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

Abstract

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.

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

“…Overall, even under the controlled conditions of this experiment, some plots were noticeably heterogeneous (for example, plot no. 16). There was also a slight difference in LAI predictions between the western and the eastern half of the study site, even within the same treatment classes.…”

“…Once these obstacles have been overcome, pushbroom scanner systems are able to record substantially less noise-affected spectra with higher resolution (spatial and spectral) than snapshot systems due to their narrower line-shaped camera aperture and higher sensor resolution [6].Since grain yield has no direct influence on the reflectance of crops, it should be derived indirectly by estimating other biophysical parameters [14]. Although [15][16][17][18] could show that an estimation of grain yield directly from reflection spectra is statistically possible, it was finally determined that this relationship can only be explained indirectly by biophysical and biochemical properties. Among other plant properties, chlorophyll content (CHL) and leaf area index (LAI) correlated with grain yield [19], which can be estimated reliably from remote sensing data of different scales and platforms [4].…”

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

“…SDS-infected plants tend to show early chlorophyll deterioration [16], resulting in spectral responses that increase reflectance in the blue and red bands while decreasing reflectance in the green and NIR regions. When plant diseases such as SDS produce necrotic or chlorotic symptoms on leaves, diseased plants show an overall increase in reflectance in the visible region, especially in the red band [16,25], and simultaneously decreased reflectance in the green and NIR regions. Reflectance in the NIR regions is affected primarily by leaf structure and canopy biomass, which are influenced by plant water and health status [22,[25][26][27].Recently, successful early detection of SDS in soybean leaves [16,25] and canopies [25,28] has been achieved using handheld, tractor-mounted, and unmanned aerial vehicle (UAV)-mounted remote sensing tools.…”

“…When plant diseases such as SDS produce necrotic or chlorotic symptoms on leaves, diseased plants show an overall increase in reflectance in the visible region, especially in the red band [16,25], and simultaneously decreased reflectance in the green and NIR regions. Reflectance in the NIR regions is affected primarily by leaf structure and canopy biomass, which are influenced by plant water and health status [22,[25][26][27].…”

“…Recently, successful early detection of SDS in soybean leaves [16,25] and canopies [25,28] has been achieved using handheld, tractor-mounted, and unmanned aerial vehicle (UAV)-mounted remote sensing tools. Although these tools have proven to be promising for early detection of SDS, they still require considerable time, capital, and equipment investments for data collection.…”