Systems and methods for delivering information related to gene markers of resistance to pest control products

Abstract

Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a processor programmed to: receive trap data for a plurality of pest traps in a geographic location, the trap data including pest gene data, the trap data includes a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving weather data of the geographic position; receiving image data of the geographic location; applying a machine learning algorithm to generate a predicted future pest pressure value at each of the plurality of pest traps; generating a first heat map at the first time point and a second heat map at the second time point; and transmitting the first heat map and the second heat map to a mobile computing device to cause a user interface on the mobile computing device to display a time-lapse heat map.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 433,554, filed on December 19, 2022.

[0003] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 433,554, filed on December 19, 2022, the entire contents of which are hereby incorporated by reference herein.

[0004] References to electronic sequence listings

[0005] The contents of the electronic Sequence Listing (Sequence Listing 38569-751 (61506-WO).xml; Size: 19,668 bytes; Creation Date: November 30, 2023) are incorporated herein by reference in their entirety. Technical Field

[0006] The present application relates generally to technologies that can be used to assist in monitoring pest pressure and, more particularly, to web-based systems and methods for generating and displaying pest pressure heat maps that convey information related to genetic markers of resistance or susceptibility to pest control products. Background Art

[0007] Due to the increase in world population and the decrease in arable land, there is a desire for methods and systems to improve crop productivity. At least one factor affecting crop productivity is pest pressure. Another factor affecting crop productivity is the resistance or sensitivity of a pest population to specific pest control products. The resistance or sensitivity of a pest population to specific pest control products can be detected by the presence of specific genetic markers for resistance or sensitivity to specific pest control products in a specific pest population.

[0008] Therefore, systems and methods for monitoring and analyzing pest pressure have been developed. For example, in at least some known systems, multiple insect traps are placed in a field of interest. To monitor pest pressure in the field of interest, the traps are periodically inspected to count the number of pests in each trap. Based on the number of pests in each trap, the level of pest pressure in the field of interest can be determined.

[0009] DNA and / or RNA can be extracted from the pests within the trap and analyzed for the presence of genetic markers for resistance or sensitivity to pest control products. DNA and / or RNA can be extracted and analyzed from a single pest. Alternatively, DNA and / or RNA from all pests of the same species within the trap can be extracted, combined, and analyzed for the presence of genetic markers for resistance or sensitivity to pest control products.

[0010] The number of pests monitored in each trap can also be used to predict future pest pressure. However, pest pressure is a relatively complex phenomenon that is constrained by a variety of factors. Therefore, accurately predicting future pest pressure based primarily on trap counts may be relatively inaccurate. Further, at least some known systems for pest pressure monitoring are focused at the individual farm level, resulting in limited visualization and significant time lags in data collection. In addition, at least some known systems for predicting future pest pressure rely on static logic (e.g., fixed phenological models and / or decision trees), and therefore their ability to accurately predict future pest pressure is limited.

[0011] Pests can be characterized by their ploidy. Typically, pests can have any suitable ploidy known in the art. In certain embodiments, the pest is haploid (i.e., monoploid), diploid, triploid, tetraploid, pentaploid, hexaploid, heptaploid, heptaploid, octaploid, polyploid, or a combination thereof. In certain embodiments, the pest is haploid. In certain embodiments, the pest is diploid.

[0012] Haploid pests do not have heterozygosity, so the pesticide resistance trait can only be characterized as resistance or sensitivity. When the pest is a haploid pest, the pest can be individually characterized as a pesticide-sensitive individual and a pesticide-resistant individual. Individual characterization can be applied to the population of each pest to perform population-level characterization. Individual characterization includes monitoring the frequency of resistance alleles and / or sensitive alleles within a single pest and / or pest population. Alternatively, individual characterization includes monitoring the percentage of individuals with sensitive alleles in the pest population.

[0013] In some embodiments, the present invention relates to a method for the treatment of insecticides that is suitable for the treatment of insecticides. For example, a plant may be a plant that is suitable ... However, characterizing genetic populations and their corresponding responses to pesticides is complex. Determining the likely response of a particular pest population with a particular genetic population to a pesticidal treatment is also complex.

[0014] The number of homozygous or heterozygous pesticide-resistant pests detected in each trap can be used to predict pest pressure that will develop resistance to a particular pest control product in the future. Alternatively, the number of homozygous or heterozygous pesticide-resistant pests detected in each trap can be used to predict pest pressure that will develop resistance to a particular pest control product in the future.

[0015] The number of traps containing pests with pesticide-resistance alleles, or the number of traps containing pests that are homozygous for pesticide-resistance or heterozygous for pesticide-resistance, can be used to predict future pest pressure that will develop resistance to a particular pest control product. Alternatively, the number of traps containing pests with pesticide-resistance alleles, or the number of traps containing pests that are homozygous for pesticide-susceptibility or heterozygous for pesticide-resistance, can be used to predict future pest pressure that will develop resistance to a particular pest control product.

[0016] It is therefore desirable to provide a system that can capture and intelligently analyze multiple different types of information, including pest genetic information, to quickly and accurately predict future pest pressure and / or the likely response of pests to pesticidal treatments. Furthermore, it is desirable to present predicted future pest pressure and the genetic profile of predicted future pest pressure to assist users in performing the technical tasks of monitoring pest pressure and, optionally, optimizing or selecting pest treatment systems to minimize pest resistance to specific pest control products. Summary of the Invention

[0017] In one aspect, a heat map generation computing device is provided. The heat map generation computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to: receive trap data for a plurality of pest traps in a geographic location, the trap data including pest genetic data, the trap data including a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receive weather data for the geographic location; receive image data for the geographic location; and apply a machine learning algorithm to the trap data, the weather data, and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps. The processor is further programmed to generate a first heat map at a first point in time and a second heat map at a second point in time, the second heat map generated using the predicted future pest pressure values, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at the associated point in time; and coloring at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the associated points in time to generate a continuous map of pest pressure values for the geographic location. The processor is further programmed to transmit the first heat map and the second heat map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

[0018] In one aspect, the first heat map and the second heat map further include pest pressure corresponding to pesticide-susceptible populations and pesticide-resistant populations.

[0019] In one aspect, the processor is further programmed to generate a first heat map for pesticide-susceptible and pesticide-resistant populations at a first point in time and a second heat map for a second point in time, the second heat map being generated using predicted future pesticide-susceptible and pesticide-resistant populations, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing the pesticide-susceptible and pesticide-resistant populations of the corresponding pest trap at the relevant point in time; and coloring at least some remaining portion of the map of the geographic location by interpolating between the values of the pesticide-susceptible and pesticide-resistant populations associated with the plurality of nodes at the relevant point in time to generate a continuous map of the pesticide-susceptible and pesticide-resistant populations at the geographic location. The processor is further programmed to transmit the first thermal map and the second thermal map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse thermal map that dynamically transitions between the first thermal map and the second thermal map over time, the user interface being implemented via an application installed on the mobile computing device.

[0020] In another aspect, a method for generating a heat map is provided. The method is implemented using a heat map generating computing device including a memory communicatively coupled to a processor. The method includes receiving trap data for a plurality of pest traps in a geographic location, the trap data including pest genetic data, the trap data including a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving weather data for the geographic location; receiving image data for the geographic location; and applying a machine learning algorithm to the trap data, the weather data, and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps. The method further includes generating a first heat map at a first point in time and a second heat map at a second point in time, the second heat map generated using the predicted future pest pressure values, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at the associated point in time; and coloring at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the associated points in time to generate a continuous map of pest pressure values for the geographic location. The method further includes transmitting the first heat map and the second heat map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

[0021] In another aspect, a method for generating a heat map is provided. The method is implemented using a heat map generating computing device including a memory communicatively coupled to a processor. The method includes receiving trap data for a plurality of pest traps in a geographic location, the trap data including pest genetic data, the trap data including a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving weather data for the geographic location; receiving image data for the geographic location; and applying a machine learning algorithm to the trap data, the weather data, and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps. The method further includes generating a first heat map for the pesticide-susceptible population and the pesticide-resistant population at a first point in time and a second heat map for the pesticide-susceptible population and the pesticide-resistant population at a second point in time, the second heat map being generated using predicted future pest pressure values for the pesticide-susceptible population and the pesticide-resistant population, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing the pest pressure value for the pesticide-susceptible population and the pesticide-resistant population for the corresponding pest trap at the associated point in time; and coloring at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the associated point in time to generate a continuous map of pest pressure values for the pesticide-susceptible population and the pesticide-resistant population for the geographic location. The method further includes: transmitting the first heat map and the second heat map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

[0022] In yet another aspect, a computer-readable storage medium is provided having computer-executable instructions embodied thereon. When executed by a heat map generating computing device comprising at least one processor in communication with a memory, the computer-readable instructions cause the heat map generating computing device to: receive trap data for a plurality of pest traps in a geographic location, the trap data comprising pest genetic data, the trap data comprising a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receive weather data for the geographic location; receive image data for the geographic location; and apply a machine learning algorithm to the trap data, the genetic data, the weather data, and the image data to generate a predicted future pest pressure value for a pesticide-susceptible population and a pesticide-resistant population at each of the plurality of pest traps. The instructions further cause the heat map generating computing device to generate a first heat map for the pesticide-susceptible population and the pesticide-resistant population at a first point in time and a second heat map for the pesticide-susceptible population and the pesticide-resistant population at a second point in time, the second heat map being generated using predicted future pest pressure values for the pesticide-susceptible population and the pesticide-resistant population, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing the pest pressure value for the pesticide-susceptible population and the pesticide-resistant population for the corresponding pest trap at the relevant point in time; and coloring at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes for the pesticide-susceptible population and the pesticide-resistant population at the relevant point in time to generate a continuous map of pest pressure values for the geographic location. These instructions further cause the heat map generating computing device to transmit the first heat map and the second heat map to the mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a block diagram of a computer system for predicting pest pressure according to the present disclosure.

[0024] Figure 2 is to demonstrate that according to this disclosure Figure 1 A block diagram of the data flow of the system is shown.

[0025] Figure 3 Demonstrates the following Figure 1 and Figure 2An example configuration of a server system such as a pest pressure prediction computing device.

[0026] Figure 4 Demonstrates the Figure 1 and Figure 2 An example configuration for a client system is shown.

[0027] Figure 5 is used in accordance with this disclosure Figure 1 A flow chart of an example method by which the system generates pest pressure data is shown.

[0028] Figure 6 is used in accordance with this disclosure Figure 1 A flowchart of an example method for generating a heat map by the system shown.

[0029] Figure 7 is available for use under this disclosure Figure 1 Screen capture of the system-generated user interface shown.

[0030] Figure 8 is available for use under this disclosure Figure 1 Screen capture of the system-generated user interface shown.

[0031] Figure 9 is available for use under this disclosure Figure 1 Screen capture of the system-generated user interface shown.

[0032] Figure 10 is available for use under this disclosure Figure 1 Screen capture of the system-generated user interface shown.

[0033] Figure 11 is the expected result of an allelic discrimination map according to the present disclosure.

[0034] Figure 12 is the expected result of an allelic discrimination map according to the present disclosure.

[0035] Figure 13 is a graph depicting the relationship between R allele frequency for certain genetic populations according to the present disclosure and bioassay under LC99.

[0036] Figure 14 is a graph depicting the relationship between R allele frequency and mortality at LC99 for certain genetic populations according to the present disclosure.

[0037] Figure 15 is a graph depicting mortality at the LC99 observed in certain genetic populations after treatment with a diamide-containing insect control formulation in accordance with the present disclosure.

[0038] Figure 16 is a graph depicting the relative resistance levels of certain genetic populations having certain mutations according to the present disclosure to various pesticides.

[0039] Figure 17 are illustrative processing window recommendations based on this disclosure.

[0040] Figure 18 are illustrative processing window recommendations based on this disclosure.

[0041] Figure 19 is an allelic discrimination plot according to the present disclosure.

[0042] Figure 20 is an allelic discrimination plot according to the present disclosure.

[0043] Figure 21 is an allelic discrimination plot according to the present disclosure.

[0044] Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. Any feature of any drawing may be referenced and / or claimed in combination with any feature of any other drawing. DETAILED DESCRIPTION

[0045] The systems and methods described herein relate to a computer-implemented system for generating and displaying pest pressure heat maps for pesticide-susceptible and pesticide-resistant populations. A heat map generating computing device receives trap data for a plurality of pest traps in a geographic location, the trap data including pest genetic data; receives weather data for the geographic location; receives image data for the geographic location; and applies a machine learning algorithm to the trap data, the weather data, and the image data to generate predicted future pest pressure values for the pesticide-susceptible and pesticide-resistant populations at each of the plurality of pest traps. A heat map generating computing device generates a first heat map for a pesticide-susceptible population and a pesticide-resistant population at a first time point and a second heat map for the pesticide-susceptible population and the pesticide-resistant population at a second time point, the second heat map being generated using predicted future pest pressure values for the pesticide-susceptible population and the pesticide-resistant population, the first heat map and the second heat map being each generated by drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps , each node having a color representing a pest pressure value for the pesticide-susceptible population and the pesticide-resistant population for the corresponding pest trap at a relevant point in time; and coloring at least some remaining portion of the map for the geographic location by interpolating between the pest pressure values associated with the plurality of nodes for the pesticide-susceptible population and the pesticide-resistant population at the relevant point in time to generate a continuous map of pest pressure values for the pesticide-susceptible population and the pesticide-resistant population for the geographic location. The first and / or heat map may also include information including genetic marker populations (e.g., Figure 11 The heat map generating computing device transmits the first heat map and the second heat map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

[0046] The systems and methods described herein facilitate accurate prediction of pest pressure in pesticide-susceptible and pesticide-resistant populations at one or more geographic locations. As used herein, 'geographic location' generally refers to an agriculturally relevant geographic location (e.g., a location comprising one or more fields and / or farms used to produce crops). Further, as used herein, 'pest pressure' refers to a qualitative and / or quantitative assessment of the distribution of pests in pesticide-susceptible and pesticide-resistant populations present at a particular location. For example, high pest pressure indicates that a relatively large distribution of pests is present at that location (e.g., compared to an expected distribution). In contrast, low pest pressure indicates that a relatively low distribution of pests is present at that location. In at least some embodiments described herein, pest pressure is analyzed for agricultural purposes. That is, pest pressure in one or more fields is monitored and predicted. However, those skilled in the art will appreciate that the systems and methods described herein can be used to analyze pest pressure in any suitable environment.

[0047] In some embodiments, the pest is non-haploid. In some embodiments, the pest is diploid. In some embodiments, the pest is haploid.

[0048] As used herein, the term 'pest' refers to an organism whose presence is generally undesirable in a particular geographic location, particularly an agriculturally related location. For example, in an embodiment analyzing pest pressure in one or more fields, the pests may include insects that have a tendency to damage crops in those fields. However, those skilled in the art will appreciate that the systems and methods described herein can be used to analyze pest pressure from other types of pests. For example, in some embodiments, pest pressure can be analyzed for fungi, weeds, and / or diseases. The systems and methods described herein refer to 'pest traps' and 'trap data.' As used herein, 'pest trap' may refer to any device capable of containing and / or monitoring the presence of a pest of interest, and 'trap data' may refer to data collected using such a device. For example, for insects, a 'pest trap' may be a traditional containment device that immobilizes the pest. Alternatively, for fungi, weeds, or diseases, a 'pest trap' may refer to any device capable of monitoring the presence and / or levels of fungi, weeds, and / or diseases. For example, in an embodiment where the 'pest' is one or more fungi, a 'pest trap' may refer to a sensing device capable of quantitatively measuring the level of spores associated with the one or more fungi in the environment surrounding the sensing device. In one embodiment, the 'pest' is one or more insects, and the terms 'pest trap' and 'pest traps' refer to an 'insect trap' and 'insect traps', respectively.

[0049] In some embodiments, when the pest is a haploid pest, monitoring pest pressure comprises monitoring changes in the frequency of resistant alleles and / or susceptible alleles within individual pests and / or populations of pests.

[0050] In some embodiments, the change in frequency of the resistance allele is less than about 1%, about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, about 20%, about 21%, about 22%, about 23%, about 24%, about 25%, about 26%, about 27%, about 28%, about 29%, about 30%, about 31%, about 32%, about 33%, about 34%, about 35%, about 36%, about 37%, about 38%, about 39%, about 40%, about 41%, about 42%, about 43%, about 44%, about 45%, about 46%, about 47%, about 48%, about 49%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about %, about 24%, about 25%, about 26%, about 27%, about 28%, about 29%, about 30%, about 31%, about 32%, about 33%, about 34%, about 35%, about 36%, about 37%, about 38%, about 39%, about 40%, about 41%, about 42%, about 43%, about 44%, about 45%, about 46%, about 47%, about 48%, about 49%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 9%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99% or about 100%.

[0051] In some embodiments, the change in frequency of the sensitive allele is less than about 1%, about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, about 20%, about 21%, about 22%, about 23%, about 24%, about 25%, about 26%, about 27%, about 28%, about 29%, about 30%, about 31%, about 32%, about 33%, about 34%, about 35%, about 36%, about 37%, about 38%, about 39%, about 40%, about 41%, about 42%, about 43%, about 44%, about 45%, about 46%, about 47%, about 48%, about 49 %, about 24%, about 25%, about 26%, about 27%, about 28%, about 29%, about 30%, about 31%, about 32%, about 33%, about 34%, about 35%, about 36%, about 37%, about 38%, about 39%, about 40%, about 41%, about 42%, about 43%, about 44%, about 45%, about 46%, about 47%, about 48%, about 49%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 9%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99% or about 100%.

[0052] In some embodiments, when the pest is a haploid pest, monitoring pest pressure includes comparing the frequency of resistance alleles and / or susceptible alleles in individual pests and / or pest populations collected at a first time with the frequency of resistance alleles and / or susceptible alleles in individual pests and / or pest populations collected at a second time. In these embodiments, a change threshold can be used to determine significance. For example, a 10% change in resistance frequency can be used as a threshold.

[0053] In some embodiments, when the pest is a non-haploid pest, the pest can be individually characterized as a homozygous individual that is susceptible to a pesticide, a homozygous individual that is resistant to a pesticide, and a heterozygous individual that is resistant to a pesticide. Individual characterization can be applied to a population of each pest to perform population-level characterization.

[0054] In some embodiments, when the pest is a polyploid pest, the pest can be individually characterized as a homozygous individual that is susceptible to a pesticide, a homozygous individual that is resistant to a pesticide, and a heterozygous individual that is resistant to a pesticide. The heterozygous individuals that are resistant to a pesticide may differ from one another and have varying degrees of pesticide resistance based on their genetic differences. Individual characterization can be applied to populations of individual pests to perform population-level characterization.

