Note: Descriptions are shown in the official language in which they were submitted.
CA 02466978 2004-05-07
1
TITLE OF THE INVENTION
METHOD FOR PEST MANAGEMENT USING PEST IDElVTITICATION
SENSORS AND NETWORK ACCESSIBLE DATABASE
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part patent application of U.S. Patent
Application Serial No. 10/084,005, filed on February 27, 2002, which is a
continuation patent application of U.S. Patent No. 6,385,544 filed on February
5,
2001 and issued on May 7, 2002, the contents of which are incorporated herein
by
reference.
STATEMENT RE: FEDERALLY SPONSORED RESEARCH/DEVELOPMENT
Not Applicable.
BACKGROUND OF THE INVENTION
The present invention relates generally to pest management systems, and more
particularly to a method for pest management using pest identification sensors
and a
network accessible database.
Integrated pest management is an informational science of obtaining accurate
information regarding the many factors that affect the density, distribution,
and
dynamics of pest populations. The ultimate goal has always been to use that
information to integrate control measures. Pest control measures are triggered
either
by the presence of a particular pest or by a threshold density of that pest,
taking into
consideration the phenology of the affected crop, and the physical and
biological
characteristics of the environment at a given time. Data on various physical
and
biological parameters must be collected, tabulated, filtered, statistically
analyzed and
compared, so that good decisions for effective pest control ran be made and
implemented in a timely fashion. There is, therefore, an ever-increasing
demand for
reliable, current data that reflect actual conditions found in the field upon
which pest
control management decisions can be based.
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Pest management, control and monitoring programs frequently suffer from a
lack of reliable information. For a monitoring prograrrr to be effective,
information
has to swiftly flow through a sequence that starts with data gathered in the
field,
which is passed through local supervisors and more central decision rr~akers,
and ends
up with those who are responsible for the implementation of pest control
measures.
A typical management program will include many branches for data
management. The branches rnay have structural differences, may be operated by
people belonging to different agencies, and may be located in different areas.
Not
surprisingly, bottlenecks in the flow of information are common. These
bottlenecks
may be caused by slaw or imprecise data gathering or' by slow and inefficient
data
processing. The result is information flowing too slowly to the decision
maker.
Further, inefficient or inconsistent data management can result in poorly
collected
data or a failure to separate relevant information from that which is
irrelevant.
Problems with information flow may generate reports with little useful
information,
resulting in poor decision-making and ineffectual control measures. Poor data
management is damaging for small programs, but the situation becomes nearly
unmanageable when data management problems occur in Large area-wide pest:
management programs.
The food production industry has been pla~~ued not only by pests that
compromise crop and food quality but also with the task of managing
information to
control these pests. Indigenous and established arthropod pests are a major
concern
for farmers and ranchers and are the subject of study for entire divisions of
large
governmental agencies. The introduction of exotic pests is especially
problematic for
the agricultural industry. The industry is affected directly, by pest damage
and extra
expenses incurred through controlling new exotic pests, and indirectly,
through trade
barriers aimed at infested commodities by pest-free importing regions. Once a
pest is
established, the cost of control is permanent. An increase in imported goods,
fostered
by trade agreements between states, increases the risk of introduction of new
pests.
Collaborative efforts will play an ever more critical role in the management
of
exotic pests. Many regional and even intercontinental task forces have been
created
to manage and combat exotic insect pests. These task forces require concerted,
area-
wide interventions, and are usually far more effective than the somewhat
erratic,
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asynchronous interventions that individual farmers may perform when not
involved in
regionally controlled management efforts.
