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REMOTE SENSING OF IPM:

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REMOTE SENSING OF IPM: Reflectance Measurements of Aphid Infestation and Density Estimation in Wheat Growing under Field Conditions. Mustafa Mirik, Gerald J. Michels ... – PowerPoint PPT presentation

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Title: REMOTE SENSING OF IPM:


1
REMOTE SENSING OF IPM Reflectance Measurements
of Aphid Infestation and Density Estimation in
Wheat Growing under Field Conditions.
Mustafa Mirik, Gerald J. Michels Jr., Norman C.
Elliott, Sabina Kassymzhanova, Roxanne Bowling,
Bob Villarreal, Vasile Catana, Timothy D. Johnson
2
How this fits with AWPM for Wheat
As a part of the participation with AWPM for
Wheat, the Entomology Program in Amarillo is
using an airborne hyperspectral spectrometer for
detecting aphid infestations.
The work is conducted as a part of the Precision
Agriculture Initiative at Texas A M in
cooperation with Oklahoma State University and
the USDA-ARS.
3
Remote sensing helps detect greenbug infestations
in wheat fields and helps demonstrate
alternatives to costly spraying. We hope to
detect infestations before wheat fields would
require insecticide application to protect crops
from economic losses. It can be used to generate
spatial, up-to-date information over time and
space in combination with statistical tools such
as GIS
4
Remote sensing is the art science of collecting
information about the earths surface using some
portions of the electromagnetic spectrum from
ground, air and space platforms without physical
contact with the objects under surveillance
To the right is the equipment that they use.
Above right Cessna 172 Right airborne aerial
spectometer.
5
Methodology
Collected aphid density and spectral reflectance
data from Texas, Oklahoma and Colorado
  • Aphid density included greenbug, bird-cherry oat,
    Russian wheat aphids
  • Data collected in over stressed and
    non-stressed 0.25m2 wheat plots
  • Reflectance data gathered by hyperspectral ground
    spectrometer over aphid infested wheat and
    non-infested wheat nearby

6
Methodology
  • Subsequently, at least 30 tillers were cut at
    ground level and transported to laboratory to
    count the number of aphids per 0.25m2 sample plot
  • Remaining tillers in each plot were tallied in
    the fields to estimate aphid density for each
    sample plot
  • Sometimes, aphid density was determined in the
    fields by counting all aphids within plots during
    the early growing season or clipping all plants
    and counting aphids in the laboratory during the
    late growing season as time permits

7
Methodology
  • All in all, aphid density was determined at
    0.25m2 level for each sample
  • The methodology was applied to all sites and
    information given during the following slides

