Fusarium virguliforme is a soil-borne pathogen that is the causal agent of sudden death syndrome (SDS). This disease is one of the top contributors to major yield losses in soybean across the United States. Characteristic symptoms of the disease include interveinal chlorosis and/or necrosis of trifoliate leaves and defoliation. In some cases, the foliar symptoms may not be present, but yield loss still occurs. This disease is evaluated using an incidence rating, the percent of plants in the plot that are expressing symptoms, and a severity rating, using a one to nine scale based on varying levels of chlorosis, necrosis, and defoliation. Using remote sensing provides an alternate approach to identify and evaluate plant diseases. It provides a non-destructive method to assess the severity of foliar symptoms and their distribution across production fields. SDS was chosen as the disease to use for this system due to the unique disease symptomology and yield loss. In 2019 and 2020, SDS trials were established in a production field location that has a history of SDS in Valmeyer, IL. This seed treatment study had different chemicals with varying levels of efficacy against SDS. Disease ratings were collected at the first sign of symptoms, and aerial imagery was collected on the same day. There were multiple dates across both years when this data was collected. ArcGIS was used to analyze the multispectral imagery and do a plot by plot analysis for each of the plots. A regression analysis was performed to test the relationship between the foliar disease ratings and the plot data collected from the multispectral imagery. Multiple vegetation indices were tested, and the results showed that overall, in 2019, GNDVI had the strongest relationship with foliar ratings. In 2020, NDRE had the strongest overall relationship with foliar ratings. The relationship between NDVI and the ratings was the most consistent at the last rating of the season.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-3971 |
| Date | 01 May 2022 |
| Creators | McKinzie, Lindsey |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
Page generated in 0.0018 seconds