When an exotic pest is the focus of a management program, it is likely that
the
program involves different organizations, including agencies from city,
county, state,
and federal governments, as well as interested private groups. The
organization for
the monitoring and detection tasks may be flexible and relaxed. Different
groups will
collect different types of information, based upon their own particular
agendas, which
is then stored in databases at various locations. It is likely that these
databases do not
use the same software and are maintained and edited by persons of varying
expertise,
who use different criteria and protocols to handle and analyze the data. The
unexpected detection of an exotic pest .results in an emergency situation
requiring a
drastic change in this flexible organization. Pest eradication requires a
program that is
well coordinated. For emergency situations the organizational structure of a
program
has to be well established. All historical data and newly collected data have
to be
readily available and rapidly analyzed so the emergency regional pest control
effort
can make rapid, effective decisions.
Existing pest management programs vary in degree of sophistication. Most
common is the approach in which farmers spray fields following a calendar
schedule.
The implementation of control measures is triggered based on historical data
and
executed regardless of the presence of or the density of the pest. This
approach is
generally attractive to growers due to its simplicity and ease of
implementation.
However, this approach frequently results in unnecessary insecticide
applications,
which may ultimately result in a plethora of agro-ecological problems
including
environmental contamination, ecological imbalance, and suppression of natural
enemy populations.
More sophisticated regional strategies exist that monitor physical and
biological environment and use the data to determine if populations are above
or
below thresholds to determine if control action is needed, referred to in the
industry as
the "threshold" approach. Such a pest control strategy has the advantage of
being a
good predictive power of pest population dynamics using modeling techniques.
If
pest control action is necessary, it is directed to the areas where pest
populations are
found at higher densities, or where they are escaping their natural enemies'
control.
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This strategy in turn has a lower impact on the argo-ecosystem, and is the
basis for the
development of more sustainable agriculture. The difficulty with this approach
is that
it requires better than average organizational skills, a conmmitment from the
farmer,
. the use of standardized methods of data collection, and enough allocation of
time to
perform the careful, consistent monitoring needed to support good decision-
making.
Accordingly, there is a need in the art for an improved method of pest
management in comparison to the prior art.
BRIEF SUMMARY OF THE IT~tVENTION
In accordance with an embodiment of the present invention, there is provided
a method of pest management of crops by a grower. Tl.~e method includes
gathering
pest sampling data in connection with a crop of the grower. The pest sampling
data
includes pest identification information gathered using a pest identification
sensor.
The pest sampling data further includes locational information thereof. The
method
1 S further includes transmitting the gathered pest sampling data to a pest
sampling
database. The pest sampling database includes pest sampling data regarding
respective crops from a plurality of other growers. The, pest sampling
database is in
electrical communication with pest management analy;~is software for'
generation of
pest management analysis. The method further includes electronically receiving
the
generated pest management analysis.
According to various embodiments, the pest identification sensor may be an
acoustic sensor, an optical sensor, or a weight sensor. The method may provide
for
using at least two different types of pest identification sensors. The pest
identification
sensor may be deployed in conjunction with a pest trap, and the pest trap may
utilize a
pest attractant and the pest sampling data includes identiification of the
attractant. The
pest identification sensor may be configured to detect wingbeat information,
pest
surface characteristics information, and size information. The pest management
analysis software may be configured to identify pests based upon the pest
identification information and the locational information. The pest sampling
data may
be gathered utilizing a portable computer, and the pest iidentification
information may
be transmitted from the pest identification sensor via a wireless device.
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In accordance with another aspect of the present invention, there is provided
a
method of providing pest management of a plurality of growers. The method
includes
establishing a relationship with the plurality of growers wherein each of the
growers
agrees to gather pest sampling data in connection with a crop of the grower.
The pest
5 sampling data includes pest identification information gathered using a pest
identification sensor. The pest sampling data further includes locational
information
thereof. The method further includes electronically recc-;wing gathered pest
sampling
data from the growers. The method further includes electronically storing the
pest
sampling data in a pest sampling database. The method further includes
generating
pest management analysis with pest management analysis software using the pest
sampling database for a crop of a respective one of the growers.
