Precision Farming Tools: Geospatial Insect Management
Carlyle Brewster*, Edwin Lewis, Bill Dimock, Ames Herbert
* Department of Entomology, Virginia Tech.
Introduction
The focus of management of plant eating insects is the protection of crops through the
regulation of pest population density. Traditionally, individual tactics such as
insecticides and biological control have been used for insect population management.
However, long-term pest suppression with these tactics often has only been achieved
within an integrated pest management (IPM) framework. IPM is an informational
science that requires knowledge of the interactions of the insect pest and its
environment within the agroecosystem.
Stern et al. (1959) were probably the first to recognize that the population density of an
insect changed through time and space. As such, changes in the population density of
the insect were conceptualized as a series of density maps (bug maps). Still, the
technologies and data analysis procedures that have been used in developing IPM
programs (such as economic thresholds, sampling procedures, and simulation
modeling) did not consider the spatial variability in insect population density. It was
often assumed that the population density of an insect did not vary over space and, as
such, the density maps that Stern et al. envisioned were summarized as an average of
the population of the pest over space at each point in time.
The wide-spread knowledge of spatial variation in agroecosystems has led to a new and
emerging technology in agricultural management known as Precision Farming (SiteSpecific Farming) or Precision Agriculture (See VCE Publication 442-500, Precision
Farming: A Comprehensive Approach). Precision farming (PF) is a technology that
modifies existing techniques and incorporates new ones to produce a new set of tools
for management. Maps that characterize the spatial distribution of crop production
variables such as soil nutrients, weed populations, and harvest yields are the most
important components in the precision approach to agriculture. Technologies such as
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Global Positioning System (GPS) enable farmers to develop and use the maps with
their map-sensitive farm equipment so that they can prescriptively apply plant
protectants and nutrients (See VCE Publication 442-503, Precision Farming Tools:
Global Positioning System – GPS).
The precision management of insect pests (precision insect pest management - PIPM)
relies on the same three elements (information, technology, and management) that are
important in the precision management of other crop production variables (such as soil
nutrients). Technology is needed for gathering information to assess and analyze the
spatial and temporal distribution of the insect within the management unit. Management
input is needed to integrate the information with other technologies in the decisionmaking process of the end-user (the farmer).
There is, however, a major difference between insect pests and the crop production
variables that typically have been the focus of PF (soil nutrients, weed populations, and
harvest yields). The latter variables have usually been more amenable to PF because
these variables are relatively static spatially and, therefore, easy to map. Constructing
maps of insect pests is much more difficult because insect populations generally are
spatially dynamic (changing density and location over time) and the methods that exist
for mapping their distribution tend to be complicated, labor intensive, and uneconomical.
The development of maps depicting the spatial distribution of insects (bug maps),
therefore, presents a challenge in PF.
Dimensions of Precision Farming
A crucial step in the planning and implementation of an IPM program is to define the
limits of the management unit (Intersociety Consortium for Plant protection 1979).
Although in IPM the management unit typically is a small area (subfield) or an entire
field, in reality, the limits of the management unit are best determined by characteristics
of the cropping system, spatial variability in pest population, and the mobility of the pest.
For species that are not highly mobile, the management unit usually will be the subfield
or single field. However, for highly mobile insects that have the ability to invade many
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fields in an agricultural region, the management unit is larger. All of the factors that
affect the size of the management unit in IPM also would affect the scale at which PF is
practiced. In the book Precision Agriculture in the 21st Century, the National Research
Council discusses the various dimensions of precision agriculture and how the scale of
variability in crop production phenomena can dictate the scale of management efforts.
The discussion also focuses on how management at the different scales of agriculture
(e.g., subfield, field, farm, and region) can be integrated into the PF framework.
Potential for Precision Farming of Insect Pests
It should be clear that PIPM can be a difficult undertaking. However, information
technologies such as remote sensing and Geographic Information Systems (GIS) exist
that when combined with GPS and quantitative approaches (e.g., predictive population
modeling) can be brought to bear on the sensing problem for insect populations in a
fairly general way to greatly increase the efficiency of data collection and the
development of bug maps for PIPM. Two examples are given of how PF technologies
and approaches can be applied at disparate spatial scales for PIPM.