[0055] The following detailed description of the disclosed embodiments refers to the accompanying drawings. The same reference numerals in different drawings may identify the same or similar elements. In addition, the following detailed description does not limit the claims.

[0056] Described herein are computer systems, such as pest pressure prediction computing devices. As described herein, all such computer systems include a processor and memory. However, any reference herein to a computer device as a processor may also refer to one or more processors, where the processor may be in a single computing device or in multiple computing devices operating in parallel. Furthermore, any reference herein to a computer device as a memory may also refer to one or more memories, where the memory may be in a single computing device or in multiple computing devices operating in parallel.

[0057] As used herein, a processor may include any programmable system, including systems using microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuits or processors capable of performing the functions described herein. The above examples are merely examples and are not intended to limit the definition and / or meaning of the term "processor" in any way.

[0058] As used herein, the term "database" may refer to a body of data, a relational database management system (RDBMS), or both. As used herein, a database may include any collection of data, including hierarchical databases, relational databases, flat file databases, object-relational databases, object-oriented databases, and any other structured collection of records or data stored in a computer system. The above examples are merely examples and are not intended to limit in any way the definition and / or meaning of the term "database." Examples of RDBMS include, but are not limited to, Database、MySQL、 DB2, SQL Server, and PostgreSQL. However, any database that implements the systems and methods described herein may be used. (Oracle is a registered trademark of Oracle Corporation of Redwood Shores, California; IBM is a registered trademark of International Business Machines Corporation of Armonk, New York; Microsoft is a registered trademark of Microsoft Corporation of Redmond, Washington; and Sybase is a registered trademark of Sybase of Dublin, California.)

[0059] In one embodiment, a computer program is provided and embodied on a computer readable medium. In an exemplary embodiment, the system is executed on a single computer system without being connected to a server computer. In another embodiment, the system is In another embodiment, the system operates in a mainframe environment and The system is configured to run on a server environment (UNIX is a registered trademark of X / Open Ltd. of Reading, Berkshire, United Kingdom). The application is highly flexible and is designed to run in a variety of different environments without affecting any of its primary functionality. In some embodiments, the system includes multiple components distributed across multiple computing devices. One or more of the components may be in the form of computer-executable instructions embodied in a computer-readable medium.

[0060] As used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are examples only and, therefore, do not limit the types of memory that can be used to store computer programs.

[0061] The systems and processes are not limited to the specific embodiments described herein. In addition, each component of the system and each process can be implemented independently and separately from the other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes.

[0062] The following detailed description illustrates embodiments of the present disclosure by way of example and not limitation. It is contemplated that the present disclosure has general application to predicting pest pressure.

[0063] Figure 1FIG1 is a block diagram of an example embodiment of a computer system 100 for predicting pest pressure according to an example embodiment of the present disclosure, the computer system including a pest pressure prediction (PPP) calculation device 112. As described herein, the PPP calculation device 112 may also be referred to herein as a heat map generation calculation device. In an example embodiment, the system 100 is used to predict pest pressure and generate a pest pressure heat map, as described herein.

[0064] More specifically, in the exemplary embodiment, system 100 includes a pest pressure prediction (PPP) computer 112 and a plurality of client subsystems, also referred to as client systems 114, connected to PPP computer 112. In one embodiment, client systems 114 are computers that include a web browser, enabling client systems 114 to access PPP computer 112 using the Internet and / or using a network 115. Client systems 114 are interconnected to the Internet through a number of interfaces, including network 115, such as a local area network (LAN) or wide area network (WAN), a dial-up connection, a cable modem, a specialized high-speed Integrated Services Digital Network (ISDN) line, and an RDT network. Client systems 114 can include systems associated with farmers, growers, scouts, and the like, as well as external systems for storing data. PPP computer 112 also communicates with one or more data sources 130 using network 115. Furthermore, client systems 114 can also communicate with data sources 130 using network 115. Furthermore, in some embodiments, one or more client systems 114 can function as data sources 130, as described herein. Client system 114 may be any device capable of interconnecting to the Internet, including a web-based phone, PDA, or other web-based connectable device.

[0065] Database server 116 is connected to a database 120 that contains information regarding various matters, as described in more detail below. In one embodiment, centralized database 120 is stored on PPP device 112 and can be accessed by a potential user at one of client systems 114 by logging into PPP computing device 112 through one of client systems 114. In alternative embodiments, database 120 is stored remotely from PPP device 112 and can be decentralized. Database 120 can be a database configured to store information used by PPP computing device 112, including, for example, transaction records, as described herein.

[0066] The database 120 may include a single database having separate sections or partitions, or may include multiple databases, each separate from the others. The database 120 may store data received from the data source 130 and generated by the PPP computing device 112. For example, the database 120 may store weather data, imaging data, trap data, scouting data, grower data, pest pressure prediction data, and / or thermal map data, as described in detail herein.

[0067] In an example embodiment, the client systems 114 may be associated with, for example, growers, scouting entities, pest management entities, and / or any other party capable of using the system 100 described herein. In an example embodiment, at least one of the client systems 114 includes a user interface 118. For example, the user interface 118 may include a graphical user interface with interactive functionality such that pest pressure forecasts and / or heat maps transmitted from the PPP computing device 112 to the client system 114 may be displayed in a graphical format. Users of the client systems 114 may interact with the user interface 118 to view, explore, and otherwise interact with the displayed information.

[0068] In an example embodiment, PPP computing device 112 receives data from multiple data sources 130 and aggregates and analyzes the received data (eg, using machine learning) to generate pest pressure forecasts and / or heat maps, as described in detail herein.

[0069] Figure 2 is a block diagram illustrating the flow of data through the system 100. Figure 2 In the illustrated embodiment, data sources 130 include a weather data source 202, an imaging data source 204, a trap data source 206, a scouting data source, a grower data source 210, and another data source 212. Those skilled in the art will appreciate that Figure 2 The data sources 130 shown in FIG. 1 are examples only, and the system 100 may include any suitable number and types of data sources.

[0070] The weather data source 202 provides weather data to the PPP computing device 112 for use in generating pest pressure forecasts. The weather data may include, for example, temperature data (e.g., indicating current and / or past temperatures measured at one or more geographic locations), humidity data (e.g., indicating current and / or past humidity measured at one or more geographic locations), wind data (e.g., indicating current and / or past wind strength and direction measured at one or more geographic locations), rainfall data (e.g., indicating current and / or past rainfall levels measured at one or more geographic locations), and forecast data (e.g., indicating predicted future weather conditions for one or more geographic locations).

[0071] The imaging data source 204 provides image data to the PPP computing device 112 for use in generating pest pressure forecasts. The image data may include, for example, satellite imagery and / or drone imagery acquired from one or more geographic locations.

[0072] The trap data source 206 provides trap data to the PPP computing device 112 for use in generating pest pressure forecasts. The trap data may include, for example, pest counts (e.g., expressed as the number of pest species, the density of pest species, etc.) from at least one pest trap in a geographic location. Further, the trap data may include, for example, in the case of insects, the pest type (e.g., taxonomic genus, species, variety, etc.) and / or the pest developmental stage and sex (e.g., larvae, juveniles, adults, males, females, etc.). The pest trap may be, for example, an insect trap. Alternatively, the pest trap may be any device capable of determining the presence of pests and providing trap data to the PPP computing device 112, as described herein. For example, in some embodiments, the pest trap is a sensing device operable to sense environmental levels of spores associated with one or more fungi. In such embodiments, the trap data may include, for example, the number of spores (representing a pest count), the fungus type, the fungus developmental stage, etc.

[0073] In some embodiments, the trap data source 206 is a pest trap that is communicatively coupled (e.g., via a wireless communication link) to the PPP computing device 112. Thus, in such embodiments, the trap data source 206 can automatically determine the pest count in the pest trap (e.g., using an image processing algorithm) and transmit the determined pest count to the PPP computing device.

[0074] In many embodiments, the trap data includes pest genetic data. In one embodiment, the pest genetic data is particularly useful for analyzing one or more pesticide resistance markers in a population. Such pesticide resistance markers can be monitored before, during, and / or after emergence.

[0075] In one embodiment, population dynamics can be combined with pest genetic data (e.g., R allele detection). In this embodiment, mapping can generate precise treatment recommendations.

[0076] Typically, gene data can be collected according to any suitable method known in the art. For example, for example, collecting gene data can include manually collecting gene data and / or automatically collecting gene data. In some instances, collecting gene data can include collecting gene data selected from phenotype, genotype, epigenotype, allele distribution and combinations thereof. In some instances, gene data can be collected using a technology selected from polymerase chain reaction (PCR), quantitative polymerase chain reaction (qPCR), real-time reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP) and combinations thereof.

[0077] Typically, the genetic data may include any suitable genetic marker known in the art. In some embodiments, the genetic data includes a genetic marker selected from single nucleotide polymorphisms and combinations thereof.

[0078] In some embodiments, the genetic data can be used as the basis for a treatment recommendation. In many embodiments, the treatment recommendation includes a composition for treating the pest population.

[0079] As used herein, a composition according to the present disclosure is a composition that is applied to a pest population and / or recommended by a treatment regimen.

[0080] In many embodiments, the treatment recommendation recommends a composition comprising at least one active ingredient. In some embodiments, the treatment recommendation recommends a composition comprising at least two active ingredients. In some embodiments, the treatment recommendation recommends a composition comprising at least two active ingredients, wherein the active ingredients have at least one different mode of action (MoA).

[0081] In many embodiments, the treatment recommendation recommends applying one or more compositions in one or more treatment windows. In some embodiments, the one or more compositions of the one or more treatment windows include active ingredients having at least one different mode of action.

[0082] In many embodiments, the active ingredient is selected from the group consisting of insecticides, herbicides, biopesticides, nematicides, bactericides, and fungicides. General references for these active ingredients (i.e., insecticides, fungicides, nematicides, acaricides, herbicides, and biocides) include The Pesticide Manual, 13th edition, edited by CDS Tomlin, British Crop Protection Council, Farnham, Surrey, UK, 2003, and The Biopesticide Manual, 2nd edition, edited by LG Copping, British Crop Protection Council, Farnham, Surrey, UK, 2001.

[0083] Non-limiting examples of insecticides include abatin, acephate, acetamiprid, acetamiprid, flumethrin, aknapyr, cyclohexanone ([(3S,4R,4aR,6S,6aS,12R,12aS,12bS)-3-[(cyclopropylcarbonyl)oxy]-1,3,4,4a,5,6,6a,12,12a,12b-decahydro-6,12-dihydroxy-4,6a,12b-trimethyl-11-oxo-9-(3-pyridyl)-2H,11H-naphtho[2,1-b]pyrano[3,4-e]pyran-4-yl]methylcyclopropanecarboxylate), sulfamectin, amitraz, avermectin, azadirachtin, azinphos-methyl, benfuracarb, cypermethrin, chlorpyrifos ... benzpyrimoxan, bifenthrin, kappa-bifenthrin, bifenazate, bistrifluan, borate, broflanilide, buprofezin, cadusafos, carbaryl, carbofuran, cartap, chlorpyrifos, chlorfenapyr, chlorfluazuron, chloroprallethrin, chlorpyrifos, chlorpyrifos-e, chlorpyrifos-methyl, chlorfenapyr, clofentazine, chloroprallethrin, clothianidin, cypermethrin, cycloheximide ((5S,8R)-1-[(6-chloro-3- [1,2-a]azepine), cypermethrin, cyfluthrin, beta-cyfluthrin, lambda-cyhalothrin, lambda-cyhalothrin, cypermethrin, cis-cypermethrin, ζ-cypermethrin, cyromazine, deltamethrin, diafenthiuron, diazinon, dichlorvos, dieldrin, diflubenzuron, tetrafluthrin, dimethoate, methimazole, dinotefuran, fenpyrad, emamectin, emamectin benzoate, endosulfan, esfenvalerate, ethomethrin, ε-methylpyridin, 1,2-dapoxetine ... Oxyfluthrin, etoxazole, fenbutatin, fenitrothion, fenthiocarb, fenoxycarb, cypermethrin, cypermethrin, fipronil, flumetolquinone (2-ethyl-3,7-dimethyl-6-[4-(trifluoromethoxy)phenoxy]-4-quinolylmethyl carbonate), flonicamid, triflumizone, flucythrin, flufenoxuron, flutolan ((αE)-2-[[2-chloro-4-(trifluoromethyl)phenoxy]methyl]-α-(methoxymethylene)phenylacetic acid methyl ester), diflunisal (5-chloro-2-[(3,4,4-trifluoro-3-buten-1-yl)sulfonyl]thiazole), fluhexenic acid, fluopyram, flupiprole (1-[2,6-dichloro-4-(trifluoromethyl)phenyl]-5-[(2-methyl-2-propen-1-yl)amino]-4-[(trifluoromethyl)sulfinyl]-1H-pyrazole-3-carbonitrile), flupyrone (4-[[(6-chloro-3-pyridyl)methyl](2,2-difluoroethyl)amino]-2(5H)-furanone), flupyrimin, fluvalinate, fluvalinate, fluoxazolidinone, flufenacet, flufenacet, flutoxamide, flufenothion, flufenamic acid, thiamethoxam, cyhalothrin, chlorfenapyr, tebufenozide, cyhalothrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 2,2-dimethyl-3-[(1Z)-3,3,3-trifluoro-1-propen-1-yl]cyclopropane esters), hexaflumuron, hexathiazolin, hydrazone, imidacloprid, indoxacarb, insecticidal soap, isopropylamine, isoxathiapiprolin, κ-tefluthrin, lambda-cyhalothrin, lufenuron, malathion, cyfluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl (1R,3S)-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylate), metaflumizone, metaldehyde, methamidophos, methidathion, methiocarb, methomyl, methoprene, methoxychlor, metofluthrin, methoxyfenozide, ε-metofluthrin, ε-cyfluthrin (momfluorothrin), monocrotophos, monofluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 1-(2-cyano-1-propen-1-yl)-2,2-dimethylcyclopropanecarboxylate), nicotine, nitenpyram, nithiazine, fluazifop, fluphenazine, N-[1,1-dimethyl-2-(methylthio)ethyl]-7-fluoro-2-(3-pyridyl)-2H-indazole-4-carboxamide, N-[1,1-dimethyl-2-(methylsulfinyl)ethyl]-7-fluoro-2-(3-pyridyl)-2H-indazole-4-carboxamide, N-[1,1-dimethyl-2-(methylsulfinyl)ethyl]-7-fluoro-2-(3-pyridyl)-2H-indazole-4-carboxamide, N-[1,1-dimethyl-2-(methylsulfonyl)ethyl]-7-fluoro-2-(3-pyridyl)-2H-indazole-4-carboxamide, N-(1-methylcyclopropyl)-2-(3-pyridyl)-2H-indazole-4-carboxamide, N-[1-(dimethyl)ethyl]-7-fluoro-2-(3-pyridyl)-2H-indazole-4-carboxamide [fluoromethyl)cyclopropyl]-2-(3-pyridyl)-2H-indazole-4-carboxamide, oxamyl, oxazosulfyl, parathion, methyl parathion, permethrin, phorate, phosalone, phosmet, phosphamidon, pirimicarb, profenofos, profluthrin, propargyl, propenthrin, pyrimethrin, pyrimethrin (1,3,5-trimethyl-N-(2-methyl-1-oxopropyl)-N-[3-(2-methylpropyl)-4-[2,2,2-trifluoro-1-methoxy-1-(trifluoromethyl)ethyl]phenyl]-1H-pyrazole-4-carboxamide), pymetrozine, pyraclostrobin, pyrethrin, pyridabenzan, pyridalyl, pyrimethrin, pyrimidine, pyrimidine, pyrimidine, pyrimidine, pyrimidine, pyrimidine, pyrimidine, pyrimidine (αE)-2-[[[2-[(2,4-dichlorophenyl)amino]-6-(trifluoromethyl)-4-pyrimidinyl]oxy]methyl]-α-(methoxymethylene)phenylacetic acid methyl ester), pyraclostrobin, pyriproxyfen, rotenone, ryanodine, flutosan, spinosad, spinosad, spirodiclofen, spiromesifen, spiropidion, spirotetramat, sulfoxaflor (N-[methyl oxide [1-[6-(trifluoromethyl)-3-pyridyl]ethyl]-λ, 4 -sulfuryl] cyanamide), tebufenozide, tebufenpyrad, flubendiamide, tefluthrin, κ-tefluthrin, terbufophos, chlorpyrifos, tetrathrin, tetrafluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 2,2,3,3-tetramethylcyclopropanecarboxylate), thiacloprid, thiomethoxam, thiodicarb, dimethoate, thiazoxan (3-phenyl-5-(2-thienyl)-1,2,4-oxadiazole), tolfenpyrad amine, tralomethrin, triazolam, trichlorfon, triflumuron (2,4-dioxo-1-(5-pyrimidinylmethyl)-3-[3-(trifluoromethyl)phenyl]-2H-pyrido[1,2-a]pyrimidinium inner salt), triflumuron, tyclopyrazoflor, beta-cypermethrin, Bacillus thuringiensis delta-endotoxin, entomopathogenic bacteria, entomopathogenic viruses or entomopathogenic fungi, and combinations thereof.

[0084] Non-limiting examples of insecticides also include diamides such as chlorantraniliprole, cyantraniliprole, tetrachlorantraniliprole, bromofenac, dichlorantraniliprole, tetrazolam, cyclofenac, cyantraniliprole, and flubendiamide.