According to various embodiments, the pest identification sensor may be an
acoustic sensor, an optical sensor, or a weight sensor. The method may provide
for
using at least two different types of pest identification sensors. The pest
identification
sensor may be deployed in conjunction with a pest trap that utilizes a pest
attractant
and the pest sampling data includes identification of the attractant. The pest
identification sensor ma.y be configured to detect wingbeat information, pest
surface
characteristics information, or size information. The pest management analysis
software may be configured to identify pests based upon the pest
identification
information and the locational information. The pest management analysis
software
may be configured to identify pests based upon the pest identification
information and
pest seasonal activity information, pest circadian rhythm information, pest
geographical distribution information, pest habitat information, and pest
attractant
information.
As such, based on the foregoing, the present invention mitigates the
inefficiencies and limitations associated with prior art pest management
methods.
Accordingly, the present invention represents a significant advance in the
art.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING
These, as well as other features of the present invention, will become more
apparent upon reference to the drawings wherein:
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Figure 1 is a symbolic relational diagram depicting the pest sampling database
utilized by growers in accordance with a method of the present invention;
Figure 2 is a flow chart of a method of an aspect of the present invention;
and
Figure 3 is a flow chart of a method of another aspect of the present
invention.
S
DETAILED DESCRIPTION OF THE INVENTION
Referring now to the drawings wherein the showings are for purposes of
illustrating a preferred embodiment of the present invention only, and not for
purposes of limiting the same, Figures 1-3 illustrate methods for implementing
aspects of the present invention.
Referring now to Figure 1 there in depicted a symbolic relational diagram
depicting the pest sampling database utilized by growers in accordance with a
method
of the present invention, a flow chart of which is depicted in Figure 2. As
will be
discussed in more detail below, the present method is specifically adapted to
provide
incentives fox many growers to participate so as to result in an integrated
database of
information that may be utilized for a variety of purposes by a variety of
entities.
As such, there is provided a method of pest management of crops 12 by
growers 10. As used herein growers 10 is used to refer generally to a grower
or
growers with specific examples depicted in Figure 1 as 10a, b and c.
Similarly, as
used herein crops I2 is used to refer generally to a crop or crops with
specific
examples depicted in Figures 1 as 12a-c.
According to an aspect of the invention, the method includes gathering 100
pest sampling data 14 in connection with a crop 12 of a grower 10. For
example,
grower IOa may gather 100 pest sampling data 14 in connection with the
grower's
2S crop 12a. In this regard, the following discussion will focus upon the
perspective of
the growers 10. The pest sampling data 14 includes pest identification
information
gathered using a pest identification sensor, such as sensors 44a-d.
Additionally, the
pest sampling data 14 includes locational information thereof. The gathered
pest
sampling data 14 is transmitted 102 to a pest sampling database 16. The pest
sampling database 16 includes pest sampling data regarding respective crops 12
from
a plurality of other growers 10. In this regard for example, to the extent
grower l0a
practices the method of the present invention, the other growers may include
growers
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lOb and lOc. The pest sampling database 16 is in electrical communication with
pest
management analysis software 18 for generation of pest management analysis 20.
The generated pest management analysis 2.0 is received 104 by the grower 10.
As mentioned above, the method includes gathering 100 pest sampling data 14
in connection with a crop 12 of a grower 10. 'The pest sampling data 14 may be
gathered 100 utilizing a portable computer 26, such as 26a and 26b as
depicted. As
such, the portable computer 26 may be advantageously taken directly into the
field for
collection of pest sampling data 14. As used herein, the term computer
includes any
of those computing devices that are well known to one of ordinary skill in the
art. For
example, such computing devices may include laptop computers, terminals, hand-
held
or palm devices, etc.
As mentioned above, the pest sampling data 1.4 includes pest identification
information gathered using a pest identification sensor, such as sensors 44a-
d. To
some degree the utilization of such sensors 44 a advantageously automates the
data
gathering process.
