Linear Support Vector Machine Classification of Plant Stress From Soybean Aphid (Hemiptera: Aphididae) Using Hyperspectral Reflectance.

Spectral remote sensing has the potential to improve scouting and management of soybean aphid (Aphis glycines Matsumura), which can cause yield losses of over 40% in the North Central Region of the United States. We used linear support vector machines (SVMs) to determine 1) whether hyperspectral samples could be classified into treat/no-treat classes based on the economic threshold (250 aphids per plant) and 2) how many wavelengths or features are needed to generate an accurate model without overfitting the data. A range of aphid infestation levels on soybean was created using caged field plots in 2013, 2014, 2017, and 2018 in Minnesota and in 2017 and 2018 in Iowa. Hyperspectral measurements of soybean canopies in each plot were recorded with a spectroradiometer. SVM training and testing were performed using 15 combinations of normalized canopy reflectance at wavelengths of 720, 750, 780, and 1,010 nm. Pairwise Bonferroni-adjusted t-tests of Cohen's kappa values showed four wavelength combinations were optimal, namely model 1 (780 nm), model 2 (780 and 1,010 nm), model 3 (780, 1,010, and 720 nm), and model 4 (780, 1,010, 720, and 750 nm). Model 2 showed the best overall performance, with an accuracy of 89.4%, a sensitivity of 81.2%, and a specificity of 91.6%. The findings from this experiment provide the first documentation of successful classification of remotely sensed spectral data of soybean aphid-induced stress into threshold-based classes.

Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield.

Remote sensing has been shown to be a promising technology for the detection and monitoring of plant stresses including insect feeding. Popillia japonica Newman, is an invasive insect species in the United States, and a pest of concern to soybean, Glycine max (L.) Merr., in the upper Midwest. To investigate the effects of P. japonica feeding injury (i.e., defoliation) on soybean canopy spectral reflectance and yield, field trials with plots of caged soybean plants were established during the summers of 2020 and 2021. In each year, field-collected P. japonica adults were released into some of the caged plots, creating a gradient of infestation levels and resulting injury. Estimates of injury caused by P. japonica, ground-based hyperspectral readings, total yield, and yield components were obtained from the caged plots. Injury was greatest in the upper canopy of soybean in plots infested with P. japonica. Overall mean canopy injury (i.e., across lower, middle, and upper canopy) ranged from 0.23 to 6.26%, which is representative of injury levels observed in soybean fields in the Midwest United States. Feeding injury from P. japonica tended to reduce measures of soybean canopy reflectance in near infra-red wavelengths (~700 to 1000 nm). These results indicate that remote sensing has potential for detection of injury from P. japonica and could facilitate scouting for this pest. Effects of P. japonica injury on total yield were not observed, but a reduction in seed size was detected in one of the two years. The threat to soybean yield posed by P. japonica alone appears minimal, but this pest adds to the guild of other defoliating insects in soybean whose combined effects could threaten yield. The results of this research will guide refinement of management recommendations for this pest in soybean and hold relevance for other cropping systems.

Floral Plantings in Large-Scale Commercial Agroecosystems Support Both Pollinators and Arthropod Predators.

Beneficial insect populations and the services that they provide are in decline, largely due to agricultural land use and practices. Establishing perennial floral plantings in the unused margins of crop fields can help conserve beneficial pollinators and predators in commercial agroecosystems. We assessed the impacts of floral plantings on both pollinators and arthropod predators when established adjacent to conventionally managed commercial potato fields. Floral plantings significantly increased the abundance of pollinators within floral margins compared with unmanaged margins. Increased floral cover within margins led to significantly greater pollinator abundance as well. The overall abundance of arthropod predators was also significantly increased in floral plantings, although it was unrelated to the amount of floral cover. Within adjacent potato crops, the presence of floral plantings in field margins had no effect on the abundance of pollinators or predators, although higher floral cover in margins did marginally increase in-crop pollinator abundance. Establishing floral plantings of this kind on a large scale in commercial agroecosystems can help conserve both pollinators and predators, but may not increase ecosystem services in nearby crops.

Implementation of a Diagnostic-Concentration Bioassay for Detection of Susceptibility to Pyrethroids in Soybean Aphid (Hemiptera: Aphididae).