Within-Field PIPM
Of the geo-information technologies available for PF, remote sensing has been the most
under used. This has been most noticeable at the level of the individual agricultural
field probably because of the lack of cost-effective and time-efficient methods for
gathering remotely sensed data at that scale. New technology, in the form of an
Unmanned Aerial Vehicle (UAV), has become available that makes it possible to collect
remotely sensed data of crop fields. A UAV with a remote sensing system can be used
to capture high-resolution imagery of a field, which then can be analyzed to create maps
of the spatial variation in plant condition in relation to pest population density. Such a
system indirectly would give pest managers the ability to gather spatially referenced
data on pest populations (bug maps) in a timely manner that would, at the farm level, be
less expensive than manual scouting.
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This idea of using UAV’s for surveillance is not new. Aerospace firms such as
AeroVironment (http://www.aerovironment.com/area-aircraft/unmanned.html) have for
some time been engaged in research and development programs on UAV systems for
military use (www.darpa.mil/tto/ then click on Programs). Researchers at Virginia Tech
currently are testing UAVs with remote sensing systems for detecting and mapping crop
condition in relation to pest infestations.
UAV Remote Sensing Systems
The form and design of UAV remote sensing systems would vary depending on the
manufacturer and the application for which the system was intended. In its simplest
form, a UAV is nothing more than a remotely-controlled aircraft. What makes a UAV
different from the classic remotely-controlled aircraft is its payload and its specialized
use for surveillance and/or targeting.
Researchers in the Department of Entomology at Virginia Tech are testing UAV
systems specially designed for agricultural applications. The first UAV that was tested
was a fixed-winged aircraft (aptly named the Bat), which measured 3 × 2.5 × 0.25 ft (0.9
× 0.76 × 0.0762 m). It was powered by a 0.06-in3 (1-cm3) engine and was light enough
(1.25 lbs) to be hand-launched (Figure 1). The craft could attain speeds from 18 to 45
mph, altitudes up to 1,500 ft (457 m), and could remain aloft for about 0.5 hour.
Although altitude hold and wing leveling autopilot were available, flight was mainly
radio-controlled (with telemetry limited to about mile radius).
The UAV carried a set of high resolution remote sensing sensors for surveillance. The
sensors consisted of two downward-facing video cameras, one that gathered color
images and the other near-infrared (NIR) images of the crop field. The radio-controlled
cameras transmitted image data in real-time to two ground video receiving and
recording stations via a handheld antenna system. A third forward-facing color camera
also was available for flight control by the remote operator.
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Flight operation mainly would be autonomous because of the ability to preprogram the
flight paths based on GPS waypoints. The ground station might include an automatic
tracking antenna and a laptop PC computer as the console and a digital image data
recording device. The console also might have a topographic-moving map display to
show the flight path of the aircraft in real-time.
Analyzing the Image Data
A UAV remote sensing system must have at least two things to be useful in PIPM
applications. The entire UAV system should be portable and easy to use, and it should
also be possible to analyze the image data collected by the system within a very short
period of time. The farmer ideally would need to have a map as quickly as possible that
shows where the problem spots occur within a field. Currently, however, it difficult to
process, in real time, the large amounts of image data obtained by the UAV into maps
that could be delivered to map-sensitive farm equipment.
In the case of the UAV system described above, it is necessary to process the color and
NIR image data before the data could be used in PIPM. For instance, a color image
would have to be separated into its red, green, and blue components. The red image
then would be combined with the NIR image to develop an image (or map) of
Normalized Difference Vegetation Index (see Figure 2). Because it is virtually
impossible to see an insect pest directly on a crop in remotely sensed imagery, it would
be important to understand the relationship between the spatial variability of crop stress
in the NDVI map and the spatial variability in the population density of the insect pest.
So far, the UAV remote sensing system has been tested over peanut fields in Suffolk.
VA. Color and NIR images have been gathered and corresponding NDVI images have
been generated that show areas within the field that were stressed or damaged (Figure
2). The next step will be to determine how that the spatial variation in crop damage (as
seen in the NDVI map) relates to the spatial distribution in pest damage (e.g., as caused
by feeding by the twospotted spider mite). Once the relationship between NDVI and
pest population density is understood completely, the NDVI map can be used directly as
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a surrogate for the bug map as input into precision management equipment to direct
management efforts.
Area-Wide PIPM
Area-wide PIPM targets insect pest populations across broad geographic areas
encompassing large acreages of cropland. The basic premise of area-wide PIPM is
that the population dynamics of a species is influenced by the spatial and temporal
composition and arrangement of its resources (host plants) in the landscape. It is
assumed that these factors can be exploited to develop management strategies. White
grub (e.g., Japanese beetle) attacks on golf course turfgrasses in the Lower Peninsula,
VA region can be used to show how an area-wide PIPM program might be developed.
