Marston, Zachary2020-08-252020-08-252019-07https://hdl.handle.net/11299/215212University of Minnesota Ph.D. dissertation. July 2019. Major: Entomology. Advisors: Robert Koch, Ian MacRae. 1 computer file (PDF); ix, 64 pages.Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is the most economically important insect pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in the north-central United States. Current management recommendations for soybean aphid include frequent scouting of soybean fields and application of foliar insecticides when soybean aphid populations exceed an economic threshold of 250 aphids per plant. The scouting process for soybean aphid is time consuming, expensive, and also fails to thoroughly assess populations across the entire field. Because of these drawbacks, 84% of soybean farmers want to reduce scouting efforts. In 2015, it was determined that soybean aphid-induced stress had a significant effect on red-edge and near-infrared (NIR) reflectance of soybean canopies, offering the potential to use remote sensing for soybean aphid scouting. Utilizing remote sensing for soybean aphid scouting may decrease human effort, increase spatial coverage, and ultimately increase the adoption of recommended management practices. However, it was unknown whether soybean aphid-induced stress could be detected from aerial platforms, whether these reflectance data of aphid-induced stress could be classified into treatment groups, and how confounding factors might affect classification results. My first chapter determined that soybean aphid-induced stress could be detected from an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. Findings indicated that NIR reflectance decreased as aphid populations increased in both caged and open-field experiments. Chapter 2 evaluated ground-based hyperspectral samples and determined that soybean reflectance samples that were above the economic threshold of 250 aphids per plant could be classified with over 86% accuracy using linear support vector machine classification. Chapter 3 further evaluated ground-based hyperspectral samples in the presence of the confounding disease, soybean sudden death syndrome (SDS) caused by the fungal pathogen Fusarium virguliforme O’Donnell and T. Aoki (Hypocraeles: Nectriaceae). Findings indicated that when using linear support vector machines, it was difficult to differentiate between healthy and diseased samples; however, including the diseased group in the classification model decreased false positives for soybean aphid-induced stress. Overall, these findings advance the use of remote sensing for soybean aphid management and provide the first documentation of spectral classification of soybean aphid into threshold-based groups.enAgricultureIntegrated Pest ManagementMachine LearningRemote SensingThesis or Dissertation