Abstract
Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. 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 using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.
Highlights
Plant disease epidemics cause substantial economic losses in agricultural settings worldwide [1]
This paper focuses on the potential of using satellite imagery to detect soybean sudden death syndrome (SDS), a disease of significant economic importance in North and South America [3,4,5]
The disease is caused by Fusarium virguliforme (Fv), a soilborne fungal pathogen [6], and is widely distributed across 23 U.S states, including those accounting for most U.S soybean production
Summary
Plant disease epidemics cause substantial economic losses in agricultural settings worldwide [1]. This paper focuses on the potential of using satellite imagery to detect soybean sudden death syndrome (SDS), a disease of significant economic importance in North and South America [3,4,5]. The pathogen starts infecting roots during early soybean growth stages [9,10] and causes root rot and poor root development [3]. The initial foliar symptoms show only as yellow traces on lower leaves, which makes the disease difficult to detect at early stages. Abundant soil moisture favors SDS foliar symptom expression [12,15], whereas infected plants may not develop foliar symptoms under dry field conditions. Disease distribution within a field is limited by the spatial distribution of the pathogen at the beginning of the growing season
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