[0085] Non-limiting examples of fungicides include fungicides such as acibenzolar-S-methyl, aldimorph, azoxystrobin, aminopyrifen, indazolesulfamide, captazole, azoxystrobin, benalaxyl (including benalaxyl-M), malathion, benomyl, benthiavalicarb (including benthiavalicarb-isopropyl), benzovindipyr, bethoxazin, binapacryl, biphenyl, triadimefon, bixafen, blasticidin-S, boscalid, fuconazole, bupirimate, thiopyralid, carboxin, cyclopropimide, captafol, captan, carbendazim, chloronazole, eb), chlorothalonil, chlozolinate, copper hydroxide, copper oxychloride, copper sulfate, syringostyrin, cyazofamid, cyfluanid, cymoxanil, cyproconazole, cyprodinil, dichlobentiazox, dichlorvos, diclocymet, diclomezine, dicloran, diethofencarb, difenoconazole, diflumetorim, dimethirimol, dimethirimol, dimethiconol, difenoconazole, diniconazole (including diniconazole-M), diclofenac, dipymetitrone, dithianon, dithiothiophene, dodecanol, econazole, ethoconazole, kefansan, enoxastrobin,Also known as enestroburin), epoxiconazole, ethaboxam, ethirimol, etridiazole, famoxadone, fenamidone, enestroburin, chlorfenapyr, fenbuconazole, furamide, fenhexamide, fenoxanil, fenpicoxamid, fenpropidine, fenpropimorph, fenpyraclostrobin, triphenyltin acetate, triphenyltin hydroxide, ferbam, ferimzone, flutomidine, florylpicoxamid, fluoxadiazine, fluazinam, fludioxonil, flutolanil, fluindapyr, flumorph, fluopicolide, fluopicolide, fluoxapiprolin, fluoxastrobin, fluquinconazole, flusilazole, flusulfamide, fluthiazolin, flutolanil, flutriafol, flupyraclostrobin, folpet, fthalide,Also known as phthalide), phthalide, furalaxyl, furaxyl, hexaconazole, hymexazole, guazatine, imazalil, iminoctadine albesilate, iminoctadine triacetate, inpyrfluxam, thiodicarb, ipconazole, ipfentrifluconazole, ipflufenoquin, isoprobenfos, iprodione, propineb, isoflurane, isoflurane, isoprothiolane, isopyrazam, isothiopyrad, kasugamycin, kresoxim-methyl, lancotrione, mancozeb, mandiprop-amide ndipropamid), mandestrobin, maneb, mapanipyrin, clofoconazole, mefenoxam, meptyldinocap, metalaxyl (including metalaxyl-M / mefenoxam), metconazole, methasulfocarb, metiram, metoclopramide, metyltetraprole, mefentrazone, myclobutanil, naftitine, ferric ammonium arsenate (ferric ferric arsenate), methanearsonate), fluphenazine, octhilamide, furamide, orysastrobin, oxadixyl, oxathiapiprolin, oxolinic acid, oxpoconazole, oxycarboxin, oxytetracycline, penconazole, pencycuron, trifloxystrobin, penthiopyrad, perfurazoate, phosphorous acid (including salts thereof, for example, fosetyl-aluminum), picoxystrobin, piperalin, polyoxin, thiabendazole, prochloraz, procymidone, propamocarb, propiconazole, propineb, proquinazid, prothiocarb, prothioconazole, trifloxystrobin, Pyraclostrobin, pyraclostrobin, pyrapropoyne, pyraclostrobin, pyraziflumid, pyraclofos, pyraclofos, pyributacarb, pyridachlometyl, pyrifenox, pyriofenone, perisoxazole, pyrimethanil, pyrrolnitrin, pyroquilon, fluquinazole, quinmethionate , quinofumelin, quinoxyfen, pentachloronitrobenzene, silthiofam, sedaxane, simeconazole, spiroxamine, streptomycin, sulfur, tebuconazole, isobutyl ethoxyquin, teclofthalam, teclofthalam, tetrachloronitrobenzene, terbinafine, fluconazole, thiabendazole, thiofuran, thiophanate-methyl, thiophanate-methyl, ceram, thiabendazole, tolprocarb, tolylfluanid, triadimefon, triadimenol, myclobutanil, triazoxide, basic copper sulfate (tribasic copper sulfate), chlorpyrifos-methyl, tridemorph, trifloxystrobin, triflumizole, trimoprhamide tricyclazole, trifloxystrobin, triamcin, trichlorfonazole, uniconazole, validamycin, valifenalate (also known as valifenal), vinclozoline, mancozeb, ziram, zoxamide, 1-[4-[4-[5-(2,6-difluorophenyl)-4,5-dihydro-3-isoxazolyl]-2-thiazolyl]-1-piperidinyl]-2-[5-methyl-3-(trifluoromethyl)-1H-pyrazol-1-yl]ethanone, and combinations thereof.

[0086] Non-limiting examples of nematicides include fluopyram, spirotetramat, thiodicarb, thiothiazolin, abatin, iprodione, diflunisal, dimethyl disulfide, thiothiazolin, 1,3-dichloropropylene (1,3-D), metamidine (sodium and potassium), dazomet, chloropicrin, fenamiphos, ethosulf, cadusaphos, terbufos, imicyafos, oxamyl, carbofuran, thiothiazolin, Bacillus firmus, Pasteuria nishizawae, and combinations thereof. A non-limiting example of a bactericide is streptomycin. Non-limiting examples of acaricides include amitraz, chlorpyrifos, chlorfenapyr, cyhexatin, dicofol, chlorfenapyr, etoxazole, fenazaquin, fenbutatin, cypermethrin, fenpyrad, hexathiacyclon, propargyl, pyridaben, tebufenpyrad, and combinations thereof.

[0087] Non-limiting examples of herbicides include acetochlor, acifluorfen and its sodium salt, fenpyroxen, acrolein (2-propenal), alachlor, fenpyroxen, ametryn, amitriptyline, amidosulfuron, cyproconazole and its esters (e.g., methyl, ethyl) and salts (e.g., sodium, potassium), aminopyralid, fenpyroxen, sulfamate, thiamethoxam, sulfamethoxam, atrazine, tetrazosulfuron, diclofenac, fluazifop-butyl, fluazifop-P, fenpyroxen, ethyl fenpyroxen, benzylpyroxen, fluazifop-butyl, quinoline, benzylpyroxen, dimethoate, bentachlor, bicyclam, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin, pyraclostrobin and its sodium salt, bromopyralid, bromophenoxime, bromoxynil, bromoxynil octanoate, butachlor, pyraclostrobin ... Amine, fluazifop-butyl, chlorpyrifos-butyl, butalin, butycloprop-butyl, butycloprop-butyl, benzophenone, carbamyl, triadimefon, catechin, methoxyfenoxam, chlorbromide, chlormethyldan, herbicide, chlorsulfuron, chlorotoluron, chlorprop-butyl, chlorsulfuron, chlorthalate dimethyl, cypermethrin, indole-butyl, cyproconazole, chlorpyrifos-butyl, chlorsulfuron, chlorpyrifos-butyl ... Salt and triethanolamine salt, dimethoate, dalapon, dalapon sodium, dazomethane, 2,4-DB and its dimethylammonium salt, potassium salt and sodium salt, betaine, dichlorvos, dicamba and its diethylene glycol ammonium salt, dimethylammonium salt, potassium salt and sodium salt, dichlorvos nitrile, dipropionic acid, diclofop-butyl, diclofop-butyl, difenac dimethyl sulfate, fluazifop-butyl, flufenac, oxazolidinone, dimethoate sulfonamide, piperidine, dimethoate sulfonamide, dimethoate, isopentanol, dimethoate, dimethoate-P, thiamethoxam, dimethylarsonic acid and its sodium salt, dimethoate, terbutal, pyridazinon, dimethoate, diquat, dithiothiopyr, diuron, DNOC, fenpyroxene, EPTC, fluazifop-butyl, pentamethylenetetramine, ethametsulfuron, ethidium bromide, ethidium bromide, chlorosulfuron, chlorosulfuron, chlorpyrifos Ether, ethoxysulfuron, ethoxybenzimid, oxadiazol, fenoxaprop-butyl, benzyloxadiazol, fenpyroxen, fenpyroxen, fenpyroxen-TCA, fluazifop-methyl, fluazifop-isopropyl, fluazifop-methyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl Chloraminopyr, flufenacet, fluazifop-butyl, flupyridazine, flupyridazine, flupyridazine-butyl, fluazifop ...Glufosinate, glufosinate ammonium, L-glufosinate ammonium, glufosinate-spiked, glyphosate and its salts such as ammonium, isopropylammonium, potassium, sodium (including sodium sesquisodium) and trimethylsulfonium (alternatively known as glufosinate), haloxifen-methyl, haloxifen-methyl, halofop-ethyl, haloxifen-methyl, hexazinone, hydantocin, imazamox, imazapic, imazapic, imazapic, imazapic, imazapic, imazapic, imazapic, pyrazosulfuron, indole, triazine indole, iofensulfuron, iodosulfuron, ioxazolidinone, ioxazolidinone octanoate, ioxazolidinone sodium, triazolam, isoproturon, isoxadiazon, isoxadiazon, isoxadiazon, lactofen, oxaclorac, cyclopentadione, cyclopentadione Cladosporin, linuron, antapoxetine, MCPA and its salts (e.g., MCPA-dimethylammonium, MCPA-potassium and MCPA-sodium, esters (e.g., MCPA-2-ethylhexyl ester, MCPA-butoxyethyl ester) and thioesters (e.g., MCPA-ethylthioester), MCPB and its salts (e.g., MCPB-sodium) and esters (e.g., MCPB-ethyl ester), 2-methyl-4-chloropropionic acid, pure 2-methyl-4-chloropropionic acid, mefenacet, sulfamylsulfuron, mesotrione, metamifenamid, metamifenamid, metamifenamid, pyrazosulfuron-methyl, methylbenzimidone, methylarsonic acid and its calcium salt, monoammonium salt, monosodium salt and disodium salt, methylthiazol, methoxybenzimidone, bromothiazol, isopropylamine, isopropylamine, sulfonic acid Oxazolin, methoxuron, metribuzin, metsulfuron-methyl, thiamethoxam, green valley, naproxen, naproxen, naproxen-M, naproxen, oxamethoxam, nicosulfuron, dafofen, pingcaodan, pyrimidisulfon-methyl, amoxicillin, propargyl oxadiazon, oxadiazon, oxamethoxam, oxaziclomefone, oxyfluorfen, paraquat dichloride, chlorpyrifos, nonanoic acid, pendimethalin, penoxsulam, meclofenamide, cyclopentadiazol, fluazifop, pethoxamid, pethoxamid, pethoxyamid, benzylpyridinium, picloram, picloram potassium, flupyridinium, pinoxaden, piperafos, pretilachlor, primisulfuron, aminopropane, cyclohexene, promethazine, promethazine, propanil, propanil, oxadiazol-butyl, propazine, amphetamine, isopropylamine Pretilachlor, propoxysulfuron, propazinesulfuron, propoxysulfuron, propoxysulfuron, pyrazogyl, pyrazobutane, pyrazobutane, pyrazobutane, pyrazobutane, pyrazobutane, pyrazobutane, pyrimisulfan, pyrimisulfan, pyrimisulfan sodium, pyrimisulfan, pyrimisulfan sodium, rock-killing sulfone, methoxysulfuron, quinclorac, clomeclorac, algae quinone, quizalofop-p-ethyl, quizalofop-p-ethyl, quizalofop-p-ethyl, sulfsulfuron, sulfsulfuron, sethoxydim, cypermethrin, simazine, simethoxam, sulfotrione, sulfosulfuron, sulfosulfuron, 2,3,6-TBA, TCA, TCA-sodium, fenpyroxen, terbuthiuron,Tefusanone, cyproconazole, demeclopramide, terbucil, terbuthion, terbuthion, terbuthion, tetraflupyralid, methoxyfenacet, thiamethoxam, thiamethoxam, thiamethoxam, thiamethoxam, thiamethoxam, thiamethoxam, pyraclostrobin, tolpyralate, benzylpyrazone, oxamethoxam, wild malt, fluazifop-butyl, etherbensulfuron-methyl, triazifop-butyl, bensulfuron-methyl, triclopyr, triclopyr, triethylammonium triclopyr, cyclopentane, trifloxysulfuron, trifluralin, trifloxysulfuron, triazole-sulfuron, trifloxysulfuron, chloranil , 3-(2-chloro-3,6-difluorophenyl)-4-hydroxy-1-methyl-1,5-naphthyridin-2(1H)-one, 5-chloro-3-[(2-hydroxy-6-oxo-1-cyclohexen-1-yl)carbonyl]-1-(4-methoxyphenyl)-2(1H)-quinoxalinone, 2-chloro-N-(1-methyl-1H-tetrazol-5-yl)-6-(trifluoromethyl)-3-pyridinecarboxamide, 7-(3,5-dichloro-4-pyridyl)-5-(2,2-difluoroethyl)-8-hydroxypyrido[2,3- b]pyrazin-6(5H)-one), 4-(2,6-diethyl-4-methylphenyl)-5-hydroxy-2,6-dimethyl-3(2H)-pyridazinone), 5-[[(2,6-difluorophenyl)methoxy]methyl]-4,5-dihydro-5-methyl-3-(3-methyl-2-thienyl)isoxazole (the aforementioned is methioxolin), 4-(4-fluorophenyl)-6-[(2-hydroxy-6-oxo-1-cyclohexen-1-yl)carbonyl]-2-methyl-1,2,4 -triazine-3,5(2H,4H)-dione, methyl 4-amino-3-chloro-6-(4-chloro-2-fluoro-3-methoxyphenyl)-5-fluoro-2-picolinate, 2-methyl-3-(methylsulfonyl)-N-(1-methyl-1H-tetrazol-5-yl)-4-(trifluoromethyl)benzamide, 2-methyl-N-(4-methyl-1,2,5-oxadiazol-3-yl)-3-(methylsulfinyl)-4-(trifluoromethyl)benzamide, or their environmentally compatible salts, acids, esters and amides. Other herbicides include bioherbicides such as Alternaria destruens Simmons, Colletotrichum gloeosporiodes (Penz.) Penz. and Sacc., Drechsiera monoceras (MTB-951), Myrothecium verrucaria (Albertini and Schweinitz) Ditmar: Fries, Phytophthora rapalmivora (Butl.) Butl.,Puccinia thlaspeos Schub, or its environmentally compatible salts, acids, esters and amides.

[0088] Non-limiting examples of herbicides also include acetyl-CoA carboxylase inhibitors (ACC), such as cyclohexenone oxime ethers, such as fenpyroxil, clethodim, cycloxydim, sethoxydim, oxaclorac, butoxycycline, cyclohexanone or chlorpyrifos; phenoxyphenoxypropionates, such as clodinafop-butyl, cyhalofop-butyl, diclofop-butyl, fenoxaprop-butyl, thiazolinone, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, fluazifop-butyl, isocyanate, quinacridone, quizalofop-butyl, quizalofop-butyl or quizalofop-butyl; or arylaminopropionic acids, such as fluazifop-methyl or fluazifop-isopropyl; para-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors, such as pyrazoline, pyrazoconazole, pyrazoconazole, sulfotrione, isoxaclorac, mesotrione, isothiocarb, pyrazoconazole ... Oxychloride, ketone, cyclosulfuron; acetolactate synthase inhibitors (ALS), for example, imidazolinones, such as imazapyr, imazaquin, imazame, imazamox, imazapic or imazapic; pyrimidinyl ethers, such as pyrimidosulfuron acid, pyrimidosulfuron sodium, bispyribac-sodium or pyrimidosulfuron; sulfonamides, such as clorac-sulam, bisclorac-sulam, bispyribac-sodium or sulfaquinoxaline; or sulfonylureas, such as amidosulfuron, tetrazosulfuron, bensulfuron-methyl, chlorimuron, chlorsulfuron, chlorsulfuron, cyprosulfuron, ethametsulfuron, ethoxysulfuron, flazasulfuron, foramsulfuron, chlorpyrisulfuron, pyrazosulfuron, iodosulfuron, metsulfuron, nicosulfuron, primisulfuron, prosulfuron , pyrazosulfuron-methyl, sulfamethoxazole-methyl, sulfamethoxazole-methyl or -3-oxetane, sulfamethoxazole-methyl, thifensulfuron-methyl, ether bensulfuron-methyl, bensulfuron-methyl, flumethoxazole-methyl or trifloxysulfuron-methyl; amides, such as dipropylene glycol, succinate, bromobutyric acid, oxacor, fenvalerate, ethoxybenzamide (benzchlomet), flufenacet, phosphinothricin or heptanoamide; auxin herbicides, such as picolinic acids, such as clopyralid or piclopram; 2,4-D or chlorpyrifos; auxin transport inhibitors, such as naphthamide or flufenacet; carotenoid biosynthesis inhibitors, such as diflubenzuron, fluazifop, flupyralid, flufenacet, flufenacet, flufenacet or flupyralid; enolpyruvylshikimate-3-phosphate synthase inhibitors (EPSPS), such as glyphosate or glufosinate; glutamine synthetase inhibitors, such as bialaphos or glufosinate; lipid biosynthesis inhibitors, such as anilines, such as cypermethrin or mefenacet; chloroacetanilides, such as dimethoate, S-dimethoate, acetochlor, alachlor, butachlor, butenesulfonamide, acetochlor, dimethochlor, pyraclostrobin, isopropyl metolachlor, S-isopropyl metolachlor, pretilachlor, pyraclostrobin, propargyl phthalate, terbutachlor, dimethenamid or dimethoate; thioureas, such as butachlor, chlorfenapyr, avenanthramide, pyraclostrobin, EPTC, pentocarb, cypermethrin, cypermethrin, benthiocarb, benthiocarb, wild oxadiazine or chlorfenapyr; or furazolidone or chlorfenapyr;Mitotic inhibitors, for example, carbamates, such as cypermethrin, cypermethrin, chlorpropamine, cypermethrin, propyzamid, cypermethrin or cypermethrin; dinitroanilines, such as fluazifop, cypermethrin, aminofluanid, ethylfluanid, chlorpropamine, amoxifen, pendimethalin, aminofluanid or trifluanid; pyridines, such as dithiopyr or thiamethoxam; or chlorpyrifos, dimethyl chlorphthalate (DCPA) or chlorpyrifos; protoporphyrinogen IX oxidase inhibitors, for example, diphenyl ethers, such as trifluorfen, triamcinol, trifloxycin ... Acifluorfen sodium, benifloxylate, cypermethrin, cypermethrin (CNP), fluazifop-butyl, penflufenacil, fluazifop-butyl, fomesafen, furosulphan, lactofen, nitroprop-butyl, trifluoroacetic acid-butyl or oxyfluorfen-butyl; oxadiazoles, such as oxadiazol-butyl or oxadiazol-butyl; cyclic imides, such as oxadiazol-butyl, fluazifop-butyl, cypermethrin-ethyl, indole-butyl, flufenacil-pentyl, fluazifop-butyl, cypermethrin, fluazifop-butyl, sulfentrazone-butyl or thiamethoxam; or pyrazoles, such as ET-751, JV 485 or fluazifop; photosynthesis inhibitors such as propanil, pyridafol or pyridafol; benzothiadiazinones such as bentazon; dinitrophenols such as bromophenol oxime, dinoseb, dinoseb-acetate, teloseb or DNOC; bipyridines such as chlorpyrifos chloride, diquat-methyl sulfate, diquat or paraquat dichloride; ureas such as chlorbromide, green wheat chlorpyrifos, such as chloranil, ... Mazine, simetryn, terbuthion, terbuthion, terbuthion or trodazine; triazinones such as metamitron or metribuzin; uracils such as bromocriptine, lenacil or terbacil; or biscarbamates such as betadine or betadine; growth substances, for example aryloxyalkanoic acids such as 2,4-DB, baclofen, dichlorvos, 2,4-DP-P, fluroxypyr, MCPA, MCPB, 2-methyl-4-chloropropionic acid, 2-methyl-4-chloropropionic acid or triclopyr; benzoic acids such as fenpyrox or dicamba; or quinolinecarboxylic acids such as quinclorac or clomeclorac; cell wall synthesis inhibitors such as isoxadiazon or dichlobenil; various other herbicides, for example dichloropropionic acids such as dalapon; dihydrobenzofurans such as fufurox; phenylacetic acids such as fenac;or azidoprop-methyl, avenaline, dimethoate, benzylthiocarb, benzofluor, buminafos, buthidone, clodinafop-methyl, mefenacet, chlorpyrifos, avenaline, cypermethrin, cyproconazole, benzylthiocarb, cyprodinil, tricyclam, benzylthiocarb, benzylthiocarb, cyprodinil, tricyclam, benzylthiocarb, cyprodinil, tricyclam, benzylthiocarb, cyprodinil, tricyclam, benzylthiocarb, cyprodinil, tricyclam, tricyclam, trimethoate, cyprodinil, tricyclam, trimethoate, cyprodinil, tricyclam, trimethylthiocarb, trimethylthiocarb, trimethylthiocarb, trimethylthiocarb, trimethylthiocarb, trimethylthiocarb, trimethylthiocarb, or trimethylthiocarb; or their environmentally compatible salts, "acids", esters and amides. ;

[0089] insect

[0090] In certain embodiments, the insect is a herbivorous insect. Herbivorous insects refer to invertebrate pests that damage plants by feeding, such as by eating branches, stems, roots, leaves, flowers, pods, fruits or seed tissues or any other nutritional or reproductive plant structures, or by sucking the vascular fluid of plants. Leaf feeders can be external (exogenous) or they can mine tissues, sometimes even specifically targeting specific cell types. There are herbivorous insect species in most insect orders, including Hemiptera, Thysanoptera, Orthoptera, Lepidoptera, Coleoptera, Heteroptera, Hymenoptera and Diptera.