Soybean aphid, Aphis glycines Matsumura, remains the most economically damaging arthropod pest of soybean in the midwestern United States and southern Canada. Foliar applications of a limited number of insecticide modes of action have been the primary management tactic, and pyrethroid resistance was documented recently with full concentration-response leaf-dip and glass-vial bioassays. Full concentration-response bioassays can be cumbersome, and a more efficient assessment tool was needed. In this study, we implemented a diagnostic-concentration glass-vial bioassay using bifenthrin and λ-cyhalothrin. Bioassays were conducted with field-collected soybean aphid populations to assess the geographic extent and severity of resistance to pyrethroids. In 2017, 10 of 18 and 11 of 21 field populations tested with bifenthrin and λ-cyhalothrin, respectively, had mean proportion mortalities less than the susceptible laboratory population. In 2018, 17 of 23 and 13 of 23 field populations tested with bifenthrin and λ-cyhalothrin, respectively, had mean proportion mortalities less than the susceptible laboratory population. Populations collected after reported field failures of a pyrethroid insecticide generally had mean proportion mortalities less than the susceptible laboratory population. In both years, there was a strong correlation between chemistries, which suggests cross-resistance between these insecticides. The diagnostic-concentration glass-vial bioassays reported here will provide the foundation for an insecticide resistance monitoring program with the ability to determine practical levels and geographic extent of insecticide resistance.

Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles.

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

Optimizing band selection for spectral detection of Aphis glycines Matsumura in soybean.

BACKGROUND: Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean in North America. Accurate estimation of A. glycines densities requires costly, time-intensive weekly counts of adults and nymphs on plants. Field studies were conducted in 2013 and 2014 to assess the potential for spectral-based remote sensing to more efficiently quantify cumulative aphid-days (CADs) using soybean canopy reflectance. RESULTS: Narrow-band wavelengths in the near-infrared spectral range were associated with CAD, but those in the visible spectral range were not associated with CAD. Simple linear regression models of CAD on reflectance were generally better than quadratic and cubic regression models. Simulated wide-band sensors centered at 740-1100 nm yielded better regression models than ones centered at 600-740 nm, regardless of bandwidth. Among the simulated wide-band sensors, increasing sensor bandwidth worsened CAD estimation or required more simulated sensors to optimize CAD estimation. Optimal combinations of spectral bands explained 83-96% of the experimentally manipulated variation in CAD. CONCLUSION: Near-infrared wavelengths at 780 ± 50 nm can effectively estimate A. glycines abundance on soybean. Our approach of simulating wide-band multispectral sensors from ground-based hyperspectral data helped to refine spectral sensors and holds potential to reduce the cost and complexity of treat/no-treat classification tasks. This study will contribute to future research aiming to quantify insect injury using customized commercial-grade sensors for detection, quantification, and differentiation of A. glycines from other stressors. © 2018 Society of Chemical Industry.

Effects of Foliar Insecticides on Leaf-Level Spectral Reflectance of Soybean.

Pest-induced changes in plant reflectance are crucial for the development of pest management programs using remote sensing. However, it is unknown if plant reflectance data is also affected by foliar insecticides applied for pest management. Our study assessed the effects of foliar insecticides on leaf reflectance of soybean. A 2-yr field trial and a greenhouse trial were conducted using randomized complete block and completely randomized designs, respectively. Treatments consisted of an untreated check, a new systemic insecticide (sulfoxaflor), and two representatives of the most common insecticide classes used for soybean pest management in the north-central United States (i.e., λ-cyhalothrin and chlorpyrifos). Insecticides were applied at labeled rates recommended for controlling soybean aphid; the primary insect pest in the north-central United States. Leaf-level reflectance was measured using ground-based spectroradiometers. Sulfoxaflor affected leaf reflectance at some red and blue wavelengths but had no effect at near-infrared or green wavelengths. Chlorpyrifos affected leaf reflectance at some green, red, and near-infrared wavelengths but had no effect at blue wavelengths. λ-cyhalothrin had the least effect on spectral reflectance among the insecticides, with changes to only a few near-infrared wavelengths. Our results showing immediate and delayed effects of foliar insecticides on soybean reflectance indicate that application of some insecticides may confound the use of remote sensing for detection of not only insects but also plant diseases, nutritional and water deficiencies, and other crop stressors.