Further, the pest identification information may be transmitted from the pest
identification sensors 44a-d via a wireless device. In this regard, the pest
identification sensors 44a-d may be readily deployed without the need for
hardwire
connections. Thus for example, the pest identification sensors 44a-b may
establish
electronic links 46a-b with the portable computer 26a for wireless
transmission of the
gathered information. Likewise, the pest identification sensors 44c-d may
establish
electronic links 46a-b with the portable computer 26b.
The pest identification sensors 44a-d may be of a variety of different types
of
sensors, such as acoustic, optical, or weight sensors. Moreover, the method
may
provide for using at least two different types of pest identification sensors
44a-d. The
pest identification sensor 44 may be deployed in conjunction with a pest trap
48, such
as sensor 44a which is deployed adjacent pest trap 48a and sensor 44c which is
deployed adjacent pest trap 48b.
The pest identification sensors 44a-d may be conf gored to detect pest
wingbeat information. A microphone may be a type of acoustic sensor that ma.y
be
used to detect the pest wingbeat information. A light photo sensor may be a
type of
optical sensor that may be used to detect the pest wingbeat infornaation. It
is
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contemplated that with respect to flying pests, different pests may have a
signature
wingbeat in terms of frequency and/or harmonics for example. Such information
may
be used to identify pests. For general. examples, mosquitoes have a wingbeat
w frequency in the range of 300-500 Hz, dragonflies in the range of 20-28 Hz,
beetles in
the range of 46-90 Hz, butterflies in the range of 9-22 Hz, hawk moths in the
range of
70-85 Hz, horseflies at about 100 Hz, honey bees at about 200 Hz, and wasps at
about
110 Hz. Moreover, within a given type of pest, such as mosquitoes, various
species
and subspecies may have specific wingbeat signatures. Further, the detected
pest
wingbeat information may include relative amplitudes of harmonics and other
waveform shapes which may be correlated to a given pe t wingbeat signature to
aid in
pest identification.
It is contemplated that various environmental conditions may influence a pest
wingbeat signature of a type of pest. For example, some pests have a signature
wingbeat that is a function of temperature or humidity. In this regard, the
pest
sampling data 14 may further include temperature and/or humidity measurements
taken in xelation to the detected pest wingbeat information.
As mentioned above, the pest identification sensors 44a-d may be weight
sensors. For example, a piezo electric film may be calibrated to detect pest
walking
across the surface of the film. Weight data taken using such weight sensors
may be
further used to aid in pest identification.
Other characteristics may be detected using the sensors 44a-d. Fox example,
pest surface characteristics information may be detected. Optical sensors may
be used
to detect such pest surface characteristics as color patterns and shapes for
example.
Another pest surface characteristic is reflectance of the outer surface of a
pest. All
arthropods have an outer shell (exoskeleton) that is often covered by a waxy f
lm.
With some arthropods this exoskeleton actually glo~,vs when illuminated with
UV
light. Sometimes, as with butterflies, moths and mosquitoes, the specimen is
covered
with little scales much Like scales on a fish. Some mosquitoes have brown
sales
others have silvery white scales and still others have scales that appear to
sparkle with
a purplish iridescent reflection. Moreover, arthropods reflect light in
specific
frequencies. This may be in response to white light or from a series of light-
emitting-
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diodes (of which each emits light in a specific wavelength, not necessarily
visible) for
example. As such, the detected reflectance may be cased for pest
identification.
Still other characteristics that may be detected using the sensors 44a-d may
be
size.information. This may be implemented with the pest identification sensors
44a-d
being optical sensors. Other characteristics may be detected such as presence
of
viruses through such sensors as those based upon enzyme-linked immunoassays.
Other sensors may be based upon chromatography or flame ionization for
detecting a
pest signature for identification.
It is contemplated the pest sampling data 14 may be collected in the field. In
20 particular, it is contemplated that precise locational data is desirable.
