Annual morningglories are consistently ranked as some of the most
troublesome weeds in Mississippi soybean fields. Many morningglory plants
survive herbicide application or emerge following herbicide application and
create problems at harvest. Harvest aids are available to desiccate
morningglory vines, but economic thresholds for morningglory vine densities
have not been established relative to harvest aids.
Field studies will be conducted to determine the level of morningglory
infestation required in soybean at harvest to economically justify a harvest
aid and to determine if critical levels of morningglory infestation can be
remotely detected with multispectral imagery. Studies will be conducted in a
weed-free location. Six levels of morningglory infestation will be
established by transplanting cotyledon-size morningglory seedlings into
plots shortly after final postemergence herbicide applications made at the
V4 soybean growth stage. This will simulate morningglory plants that emerge
as problems late in the growing season and that cause problems at harvest.
Pitted morningglory biomass will be determined from a designated area within
each plot prior to harvest aid application and immediately after remotely
sensed images have been acquired. Percent canopy cover occupied by pitted
morningglory and soybean will also be visually estimated for each plot.
Remote sensing will be incorporated into the study to determine if
multispectral imagery can be used to assess pitted morningglory densities
and canopy composition present in soybean fields near harvest and in turn be
used to determined if harvest aide applications are economically feasible.
Images will be acquired when soybean plants are in the R7 growth stage
(beginning maturity, plants senescing and dropping leaves) and morningglory
vines are still green. Efforts will be made to correlate actual plant
densities and canopy cover estimates collected on the ground with remotely
derived data. Main plots will contain a specified morningglory density. Main
plots will be subdivided into subplots that receive a harvest aid and those
that do not. Data from this study should provide critical information that
can be used by producers to determine when harvest aids are economically
feasible and if remotely sensed imagery can be used to detect these levels
of weed infestations.
Establish economic thresholds
for harvest aids relative to morningglory infestation levels present in
soybean fields at harvest.
Determine if multispectral imagery can be used to assess levels of
morningglory infestations present in soybean fields at harvest.
Studies will be initiated in 2002 at the Delta Research and Extension Center
in Stoneville, MS. A location will be selected that has been maintained weed
free for several years. Metolachlor will be applied preemergence to remove
annual grasses from the test area and postemergence graminicides will be
used if necessary to remove grasses that escape soil applied herbicides.
Roundup Ready MG III or IV (depending on planting date) soybeans will be
planted using 15-inch row spacings. Plots will be tilled prior to planting
to remove existing vegetation. Glyphosate applications will be made at V2
and V4 soybean growth stages, but may vary depending on weed emergence
patterns and soybean development. Following the final glyphosate
application, cotyledon-stage morningglory plants will be transplanted into
row middles to simulate morningglory plants that emerge following glyphosate
applications and become a problem at harvest. Plant densities of 0, 0.5, 1,
2, 4, and 8 plants m-2 will be established. Plants that do not
survive will be replaced with new ones. Other broadleaf weeds that emerge in
plots will be removed by hand weeding or hoeing. Plots will be 30 x 40 feet
arranged in a randomized complete block design with 4 replications and a
split plot arrangement of treatments. Main plots will contain a specified
morningglory density. Main plots will be sub-divided into subplots that
receive a harvest aid and those that do not. Above ground pitted
morningglory biomass will be determined by harvesting vines from one square
meter of each plot just prior to harvest aid application, image collection,
and soybean harvest. Percent canopy composition occupied by pitted
morningglory and soybean will be visually estimated for each plot during the
same time frame. Harvest aids will be applied to appropriate plots following
collection of vine and canopy composition data and multispectral images.
Soybean will be harvested 7 to 10 days after harvest aid applications. Seed
samples will be screened and percent foreign matter and soybean yield
Image collection will be coordinated through the RSTC using a Geovantage
multispectral system. In addition, several other vendors are available as
backups if flights scheduled flights by RSTC are interrupted or delayed for
various reasons. GPS coordinates for the corners of the test area will be
provided to RSTC personnel to ensure the collection of images for the study
area. Plots are located in established RSTC flight lines. Multispectral
imagery will be collected immediately prior to biomass collection and in the
same timeframe as visual ratings. This imagery will correspond with R7 to R8
soybean growth stages when soybeans are senescing and have lost most or all
of their green foliage. MG III soybeans planted in early-May will mature in
late-August to early-September depending on environmental conditions.
Multispectral flights coordinated by the RSTC are scheduled every 2 weeks
for Stoneville through September and possibly into November. This should
allow ample opportunities to collect imagery of plots during the desired
time frame. Spatial resolution will be 0.5m. Therefore, plot sizes of 30 x
40 feet should be more than adequate for this study. For each image,
reflectance data will be collected in the visible blue, green, red, and
near-infrared (NIR) spectrums. Imagery will be geo-referenced at time of
data acquisition. This will allow images to be processed and received within
approximately 24 hours of time of data acquisition. Reflectance data for
each spectral band will be extracted from a 4.0- by 4.0-m sampling area for
each plot center. To account for 1.0-m accuracy and minute errors associated
with image geo-referencing, reflectance data for each 1.0-m2
pixel within each sampling area for each spectral band will be averaged.
Reflectance data for the red and NIR spectral bands will be used to derive a
Normalized Difference Vegetation Index (NDVI) for each plot. The spectral
band and NDVI data will be as classification features for discriminating
pitted morningglory infested and weed-free soybean plots.
