Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

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.The pathogen starts infecting roots during early soybean growth stages [9,10] and causes root rot and poor root development [3]. Root infections are favored by cool, wet soil environments [11,12]. Foliar symptoms of interveinal chlorosis (yellowing between leaf veins) and necrosis (tissue browning following cell death) typically appear during reproductive stages [13] and cause premature defoliation and senescence under severe disease pressure [14]. 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.Scouting for SDS foliar symptoms in the field is made difficult by the relatively late onset of visible foliar symptom expression, which often occurs after the soybean canopy has closed, and by the patchy distribution of SDS in soybean fields. Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be di...

Plant pest surveillance: from satellites to molecules

“…Recent advances in remote sensing technologies, methodologies, and analytics have helped to resolve some of these historical challenges. Commercial satellites, such as Planet Labs and Maxar, for example, now enable frequent, precise returns with high spatial and temporal resolution with growing spectral resolution and have been recently established to be capable of disease monitoring [26,27]. Unmanned aerial vehicles (UAV, or 'drones') and low-altitude aircraft are well established to be capable of disease detection and monitoring, though reliable differentiation and diagnosis remains challenging [28][29][30][31][32].…”

Farmer perspectives on benefits of and barriers to extended crop rotations in Iowa, USA

“…Crop rotations can also decrease the "less is better" indicators of soil health such as bulk density (−0.8%) (McDaniel et al, 2014). Furthermore, crop rotations have been found to decrease weed density by 49% (Weisberger et al, 2019) and certain soil-borne pathogens up to 17-fold (Leandro et al, 2018;Peralta et al, 2018;Raza et al, 2020). These effects in combination likely contribute to a 28% increase in maize yield (Bowles et al, 2020).…”

Current recommendations and novel strategies for sustainable management of soybean sudden death syndrome

“…Along with these agronomic measures, precision farming practices, such as the quantification of inoculum level and the determination of the disease risk, are being implemented in some countries 45,74,75,89,107‐109 . For example, Roth et al 45 .…”

“…This tool may allow farmers to minimize the risk of yield loss, reducing treatment costs and the environmental impact associated with chemical treatments by applying these treatments only where they are required 89 . Additionally, detecting SDS‐diseased patches using remote sensing methods is a promising tool that would allow the identification of infected fields and adapt the management strategy for the following growing seasons in specific field‐patches 74,107‐109 …”