology curves/metrics (VPM). The objective of this study was to investigate the use of remote sensing and VPMs as a method to detect soybean infested with D. texanus. The study was conducted in small soybean plots near Scandia, KS during 2014. Open plots were arranged in a randomized complete block design (n = 8) with two treatments: 1) treated control (insecticide applied to foliage to treat and prevent D. texanus infestation), and 2) natural colonization of soybean by D. texanus adults. For image acquisition, 2-m sections (3 per plot) were monitored through time using a modified Cannon S100 camera. Using AgPixel™ software, the green normalized difference vegetative index (GNDVI) was calculated from the near-infrared images to create vegetation phenology curves. We will use the VPM's to quantify the physiological impact of D. texanus when feeding on soybean. The results will lead to a better understanding of how D. texanus feeding changes soybean phenology, which will aid in developing and implementing site-specific management and sampling strategies.