[0091] Examples of agronomic or non-agronomic invertebrate pests include eggs, larvae and adults of the order Lepidoptera, such as armyworms, cutworms, loopers and heliothines of the family Noctuidae (e.g., pink stem borer (Sesamia inferens Walker), cornstalk borer (Sesamia nonagrioides Lefebvre), southern armyworm (Spodoptera eridania Cramer), fall armyworm (Spodoptera frugiperda J.E. Smith), beet armyworm (Spodoptera exigua Hübner), cotton leafworm (Spodoptera littoralis Boisduval), yellowstriped armyworm (Spodoptera ornithogalli Guenée), black cutworm (Spodoptera spp. cutworm (Agrotis ipsilon Hufnagel), velvet bean caterpilla (Anticarsia gemmatalis Hübner), green fruitworm (Lithophane antennata Walker), cabbage armyworm (Barathra brassicae Linnaeus), soybean looper (Pseudoplusia includens Walker), cabbage looper (Trichoplusia ni Hübner), tobacco budworm (Heliothis virescens Fabricius); borers, casebearers, webworms, coneworms, cabbageworms and skeletonizers from the family Pyralidae (e.g., European corn borer,Ostrinia nubilalis Hübner), navel orangeworm (Amyelois transitella Walker), corn root webworm (Crambus caliginosellus Clemens), sod webworms (Crambinae), such as sod worm (Herpetogramma licarsisalis Walker), sugarcane stem borer (Chilo infuscatellus Snellen), tomato small borer (Neoleucinodeselegantalis Guenée), green leafroller (Cnaphalocrocism medinalis), grape leafroller (Cranphalocrocism medinalis), and sorghum worms (Crambinae). leaffolder (grape borer (Desmia funeralis Hübner)), melon worm (cucumber silk borer (Diaphania nitidalis Stoll)), cabbage center grub (Helluala hydralis Guenée), yellow stem borer (Scirpophaga incertulas Walker), early shoot borer (Sugarcane borer (Scirpophaga infuscatellus Snellen)), white stem borer (Scirpophaga innotata Walker), top shoot borer (Sugarcane white borer (Scirpophaga nivella Fabricius)), dark-headed rice borer (Chilo polychrysus Meyrick), striped rice borer (Chilo suppressalis Walker), cabbagecluster caterpillar,Crocidolomia binotalis English); leafrollers, budworms, seed worms, and fruit worms of the family Tortricidae (e.g., codling moth (Cydia pomonella Linnaeus), grape berry moth (Endopiza viteana Clemens), oriental fruit moth (Grapholita molesta Busck), citrus false codling moth (Cryptophlebia leucotreta Meyrick), citrus borer (Ecdytolophaaurantiana Lima), redbanded leafroller (Argyrotaenia velutinana Walker), obliquebanded leafroller (Choristoneura rosaceana Harris), light brown apple moth (Epiphyas postvittana Walker), European grape berry moth (Eupoecilia ambiguella Hübner), apple bud moth (Pandemis pyrusana Kearfott), omnivorous leafroller (Platynota stultana Walsingham), barred fruit-tree tortrix (Pandemis cerasana Hübner), apple brown tortrix (Pandemis heparana Denis & Schiffermüller); and many other economically important Lepidoptera (e.g., diamond back moth (Plutella xylostella Linnaeus), pink bollworm (Plutella xylostella Linnaeus),Pectinophora gossypiella Saunders), gypsy moth (Lymantriadispar Linnaeus), peach fruit borer (Carposina niponensis Walsingham), peach twig borer (Anarsia lineatella Zeller), potato tuberworm (Phthorimaea operculella Zeller), spotted teniform leafminer (Lithocolletis blancardella Fabricius), Asiatic apple leafminer (Lithocolletis ringoniella Matsumura), rice leaffolder (Lerodea eufala Edwards), apple leafminer (Leucoptera scitella Zeller); eggs, nymphs, and adults of the order Blattellidae, including cockroaches from the families Blattellidae and Blattidae (e.g.,Oriental cockroach (Blatta orientalis Linnaeus), Asian cockroach (Blatella asahinai Mizukubo), German cockroach (Blattella germanica Linnaeus), brownbanded cockroach (Supella longipalpa Fabricius), American cockroach (Periplaneta americana Linnaeus), brown cockroach (Periplaneta brunnea Burmeister), Madeira cockroach (Leucophaea maderae Fabricius), smoky brown cockroach (Periplaneta fuliginosa Service), Australian cockroach cockroach (Periplaneta australasiae Fabr.), lobster cockroach (Nauphoeta cinerea Olivier), and smooth cockroach (Symplocepallens Stephens); eggs, leaf-feeding, fruit-feeding, root-feeding, seed-feeding, and vesicle-feeding larvae and adults of the order Coleoptera, including weevils from the families Anthribidae, Bruchidae, and Curculionidae (e.g., boll weevil (Anthonomus grandis Boheman), rice water weevil (Lissorhoptrusoryzophilus Kuschel), granary weevil (Sitophilus granarius Linnaeus), rice weevil (Sitophilus granarius Linnaeus), and rice weevil (Sitophilus granarius Linnaeus). weevil,oryzae Linnaeus), annual bluegrassweevil (Listronotus maculicollis Dietz), bluegrass billbug (Sphenophorus parvulus Gyllenhal), hunting billbug (Sphenophorus venatus vestitus), Denver billbug (Sphenophorus cicatristriatus Fahraeus); flea beetles, cucumber beetles, rootworms, leaf beetles, potato beetles, and leaf miners (e.g., Colorado potato beetle (Leptinotarsa decemlineata)) of the family Chrysomelidae. Say), western cornrootworm (Diabrotica virgifera LeConte); scarab beetles and other beetles from the family Scarabaeidae (e.g., Japanese beetle (Popilliajaponica Newman), oriental beetle (PopilliajaponicaAnomala orientalis Waterhouse, Exomala orientalis (Waterhouse) Baraud), northern masked chafer (Cyclocephala borealis Arrow), southern masked chafer (Cyclocephala immaculata Olivier or C. lurida Bland), dung beetles and white grubs (Aphodius species), black turfgrass ataenius (Ataenius spretulus Haldeman), green June beetle (Cotinis nitida Linnaeus), Asiatic garden beetle (Maladera castanea Arrow), May / June beetles (Phyllophaga species), and European chafers (Rhizotrogus majalis Razoumowsky); carpet beetles from the Dermestidae family; wireworms from the Elateridae family; bark beetles from the Scolytidae family, and flourbeetles from the Tenebrionidae family.

[0092] In addition, agronomic and non-agronomic pests include: eggs, adults and larvae of the order Dermoptera, including earwigs from the family Forficulidae (e.g., European earwig (Forficulaauricularia Linnaeus), black earwig (Chelsoches morio Fabricius));Eggs, larvae, adults and nymphs of the Hemiptera and Homoptera, such as plant bugs from the family Miridae, cicadas from the family Cicadidae, leafhoppers from the family Cicadellidae (e.g., Empoasca species), potato leafhoppers, bed bugs from the family Cimicidae (e.g., Cimex lectularius Linnaeus), planthoppers from the families Fulgoroidae and Delphacidae, treehoppers from the family Membracidae, psyllids from the family Psyllidae, whiteflies from the family Aleyrodidae, aphids from the family Aphididae, phylloxera from the family Phylloxeridae, mealybugs from the family Pseudococcidae, scales from the families Coccidae, Diaspididae, and Margarodidae, lace bugs from the family Tingidae, stink bugs from the family Pentatomidae, bugs), chinchbugs from the Lygaeidae family (e.g., hairy chinch bug (Blissus leucopterus hirtus Montandon) and southern chinch bug (Blissus insularis Barber), and other seed bugs from the Lygaeidae family, spittlebugs from the Cercopidae family, squash bugs from the Coreidae family, and red bugs and cottonstainers from the Coreidae family.

[0093] Agronomic and non-agronomic pests also include: eggs, larvae, nymphs and adults of the order Acari (mites), such as spider mites and red mites of the family Tetranychidae (e.g., European red mite, Panonychus ulmi Koch, two spotted spider mite, Tetranychus urticae Koch, McDaniel mite, Tetranychus mcdanieli McGregor); flat mites of the family Tenuipalpidae (e.g., citrus flat mite (Brevipalpus lewisi McGregor)); rust mites and bud mites of the family Eriophyidae. mite, and other leaf-feeding mites and mites important in human and animal health, namely, dust mites of the family Epidermoptidae, hair mites of the family Demodicidae, grain mites of the family Glycyphagidae; ticks of the family Ixodidae, commonly known as hard ticks (e.g., deer tick (Ixodes scapularis Say), Australian paralysis tick (Ixodes holocyclus Neumann), American dog tick (Dermacentor variabilis Say), lone star tick (Amblyomma americanum Linnaeus)) and ticks of the family Argasidae, commonly known as soft ticks (e.g., relapsing fever tick (Ornithodoros turicata), the common fowl tick (Argas radiatus); scab mites and itch mites of the Psoroptidae, Pyemotidae, and Sarcoptidae families;Eggs, adults and larvae of the order Orthoptera, including grasshoppers, locusts and crickets (e.g., migratory grasshoppers (e.g., Melanoplus sanguinipes Fabricius, M. differentialis Thomas), American grasshoppers (e.g., Schistocerca americana Drury), desert locust (Schistocerca gregaria Forskal), migratory locust (Locustamigratoria Linnaeus), bush locust (Zonocerus species), house cricket (Acheta domesticus Linnaeus), mole crickets (e.g., tawny mole cricket (Scapteriscus vicinus Scudder) and southern mole cricket (Scapteriscus vicinus Scudder)). cricket, Scapteriscus borellii Giglio-Tos)));Eggs, adults, and larvae of the order Diptera, including leafminers (e.g., Liriomyza species, such as serpentine vegetable leafminer (Liriomyza sativae Blanchard)), midges, fruit flies (Tephritidae), frit flies (e.g., Oscinella frit Linnaeus), soil maggots, house flies (e.g., Musca domestica Linnaeus), lesser house flies (e.g., Fannia canicularis Linnaeus, F. femoralis Stein), stable flies (e.g., Stomoxys calcitrans Linnaeus), face flies, horn flies, blow flies (e.g., flies (e.g., Chrysomya species, Phormia species) and other muscoid fly pests, horse flies (e.g., Tabanus species), bot flies (e.g., Gastrophilus species, Oestrus species), cattle grubs (e.g., Hypoderma species), deer flies (e.g., Chrysops species), keds (e.g., Melophagus ovinus Linnaeus) and other Brachycera, mosquitoes (e.g., Aedes species, Anopheles species, Culex species), black gnats (e.g., Eurasian gnats), blackflies ... flies) (e.g., Prosimulium species, Simulium species), biting midges, sand flies, sciarids, and other Nematocera;Eggs, adults, and larvae of the order Thysanoptera, including onion thrips (Thrips tabaci Lindeman), flower thrips (Frankliniella species), and other leaf-feeding thrips; insect pests of the order Hymenoptera, including ants of the family Formicidae, including Florida carpenter ants (Camponotus floridanus Buckley), red carpenter ants (Camponotus ferrugineus Fabricius), black carpenter ants (Camponotus pennsylvanicus De Geer), white-footed ants (Technomyrmex albipes fr. Smith), bigheaded ants (Pheidole species), ghost ants (Tapinoma melanocephalum Fabricius); Pharaoh ant (Monomorium pharaonis Linnaeus), little fire ant (Wasmannia auropunctata Roger), fire ant (Solenopsis geminata Fabricius), red imported fire ant (Solenopsis invicta Buren), Argentine ant (Iridomyrmex humilis Mayr), crazy ant (Paratrechina longicornis Latreille), pavement ant (Tetramorium caespitum Linnaeus), cornfield ant (Lasius alienus); ) and odorous house ant (Tapinoma sessile Say). Other Hymenoptera, including bees (including carpenter bees), hornets, yellow jackets, wasps, and sawflies (Neodiprion species; Cephus species); insect pests of the order Isoptera, including termites of the families Termitidae (e.g., Macrotermes species, Odontotermes obesus Rambur), Kalotermitidae (e.g., Cryptotermes species), and Rhinotermitidae (e.g., Reticulitermes species, Coptotermes species, Heterotermes tenuis Hagen), eastern subterranean termites (Reticulitermes species), and the eastern subterranean termite (Reticulitermes species). flavipes Kollar), western subterranean termite (Reticulitermes hesperus Bank), Formosan subterranean termite (Coptotermes formosanus Shiraki), West Indian drywood termite (Incisitermes immigrans Snyder), powder posttermite (Crytotermes brevis Walker), drywood termite (Incisitermessnyderi Light), southeastern subterranean termite (Reticulitermes virginicus Banks), western drywood termite (Incisitermes minor Hagen), arboreal termites such as species of the genus Nasutitermes, and other termites of economic importance;Insect pests of the order Thysanura, such as silverfish (Lepisma saccharina Linnaeus) and firebrat (Thermobia domestica Packard); Insect pests of the order Trichophagus, including head louse (Pediculus humanus capitis De Geer), body louse (Pediculus humanus Linnaeus), chicken body louse (Menacanthus stramineus Nitszch), dog biting louse (Trichodectes canis De Geer), fluff louse (Goniocotes gallinae De Geer), sheep body louse (Bovicolaovis Schrank), short-nosed cattle louse (Haematopinus eurysternus Nitzsch), long-nosed cattle louse (Hemiptera: Trichodectes canis De Geer), long-nosed cattle louse (Hemiptera: Trichodectes canis De Geer), long-nosed cattle louse (Hemiptera: Trichodectes gallinae De Geer), short ... louse) (Linognathus vituli Linnaeus) and other sucking and chewing parasitic lice that attack humans and animals;Insect pests of the order Siphonoptera, including the oriental rat flea (Xenopsylla cheopis Rothschild), cat flea (Ctenocephalides felis Bouche), dog flea (Ctenocephalides canis Curtis), hen flea (Ceratophyllus gallinae Schrank), sticktight flea (Echidnophaga gallinacea Westwood), human flea (Pulex irritans Linnaeus), and other fleas that afflict mammals and birds. Additional arthropod pests covered include spiders of the order Araneae, such as the brown recluse spider (Loxosceles reclusa Gertsch & Mulaik) and the black widow spider (Latrodectus mactans Fabricius), and centipedes of the order Scolopendra, such as the house centipede (Scutigera coleoptrata Linnaeus).

[0094] Examples of invertebrate pests of stored grain include larger grain borer (Prostephanus truncatus), lesser grain borer (Rhyzopertha dominica), rice weevil (Stiophilus oryzae), maize weevil (Stiophilus zeamais), cowpea weevil (Callosobruchus maculatus), red flourbeetle (Tribolium castaneum), granary weevil (Stiophilus granarius), Indian meal moth (Plodia interpunctella), Mediterranean flourbeetle (Ephestia kuhniella), and flat or rusty grain beetle. beetle)(Cryptolestis ferrugineus).

[0095] The compounds of the present disclosure may have activity against members of the classes Nematoda, Cestoda, Trematoda, and Acanthocephala, including economically important members of the orders Strongylida, Ascaridida, Oxyurida, Rhabditida, Spirurida, and Enoplida, such as, but not limited to, economically important agricultural pests (i.e., root-knot nematodes in the genus Meloidogyne, lesion nematodes in the genus Pratylenchus, stubby root nematodes in the genus Trichodorus, etc.), as well as animal and human health pests (i.e., all economically important trematodes, cestodes, and roundworms, such as Strongylus vulgaris in horses). vulgaris in dogs, Toxocara canis in dogs, Haemonchus contortus in sheep, Dirofilaria immitis Leidy in dogs, Anoplocephala perfoliata in horses, Fasciola hepatica Linnaeus in ruminants, etc.