In this regard, in
an embodiment of the present invention, the locational information is gathered
using
an electronic locational device which may be integrated with the portable
computer
26, such as 26a and 26b as depicted. Preferably, the electronic locational
device is a
global positioning system (GPS) based device such as portable computer 26a. In
this
I5 regard, the portable computer 26a is symbolically depicted as being in
electronic
communication with a GPS satellite 28 via a data link 30. In another
arrangement, the
portable computer 26 may be provided with an optical scanner. An operator may
simply scan in predetermined locational data which may be stored in the fomn
of a bar
code which is disposed adjacent a pest sampling or trapping location, for
example. In
20 this regard, it is envisioned that because the pest sampling data 14 is
electronically
inputted, such GPS and data scanning technologies may provide a means for
capturing highly accurate locational data while being relatively easy to
employ or
implement by a grower 10.
It is contemplated that the pest management analysis software 18 may utilize a
25 variety of filters 50 to aid in pest identification. In this regard, the
filters 50
symbolically represent the programming Logic or algorithms that take into
consideration information in addition to the pest identification information
gathered
100 by the pest identificafiion sensors 44a-d. This information may be pest
seasonal
activity information, pest circadian rhythm information, pest geographical
distribution
30 information, pest habitat information, and pest attractant information for
examples.
In this regard, where the gathered pest identification information is wingbeat
information, there may be several pests that have a similar wing beat
signature. By
CA 02466978 2004-05-07
overlaying further information, however, the number of possible pests may be
narrowed. For example, by knowing pest seasonal activity information and
knowing
the current date, several pests may be ruled out as possibilities as such pest
may not
be active. Similarly, certain pests may only be active at night (nocturnal) or
during
5 the day (diurnal) or in the transition between day and night (crepuscular).
As such, by
knowing when a pest was detected by the sensors 44a-d, such time stamp data
may be
used as a pest circadian rhythm informational filter for pest identification.
Another
example of a filter 50 relates to pest geographical distribution. infornation.
Certain
pest may be off Bated with certain geographical areas. By knowing where the
pest
10 identification information was gathered 100 either via the gathered
locational
information or otherwise, such information may be used as a filter for pest
identification. Another type of information that may be used as by the filters
50 is
pest habitat information. Certain pests are only affiliated with certain
habitats
(swamps, woodlands, fields, etc.). As such, this information may be used by
the
filters 50 for pest identification.
As mentioned above, the pest identification sensors 44a,c may be deployed
adjacent pest traps 48a,b. The pest traps 48a,b may utilize a pest attractant
and the
pest sampling data includes identification of the attractant. In this regard,
certain
attractants are specific to specific types of pests. As such, it is
contemplated that by
knowing the identification of the attractant utilized together with the pest
identification information gathered from the sensor 44a,c the likelihood of
accurate
pest identification may be enhanced. Thus, the inforn:aation regarding the
attractant
may be used by the programming logic or filters SO for use by the pest
management
analysis software 18 to accurately identify pests.
It is contemplated that the pest sampling data 14 need not be gathered 100 and
transmitted 102 in its entirety all at the same time. In this regard, some of
the pest
sampling data 14 may be initially gathered 100 and transmitted 102 to the pest
sampling database 16 upon an initial set up of the various system components
Furthermore, the pest sampling data l4 may include additional information
which is well known to one of ordinary skill in the art such as information
regarding
pest populations and infestation, crop condition, climatological information,
method
of pest control used, for exarriple. It is understood that the nature of the
data will vary
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11
in degree, format and type depending upon the level of sophistication of the
pest
management analysis software 18 that is utilized.
Having gathered I00 the pest sampling data I4, the method provides for
transmitting 102 such pest sampling data 14 to the pest sampling database 16.
The
present method contemplates that such a data transfer may be affected in any
number
of ways. In one embodiment, the portable computer 26 may be a wireless device
that
may transmit from the field the pest sampling data 14. In this regard,
portable
computer 26a is depicted as having a symbolic data link 32 to the pest
sampling
database 16. The particular techniques and hardware and software requirements
.for
affecting such a wireless transfer may be chosen from those which are well
known to
one of ordinary skill in the art and may include cellular or radio frequency
technology
for example.