8
Distractive sampling to count aphids in the lab
when wheat was at mid- and late growth stages.
9
TAMU employees at work counting Russian wheat
aphids in the lab
Russian wheat aphid population in a 0.25m2 wheat
plot
10
Survey team from the USDA-ARS, Stillwater, and
TAMU collecting aphid and remote sensing data in
the field
11
The plot at the top represents the greenbug (GB)
and bird-cherry oat aphid (BCOA) (mainly GB),
combination of aphid and abiotic stress, and
no-stress on volunteer wheat. The data were
collected over infested wheat near Dumas, Texas
in late fall 2003.
12
The plot above is the Russian wheat aphid (RWA)
and abiotic stress, no-stress on winter wheat,
and exposed soil collected in a field near
Amarillo, Texas in mid-April 2004.
13
Spectral reflectance of GB and RWA data were
plotted across the Visible and Near Infrared
(NIR) wavelengths.
These plots clearly indicates that hot spots of
GB and RWA can be accurately detected and
discriminated from the soil, abiotic stress and
non-stressed wheat in fields with air- and
space-borne remote sensing platforms at an
appropriate scale
14
The graph and table below depicts sampling done
on wheat under 3 levels of stress healthy
plants, plants stressed by greenbug alone, and
plants stressed by a combination of greenbug and
abiotic factors (bar plot on the left) and RWA
stress (table on the right).
15
We found there were statistically significant
differences in the reflectance from each of these
wheat conditions.
A similar comparison of wheat stressed by Russian
wheat aphid versus healthy plants also showed
significant differences in reflected light
These data substantiates the trend seen in the
two previous graphs it suggests we can use air-
and space-borne imageries to detect aphid stress.
16
Healthy wheat
Abiotic and Aphid Stressed Wheat
Aphid Stressed Wheat
17
These are the sample pictures of aphid stress,
combination of aphid and abiotic stress, and
no-stress on volunteer wheat taken in the same
field near Dumas, Texas.
Aphid and combination of aphid and abiotic
stresses are visually assessable in these
pictures. We found eight spots heavily damaged by
aphid, both GB and BCOA we also found 22 spots
stressed by abiotic and biotic factors.
These eight pictures were analyzed using ASSESS,
Image Analysis Software for Plant Disease
Quantification, to determine percent damage
caused by aphid feeding on wheat, (see next
slide).
Then total aphid (GB BCOA), GB, and BCOA
densities were regressed against percent aphid
damage to estimate aphid density. See slides 20,
21, 22 for these results.
18
This picture shows how the percent aphid damage
was assessed by masking either healthy or
unhealthy parts of the canopy.
Aphid damage was outlined, and percent damage was
estimated on wheat leaves.
19
We found strong correlations between percent
aphid damage and density of total aphid, GB, and
BCOA (R2 0.85 for total aphid, GB, and BCOA
Densities). The next three slides present slides
that illustrate this.
20
This graph shows strong correlation found between
percent aphid damage and total aphid density,
greenbug and bird-cherry oat aphid. R2 0.85
21
This graph shows percent damage and the
correlation with greenbug density (R2 0.85)
22
This graph shows percent damage and its
correlation with bird-cherry oat aphid density
(R2 0.85)
23
Reflectance data were analyzed by calculating 25
existing spectral vegetation indices and
regressing them against density of total aphid,
GB, and BCOA for 30 samples situated near Dumas.
Of which, the Carotenoid Reflectance Index (CRI)
was best correlated with density of aphids.
24
This graph shows the correlation between CRI and
total aphid (GB BCOA) number.
25
This graph shows the correlation between CRI and
greenbug density.
26
This graph shows the correlation between CRI and
bird-cherry oat aphid density.
27
Spectral reflectance data gathered in Oklahoma
winter wheat fields exhibited similar trends to
the data collected near Dumas for aphid density
estimation.
This graph shows the relationship between NDVI
(Normalized Difference Reflectance Index) and
total aphid (GB BCOA) density.
28
The graph shows the relationship between NDVI and
greenbug density.
29
The graph shows the relationship between green
NDVI and bird-cherry oat aphid density.
30
Strong linear relationship were found between RWA
density and spectral vegetation indices for both
Texas (slide 31) and Colorado (slide 32) winter
wheat fields. Correlations were 97 for Texas and
77 for the Colorado wheat fields.
31
Texas This slide shows the correlation between CR
(Chlorophyll Ratio) and RWA density
32
Colorado This slide shows the correlation between
NDVI and RWA density
Colorado
33
We have also worked on estimating and
comparing wet and dry biomass of stressed by RWA
and non-stressed wheat near Amarillo. The next
slide shows that there were significant
differences between wet and dry biomass of
stressed and non-stressed wheat. The next slide
gives a slide illustrating this.
34
(No Transcript)
35
This slide shows dry biomass estimation for RWA
infested and non-infested wheat. High R2 values
(0.80 and 0.69 for infested and non-infested
wheat, respectively) indicate the usefulness of
remote sensing technology and techniques to
predict wheat biomass regardless of aphid
infestation.
36
Last year, we collected baseline data to
correlate observed aphid density and damage in
wheat to ground-based remote sensing data.
These preliminary results showing established
correlations strongly force us to move to
forward. In addition to these, remote sensing
technologies and techniques are highly promising
to detect aphid stress in other field crops.
37
In the next year, we plan to move from the
ground-based remote sensing to air-borne
hyperspectral and/or satellite multispectral
remote sensing. We expect to use hyperspectral
or multispectral imageries to detect
aphid-induced stress in wheat and sorghum, and
possibly other crops at larger scales if the
conditions permit.
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