[0096] The compositions of the present disclosure may have activity against pests in the order Lepidoptera (e.g., Alabama argillacea Hübner (cotton leafworm), Archips argyrospila Walker (fruit leaf roller), A. rosana Linnaeus (European leaf roller) and other Archips species, Chilo suppressalis Walker (rice borer), Cnaphalocrosis medinalis Guenée (rice leaf roller), Crambus caliginosellus Clemens (corn root webworm), Crambus teterrellus Zincken (bluegrass webworm), Cydia pomonella Linnaeus (codling moth), Earias insulana Boisduval (spiny stem borer), Earias vittella Fabricius (spotted stem borer), Helicoverpa armigera Hübner (American bollworm), Helicoverpa zea Boddie (corn earworm), Heliothis virescens Fabricius (tobacco budworm), Herpetogramma licarsisalis Walker (sod webworm), Lobesia botrana Denis & Schiffermüller (grape leaf roller), Pectinophoragossypiella Saunders (pink bollworm), Phyllocnistis citrella Stainton (citrus leafminer), Pieris brassicae Linnaeus (large white cabbage butterfly), Pieris rapae Linnaeus (small white butterfly), Plutella xylostella Linnaeus (diamond back moth), Spodoptera exigua Hübner (beet armyworm),beet armyworm), Spodoptera litura Fabricius (tobacco cutworm, cluster caterpillar), Spodoptera frugiperda (fall armyworm), Trichoplusia ni Hübner (cabbage looper), and Tuta absoluta Meyrick (tomato leafminer).

[0097] The compositions of the present disclosure can have significant activity against members from the order Homoptera, including: Acyrthosiphon pisum Harris (pea aphid), Aphis craccivora Koch (cowpea aphid), Aphis fabae Scopoli (black bean aphid), Aphis gossypii Glover (cotton aphid, melon aphid), Aphis pomi DeGeer (apple aphid), Aphis spiraecola Patch (spirea aphid), Aulacorthum solani Kaltenbach (foxglove aphid), Chaetosiphon fragaefolii Cockerell (strawberry aphid), Diuraphis noxia (wheat aphid), Kurdjumov / Mordvilko (Russian wheat aphid), Dysaphis plantaginea Paaserini (rosy apple aphid), Eriosoma lanigerum Hausmann (wooly apple aphid), Hyalopterus pruni Geoffroy (mealy plum aphid), Lipaphis erysimi Kaltenbach (turnip aphid), Metopolophium dirrhodum Walker (cereal aphid), Macrosiphum euphorbiae Thomas (potato aphid), Myzus persicae Sulzer (peach-potato aphid, green peach aphid), Nasonovia ribisnigri Mosley (lettuce aphid),lettuce aphid), Pemphigus species (root aphids and gallaphids), Rhopalosiphum maidis Fitch (corn leaf aphid), Rhopalosiphum padi Linnaeus (bird cherry-oat aphid), Schizaphis graminum Rondani (greenbug), Sitobion avenae Fabricius (English grain aphid), Therioaphis maculata Buckton (spotted alfalfa aphid), Toxoptera aurantii Boyer de Fonscolombe (black citrus aphid), and Toxoptera citricida Kirkaldy (brown citrus aphid). aphid; Adelges species (adelgids); Phylloxera devastatrix Pergande (pecan phylloxera); Bemisia tabaci Gennadius (tobacco whitefly, sweetpotato whitefly), Bemisia argentifolii Bellows & Perring (silverleaf whitefly), Dialeurodes citri Ashmead (citrus whitefly) and Trialeurodes vaporariorum Westwood (greenhouse whitefly); Empoasca fabae Harris (potato leafhopper), Laodelphax striatellus Fallen (smaller brown planthopper), Macrolestes quadrilineatus Forbes (aster leafhopper), Nephotettix cinticeps Uhler (green leafhopper), Nephotettix nigropictus, ) (rice leafhopper), brown planthopper (Nilaparvatalugens brown planthopper), Peregrinus maidis Ashmead (corn planthopper), Sogatella furcifera Horvath (white-backed planthopper), Sogatodes orizicola Muir (rice delphacid), Typhlocyba pomaria McAtee (white apple leafhopper), Erythroneoura species (grape leafhoppers); Magicidada septendecim Linnaeus (periodical cicada); Icerya purchasi Maskell (cottony cushion scale), Quadraspidiotus perniciosus Comstock (San Jose scale); Planococcus citri Risso (citrus mealybug), and other pests. mealybug); Pseudococcus species (other mealybug groups); pear psylla (Cacopsylla pyricola Foerster, pearpsylla), persimmon psylla (Trioza diospyri Ashmead, persimmon psylla).

[0098] The compositions of the present disclosure may also have activity against members from the order Hemiptera, including: Acrosternum hilare Say (green stink bug), Anasa tristis DeGeer (squash bug), Blissus leucopterus Say (chinchbug), Cimex lectularius Linnaeus (bedbug), Corythuca gossypii Fabricius (cotton lace bug), Cyrtopeltis modesta Distant (tomatobug), Dysdercus suturellus Herrich- ) (cottonstainer), Euchistus servus Say (brown stink bug), Euchistus variolarius Palisot de Beauvois (one-spotted stink bug), Graptosthetus species (complex of seed bugs), Halymorpha halys ) (brown marmorated stink bug), Leptoglossus corculus Say (leaf-footed pine seed bug), Lygus lineolaris Palisot de Beauvois (tarnished plant bug), Nezara viridula Linnaeus (southern green stink bug), Oebalus pugnax Fabricius (rice stink bug), Oncopeltus fasciatus Dallas (large milkweed bug), Pseudatomoscelis seriatus Reuter (cotton fleahopper). Other insect orders controlled by the compounds of the present disclosure include Thysanoptera (e.g., Frankliniella occidentalis Pergande (western flower thrips), Scirthothrips citri Moulton (citrus thrips), Sericothrips variabilis Beach (soybean thrips), and Thrips tabaci (onion thrips)); and Coleoptera (e.g., Colorado potato beetle, Mexican bean beetle, and wireworms of the genera Agriotes, Athous, or Limonius).

[0099] In some aspects, the compositions of the present disclosure can be used to control western flower thrips (Frankliniella occidentalis). In some aspects, the compositions of the present disclosure can be used to control potato leafhoppers (Empoasca fabae). In some aspects, the compositions of the present disclosure can be used to control cotton melon aphids (Aphis gossypii). In some aspects, the compositions of the present disclosure can be used to control diamondback moths (Plutella xylostella L.). In some aspects, the compositions of the present disclosure can be used to control silverleaf whiteflies (Bemisia argentifolii Bellows & Perring).

[0100] In terms of cyantraniliprole of the present disclosure, the composition of the present disclosure is effective against the following: Coleoptera, Chrysomelidae, Cerotoma trifurcata bean leaf beetle, Chaetocnemaconcinna beet flea beetle, Epilachna varivestis Mexican bean beetle, Epitrix cucumeris potato flea beetle, Leptinotarsa decemlineata Colorado potato beetle, Oulemamelanopus cereal leaf beetle, Oulema oryzae rice leaf beetle, Phyllotreta cruciiferae cabbage flea beetle, Phyllotretastriolata striped flea beetle, Psylliodes spp. flea beetle, beetles), Curculionidae, Anthonomus eugenii pepper weevil, Ceutorhynchus napi cabbage stem weevil, Ceutorhynchus quadridens cabbage seed-stalk curculio, Conotrachelus nenuphar plum curculio, Hypera bruneipennis Egyptian alfalfa weevil, Hypera postica alfalfa weevil, Lissorhoptrus oryzophilus ricewater weevil, Nitidulidae, Meligethes aeneus pollenbeetle, blossom beetle, Scarabaeidae, Cotinisnitida green June beetle beetle), Phyllophaga junceaspp. Junebeetles), grubs, Popillia japonica Japanese beetle, Diptera, Agromyzidae, Liromyza chinensis stone leek leafminer, Liromyza huidobrensis pea leafminer, Liriomyza sativaeserpentine / vegetable leafminer, Liromyza trifolii Americanserpentine leafminer, Anthomyiidae, Delia antiqua onion fly, Delia platura seedcorn maggot, Muscidae, Atherigonaoryzae rice seedling fly, Psilidae, Psila rosae carrotfly), Tephritidae, Anastrepha fraterculus South American fruit fly, Anastrepha ludens Mexican fruit fly, Anasterpha striata guava fruit fly, Bactrocera cucurbitae melon fly, Bactrocera dorsalis oriental fruit fly, Bactrocera oleae olive fly, Ceratitis capitata Mediterranean fruit fly, Chromatomyia horticola garden pea leafminer, Rhagoletis cerasi cherry fruit fly, Rhagoletis cingulata cherry fruit fly, Rhagoletis indifferens western cherry fruit flyfly), Rhagoletis pomonella apple maggot, Hemiptera, Aleyrodidae, Aleyrodes proletella cabbage whitefly, Bemisia tabaci sweetpotato whitefly, cotton whitefly, Dialeurodes citri citrus whitefly, greenhouse whitefly, greenhouse whitefly, Aphididae, Acyrthosiphon pisumpea aphid, Aphis craccivora cowpea aphid, Aphis fabae blackbean aphid, Aphis glycines soybean aphid, Aphis gossypii cottonaphid, melon aphid, Aphis nasturtii buckthorn aphid, Aphis pomigreen apple aphid aphid), Aphis spiraceola spirea aphid, Aulacorthum solani foxglove aphid, Brachycaudus persicae black peach aphid, Brevicoryne brassicae cabbage aphid, Chromaphis juglandicola European walnut aphid, Dysaphis plantaginea rosy apple aphid, Hyalopterus pruni mealy plum aphid, Lipaphis erysimi mustard aphid, turnip aphid, Macrosiphum euphorbiae potato aphid, Myzus persicae green peach aphid, peach potato aphid, Rhopalosiphum padi bird cherry aphidoataphid), Rhopalosiphum nymphaeae plum aphid, Schizaphis graminum greenbug, Sitobion avenae English grain aphid, Therioaphis maculata spotted alfalfa aphid, Toxopteracitricida brown citrus aphid, oriental citrus aphid, Cicadellidae, Empoasca fabae leafhopper / jassid complex, Empoasca vitis green frogfly, Hortensia similis common green leafhopper, Idioscopus spp. mango leafhopper, Jacobiasca lybica cotton leafhopper jassid), Nephotettix spp. rice green leafhopper complex, Typhlocyba rosaerose leafhopper, Typhlocyba pomaria white apple leafhopper, Coreidae Leptocorisa oratorius rice bug, rice ear bug, paddy bug, Delphacidae, Nilaparvata lugens rice brown planthopper, Diaspididae, Aonidiella aurantii citrusscale, Flatidae, Metcalfa pruinosa citrus flatidplanthopper, Pentatomidae, Euschistus spp. spp.brownstinkbugs), Edessa spp.stinkbugs), Psyllidae, Diaphorina citri Asian citrus psyllid, Paratriozacockerelli potato psyllid, tomato psyllid, Triozaeugeniae eugenia psyllid, lillypilly psyllid, Hymenoptera Tenthredinidae, Hoplocampa testudinea European apple sawfly, Lepidoptera, Crambidae, Scirpophaga incertulas yellow (rice) stemborer, Gelechiidae, Anarsia lineatella peach twigborer, Keiferia lycopersicella tomato pinworm, Pectinophora gossypiella pink bollworm bollworm), Tuta absoluta tomato leafminer, Gracilariariidae, Gracilaria theivora tea leafroller, Phyllonorycter blancardella spotted tentiform leafminer, Phyllonorycter coryfoliella nut leaf blister moth, Phyllonorycter crataegella apple blotch leafminer, Phyllonorycter ringoniella apple leafminer, Phyllonorycter elmaella western tentiform leafminer, Hesperiidae, Borbocinara rice leafroller, Lyonetiidae, Leucoptera coffeellawhiteCoffee leafminer), Leucoptera scitella pear leaf blistermoth, Lyonetia clerkella peach, leaf miner, Noctuidae, Agrotis segetum common cutworm, Alabamaargillacea cotton leafworn, Autographa californica alfalfalooper, Barathra brassicae cabbage armyworm, Chrysodeixis chalcites green garden looper, Chrysodeixis eriosoma green semi-looper, Earias insulana Egyptian bollworm, Earias vittella northern rough bollworm, Feltia subterranea granulate cutworm, Helicoverpa armigera American bollworm), cotton bollworm, Helicoverpa punctigera climbing cutworm, Heliothis virescens tobacco budworm, Helicoverpa zea corn earworm, Prodenia ornithogalli yellow-striped armyworm, Pseudaletia unipuncta true armyworm, Pseudoplusia includens soybean looper, Sesamia inferens pink (rice) stem borer, Spodoptera eridania southern armyworm, Spodoptera exigua beet armyworm, Spodopterafrugiperda fall armyworm), Spodoptera littoralis cotton leafworm, Spodoptera litura cluster caterpillar, Thermesia gemmatalis velvet bean caterpillar, Trichoplusia nicabbage looper, Phyllocnistidae, Phyllocnistis citrellacitrus leafminer, Pieridae, Colias eurytheme alfalfa caterpillar, Leptophobia aripa green-eyed white, Pieris brassicae cabbage butterfly, large white, Pieris rapae imported cabbage worm, cabbage white, Plutellidae, Plutellaxylostella Diamondback moth), Pyralidae, Chilo suppressalis Asiatic rice stemborer, Cnaphalocerus medinalis rice leaffolder, Crocidolomia binotalis cabbage caterpillar, Desmia funeralis grape leaffolder, Diaphania indica cotton caterpillar, Diaphania nitidaltis melonworm, Hellula hydralis cabbage center grub, Hellula undalis cabbage webworm, Lerodea eufala rice leaffolder, Leucinodes orbonalis brinjal fruit borer, Maruca testulalis bean pod borerborer), Neoleucinodes elegantalis small tomato borer, Nymphula depunctalis rice caseworm, Ostrinia furnicalis Asian corn borer, Ostrinia nubilalis European corn borer, Sphingidae, Manduca sexta tomato hornworm, tobacco hornworm, Smerinthus spp. sphinx moths, Tortricidae, Adoxophyes orana summer fruit tortrix, Argyrotaenia pulchellana grape tortrix, Argyrotaenia velutinana red-banded leafroller, Choristoneura rosaceana oblique-banded leaf roller), Eupoecilia ambiguella grape berry moth, Cydia pomonella codling moth, Cydia prunivora lesser apple worm, Grapholita molesta oriental fruit moth, Lobesia botrana grape vine moth, Pandemis heparana apple brown tortrix, Pandemis limitata three-lined leaf roller, Paramyelois transitella navel orangeworm, Platynota idaeusalis tufted apple bud moth, Platynota stultana omnivorusleafroller), Thysanoptera, Thripidae, Enneothrips flavens, Frankliniellafusca tobacco thrips, Frankliniella intonsa European flower thrips, Frankliniella occidentalis western flower thrips, Frankliniella schultzei common blossom thrips, Frankliniellatritici eastern flower thrips, Megalurothrips sjostedti cowpea thrips, Megalurothrips usitatus bean blossom thrips, Scirthothrips citri citrus thrips, Scirthothrips dorsalis yellowtea thrips, chilli thrips thrips), Sericothrips variabilis soybean thrips, Stenchaetothrips biformis oriental rice thrips, Thrips arizonensis cotton thrips, Thrips meridionalis peach thrips, Thrips palmi melon thrips and Thrips tabaci onion thrips, common cotton thrips.

[0101] In some aspects of the present disclosure, the compositions of the present disclosure are effective against: potato beetles, rice mudworms, cabbage flea beetles, yellow striped flea beetles, flea beetles of the genus Leptinotarsa, pepper weevils, plum cone weevils, rice water weevils, rapeseed beetles, cherry blossom beetles, onion leafminers, pea plantminers, American leafminers, clover leafminers, onion field flies, gray field flies, carrot stem flies, citrus fruit flies, olive fruit flies, Mediterranean fruit flies, European Cherry fruit fly, apple fruit fly, sweet potato whitefly, cotton whitefly, greenhouse whitefly, greenhouse whitefly, pea aphid, cowpea aphid, broad bean aphid, cotton aphid, melon aphid, green apple aphid, spiraea aphid, foxglove aphid, cabbage aphid, rose apple aphid, mustard aphid, radish aphid, potato aphid, green peach aphid, peach aphid, cereal aphid, wheat aphid, English wheat aphid, brown citrus aphid, oriental citrus aphid, false-eyed green leafhopper, flat leafhopper, and species of cicadas. Fruit brown leafhopper, rice brown planthopper, citrus red scale, American brown stink bug, Asian citrus psyllid, potato psyllid, tomato psyllid, yellow stem borer, peach moth, tomato leafminer, white coffee leafminer, cotton leafworm, American bollworm, cotton bollworm, leafworm, tobacco budworm, corn earworm, soybean armyworm, pink rice stem borer, southern armyworm, beet armyworm, fall armyworm, cotton armyworm, armyworm, dry moth, cabbage Silver-striped armyworm, citrus leafminer, cabbage white butterfly, large-striped cabbage butterfly, cabbage white cabbage butterfly, diamondback moth, striped stem borer, rice leaf roller, eggplant yellow moth, Asian corn borer, European corn borer, rose oblique-striped leaf roller, grape leaf roller, apple leaf roller, pear leaf roller, grape flower roller, tobacco thrips, European flower thrips, western flower thrips, orange thrips, tea yellow thrips, pepper thrips, melon thrips and onion thrips, common cotton black thrips.

[0102] In some aspects of the present disclosure, the compositions of the present disclosure are effective against: plum cone weevil, pea plant miner, American leafminer, clover leafminer, sweet potato whitefly, cotton whitefly, greenhouse whitefly, greenhouse whitefly, pea aphid, cowpea aphid, cotton aphid, melon aphid, cabbage aphid, rose apple aphid, green peach aphid, peach aphid, Asian citrus psyllid, potato sharp-winged psyllid, tomato psyllid, yellow stem borer, peach moth, tomato leafminer, white coffee leafminer, cotton leaf ripple moth , American bollworm, cotton bollworm, leaf borer, tobacco budworm, corn earworm, soybean armyworm, pink rice stem borer, southern gray armyworm, beet armyworm, fall armyworm, cotton armyworm, Spodoptera litura, citrus leaf miner, diamondback moth, striped suppressalis, rice leaf roller, rose leaf roller, grape leaf roller, apple leaf roller, pear leaf roller, grape flower roller, tobacco thrips, western flower thrips, tea yellow thrips, pepper thrips, melon thrips and onion thrips, common cotton black thrips.

