Hyperspectral Spectrometry as a Means to Differentiate Uninfested and Infested Winter Wheat by Greenbug (Hemiptera: Aphididae)

Abstract

Although spectral remote sensing techniques have been used to study many ecological variables and biotic and abiotic stresses to agricultural crops over decades, the potential use of these techniques for greenbug, Schizaphis graminum (Rondani) (Hemiptera: Aphididae) infestations and damage to wheat, Triticum aestivum L., under field conditions is unknown. Hence, this research was conducted to investigate: 1) the applicability and feasibility of using a portable narrow-banded (hyperspectral) remote sensing instrument to identify and discern differences in spectral reflection patterns (spectral signatures) of winter wheat canopies with and without greenbug damage; and 2) the relationship between miscellaneous spectral vegetation indices and greenbug density in wheat canopies growing in two fields and under greenhouse conditions. Both greenbug and reflectance data were collected from 0.25-, 0.37-, and 1-m2 plots in one of the fields, greenhouse, and the other field, respectively. Regardless of the growth conditions, greenbug-damaged wheat canopies had higher reflectance in the visible range and less in the near infrared regions of the spectrum when compared with undamaged canopies. In addition to percentage of reflectance comparison, a large number of spectral vegetation indices drawn from the literature were calculated and correlated with greenbug density. Linear regression analyses revealed high relationships (R2 ranged from 0.62 to 0.85) between greenbug density and spectral vegetation indices. These results indicate that hyperspectral remotely sensed data with an appropriate pixel size have the potential to portray greenbug density and discriminate its damage to wheat with repeated accuracy and precision.

Paprastojo kukurūzo (Zea mays L.) drėgmės streso nustatymas, taikant spektrinį atspindį ir klasifikavimo medžio metodą

“…In addition, remote sensing is a better method to detect and quantify the impact of plant stress compared to visual techniques because a vegetative unit can be repeatedly, objectively, and nondestructively examined in a fast, robust, accurate, and inexpensive way (Mirik et al, 2006;Elsayed et al, 2011). The objectives of our study were 1) to examine the use of spectral reflectance characteristics and chlorophyll readings to discern water stress severity imposed with four irrigation levels (0, 33, 66 and 100 % of pot capacity (PC) on sweet corn (Zea mays L.) grown in controlled conditions and 2) to explore the effectiveness of CT for discriminating water stress severity using spectral indices before and after irrigation.…”

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

“…Spectral characteristics of uninfested and infested wheat by RWA found in this study are similar to the results found by previous studies. 31,[32][33][34][35] Spectral characteristics of field been (Vicia faba) leaf infected by Botrytis fabae, 32 wheat infested by sunn pest (Eurygaster integriceps) 33 and greenbug (Schizaphis graminum) 31,[34][35] showed similar patterns in reflectance from infested infected and health plants. Reflectance from infested-infected plants was lower in the NIR and higher in the visible spectrum than uninfected-uninfested plants studied.…”

Remote Sensing and Reflectance Profiling in Entomology

“…With the recent surge in food commodity prices and the growing interest and concern about food security and agricultural sustainability, technologies such as airborne remote sensing systems are needed to improve the productivity of food production systems (9,15). A large body of research exists on the use of handheld spectrometers and ground-based multispectral and hyperspectral imaging technologies to detect and quantify arthropod-induced stress in crops (85,86,91,94,95,116,150,151). Airborne platforms include satellites, manned aircraft, and unmanned aerial vehicles (UAVs) (also called unmanned aerial systems, UAS).…”

“…Even with the newest source of commercially available satellite imagery (e.g., Quickbird, Worldview-2, and GeoEye-1), practical constraints for use of satellite imagery in agricultural insect pest management include slow data delivery time to end users, fixed orbital cycles with generally low temporal resolution, generally low spatial resolution, and weather dependency (challenges imposed by cloud cover) of image quality (111). Numerous studies describe the use of airborne multispectral and hyperspectral reflectance data acquired from crop plants (17,19,86,131,150,152,156) and forests (130) under arthropod-induced stress.…”