Potential technology products from this project may include the development
of software packages that use data from images to estimate weed densities
and generate prescription spray maps. Any intellectual property issues will
be handled according to Mississippi State University intellectual property
Pitted morningglory (Ipomoea lacunosa L.) and entireleaf morningglory
(Ipomoea hederacea var. integriuscula L.) are the 2nd and
3rd most troublesome weeds in Mississippi soybeans, respectively
(Byrd, 2001). Morningglories often emerge as a problem as early-maturing
(maturity groups III and IV) soybeans are nearing harvest thereby reducing
yields and harvest efficiency. Harvest aids applied 7 to 14 days prior to
harvest can be used to desiccate vines and improve harvestability, but
threshold levels for morningglory infestation have not been established
relative to harvest aids. Based on research conducted in 2000 and 2001,
harvest aids were generally economical when morningglory control was 70% or
less (Poston et al. 2001) With actively growing morningglories in soon to be
harvested fields, moisture can be elevated causing loss in seed quality and
harvesting speed (Shaw, 1998). Bennet and Shaw (2000) found that 0.25 lb ai/A
paraquat + 3 lb ai/A sodium chlorate applied as a pre-harvest desiccant
provided the most economical and efficacious control of morningglory species
and other weeds. Unfortunately, no threshold levels for weed infestation at
harvest have been established. Therefore, efficacious harvest aids are
available but producers have no method of assessing when weed infestations
are at high enough levels to make harvest aids profitable. Additionally,
farm size has increased in recent years as profit margins for farm
commodities have narrowed and scouting larger land areas manually is likely
to be cost prohibitive.
Interest has been expressed in using remote sensing techniques to detect
weed infestations in fields and developing customized spray maps from the
data (Christensen et al. 1999, Thornton et al. 1990). Several problems have
been encountered with attempts to discriminate weeds and crops early in the
growing season. Medlin et al. (2000) were able to successfully detect pitted
morningglory and sicklepod early in the growing season with 90% accuracy
provided weeds were 5 to 10 cm tall and at populations of 10 plants m-2.
Medlin et al. (2000) also noted that reflectance of background soil and
vegetation interfered with the ability to discriminate between weed-free and
Multispectral imagery has proven more effective for detecting late-season
weed infestations in soybean. Multispectral imagery has been used with at
least 90% accuracy to discriminate weed-free soybean from soybean infested
with barnyardgrass, browntop millet, and large crabgrass after soybean
canopy closure when soybean was in the vegetative to late-senescence growth
stages (Koger et al. 2001). Richardson et al. (1985) as well as Menges et
al. (1985) used multispectral remote sensing to differentiate weed-free
cotton from various monocot and dicot weed species late in the growing
season. Multispectral imaging is also useful for discriminating weed
infestations using discriminant functions developed from other images. Koger
et al. (2001) used multispectral imagery to detect late-season weed
infestations for one image with 81 to 90% accuracy using discriminant
function developed for another image. Based on this information,
multispectral imagery may have the potential for detecting late-season
pitted morningglory infestations at or below levels that require pre-harvest
desiccation and in turn help determine when harvest aid applications are
cost effective. In addition, detection of late-season weed infestations can
provide growers useful information as to where weed problems occur in fields
thereby assisting in the development of future herbicide programs.
Late in the growing season interference from background soil should also be
reduced because of crop canopy, especially in narrow-row soybeans.
Additionally, green weed should be easily distinguishable from yellowish
brown senescing soybeans.
Bennet, A.C. and D.R. Shaw. 2000. Effect of preharvest desiccants on weed
production and viability. Weed Technol. 14:530-538.
Byrd, J. 2001. The Southern States Most Common and Troublesome weeds in
Soybean. Proc. South. Weed Sci. Soc 54:253.
Christensen, S., A. M. Walter, and T. Heisel. 1999. The patch treatment of
cereals. Pages 591-600 in Proc. Brighton Crop Protection Conf. Weeds.
UK: British Crop Protection Council.
Koger, C.H., D.R. Shaw, C.E. Watson, and K.N. Reddy. 2001. Potential of
sensing for detecting late-season weed infestations in soybean (Glycine
Weed Sci. (In Review).
Medlin, C. R., D. R. Shaw, P. D. Gerard, and F. E. Lemastus. 2000. Using
remote sensing to detect weed infestations in Glycine max.
Menges, R.M., P.R.
Nixon, and A.J. Richardson. 1985. Light reflectance and remote
sensing of weeds in agronomic and horticulture crops. Weed Sci. 33:569-581.
Poston, D. H., D. R. Shaw, C. Smith, and R. M. Griffin. 2001. Weed control
alternatives for maturity group III soybeans in Mississippi. Proc. South.
Weed Sci. Soc. 54:48.
Richardson, A.J., R.M.
Menges, and P.R. Nixon. 1985. Distinguishing weed from crop
plants using video remote sensing. Photogramm. Eng. Remote Sens.
Shaw, D. R. 1998. In Getting the green out of soybean harvest. E. A.
Farmer. August 1998.
Thornton, p. K., R. H. Fawcett, J. B. Dent, and T. J. Perkins. 1990. Spatial
distribution and economic thresholds for weed control. Crop Prot. 9:337-342.
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