[0103] In terms of the chlorantraniliprole of the present disclosure, the composition of the present disclosure is effective against the following: Coleoptera (Chrysomelidae, Potato Beetle, Curculionidae, Rice Water Weevil, Listronotus maculicollisannual bluegrass weevil, Rice Water Weevil, Sphenophorus spp. Billbug, Scarabaeidae Ataenius spretulus black turfgrass ataenius, Aphodius spp. scarab beetles, Cotinis nitida green June beetle, Cyclocephala spp. masked chafers, Exomala orientalis oriental beetle grub, Maladera castanea Asiatic garden beetle beetle, Phyllophaga spp. June beetles, Japanese beetles, and Rhizotrogus majalis European chafer); Diptera (Myrmecophaga, Liriomyza spp. Leafminers); Hemiptera (Aleyrodidae, Bemisia spp.Whitefly, Trialeurodes abutiloneus banded-winged whitefly, Cicadellidae, and Typhlocyba pomaria white apple leafhopper); Isoptera (Rhinotermitidae, Heterotermes tenuis sugarcane termite, Termitidae, Microtermes obesi sugarcane termite, and Odontotermes obesus sugarcane termite); and Lepidoptera (Arctiidae, Estigmene acreasaltmarsh caterpillar, Ornithorhynchidae, Achyra rantalis garden moth). webworm), grape leaf roller, European corn borer, Phthorimaea operculella, peach moth, tomato stem moth, potato moth, Tuta absoluta S. American tomato pinworm, Geometridae, Operophthera brumata winter moth, Gracilaridae, Phyllocnistis citrella citrus leafminer, Lithocolletis ringoniella apple leafminer, Phyllonorycterblancardella spotted tentiform leafminer, Lyonetidae, Leucoptera spp.coffee leafminer (i.e., malifoliella, coffee leafminer), pear leaf blister moth, Noctuidae, Agrotis ipsilon black cutworm, Alabama argillacea cotton leafworm, Amphipyra pyramidoides humped green fruitworm, Anticarsia gemmatalis velvetbean caterpillar, Autographa gammacommon silver Y moth, Barathra brassicae cabbage armyworm, Earias spp. (i.e., huegeliana, insulana, vitella) rough, spiny, northern rough bollworm, Helicoverpa species spp. (i.e., armigera, punctigera, zea) bollworms / budworms / fruitworms), tobacco budworm, Lithophane antennata green fruitworm, Mamestra brassicae cabbage moth, Orthosia hibisci green fruitworm, Phalaenoides glycinae grapevine moth, Phytometra acuta tomato semi-looper, soybean armyworm, Spodoptera spp. (i.e., exigua, frugiperda, littoralis) beet armyworm, fall armyworm, Egyptian cotton leafworm, Trichoplusia ni cabbage looper, Pieridae, Pieris spp.(i.e., Brassica rapae) large white), imported cabbageworm, Plutellidae, diamondback moth, Pyralidae, Amyelois transitella navelorangeworm, Chilo spp. (i.e., infuscatellus, polychrysus, suppressalis) sugarcane / rice stem borers, Cnaphalocrocis medinalis rice leafroller, Crambus spp. sod webworm, Crocidolomiabinotalis cabbage cluster caterpillar, Diaphania species melon borer spp. (i.e., hyalinata, nitidalis) melonworm), pickleworm, Diatraea saccharalis, Brazilian sugarcane borer, Elasmopalpus lignosellus lesser stalk borer, Evergestis rimosalis cross-stripped cabbageworm, Hedylepta indicata soybean leaffolder, Hellula spp. (i.e., hydralis, undalis) cabbage centre-grub, cabbage webworm, Leucinodes orbonalis eggplant shoot and fruit borer, Maruca spp. pod borer borer), Neoleucinodes elegantalis tomato small borer, Scirpophaga spp. sugarcane / rice stem borer, Sesamia spp.(i.e., Inferens, Nonagrioides) pink stem borer / corn stalk borer), Sphingidae, Manduca spp. (i.e., Quinquemaculata, Sexta) tomato / tobaccohornworm), Tortricidae, Adoxophyes orana summer fruittortrix, Argyrotaenia spp. (i.e., Pulchellana, Velutinana) grape tortrix, redbanded leafroller, Bonagotacranaodes Brazilian apple leafroller, Carposina spp. (i.e., Niponensis, Sasak) peach fruit borer, peach fruit moth, Choristoneura rosaceana obliquebanded leafroller leafroller), Cryptophlebia leucotreta false codling moth, Cydia pomonella codling moth, Ecdytolopha aurantiana citrus borer, Endopiza vitana grape berry moth, Epiphyas postvittana lightbrown apple moth, Eupoecilia ambiguella European grape berry moth, Grapholita molesta oriental fruit moth, Lobesia botrana European grapevine moth, Pandemis spp.(i.e., cerasana, heparana, barred fruit tree tortrix, limitata, pyrusana) apple brown tortrix), three-lined leafroller, (apple pandemic), Platynota spp. (i.e., idaeusalis, stultana) tufted apple bud moth, omnivorous leafroller, Zygaenidae, and Harrisina spp. (i.e., americana, brillian) grapeleaf / western grapeleaf skeletonizer.

[0104] In some chlorantraniliprole aspects of the present disclosure, the compositions of the present disclosure are effective against: potato beetles, leafminers of the genus Liriomyza, whiteflies of the genus Aleurodes, banded-winged whiteflies, sugarcane termites, sugarcane termites of the genus Orbes, and sugarcane termites of the genus Termitomyces, European corn borer, peach moth, potato moth, American tomato pinworm, citrus leafminer, spotted veil moth, coffee leafminer of the genus Argenteus (i.e., spiralling leafminer, coffee leafminer), black cutworm, cotton leafworm, velvet beanworm, stem borer / moth / fruitworm of the genus Helicoverpa (i.e., armigera, punctigera, zea), tobacco budworm, soybean armyworm, beet armyworm of the genus Spodoptera (i.e., exigua, frugiperda, littoralis), fall armyworm, Egyptian cotton leafworm , cabbage armyworm, Pieris species (i.e. Brassica rapae), imported cabbage worm, diamondback moth, navel orange borer, Cnaphalocrocis species (i.e. Infuscatellus, Polychrysus, Suppressalis), rice leaf roller, small sugarcane stalk borer, Brazilian sugarcane borer, eggplant yellow spot borer, Striped stem borer species (i.e. Inferens, Nonagrioides), peach fruit borer species (i.e. Niponensis, Sasaki), peach fruit moth, rose leaf roller, apple leaf roller, European grape leaf roller, pear fruit borer and European grape vine moth.

[0105] In some chlorantraniliprole aspects of the present disclosure, the compositions of the present disclosure are effective against: Liriomyza spp. leafminer, Bemisia spp. Whitefly, Trialeurodesabutiloneus bandedwinged whitefly, Heterotermes tenuis sugarcanetermite, Microtermes obesi sugarcane termite, and Odontotermes obesus sugarcane termite, European corn borer, Peach moth, Tuta absoluta S. American tomato pinworm, Velvet bean moth, Helicoverpa spp. stem borer / moth / fruit worm spp. (i.e., armigera, punctigera, zea) bollworms / budworms / fruitworms), tobacco budworm, soybean armyworm, Spodoptera spp. (i.e., exigua, frugiperda, littoralis) beet armyworm, fall armyworm, Egyptian cotton leafworm, diamondback moth, Amyelois transitella navel orangeworm, Chilo spp. (i.e., infuscatellus, polychrysus, suppressalis) sugarcane / ricestem borers, Cnaphalocrocis medinalis rice leafroller, Diatraea saccharalis, Brazilian sugarcane borer The main pests are the sugarcane / rice stem borer, Scirpophaga spp. sugarcane / rice stem borer, Cydia pomonella codling moth, Grapholita molesta oriental fruit moth, and Lobesia botrana European grapevine moth.

[0106] plant

[0107] The compositions of the present invention can be used to protect agronomic field crops, other non-agronomic horticultural crops and plants from the infringement of herbivorous invertebrate pests. This utility includes protecting crops and other plants (i.e., agronomic and non-agronomic) containing genetic material introduced by genetic engineering (i.e., transgenic) or modified by mutagenesis to provide favorable traits. Examples of such traits include tolerance to herbicides, resistance to herbivorous pests (e.g., insects, mites, aphids, spiders, nematodes, snails, plant pathogenic fungi, bacteria and viruses), improved plant growth, increased tolerance to adverse growth conditions (such as high or low temperatures, low soil moisture or high soil moisture, and high salinity), increased flowering or fruiting, larger harvest yields, faster maturation, higher quality and / or nutritional value of harvested products, or improved storage or processing characteristics of harvested products. Transgenic plants can be modified to express a variety of traits. Examples of plants containing traits provided by genetic engineering or mutagenesis include corn, cotton, soybean and potato varieties expressing Bacillus thuringiensis insecticide toxins, such as YIELD and INVICTA RR2 PRO TM , and herbicide-tolerant corn, cotton, soybean, and rapeseed varieties, such as ROUNDUP and The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention. The present invention also provides a method for the treatment of herbicides comprising the use of the present invention and the invention.

[0108] Plants within the scope of the present disclosure include crops, vegetables, fruits, trees other than fruit trees, lawns and other uses (flowers, biofuel plants and ornamental foliage). Crops include: corn, rice, wheat, barley, rye, oats, sorghum, cotton, soybeans, peanuts, buckwheat, sugar beets, rapeseed, sunflower, sugar cane, tobacco and other crops known in the art. Vegetables include: Solanaceae vegetables (e.g., eggplant, tomato, bell pepper, pepper, and potato); Cucurbitaceae vegetables (e.g., cucumber, pumpkin, zucchini, watermelon, and cantaloupe); Cruciferous vegetables (e.g., Japanese radish, white radish, horseradish, Brussels sprouts, Chinese cabbage, kale, mustard greens, broccoli, and cauliflower); Asteraceae vegetables (e.g., burdock, garland chrysanthemum, artichoke, and lettuce); Liliaceae vegetables (e.g., green onion, onion, garlic, and asparagus); Apiaceae vegetables (e.g., carrot, parsley, celery, and parsnip); Chenopodiaceae vegetables (e.g., spinach and Swiss chard); and Mint family vegetables (e.g., perilla, mint, and basil). Fruits include: pome fruits (e.g., apples, pears, Japanese pears, tangerines, and quinces); hard-fleshed fruits (e.g., peaches, plums, nectarines, apricots, prunes, citrus fruits (e.g., mandarins, oranges, lemons, limes, and grapefruits); nuts (e.g., chestnuts, walnuts, hazelnuts, almonds, pistachios, cashews, and macadamia nuts); berry fruits (e.g., blueberries, cranberries, blackberries, strawberries, and raspberries); grapes; kaki; persimmon; olives; Japanese plum; bananas; coffee; date palms; coconuts; and oil palms. Trees other than fruit trees include: tea trees; mulberry trees; and other trees (e.g., ash, birch, dogwood, eucalyptus, ginkgo, lilac, maple, oak, poplar, Judas tree, sweetgum, plane tree, beech, Japanese arborvitae, fir, hemlock, juniper, pine, spruce, northeastern yew, elm, and Japanese horse chestnut, coral tree, podocarp, Japanese cedar, Japanese cypress, croton, Japanese sea euonymus, and photinia). Lawn uses include: turf (e.g., zoysia, abaca); bermudagrass; chaff; fescue; and ryegrass. Flowers used for production include roses, carnations, chrysanthemums, lisianthus, baby's breath, gerberas, marigolds, sage, petunias, verbena, tulips, asters, gentians, lilies, pansies, cyclamen, orchids, lily of the valley, lavender, violets, ornamental cabbages, primroses, poinsettias, gladioli, cattleyas, daisies, orchids, and begonias. Plants used for biofuel production include jatropha, safflower, camelina, switchgrass, phalaenopsis, phalaenopsis, arundo donax, kenaf, cassava, and willow.

[0109] Non-agricultural uses

[0110] Non-agricultural uses refer to invertebrate pest control in areas outside of crop plant fields. Non-agricultural uses of the compositions of the present invention include invertebrate pest control in stored grains, legumes and other foods, and textiles such as clothing and carpets. Non-agricultural uses of the compositions of the present invention also include invertebrate pest control in ornamental plants, forests, gardens, roadsides and railroad lands, and turf such as lawns, golf courses, and pastures. Non-agricultural uses of the compositions of the present invention also include invertebrate pest control in houses and other buildings that may be occupied by humans and / or companion animals, farm animals, ranch animals, zoo animals, or other animals. Non-agricultural uses of the compositions of the present invention also include the control of pests such as termites that may damage wood or other structural materials used in buildings.

[0111] The non-agricultural uses of the composition of the present invention also include protecting human and animal health by preventing and treating invertebrate pests that parasitize or spread infectious diseases. The prevention and treatment of animal parasites includes preventing and treating external parasites that parasitize on the surface of the host animal (e.g., shoulders, armpits, abdomen, inner thighs) and internal parasites that parasitize in the host animal body (e.g., stomach, intestines, lungs, veins, subcutaneous, lymphatic tissue). External parasitic or disease-propagating pests include, for example, chiggers, ticks, lice, mosquitoes, flies, mites and fleas. Internal parasites include heartworms, hookworms and worms. The composition of the present disclosure is applicable to systemic and / or non-systemic prevention and treatment of parasites on animals or infections. The composition of the present disclosure is particularly suitable for resisting external parasitic or disease-propagating pests. The compositions of the present disclosure are suitable for combating parasites that infest the following animals: agricultural working animals, such as cattle, sheep, goats, horses, pigs, donkeys, camels, buffaloes, rabbits, hens, turkeys, ducks, geese, and bees; pet animals and domestic animals, such as dogs, cats, pet birds, and aquarium fish; and so-called laboratory animals, such as hamsters, guinea pigs, rats, and mice. By combating these parasites, mortality and performance decline (in terms of meat, milk, wool, fur, eggs, honey, etc.) are reduced, and thus the administration of the compositions of the present disclosure allows for more economical and simple animal husbandry.

[0112] Can handle all plants or any part of plant according to this disclosure.As used herein, term " plant " should be construed as all plants and plant population, for example as desired and undesirable wild plants or crop plants (comprising naturally occurring crop plants).Crop plants can be the plants that the combination of conventional breeding and optimization method or biotechnology and genetic engineering method or these methods obtains, comprises transgenic plants and comprises can or can not be subject to the plant cultivars protected by plant breeders' rights.Plant parts should be construed as meaning all parts and the organ of plant on and below the ground, as plumule, leaf, flower and root, and the example that can mention is leaf, needle, stalk, stem, flower, fruiting body, fruit and seed and root, stem tuber and rhizome.Plant parts also comprise harvesting material and vegetative and reproductive propagation material, for example cutting, stem tuber, rhizome, branch and seed.

[0113] The treatment of plants and plant parts with the compositions according to the present disclosure is carried out by direct contact with the plants or plant parts or by acting on the environment, habitat or storage space of the plants using customary treatment methods. For example, the treatment as described herein can be carried out by dipping, spraying, evaporating, atomizing, broadcasting, spreading, injection, and in the case of propagation material, in particular in the case of seeds, applying a coating comprising the composition (optionally with further layers).

[0114] In one embodiment, the compositions of the present disclosure are delivered to the plant aerially. In another embodiment, the compositions of the present disclosure are delivered via an unmanned aerial vehicle (UAV).

[0115] Can process wild plant species and plant cultivars or those that obtain by conventional biological breeding methods such as hybridization or protoplast fusion, and parts thereof.In addition, processed by genetic engineering methods, if appropriate with conventional methods (genetically modified organisms) combination of transgenic plants and plant cultivars, and parts thereof.Process the plant of plant cultivars that are commercially available or in use in each case according to this disclosure.Plant cultivars should be understood to mean plants with novel characteristics (" traits"), which plants have been obtained by conventional breeding, mutagenesis or recombinant DNA technology.These can be cultivars, biotypes or genotypes.

[0116] Transgenic plants and plant cultivars (obtained by genetic engineering) that can be treated according to the present disclosure include all plants that have been genetically modified to obtain genetic material that confers particularly advantageous, useful traits on these plants. Examples of such traits are better plant growth, increased tolerance to high or low temperatures, increased tolerance to drought or water or soil salinity, increased flowering performance, easier harvesting, faster maturation, higher harvest yields, higher quality and / or higher nutritional value of harvested products, better storage stability and / or better processability of harvested products. Further and particularly emphasized examples of such traits are better resistance of plants to animal and microbial pests (such as insects, mites, phytopathogenic fungi, bacteria and / or viruses), and increased tolerance of plants to certain herbicidally active compounds. Examples of transgenic plants include important crop plants such as cereals (wheat, rice), maize, soybeans, potatoes, sugar beets, tomatoes, peas and other vegetable varieties, cotton, tobacco, rapeseed and fruit plants (fruits such as apples, pears, citrus fruits and grapes), with an emphasis on maize, soybeans, potatoes, cotton, tobacco and rapeseed. Traits include increased resistance of plants to insects, spiders, nematodes and slugs and snails by toxins formed in the plant, in particular those formed in the plant by genetic material from Bacillus thuringiensis (e.g., genes CryIA(a), CryIA(b), CryIA(c), CryIIA, CryIIIA, CryIIIB2, Cry9c, Cry2Ab, Cry3Bb, CryIF, Vip3A, and combinations thereof) ("Bt plants"). Other traits are increased resistance of plants to fungi, bacteria and viruses through systemic acquired tolerance (SAR), systemins, phytoalexins, elicitors and resistance genes and correspondingly expressed proteins and toxins. Traits also include increased tolerance of plants to certain herbicidally active compounds such as imidazolinones, sulfonylureas, glyphosate or phosphinotricin (e.g. "PAT" genes). Genes conferring the desired traits in question can also be present in transgenic plants in combination with one another. Examples of "Bt plants" include those marketed under the trade name YIELD (e.g. corn, cotton, soybeans), (e.g. corn), (e.g. corn), (cotton), (cotton) and (potato) maize varieties, cotton varieties, soybean varieties and potato varieties sold under the trade name Roundup (glyphosate-tolerant, e.g., maize, cotton, soybeans), Liberty (tolerant to glufosinate, such as rapeseed), (resistant to imidazolinone) and Herbicide-tolerant plants (plants bred in a conventional manner for tolerance to herbicides) include those sold under the names The agricultural crop is selected from the group consisting of cereals, fruit trees, citrus fruits, pulses, horticultural crops, melons, oil plants, tobacco, coffee, tea, cocoa, sugar beets, sugar cane and cotton.