In another arrangement, the pest sampling data 14 may be downloaded from a
portable computer 26b to a local computer 34. In this regard, a given grower
10 (such
as grower lOb) may utilize many portable computers 26 (such as portable
computer
26b) in the field that may be then taken back to the grower's facility having
the Local
computer 34 thereat. In this regard, the collected pest sampling data I4 may
be
transferred and compiled at the local computer 34. Such a data transmission or
transfer may simply take the form of the data being recorded upon a data disk
and
physically downloaded to the local computer 34. A more sophi sticated
arrangement
may include a more automated transmission which may include the portable
computer
26b establishing an electronic data link 36 with the local computer 34. As
such, the
pest sampling data 14 may be transmitted via a more direct electronic
connection,
such as by the local computer 34 having a docking bay for receiving
communications
from the portable computer 26b. In another arrangement, the portable computer
26b
may communicate with the local computer 26b via a wireless link. As such, the
electronic data link 36 symbolically indicates some form of data transfer from
the
portable computer 26b to the local computer 34. The particular techniques,
hardware
and software requirements for affecting such data transfer from the portable
computer
26b to the local computer 34 may be .chosen from those which are well known to
one
of ordinary skill in the art and may include cellular or radio frequency
technology for
example.
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12
In addition, it is contemplated that the local computer 34 electronically
communicates with the pest sampling database 16. Conveniently, the pest
sampling
data 14 may be transferred from the Iocal computer 34 to the pest sa~:npling
database
16 via a computer network. While the computer network is preferably what is
currently understood as the Internet, other computer network an-angements may
be
included, such as local area networks (LANs), intranets, extranets, private
networks,
virtual private networks, integrated services digital networks (ISDN s), etc.
The
particular techniques and hardware and software requirements for affecting
such data
transfer from the local computer 34 to the pest sampling database 16 may be
chosen
from those which are well known to one of ordinary skill in the art a:zd may
include
telephony based systems, cable ();igital Subscriber Lines (I3SL) and
variations
thereof, wire, optical, etc.), optical communications (including infrared),
and wireless
forms of communications, such as those based upoza cellular, satellite, radio
frequency
(RF) and other forms of electromagnetic wave based mediums.
It is contemplated that a remote or host computer system 40 may host or
otherwise be disposed in electronic communication with the pest sampling
database
16 and the pest management analysis software I8. The host computer system 40
may
be disposed in communication with a computer network. In the case where the
computer network is the Internet, it is contemplated that host computer system
40 may
be interfaced or hosted at a web address. As such, access or utili~;ation of
the host
computer system 40 may be provided by an application service provider (ASP)
for
example. The particular techniques and hardware and software requirements for
operation of the host computer system 40 insofar as data processing between
the pest
sampling database 16 and the pest management analysis software 18 may be
chosen
form those which are well known to one of ordinary skill in the art. It is
also
contemplated that the pest sampling database 16 and/or the pest management
analysis
software 18, including portions thereof, need not be hosted remotely as
discussed
above but may also be deployed at a local computer 34 or even in a portable
computer
26 (such as 26a or 26b) for use in the field.
As will be discussed further below, the pest management analysis software 18
may be based upon any variety of algorithms and software modules. In this
regard,
the pest management analysis software 18 is configured to generate pest
management
CA 02466978 2004-05-07
13
analysis 20 regarding a given grower's crops 12, such as i.n connection with
the crops
12c of grower lOc. Such analysis may be received 104 or otherwise accessed by
the
grower l Oc via a computer network.
In addition, the pest control analysis 20 may include a pest control
recommendation, such as utilization of certain pesticide applications for
example. In
this regard, the method may further include implementing the pest control
recommendation, and subsequently repeating the steps of gathering L00,
transmitting
102 and receiving 104 pest control analysis 20.
