Browsing by Author "Cira, Theresa M."

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    Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles
    (Journal of Economic Entomology, 2019-11) Marston, Zachary P. D.; Cira, Theresa M.; Hodgson, Erin W.; Knight, Joseph F.; MacRae, Ian V.; Koch, Robert L.
    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.
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    Economic-threshold-based classification of soybean aphid, Aphis glycines, infestations in commercial soybean fields using Sentinel-2 satellite data
    (Crop Protection, 2023-12) Ribeiro, Arthur V.; Lacerda, Lorena N.; Windmuller-Campione, Marcella A.; Cira, Theresa M.; Marston, Zachary P.D.; Alves, Tavvs M.; Hodgson, Erin W.; MacRae, Ian V.; Mulla, David J.; Koch, Robert L.
    The soybean aphid (SBA), Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), and field treatment decisions for this pest are based on average field populations. Previous studies indicated that ground- and drone-based red-edge and near-infrared remote sensing can be used to detect plant stress caused by SBA infestations in soybean. However, it remains to be determined if remote sensing for SBA can be expanded to field or landscape scale using satellite-based platforms. Thus, this research was conducted in three steps to determine the potential of using Sentinel-2 satellite data for the classification of SBA infestations in soybean fields using simulated and actual Sentinel-2 satellite spectral reflectance. In the first step, as a proof of concept, hyperspectral data from cage studies were used to simulate Sentinel-2 bands and vegetation indices (VIs), conducted in nine trials at multiple locations between 2013 and 2021. The effects of SBA from caged plants on simulated data were evaluated with random intercept linear mixed models. The satellite simulation indicated a significant effect of SBA on the spectral reflectance of caged soybean plants (p < 0.05) for four satellite bands (5, 6, 7, and 8A) and five VIs (NDVI, GNDVI, SAVI, OSAVI, and NDRE). In the second step, actual Sentinel-2 spectral reflectance and corresponding aphid counts of commercial soybean fields, collected from 2017 to 2019, were obtained. The relationship between SBA counts and Sentinel-2 spectral reflectance from commercial soybean fields were evaluated with general linear models. A significant effect of SBA was observed for three satellite bands (6, 7, and 8A) and three VIs (NDVI, SAVI, and OSAVI). In the third step, linear support vector machine (LSVM) models for the classification of SBA infestations as above or below a previously determined economic threshold of 250 aphids per plant were developed using simulated Sentinel-2 bands and VIs from the caged plots, and were tested on actual Sentinel-2 data from commercial soybean fields. The best LSVM model for the classification of aphids in soybean reached 91% accuracy, 85.7% sensitivity, and 93.3% specificity. Thus, simulations with caged plots can be used as an indication of the potential of using satellite data for the detection of plant stresses on a larger scale. Furthermore, this study advances decision-making for SBA, and the developed LSVM model can be used to update regional and local monitoring for the management of SBA.