[0117] Depending on the plant species or plant cultivars, their location and growth conditions (soil, climate, growing season, diet), the treatment according to the present disclosure may also result in superadditive ("synergistic") effects. Thus, for example, it is possible to reduce the application rate and / or broaden the activity spectrum and / or increase the activity of the substances and compositions that can be used according to the present disclosure, better plant growth, increased tolerance to high or low temperatures, increased tolerance to drought or water or soil salinity, increased flowering performance, easier harvesting, faster maturation, higher harvest yields, higher quality and / or higher nutritional value of the harvested products, better storage stability and / or better processability of the harvested products, which go beyond the actual expected effects.

[0118] Crops that can be protected with the compositions of the present disclosure include, for example, cereals (wheat, barley, rye, oats, rice, maize, sorghum, etc.), fruit trees (apples, pears, plums, peaches, almonds, cherries, bananas, grapes, strawberries, raspberries, blackberries, etc.), citrus trees (oranges, lemons, tangerines, grapefruits, etc.), legumes (beans, peas, lentils, soybeans, etc.), vegetables (spinach, lettuce, asparagus, cabbage, carrots, onions, tomatoes, potatoes, eggplants, peppers, etc.), melons (pumpkin, zucchini, cucumbers, melons, watermelons, etc.), oil plants (sunflower, rapeseed, peanuts, castor, coconut, etc.), tobacco, coffee, tea, cocoa, sugar beets, sugar cane, and cotton.

[0119] For the protection of agricultural crops, the compositions of the disclosure may be applied to any part of the plant, or to the seed before sowing, or to the soil in which the plant is growing.

[0120] about Figure 2The scouting data source 208 provides scouting data to the PPP computing device 112 for use in generating pest pressure forecasts. The scouting data may include any data provided by a human scout monitoring one or more geographic locations. For example, the scouting data may include crop conditions, pest counts (e.g., manually counted by a human scout at a pest trap), and the like. In some embodiments, the scouting data source 208 is one of the client systems 114. That is, a scout may both provide scouting data to the PPP computing device 112 and view pest pressure forecast data and / or heat map data using the same computing device (e.g., a mobile computing device).

[0121] The grower data source 210 provides grower data to the PPP computing device 112 for use in generating pest pressure forecasts. The grower data may include, for example, field boundary data, crop condition data, and the like. Further, similar to the scouting data source 208, in some embodiments, the grower data source 210 is one of the client systems 115. That is, a grower can both provide scouting data to the PPP computing device 112 and view pest pressure forecast data and / or heat map data using the same computing device (e.g., a mobile computing device).

[0122] Other data sources 212 may provide other types of data not available from data sources 202-210 to PPP computing device 112. For example, in some embodiments, other data sources 212 include a map database that provides map data (e.g., topographic maps of one or more geographic locations) to PPP computing device 112.

[0123] In an example embodiment, the PPP computing device 112 receives data from at least one data source 202-212 and aggregates and analyzes the data (e.g., using machine learning) to generate pest pressure prediction data, as described herein. Furthermore, the PPP computing device 112 may also aggregate and analyze the data to generate heat map data, as described herein. The pest pressure prediction data and / or heat map data may be transmitted to the client system 114 (e.g., for display to a user of the client system 114).

[0124] In some embodiments, data from at least one data source 202-210 is automatically pushed to the PPP computing device 112 (e.g., without the PPP computing device 112 polling or querying the data source 202-210). Further, in some embodiments, the PPP computing device 112 polls or queries (e.g., periodically or continuously) at least one data source 202-210 to retrieve the associated data.

[0125] Figure 3 PPP computing device 112 ( Figure 1 and Figure 2 ). The server system 301 may also include, but is not limited to, the database server 116. In an example embodiment, the server system 301 generates pest pressure prediction data and heat map data as described herein.

[0126] Server system 301 includes a processor 305 for executing instructions. For example, the instructions may be stored in memory area 310. Processor 305 may include one or more processing units (e.g., in a multi-core configuration) for executing instructions. The instructions may be executed in a variety of different operating systems on server system 301, such as UNIX, LINUX, Microsoft It should also be understood that various instructions may be executed during initialization when starting the computer-based method. Some operations may be required to perform one or more of the processes described herein, while other operations may be more general and / or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.).

[0127] The processor 305 is operatively coupled to the communication interface 315 so that the server system 301 can communicate with a remote device such as a user system or another server system 301. For example, the communication interface 315 can receive a request from the client system 114 via the Internet, such as Figure 2 shown.

[0128] The processor 305 may also be operably coupled to a storage device 134. The storage device 134 is any computer-operated hardware suitable for storing and / or retrieving data. In some embodiments, the storage device 134 is integrated into the server system 301. For example, the server system 301 may include one or more hard disk drives as the storage device 134. In other embodiments, the storage device 134 is external to the server system 301 and may be accessed by multiple server systems 301. For example, the storage device 134 may include multiple storage units, such as hard disks or solid-state disks in a redundant array of inexpensive disks (RAID) configuration. The storage device 134 may include a storage area network (SAN) and / or a network-attached storage (NAS) system.

[0129] In some embodiments, the processor 305 is operatively coupled to the storage device 134 via a storage interface 320. The storage interface 320 is any component capable of providing the processor 305 with access to the storage device 134. The storage interface 320 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides the processor 305 with access to the storage device 134.

[0130] Memory area 310 may include, but is not limited to, random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are merely examples and therefore do not limit the types of memory that can be used to store computer programs.

[0131] Figure 4 An example configuration of a client computing device 402 is shown. Client computing device 402 may include, but is not limited to, client system ("client computing device") 114. Client computing device 402 includes a processor 404 for executing instructions. In some embodiments, executable instructions are stored in a memory area 406. Processor 404 may include one or more processing units (e.g., in a multi-core configuration). Memory area 406 is any device that allows information, such as executable instructions and / or other data, to be stored and retrieved. Memory area 406 may include one or more computer-readable media.

[0132] The client computing device 402 also includes at least one media output component 408 for presenting information to the user 400. The media output component 408 is any component capable of conveying information to the user 400. In some embodiments, the media output component 408 includes an output adapter, such as a video adapter and / or an audio adapter. The output adapter is operatively coupled to the processor 404 and is operatively coupled to an output device, such as a display device (e.g., a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a cathode ray tube (CRT), or an "electronic ink" display) or an audio output device (e.g., a speaker or headphones).

[0133] In some embodiments, the client computing device 402 includes an input device 410 for receiving input from the user 400. The input device 410 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad or touch screen), a camera, a gyroscope, an accelerometer, a position detector, and / or an audio input device. A single component (such as a touch screen) may serve as both an output device for the media output component 408 and an input device 410.

[0134] The client computing device 402 may also include a communication interface 412 that is communicatively coupled to a remote device, such as the server system 301 or a network server. The communication interface 412 may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile Communications (GSM), 3G, 4G, 5G, or Bluetooth) or other mobile data network (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).

[0135] Stored in memory area 406 are computer-readable instructions for, for example, providing a user interface to user 400 via media output component 408 and optionally receiving and processing input from input device 410. The user interface may include, among other possibilities, a web browser and a client application. The web browser enables user 400 to display and interact with media and other information typically embedded in a web page or website from a web server. The client application allows user 400 to interact with the server application. The user interface facilitates displaying pest pressure information provided by PPP computing device 112 via one or both of the web browser and the client application. The client application is capable of operating in both an online mode (wherein the client application communicates with PPP computing device 112) and an offline mode (wherein the client application does not communicate with PPP computing device 112).

[0136] Figure 5 is a flow chart of an example method 500 for generating pest pressure data. The method 500 may be implemented, for example, using the PPP computing device 112.

[0137] Method 500 includes receiving 502 trap data for a plurality of pest traps in a geographic location. In an example embodiment, the trap data includes current pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population at each of the plurality of traps, as well as historical pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. The data may be obtained, for example, from trap data source 206 ( Figure 2) receives 502 trap data. Further, the PPP computing device 112 can analyze the received 502 trap data to generate additional data. For example, based on the received 502 trap data, the PPP computing device 112 can determine the number of traps for each level for multiple different pest pressure levels (e.g., defined by suitable upper and lower thresholds) for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population. Further, the PPP computing device 112 can determine an average pest pressure across multiple traps and / or across at least a portion of a geographic location. As described herein, this additional data can be used to identify correlations and predict future pest pressure.

[0138] Method 500 further includes receiving 504 weather data 502 for a geographic location. In an example embodiment, the weather data includes both current weather conditions and historical weather conditions for the geographic location. Further, in some embodiments, the weather data may include predicted future weather conditions for the geographic location. The weather data may be obtained from, for example, weather data source 202 ( Figure 2 Receive 504 weather data.

[0139] In an example embodiment, the method 500 further includes receiving 506 image data of the geographic location. The image data may include, for example, satellite and / or drone image data. The image data may be obtained from, for example, the imaging data source 204 ( Figure 2 As shown) receives 506 image data.

[0140] Further, method 500 includes identifying 508 at least one geospatial feature within or near the geographic location.

[0141] As used herein, 'geospatial features' refer to geographic features or structures that may influence pest pressure. For example, geographic features may include bodies of water (e.g., rivers, streams, lakes, etc.), elevation features (e.g., mountains, hills, canyons, etc.), transportation routes (e.g., roads, railroad tracks, etc.), and the location of farms or factories (e.g., cotton factories).

[0142] In one embodiment, at least one geospatial feature is identified 508 from existing map data. For example, the PPP computing device 112 may identify at least one geospatial feature from a map data source (e.g., other data sources 212 ( Figure 2 As shown)) retrieves previously generated maps (e.g., terrain maps, elevation maps, road maps, surveys, etc.) that delineate boundaries of one or more geospatial features.

[0143] In another embodiment, the PPP computing device 112 identifies 508 one or more geospatial features by analyzing the received 506 image data. For example, the PPP computing device 112 may apply raster processing to the image data to generate a digital elevation map, wherein each pixel (or other similar subdivision) of the digital elevation map is associated with an elevation value. Based on the elevation values, the PPP computing device 112 then identifies 508 one or more geospatial features from the digital elevation map. For example, such techniques may be used to identify elevation features and / or bodies of water.

[0144] The method 500 further includes applying 510 a machine learning algorithm to the trap data, the weather data, the imagery data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature. Applying 510 the machine learning algorithm to the trap data, the weather data, the imagery data, and the at least one identified geospatial feature can be viewed as applying 510 a machine learning-based approach to the trap data, the weather data, the imagery data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature. In one or more example embodiments, applying 510 the machine learning algorithm to the trap data, the weather data, the imagery data, and the at least one identified geospatial feature can include determining a pest pressure value associated with the pest trap based on a relationship (e.g., a correlation) between the pest pressure and the at least one geospatial feature.

[0145] In some embodiments, the PPP computing device 112 may determine that pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population (e.g., at a location of a pest trap) varies based on distance from at least one identified geospatial feature by applying 510 a machine learning algorithm. For example, the PPP computing device 112 may determine that pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population is higher at locations near a body of water (e.g., due to increased pest levels at the body of water). In another example, the PPP computing device 112 may determine that pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population is higher at locations near a transportation route (e.g., due to increased pest levels caused by materials transported along the transportation route). In yet another example, the PPP computing device 112 may determine that pest pressure is higher for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at a location near a factory (e.g., due to increased pest levels caused by materials processed at the factory). In yet other examples, the PPP computing device 112 may determine that pest pressure is reduced for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at a location near at least one identified geospatial feature. The at least one identified geospatial feature may be an area that has been treated with a pest control product.

[0146] Those skilled in the art will appreciate that applying 510 a machine learning algorithm can identify other correlations between pest pressure in a homozygous pesticide-susceptible population, a homozygous pesticide-resistant population, and a heterozygous pesticide-resistant population at at least one geospatial feature. Specifically, the machine learning algorithm combines trap data, weather data, imagery data, and at least one identified geospatial feature and can detect complex interactions between these different types of data that may not be determined by a human analyst. For example, in some embodiments, non-distance-based correlations between at least one identified geospatial feature and pest pressure can be identified.

[0147] For example, in one or more example embodiments, applying 510 a machine learning algorithm to trap data, weather data, image data, and at least one identified geospatial feature may include determining pest pressure values associated with pest traps for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population based on a model (e.g., a machine learning model, a pest life cycle model) that characterizes a relationship (e.g., a correlation) between pest pressure and trap data (optionally, where the trap data includes insect data and / or insect developmental stage data). Further, in one or more example embodiments, applying 510 a machine learning algorithm to the trap data, weather data, image data, and at least one identified geospatial feature may include determining pest pressure values associated with the pest trap for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population based on a model (e.g., a machine learning model) that characterizes the relationship (e.g., correlation) among pest pressure, the trap data, and the weather data.

[0148] Further, in some embodiments, the pest pressure of a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population for a first pest can be correlated with the pest pressure of a different second pest, and the correlation can be detected using the PPP computing device 112. For example, at least one geospatial feature is a particular field with high pest pressure of a known second pest. Using the systems and methods described herein, the PPP computing device 112 can determine that locations near the particular field typically have high pest pressure of the first pest, which is correlated with the level of pest pressure of the second pest in the particular field. These "inter-pest" correlations can be complex relationships that the PPP computing device 112 can recognize but a human analyst cannot. Similarly, the PPP computing device 112 can identify "inter-crop" correlations between nearby geographic locations that produce different crops.

[0149] Subsequently, method 500 includes generating 512 predicted future pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population for the geographic location based at least on the identified correlations. Specifically, the PPP calculation device 112 uses the identified correlations in combination with one or more models, algorithms, etc. to predict future pest pressure values for the geographic location. For example, the PPP calculation device 112 may utilize a spray timer model, a pest life cycle model, etc., in combination with the identified correlations, trap data, weather data, and image data to generate 512 predicted future pest pressure based on the identified patterns. Those skilled in the art will appreciate that other types of data may also be combined to generate 512 predicted future pest pressure. For example, previously planted crop data, neighboring farm data, field water level data, and / or soil type data may be considered when predicting future pest pressure.

[0150] As an example of a model, the developmental stage of a pest of interest (e.g., an insect or fungus) may be dictated by ambient temperature. Thus, using a "degree-day" model, the developmental stage of the pest can be predicted based on heat accumulation (e.g., determined from temperature data).

[0151] The predicted future pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population generated 512 is an example of pest pressure prediction data that can be transmitted to and displayed on a user computing device, such as client system 114 ( Figure 1 and Figure 2 For example, the predicted future pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population can be transmitted to a user computing device so that the user computing device presents the predicted future pest pressure in text, graphical, and / or audio format, or any other suitable format. As described in detail below, in some embodiments, one or more heat maps showing the predicted future pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population are displayed on the user computing device.

[0152] Based on the predicted future pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population generated 512, in some embodiments, the systems and methods described herein can also be used to generate (e.g., using machine learning) treatment recommendations for a geographic location to address the predicted future pest pressure. For example, with an accurate prediction of the future pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, the PPP calculation device 112 can automatically generate a treatment plan for the geographic location to alleviate the future high pest pressure level. The treatment plan can specify, for example, one or more substances (e.g., pesticides, fertilizers, etc.) and the specific time (e.g., daily, weekly, etc.) when the one or more substances should be applied. Alternatively, the treatment plan can include other data to facilitate improved agricultural production conditions, taking into account the predicted future pest pressure.

[0153] Furthermore, in some embodiments, the predicted future pest pressures for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population generated 512 are used (e.g., by the PPP computing device 112) to control additional systems. In one embodiment, a system for monitoring pest pressure (e.g., a system including pest traps) can be controlled based on the predicted future pest pressure. For example, the reporting frequency and / or type of trap data reported by one or more pest traps can be modified based on the predicted future pest pressure. In another example, spraying equipment (e.g., for spraying pesticides) or other agricultural equipment can be controlled based on the predicted future pest pressure.

[0154] As described above, the PPP computing device 112 may also generate one or more heat maps using the pest pressure prediction data. For the purposes of this discussion, the PPP computing device 112 may be referred to herein as a heat map generating computing device 112.

[0155] Figure 6 6 is a flow chart of an example method 600 for generating a heat map of a homozygous population of pesticide-susceptible type, a homozygous population of pesticide-resistant type, and a heterozygous population of pesticide-resistant type. The method 600 may be generated, for example, using the heat map generating computing device 112 ( Figure 1 shown) to implement.

[0156] Method 600 includes receiving 602 trap data for a plurality of pest traps in a geographic area. In an exemplary embodiment, the trap data includes current pest pressure for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population at each of the plurality of traps, as well as historical pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. The data may be obtained, for example, from trap data source 206 ( Figure 2 As shown) receives 602 trap data.

[0157] Further, method 600 includes receiving 604 weather data for the geographic location. In an example embodiment, the weather data includes both current weather conditions and historical weather conditions for the geographic location. Further, in some embodiments, the weather data may include predicted future weather conditions for the geographic location. The weather data may be obtained from, for example, weather data source 202 ( Figure 2 ) receives 604 weather data.

[0158] In an example embodiment, the method 600 further includes receiving 606 image data of the geographic location. The image data may include, for example, satellite and / or drone image data. The image data may be obtained from, for example, the imaging data source 204 ( Figure 2 As shown) receives 606 image data.

[0159] The method 600 further includes applying 608 a machine learning algorithm to the trap data, weather data, and image data to generate predicted future pest pressure values for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at each of the plurality of pest traps.

[0160] Additionally, method 600 includes generating 610 a first heat map of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, and generating 612 a second heat map of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. In an exemplary embodiment, the first heat map is associated with a first time point and the second heat map is associated with a second, different time point. The first heat map 610 and the second heat map 612 may be generated as follows.

[0161] In an example embodiment, each heat map is generated by drawing a plurality of nodes on a map of a geographic location. Each node corresponds to the location of a particular pest trap among a plurality of pest traps. Further, in an example embodiment, each node is displayed in a color representing the pest pressure value of the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population for the corresponding test trap at the relevant point in time. In one example, each node is displayed in green (indicating a low pest pressure value), yellow (indicating a medium pest pressure value), or red (indicating a high pest pressure value). Figures 7 to 9 In the embodiment of the present invention, green is represented by a diagonal line pattern, yellow is represented by a cross-hatched pattern, and red is represented by a dot pattern. It will be understood by those skilled in the art that other numbers of colors and different colors can be used in the embodiments described herein. Depending on the time point associated with the heat map, the color of the node can indicate the past pest pressure value of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population (if the time point is in the past), the current pest pressure value of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population (if the time point is now), or the predicted future pest pressure value of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population (if the time point is in the future). As described herein, future predicted pest pressure values for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population can be generated, for example, using a machine learning algorithm.