At some point the growers 10 will harvest their crops 12 resulting in
harvested
crops 22. It is contemplated that such crops 12 or 22 may need to be inspected
by a
variety of entities, ranging from regulatory agencies to anyone in the supply
chain of
disposition of the harvested crops 22. Importantly, according to an aspect of
the
present invention, a crop certification 24 of the harvested crop 22 i.s
received based
upon the generated pest management analysis 20. The crop certification 24 may
be
received via a computer network. This ma.y take the form of access to such
information or an actual electronic communication. It is contemplated that
such a
certification 25 may parallel or at least be in a form and content required to
base a
certain regulatory approval or decisions concerning the disposition of the
crop 12 or
harvested crop 22 at issue. In this regard, such a certification pracess may
circumvent
or at least mitigate pest investigation and testing burdens or duties by those
entities
responsible for such regulatory approval or decisions concerning the
disposition of the
crops I2 or harvested crops 22 at issue. such reduction in burden may directly
translate to mitigation of fees or costs that may have been borne by the
growers 10 or
passed along to those in the distribution chain. Further, such a certification
process is
efficient from a time saving point of view thereby speeding the approval or
decision
making processes allowing for the harvested crops 22 to more readily enter the
distribution chain.
Referring now to Figures 1 and 3, in accordance with another aspect of the
present invention, there is provided a method of providing pest management and
crop
certification of crops 12 of a plurality of growers 10. In this regard, the
following
discussion will focus upon the perspective of ao operator of the host computer
system
40.
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14
The method includes establishing 110 a relationship with the plurality of
growers IO wherein each of the growers agrees to gather pest sampling data 14
in
connection with a crop 12 of the grower 10. The pest sampling data 14 includes
pest
. identification information gathered using a pest identification sensor (such
as any of
44a-d) and locational information thereof. The gathered pest sampling data 14
is
electronically received 112 from the growers 10. The pest sampling data I4 is
electronically stored 114 in a pest sampling database 16. Pest management
analysis
20 is generated II6 with pest management analysis software 18 using the pest
sampling database 16 for a crop 12 of a respective one of the growers 10.
The method may further include issuing of a crop certification 24 of a crop 12
or harvested crop 22 of the respective one of the growers based upon the
generated
pest management analysis 20. The method may fiuther include providing access
to
data from the pest management database 16 to a third party 42. In this regard,
database access criteria may be received form a respective one of the growers
10, and
access to data from the pest management database 18 regarding crops 12 of the
respective one of the growers 10 may be selectively provided to tile third
party 42
based upon the received database access criteria. It is contemplate~~ that a
variety of
third parties 42 may have interest in the data or derivative data from the
pest
management database 16. For example, such information may be useful to
governmental agencies, entities conducting research or education, entities
involved in
pest control (such as pesticide manufacturers), health related organizations,
and even
those interested in commodities trading. Moreover, it is contemplated that
such
access to data from the pest management database 16 to such third party 42 may
take
the form of insertion of data as well.
In view of the foregoing, it is contemplated that the various efficiencies of
the
methods of the present invention provide substantial incentives fox
participation by
growers 10. Through timely, disciplined and automated gathering 100 of the
pest
sampling data 14, the growers 10 are provided with timely, more accurate pest
data
analysis 20 which may include corrective or control recommendations. Thus, the
earlier a grower 10 can implement such recommendations, costly pest control
infestation and establishment problems may be avoided. Further, it is
contemplated
that as the number of participating growers 10 increases, a more
comprehensive,
CA 02466978 2004-05-07
integrated, and accurate pest sampling database 16 results. This in turn is
contemplated to result in a higher, more comprehensive, and timely pest
management
analysis 20.
Additional modifications and improvements of the present invention may also
5 be apparent to those of ordinary skill in the art. Thus, the particular
combination of
parts described and illustrated herein is intended to represent only one
embodiment of
the present invention, and is not intended to serve as limitations of
alternative devices
within the spirit and scope of the invention.