Evidence for Soybean Aphid (Hemiptera: Aphididae) Resistance to Pyrethroid Insecticides in the Upper Midwestern United States.

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a damaging invasive pest of soybean in the upper Midwest. Threshold-based insecticide applications are the primary control method for soybean aphid, but few insecticide groups are available (i.e., pyrethroids, organophosphates, and neonicotinoids). To quantify current levels of soybean aphid susceptibility to pyrethroids in the upper Midwest and monitor for insecticide resistance, leaf-dip bioassays were performed with λ-cyhalothrin in 2013-2015, and glass-vial bioassays were performed with λ-cyhalothrin and bifenthrin in 2015 and 2016. Soybean aphids were collected from 27 population-years in Minnesota and northern Iowa, and were compared with a susceptible laboratory colony with no known insecticide exposure since discovery of soybean aphid in North America in 2000. Field-collected aphids from some locations in leaf-dip and glass-vial bioassays had significantly lower rates of insecticide-induced mortality compared with the laboratory population, although field population susceptibility varied by year. In response to sublethal concentrations of λ-cyhalothrin, adult aphids from some locations required higher concentrations of insecticide to reduce nymph production compared with the laboratory population. The most resistant field population demonstrated 39-fold decreased mortality compared with the laboratory population. The resistance documented in this study, although relatively low for most field populations, indicates that there has been repeated selection pressure for pyrethroid resistance in some soybean aphid populations. Integrated pest management and insecticide resistance management should be practiced to slow further development of soybean aphid resistance to pyrethroids.

Soybean Aphid (Hemiptera: Aphididae) Affects Soybean Spectral Reflectance.

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is the most economically important insect pest of soybean in the north central United States. Scouting-based integrated pest management (IPM) programs could become more efficient and more widely adopted by using plant spectral reflectance to estimate soybean aphid injury. Our objective was to determine whether plant spectral reflectance is affected by soybean aphid feeding. Field trials were conducted in 2013 and 2014 using caged plots. Early-, late-, and noninfested treatments were established to create a gradient of soybean aphid pressure. Whole-plant soybean aphid densities were recorded weekly. Measurements of plant spectral reflectance occurred on two sample dates per year. Simple linear regression models were used to test the effect of cumulative aphid-days (CAD) on plant spectral reflectance at 680 nm (RED) and 800 nm (NIR), normalized difference vegetation index (NDVI), and relative chlorophyll content. Data indicated that CAD had no effect on canopy-level RED reflectance, but CAD decreased canopy-level NIR reflectance and NDVI. Canopy- and leaf-level measurements typically indicated similar plant spectral response to increasing CAD. CAD generally had no effect on relative chlorophyll content. The present study provides the first documentation that remote sensing holds potential for detecting changes in plant spectral reflectance induced by soybean aphid. The use of plant spectral reflectance in soybean aphid management may assist future IPM programs to reduce sampling costs and prevent prophylactic insecticide sprays.

Remote Sensing for Assessing Rhizoctonia Crown and Root Rot Severity in Sugar Beet.

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani AG-2-2, is an increasingly important disease of sugar beet in Minnesota and North Dakota. Disease ratings are based on subjective, visual estimates of root rot severity (0-to-7 scale, where 0 = healthy and 7 = 100% rotted, foliage dead). Remote sensing was evaluated as an alternative method to assess RCRR. Field plots of sugar beet were inoculated with R. solani AG 2-2 IIIB at different inoculum densities at the 10-leaf stage in 2008 and 2009. Data were collected for (i) hyperspectral reflectance from the sugar beet canopy and (ii) visual ratings of RCRR in 2008 at 2, 4, 6, and 8 weeks after inoculation (WAI) and in 2009 at 2, 3, 5, and 9 WAI. Green, red, and near-infrared reflectance and several calculated narrowband and wideband vegetation indices (VIs) were correlated with visual RCRR ratings, and all resulted in strong nonlinear regressions. Values of VIs were constant until at least 26 to 50% of the root surface was rotted (RCRR = 4, wilting of foliage starting to develop) and then decreased significantly as RCRR ratings increased and plants began dying. RCRR also was detected using airborne, color-infrared imagery at 0.25- and 1-m resolution. Remote sensing can detect RCRR but not before initial appearance of foliar symptoms.