Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa

Li, X.; Chen, C.; Kyveryga, Peter; Yang, X. B.

doi:10.1094/phyto-11-15-0303-r

Cited by 12 publications

(9 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of crop rotation information for SDS detection in our study suggests that features specific to a study site (e.g., prior knowledge of disease occurrence and intensity, landscape features, environmental inputs, or cultivar susceptibility) may be as important as spectral features. Yang et al [62] used a combination of geographic features and satellite image data to predict SDS risk in commercial soybean fields throughout the state of Iowa. Their field survey data confirmed that information from early-season NDVI values and geographical features (e.g., field slope and distance from water flow lines) were correlated with late-season SDS intensity.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

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...

show abstract

Section: Discussionmentioning

confidence: 99%

“…Our study also did not encode the location of the quadrats relative to each other for the random forest model, and this spatial (distance) information could also be used. Depending on their scale, future studies might potentially include soil types, distance to flowlines [62], and climatic and environmental trends [15].…”

Section: Limitations and Future Workmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Information technology enabled us to study this disease using a non-traditional approach, which may provide new insight on the sudden occurrence of this disease. Yang et al [146] showed SDS to be a model system in the study of satellite remote sensing for disease detection. This is the first reported case of occurrence of a plant disease that can be seen and identified from satellite due to its unique nature.…”

Section: Conclusion and Future Research Opportunitiesmentioning

confidence: 99%

Sudden death syndrome - a growing threat of losses in soybeans.

Navi¹,

Yang²

2016

CABI Reviews

Self Cite

View full text Add to dashboard Cite

Sudden death syndrome (SDS) caused by Fusarium virguliforme is one of the major yield-limiting soil borne diseases of soybean (Glycine max). The SDS has been reported from 21 U.S. states and is known to occur in Africa, North America, and South America. In the U.S. the losses due to SDS was estimated at $3.06 billion for a period from 1988 to 2010. Since 1983, several management approaches have been investigated to reduce SDS and yet, continued efforts are necessary to develop long term disease management programs and to sustain disease below economic threshold levels. Integrating available control measures is an option, but adaptability and real-world assessments are equally important. Support of several funding agencies to better understand the disease in identifying suitable control measures to reduce yield losses in commercial cultivations has been indispensable in accomplishing these goals. In spite of sustained efforts, SDS continued to spread within the U.S. and reported in seven other countries since its first report in 1971.Comprehensive reviews have previously been published on this disease by Roy et al. [98], Leandro et al. [58], and Hartman et al. [32]. In this review, updated information on geographic distribution and economic significance of SDS, epidemiology, factors affecting SDS, and management options for SDS including screening techniques have been compiled. Also, discussed significant gaps in use of plant, fungi and bacteria based biocontrol agents in addressing management of SDS.Keywords: Soybean sudden death syndrome, SDS, Fusarium virguliforme, biocontrol, global reports, yield losses due to SDS, epidemiological factors, greenhouse and field screening. ______________________________________________________________________________This is a manuscript of an article from CAB Reviews 11 (2016): 039, doi: 10.1079/PAVSNNR201611039. Navi, S. S.; Yang, X. B. 2016. Sudden death syndrome -a growing threat of losses in soybeans. CAB International, Wallingford, UK. Aoki et al., and F. virguliforme,90] is an economically significant soil-borne disease, and a risk to many soybean production areas worldwide. The disease was first observed by H.J. Walters in Arkansas, United States in 1971[100], but it was only in 1983 that the disease was named as sudden death syndrome (SDS) of unknown etiology [40]. As of this review, the disease has been reported in three continents, North America (Canada and United States), South America (Argentina, Bolivia, Brazil, Paraguay, and Uruguay), and Africa (South Africa), and within the United States 21 states (Fig. 1) Whether the pathogens causing SDS have been introduced in to new regions or if they have been present in the soil in production fields of these countries for some time without being detected is unknown. After the SDS was detected in Iowa [150], Scherm and Yang [114] developed a risk assessment model to determine potential geographic range of development of this disease, which was considered a southern disease by then, in North America. They predi...

show abstract

“…There is no in-season treatment for infected plants, making it important to detect Fv infection to take preventive actions in the next planting cycle [32] and consider impact on yield for the current season. Although risk of Fv infection has been studied [33], a nondestructive method to identify Fv infected soybeans prior to visual symptoms is needed.…”

Section: Introductionmentioning

confidence: 99%

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

et al. 2018

View full text Add to dashboard Cite

Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf and canopy level spectral measurements to detect non-visual foliage symptoms induced by Fusarium virguliforme (Fv, which causes sudden death syndrome). Canopy reflectance measurements were made using the Piccolo Doppio dual field-of-view, two-spectrometer (400 to 1630 nm) system on a tractor. Leaf level measurements were obtained, in different plots, using a handheld spectrometer (400 to 2500 nm). Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plants. Canopy and leaf spectral data allowed identification of Fv infection, prior to visual symptoms, with classification accuracy of 88% and 91% for calibration, 79% and 87% for cross-validation, and 82% and 92% for validation, respectively. Differences in wavelengths important to prediction by canopy vs. leaf data confirm that there are different bases for accurate predictions among methods. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, with calibration, cross-validation and validation R 2 values 0.71, 0.59 and 0.62 (p < 0.01), respectively, and validation root mean square error of 0.31 t•ha −1. Spectral data from the tractor mounted system are thus sensitive to the expression of Fv root infection at canopy scale prior to canopy symptoms, suggesting such systems may be effective for precision agricultural research and management.

show abstract

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa

Cited by 12 publications

References 56 publications

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

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

Sudden death syndrome - a growing threat of losses in soybeans.

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

Contact Info

Product

Resources

About