Regional Data
Because of the patchy nature of turfgrass land cover (such as golf courses) within the
land matrix, it is relatively easy to identify and develop maps of these areas relative to
other land cover types. Remote sensing technology in the form of satellite data (e.g.,
Landsat TM data) is useful in this instance.
First, it would be necessary to develop a land cover map that identified turfgrass sites in
the Lower Peninsula, VA (Figure 3). The land cover map then would be used to derive
landscape metrics relevant to the golf courses where surveys of white grub populations
would be conducted. For example, Figure 4 summarizes the results of surveys
conducted over five seasons (summer 2000 and spring and summer 2001 and 2002) on
white grub populations on nine golf courses in the Lower Peninsula. The data show that
grub densities generally were lower on golf courses in the southernmost part of the
Peninsula and that there was large-scale spatial variability (a primary requirement of
PF) in grub density on the golf courses throughout the region (Figure 4).
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Local-scale Data
Spatial variability also is likely to occur at the local-scale of the individual golf course.
Therefore, data relevant to local-scale factors such as course architecture, site history,
environmental conditions, and management inputs (pesticide, fertilizer, and irrigation)
would also have to be collected. All of the data could analyze to identify the most
important factors for predicting the patterns of white grub populations on individual golf
courses.
Decision Support System
The land-cover map derived from the satellite data and data on the environment and
pest population could be used to develop a GIS knowledge-base. The GIS could then
be integrated with a mathematical model for predicting the risk of white grub infestation
on golf courses throughout the region. This combination of the GIS with the model
(what might be referred to as an Intelligent GIS) provides a decision-support tool that
could be made available to pest managers over the Internet.
The decision support system would contain a variety of utilities to link the knowledge
base on turfgrass landscapes, pest populations, and the environment for the study area
with the predictive model. Pest managers who log into the web site would use a pointand-click graphical user interface to query the knowledge base and predictive model,
and the results will be delivered to them in the form of maps (e.g., contour maps) and
graphs of the projected occurrence of white grub populations throughout the region.
Pest managers then will be able to use the system to decide on a course of action that
might involve further surveillance or preventative management.
Conclusions
The most important component of PIPM is a bug map of the spatial distribution
(intensity levels) of the insect pest population within management unit.
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Figure 1. Custom-built UAV (above left). The unit carries onboard color and infrared cameras (above
center) and is remotely controlled as it acquires small-scale imagery (above right).
Figure 2. Images from the Custom built UAV over Peanut fields. On the left are natural-color
images, middle are NIR images and to the right are NDVI images form the red and NIR portions
of the spectrum.
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Turfgrass
Urban
Woodlands
Wetlands
Water
Figure 3. Satellite image of Lower Peninsula, VA classified into 5 main landcover types.
Enlarged area shows the convoluted pattern of fairways and greens on a golf course.
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SH (0.91)
CG (3.92)
KK (8.48)
FC (0.44)
WN (3.46)
KM (0.00)
WC (0.46)
NN (0.00)
JR (0.07)
Figure 4. Mean number of white grubs per ft2 on golf courses in Lower Peninsula, VA
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Manufacturers
Handheld
GPS
receivers
from
Garmin
(www.
garmin.com)
and
Trimble
(www.trimble.com) to recorded the geographic coordinators of fields and survey sites.
The UAV carried two MC1001 color CMOS microcameras and an MC1000 black-andwhite CMOS micro camera from Vigitron Inc. (www.vigitron.com/). MLB Co.
(www.spyplanes.com) helped build the UAV.
Table 1. Glossary of terms.
AIPM – Area-wide insect pest management.
GPS –
GIS NDVI – Normalized Difference Vegetation Index) is calculated based on the following
relationship between a red and near infrared image: NDVI = (NIR - red)/(NIR + red).
NIR – Near infrared spectrum
PIPM – Precision insect pest management
RGB – Red, green, blue of the visible spectrum
SEPM – Spatially explicit population model
TM – Thematic mapper
TSSM - Two-spotted spider mite
UAV – Unmanned aerial vehicle
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References
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National Research Council. 1997. Precision agriculture in the 21st century. Geospatial
and information technologies in crop management. National Academy Press, Wash.,
D.C.
Rosen, D., J. L. Capinera, and F. D. Bennett. 1996. Integrated pest management: an
introduction, pp. 3–7. In D. Rosen, F. D. Bennett, and J. L. Capinera [eds.], Pest
management in the subtropics. Integrated pest management—a Florida perspective.
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