Root Rot Detection in Sugar Beet Using Digital Multispectral Video
Allen Hope, Lloyd Coulter, Douglas Stow, Seth Peterson and Dawn Service
San Diego State University, San Diego, California, U.S.A.
Tel: (1)-619-594-2777 Fax: (1)-619-594-4938
E-mail: hope1@mail.sdsu.edu
Alan Telck and David Melin
Holly Agricultural Resource Center, Sheridian, Wyoming, U.S.A.
Jan SVEJKOVSKY and Jeffrey CONGER
Ocean Imaging Inc., Solana Beach, California, U.S.A.
Abstract
A study to determine whether root rot infestations can be detected using high spatial
resolution digital multispectral video (DMSV) data was conducted in the Imperial Valley of
California. DMSV data were collected on two dates over 25 fields during the summer of
1999 using a light aircraft. The DMSV images included four spectral bands centered on
530 nm, 570 nm, 650 nm and 750 nm, with nominal widths of the first two bands being 10
nm and the last two bands 20 nm. Data collection missions were flown on July 15 and July
30 and the fields were harvested in late July and early August. Data from the two narrow
band filters (525 - 535 nm and 565 - 575 nm) were used to calculate the physiological
reflectance index (PRI) while data from the red (640 - 660 nm) and near infrared (740 - 760
nm) bands were used to calculate the normalized difference vegetation index (NDVI). The
NDVI was found to be a better predictor of percent root rot than the PRI while the change in
NDVI between the two flight dates was a better predictor than single date NDVI models.
Introduction
Root rot is a soil-borne fungal disease that can cause serious damage to sugar beets
and reduce yields significantly. Early detection of infestations helps managers prioritize
fields for harvest and minimizes production losses. Farmers visually inspect their fields to
identify changes in canopy condition (e.g., wilting leaves, dead leaves) that may signify root
rot or other types of stress. The large area under sugar beet cultivation in the Imperial
Valley of California makes it impractical to implement a comprehensive monitoring
program based on ground observations. Furthermore, a regular monitoring schedule is
necessary to detect the onset of root rot.
High spatial resolution remote sensing with ground resolution elements less than
two meters may be a viable technique for the regular monitoring of sugar beet over large
areas if
spectral bands or spectral vegetation indices (SVIs) can be identified that are
responsive to changes in the vegetation canopy which accompany the onset of root rot, and
the data can be collected and processed in a timely manner (24 - 36 hours). A study was
conducted in the summer of 1999 to determine whether root rot infestations in the Imperial
Valley of California can be detected using high spatial resolution digital multispectral video
(DMSV) data.
Spectral vegetation indices reduce multispectral data to a single value and have been
used widely to infer vegetation characteristics such as leaf area index and biomass
(Choudhury, 1988). A common form of a spectral vegetation index based on two spectral
bands is:
Index = (band 1 - band 2)/(band 1 + band 2) (1)
The normalized difference vegetation index (NDVI) follows this form with band 1 and band
2 being near infrared and red reflectances, respectively. This index has been shown to have
a stable relationship with the fraction of photosynthetically active radiation absorbed by a
plant canopy (Prince, 1991). Consequently, the NDVI has also been used to model
vegetation growth (Choudhury, 1988).
While the NDVI has been used extensively over the past two decades with broad
band spectroradiometric data, a new suite of indices have been developed that exploits
narrow band spectroradiometric data. The physiological reflectance index (PRI) introduced
by Gamon et al. (1992) follows the form of equation 1 and is based on reflectances at 531
nm (band 1) and 570 nm (band 2) wavelengths. Gamon et al. (1997) renamed this the
photochemical reflectance index and suggested that the index was a good predictor of
photosynthetic radiation use efficiency at the leaf or canopy level, if the canopy is uniform
and completely covers the soil background. The index has not shown sensitivity to nutrient
stress, but it is affected by water stress with severe wilting (Gamon et al., 1997).
Objectives and Approach
The goal of this project was to investigate whether root rot in sugar beets could be
detected using the NDVI or PRI derived from high spatial resolution digital multispectral
video (DMSV) data. The study was also intended to determine whether data collection,
processing and analysis could be routinely executed within a 24 - 36 hour time frame. The
turn-around time for collecting data and delivering information to farm managers often
limits the utility of remote sensing for agricultural applications.
The four-band DMSV camera was fitted with filters having the following nominal
bandwidths: 525 - 535 nm; 565 - 575 nm; 640 - 660; nm; 740 - 760 nm. Two data
collection missions were flown in the summer of 1999 over 25 fields in the Imperial Valley
of southern California. The first flight was on July 15 and the second on July 30, with all
radiometric data being collected in a four-hour window centered on solar noon. The aircraft
flew at 1700 m AGL and the DMSV imagery had a ground sampling distance of 1.25 m.
Differences between the spectroradiometric properties of light and dark targets that were
assumed to be invariant between the two flight dates were used to determine global
corrections to normalize the spectral indices for differences in illumination conditions.
Detailed data on the spatial distribution and degree of root rot infestation (percent of
sugarbeet harvested) were collected in a selected field at the time of harvest (August). The
percent root rot was obtained for 13 sub-areas in this field, their areas ranging between 1.2
and 6.5 ha (average = 3.8 ha). In addition to these large area samples, samples based on 20
to 30 sugar beet plants were obtained at six sites in the field prior to harvest.
The mean NDVI and PRI values were calculated for the each of the 13 sub areas on
the two dates and related to the corresponding percent root rot. The average change in
index values between the two dates was also considered a possible predictor variable for
root rot. The original 1.25 m DMSV data was resampled to 5 m pixels to minimize
misregistration errors for the multidate comparisons.
