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POSTERS: Remote Sensing and Sensor Technology

Early detection of soybean sudden death syndrome using high-resolution satellite imagery
Muhammad Raza - Iowa State University. Leonor Leandro- Iowa State University, Forrest Nutter- Iowa State University, Sharon Eggenberger- Iowa State University

Sudden death syndrome (SDS) is a major yield-limiting disease of soybean in the Midwest. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of high-resolution (3 m) PlanetScope satellite imagery for early and accurate detection of SDS using random forest classification algorithm. We used four spectral bands including red, blue, green, and near-infrared (NIR) and calculated normalized difference vegetation index (NDVI) to detect healthy and SDS-infected quadrats (3 m wide × 1.5 m in length) in a soybean field experiment located in Boone, Iowa. Data collected during the 2016, 2017 and 2018 soybean growing seasons were analyzed in this study. The results indicate that spectral bands of PlanetScope imagery, along with calculated NDVI, can accurately predict SDS in soybean plots, even before foliar symptoms onset. Healthy and diseased soybean quadrats were detected with more than 85% accuracy and with kappa statistics (a measure of inter-rater agreement)f more than 68%, in all growing seasons. These promising results suggest that high-resolution satellite imagery has a high potential for early detection of SDS in soybean fields. Our findings highlight that this technology can facilitate large-scale monitoring of SDS and possibly other economically important soybean diseases to guide recommendations for site-specific management in current and future seasons.