[0162] To complete the heat map of the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, at least some remaining portion of the map, including the colored nodes, is colored. Specifically, the remaining portion of the map is colored to generate a continuous map of pest pressure values for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. In an exemplary embodiment, the remaining portion is colored by interpolating between the pest pressure values at multiple nodes.

[0163] In one embodiment, interpolation is performed using an inverse distance weighted (IDW) algorithm, wherein points on the remainder of the map are colored based on their distance from known pest pressure values for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at the node. For example, in such an embodiment, pest pressure values for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at locations without nodes can be calculated based on a weighted average of the inverse distances near the node. This embodiment is based on the assumption that the pest pressure of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at a particular point will be more affected by nodes that are closer than by nodes that are farther away. In other embodiments, interpolation can be performed based on other criteria in addition to or in lieu of distance from a node.

[0164] By generating pest pressure values for pesticide-sensitive homozygous populations, pesticide-resistant homozygous populations, and pesticide-resistant heterozygous populations for at least some of the remaining portions of the map (using interpolation, as described above), the portions are colored based on the generated pest pressure values for the pesticide-sensitive homozygous populations, pesticide-resistant homozygous populations, and pesticide-resistant heterozygous populations. As with the nodes, in one example, green indicates low pest pressure values for the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, yellow indicates medium pest pressure values for the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, and red indicates high pest pressure values for the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. The pest pressure of the pesticide-sensitive population and the pesticide-resistant population can be colored similarly.

[0165] For example, different color thresholds can be set based on historical pest pressure and can be adjusted over time (automatically or based on user input). Those skilled in the art will understand that these three colors are just examples, and any suitable coloring scheme can be used to generate the heat maps described herein.

[0166] In an example embodiment, the first heat map and the second heat map of the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population are stored in a database, such as database 120 (e.g., Figure 1Thus, in this embodiment, when a user views a heat map on a user device (e.g., a mobile computing device) as described below, the heat map has previously been generated and stored by heat map generation computing device 112. Alternatively, the heat map may be generated and displayed in real time based on a user's request.

[0167] With the first and second heat maps generated 610, 612, in an example embodiment, the method 600 further includes causing a user interface to display 614 the time-lapse heat maps. The user interface may be, for example, a display on the client device 114 ( Figure 1 and Figure 2 For example, the user interface may be implemented via an application installed on the client device 114 (eg, an application provided by the entity operating the thermal computing device 112).

[0168] The time-lapse heat map displays an animation on the user interface. Specifically, in an example embodiment, the time-lapse heat map dynamically transitions between a plurality of previously generated heat maps (e.g., a first heat map and a second heat map) over time, as described below. Thus, by viewing the dynamic heat map, a user can easily view and understand how pest pressure in a geographic area changes over time. The time-lapse heat map can display past, current, and / or future pest pressure values for a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population in the geographic area.

[0169] It should be understood that in example embodiments, the second heat map of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at a second time point is generated using the predicted pest pressure values for the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population, and that the second time point refers to a time point that is later than the most recent current and historical pest pressure values (e.g., included in the trap data) for the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population that are incorporated into the machine learning algorithm. That is, in such embodiments, the second time point refers to a future time point.

[0170] Regarding the first heat map at the first time point, in an exemplary embodiment, this is generated using the pest pressure values of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at a time point earlier than the second time point. Therefore, the pest pressure values used to generate the first heat map of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population are generally current or historical pest pressure values of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. In another embodiment, the first time point is also a future time point, but a time point different from the second time point. Therefore, the pest pressure values used to generate the first heat map of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population are also predicted pest pressure values.

[0171] Within the scope of the present disclosure, it should be understood that references herein to a "first heat map" and a "second heat map" and a "first heat map and a second heat map" may imply the generation of one or more (e.g., multiple) "intermediate heat maps" using pest pressure values (e.g., current, historical, or predicted pest pressure values, as the case may be) for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at various time points between the first time point and the second time point. In this case, the time-lapse heat map displays the dynamic transitions between the first heat map, the one or more intermediate heat maps, and the second heat map over time. In one embodiment, the intermediate heat maps include one or more (e.g., multiple) intermediate heat maps generated using predicted pest pressure values. In another embodiment, the intermediate heat maps include one or more (e.g., multiple) intermediate heat maps generated using current and / or historical pest pressure values. In yet another embodiment, the intermediate heat map includes one or more (e.g., multiple) intermediate heat maps generated using predicted pest pressure values and one or more (e.g., multiple) intermediate heat maps generated using current and / or historical pest pressure values.

[0172] In one embodiment, to display a time-lapse heat map, each previously generated heat map is displayed for a short period of time before instantly transitioning to the next heat map (e.g., in a slideshow format). Alternatively, in some embodiments, the heat map generation computing device 112 performs temporal interpolation between successive heat maps to generate transition data between these heat maps (e.g., using machine learning). In such embodiments, the time-lapse heat map displays a smooth evolution of pest pressure over time, rather than a series of static images.

[0173] Figure 7 It can be displayed on, for example, the client system 114 ( Figure 1 and Figure 2 700 of a first screen shot of a user interface on a computing device such as . For example, the computing device may be a mobile computing device.

[0174] First screenshot 700 includes a pest pressure heat map 702 that displays pest pressure associated with a particular pest and crop in area 704, including field 706. In the example shown in first screenshot 700, the pest is cotton boll weevil and the crop is cotton. Those skilled in the art will appreciate that the heat maps described herein can display pest pressure information for any suitable pest and crop. Furthermore, in some embodiments, the heat map can display pest pressure for multiple pests in the same crop, for a single pest in multiple crops, or for multiple pests in multiple crops.

[0175] like Figure 7 As shown, field 706 is bounded on heat map 702 by field boundary 708. Field boundary 708 may be drawn on heat map 702 by heat map generating computing device 112 based on, for example, information provided by a grower associated with field 706. For example, a grower may obtain data from a grower computing device (such as grower data source 210 ( Figure 2 As shown)) provides information to the heat map generation computing device 112.

[0176] Heat map 702 includes three nodes 710 corresponding to three pest traps in field 706. Figure 7 As shown, each node 710 has an associated color (here, two red nodes and one yellow node). Further, in the heat map 702, the positions excluding the node 710 are colored by interpolating the pest pressure values of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at the node 710, thereby generating a continuous map of the pest pressure values of the pesticide-sensitive homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. Although Figure 7702. Only three nodes 710 are shown in FIG. 702, but one skilled in the art will appreciate that additional pest traps can be used to color portions of the heat map 702. The nodes 710 can be subdivided into a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population, and colored separately for the first and second heat maps 702. In this example, the heat map 702 is a static heat map (e.g., one of the first and second heat maps described above) showing pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population at a specific point in time.

[0177] The first screenshot 700 further includes a time-lapse button 712 that, when selected by a user, causes a time-lapse heat map to be displayed, as described herein.

[0178] Figure 8 It can be displayed on, for example, the client system 114 ( Figure 1 and Figure 2 ). Specifically, the second screenshot 800 shows a zoomed-in view of the heat map 702 of the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population. For example, the zoomed-in view can be generated in response to a user selecting to change the zoom level on the user interface.

[0179] like Figure 8 As shown, additional information not shown in the first screenshot 700 is shown in the zoomed-in view. For example, additional nodes 802 (representing additional traps) are now visible. Further, the associated trap name is displayed with each node 710. In the exemplary embodiment, in the zoomed-in view, the user can select a particular node 710 to cause the user interface to display pest pressure data for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population for that node 710. This will be discussed below in conjunction with Figure 10 Describe in more detail.

[0180] Figure 9 It can be displayed on, for example, the client system 114 ( Figure 1 and Figure 2 ). Specifically, the third screenshot 900 shows a time-lapse heat map 902 of a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population. For example, in response to a user selecting the time-lapse button 712 ( Figure 7 and Figure 8), a time-lapse heat map 902 may be displayed.

[0181] like Figure 9 902. As shown, a timeline 904 is displayed in association with a time-lapse heat map 902 of a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population. The timeline 904 enables a user to quickly determine the time at which pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population is currently displayed. The timeline 904 shows a date range, which in the exemplary embodiment includes historical dates and future dates. Further, the timeline 904 includes a current time marker 906 indicating the present time (i.e., the present time) and a selected time marker 908 indicating a time associated with the pest pressure for the pesticide-susceptible homozygous population, the pesticide-resistant homozygous population, and the pesticide-resistant heterozygous population shown on the time-lapse heat map 902.

[0182] For example, in Figure 9 In Figure 9, timeline 904 extends from January 5 to February 2, which is January 26, and time-lapse heatmap 902 shows pest pressure on January 29. Figure 9 The pest pressure shown in is the predicted future pest pressure because the selected time stamp 908 is later than the current time stamp 906 .

[0183] In one embodiment, the user can adjust the selected time marker 908 (e.g., by selecting and dragging the selected time marker 908) to manipulate the time displayed by the time-lapse heat map 902. Further, in the exemplary embodiment, when the user selects the activation icon 910, the time-lapse heat map 902 is displayed as an animation, automatically transitioning between different static heat maps to illustrate the evolution of pest pressure over time. A stop icon 912 is also shown in the screenshot 900. When the user previously selected the activation icon 910, the user can select the stop icon 912 to stop the animation and freeze the time-lapse heat map 902 at the desired point in time.

[0184] Figure 10 It can be displayed on, for example, the client system 114 ( Figure 1 and Figure 2 1000 of a user interface on a computing device such as a computer (e.g., FIG. 1000 ). Specifically, the fourth screenshot 1000 shows pest pressure data 1002 for a particular trap of a pesticide-susceptible homozygous population, a pesticide-resistant homozygous population, and a pesticide-resistant heterozygous population. For example, the fourth screenshot 1000 may be displayed in response to a user selecting a particular node 710 (as described above with reference to FIG. 1001 ). Figure 8) to display pest pressure data 1002. In one embodiment, pest pressure data 1002 includes graphical data 1004 showing pest pressure over time (e.g., current and historical pest pressure) and textual data 1006 summarizing predicted future pest pressure.

[0185] Furthermore, in some embodiments, the generated heat map facilitates the control of additional systems. In one embodiment, a system for monitoring pest pressure (e.g., a system including pest traps) can be controlled based on the heat map. For example, the reporting frequency and / or type of trap data reported by one or more pest traps can be modified based on the heat map. In another example, spraying equipment (e.g., for spraying pesticides) or other agricultural equipment can be controlled based on the heat map.

[0186] The system addresses at least one technical problem including: i) the inability to accurately monitor pest pressure; ii) the inability to accurately predict future pest pressure; and iii) the inability to communicate pest pressure information to users in a comprehensive and straightforward manner.

[0187] The technical effects provided by the embodiments described herein include at least: i) real-time monitoring of pest pressure; ii) accurate prediction of future pest pressure using machine learning; iii) control of other systems or equipment based on the predicted future pest pressure; iv) generation of a comprehensive heat map illustrating pest pressure; v) generation of a time-lapse heat map that dynamically displays changes in pest pressure over time; and vi) control of other systems or equipment based on the generated heat map.

[0188] Furthermore, the technical effects of the systems and processes described herein are achieved by performing at least one of the following steps: (i) receiving trap data for a plurality of pest traps in a geographic location, the trap data comprising pest genetic data, the trap data comprising a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; (ii) receiving weather data for the geographic location; (iii) receiving image data for the geographic location; (iv) applying a machine learning algorithm to the trap data, the weather data, and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps; and (v) generating a first heat map for a first point in time and a second heat map for a second point in time, the second heat map being generated using the predicted future pest pressure values. The first heat map and the second heat map are each generated by: a) plotting a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node having a color representing a pest pressure value for the corresponding pest trap at an associated point in time; and b) coloring at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the associated points in time to generate a continuous map of pest pressure values for the geographic location; and (vi) transmitting the first heat map and the second heat map to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

[0189] The processor or processing element in the embodiments described herein can use artificial intelligence, and / or can be trained using supervised or unsupervised machine learning, and the machine learning program can use a neural network, which can be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more areas or aspects of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to make predictions about subsequent data. Models can be created based on example inputs so that new inputs can be effectively and reliably predicted.

[0190] Additionally or alternatively, a machine learning program can be trained by inputting sample data sets or certain data (such as image data, text data, report data and / or numerical analysis) into the program. The machine learning program can utilize deep learning algorithms that may focus primarily on pattern recognition and can be trained after processing multiple examples. The machine learning program can include Bayesian program learning (BPL), speech recognition and synthesis, image or object recognition, optical character recognition and / or natural language processing - alone or in combination. The machine learning program can also include natural language processing, semantic analysis, automated reasoning and / or machine learning.

[0191] In supervised machine learning, a processing element may be provided with example inputs and their associated outputs, and may seek to discover general rules that map inputs to outputs, so that when new inputs are subsequently provided, the processing element can accurately predict the correct output based on the discovered rules. In unsupervised machine learning, the processing element may need to find its own structure in unlabeled example inputs. In one embodiment, machine learning techniques may be used to extract data related to a computer device, a user of the computer device, a computer network hosting the computer device, a service executed on the computer device, and / or other data.

[0192] Based on these analyses, the processing element can learn how to identify characteristics and patterns, which can then be applied to analyze trap data, weather data, imagery data, geospatial data (e.g., using one or more models) to predict future pest pressure.

[0193] As used herein, the term "non-transitory computer-readable medium" is intended to represent any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as computer-readable instructions, data structures, program modules and submodules, or other data in any device. Thus, the methods described herein can be encoded as executable instructions embodied in a tangible non-transitory computer-readable medium (including but not limited to storage devices and / or memory devices). Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Furthermore, as used herein, the term "non-transitory computer-readable medium" includes all tangible computer-readable media, including but not limited to non-transitory computer storage devices, including but not limited to volatile and non-volatile media, and removable and non-removable media, such as firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital resources, such as a network or the Internet, as well as digital means yet to be developed, with the sole exception of transient propagation signals.

[0194] Examples

[0195] Without further elaboration, it is believed that one skilled in the art can, using the preceding description, utilize the present disclosure to its fullest extent.Therefore, the following examples should be construed as merely illustrative, and not limiting of the present disclosure in any way.

[0196] In the following examples, FAM is 6-carboxyfluorescein, MGBQ is a minor groove binding non-fluorescent quencher, HEX is hexachlorofluorescein, VIC is an asymmetric xanthene dye, and BHQ1 is black hole quencher 1.

[0197] Example 1. qPCR assay.

[0198] qPCR assay was performed using Taqman probes targeting the I4790M SNP (Plutella xylostella RyR numbering) in the ryanodine receptor gene of Fall Armyworm (FAW). The oligomers used were as follows.

[0199]

[0200] The PCR setup was as follows. PCR was performed on a 7500qPCR machine using standard thermal cycler conditions and endpoint fluorescence measurement. Initial denaturation was performed at 95°C for 5 minutes. This was followed by 35 cycles of 95°C for 15 seconds and 60°C for 30 seconds. Endpoint analysis was performed.

[0201]

[0202] The expected result is shown in Figure 11 In. Figure 11 In the figure, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, and red represents the homozygous sensitive SS genotype.

[0203] Example 2. qPCR assay.

[0204] qPCR assay was performed using Taqman probes for the G4903E SNP (G4946E according to the RyR numbering of Plutella xylostella) in the Tuta ryanodine receptor gene. The oligomers used were as follows.

[0205]

[0206] The PCR setup was as follows. PCR was performed on a 7500qPCR machine using standard thermal cycler conditions and endpoint fluorescence measurement. Initial denaturation was performed at 95°C for 5 minutes. This was followed by 35 cycles of 95°C for 15 seconds and then 58°C–60°C for 20 seconds. Endpoint analysis was performed.

[0207]

[0208]

[0209] The expected result is shown in Figure 12 In. Figure 12 In the figure, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, and red represents the homozygous sensitive SS genotype.

[0210] Example 3. Molecular monitoring of I4790M in FAW.

[0211] based on Probe-based qPCR methods (e.g., the methods of Examples 1 and 2) can be used for allelic discrimination on a large scale. In the context of pesticidal resistance, genotyping of a single individual can determine the resistant, heterozygous, or susceptible genotype. Such genotyping can be performed rapidly, with a turnaround time of approximately one day or less.

[0212] The frequency of the I4790M allele in the F1 generation of Fall Armyworm (FAW) on crops was monitored according to the disclosed method. The results are shown in the table below.

[0213]

[0214] *0% means that the R allele frequency was below the detection level (1.7% determined based on method sensitivity).

[0215] Extremely high R allele frequencies (>20%) were observed at several positions. Very high R allele frequencies (11%-20%) and high R allele frequencies (5%-10%) were also observed. In all positions with high to extremely high R allele frequencies, the heterozygous RS frequency was at least 10%, indicating an R allele.

[0216] The main carrier of the gene.

[0217] The R allele frequencies of these populations were measured using the LC99 bioassay. Figure 13 As shown, for 12 of the 18 FAW populations, some correlation between the R allele frequency and the bioassay data was observed (R2 = 0.62; p = 0023). The SNP conferring 14790M appears to be widely distributed in the maize-producing region. For approximately 70% of the FAW field populations, the correlation between the R allele frequency and the bioassay data was accurate, indicating that the bioassay is crucial for identifying an alternative R mechanism.

[0218] The R allele frequencies and mortality rates at LC99 were measured in these populations. Figure 14 As shown, red circles indicate indirect associations, blue circles indicate potential fitness costs, and green circles indicate possible involvement of other single nucleotide polymorphisms (SNPs) or other resistance mechanisms.

[0219] The mortality of various diamide pesticides at the LC99 in these populations was determined. Figure 15 As shown, for some diamides, different allele frequencies experienced higher mortality rates. SS homozygotes showed the highest relative mortality rate, while RR homozygotes showed the lowest relative mortality rate.

[0220] Example 4. Insecticidal Resistance Management (IRM) Recommendations.

[0221] Methods according to the present disclosure may include providing recommendations for managing insecticidal resistance.

[0222] One recommendation involves treating successive generations of FAW with products that have different modes of action (MoA). Another recommendation involves using a treatment window approach to treating FAW, rotating the MoA as needed within each window. Figure 17-18 Each depicts an exemplary processing window suggestion.

[0223] Example 5. qPCR assay.

[0224] qPCR assay was performed using Taqman probes for the G4903E SNP (numbered G4946E based on the RyR number of Plutella xylostella) in Tuta tomato leafminer. The oligomers used were as follows.

[0225]

[0226] The results are shown in Figure 19 In. Figure 19 In the figure, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, light blue represents the control of each genotype, red represents the homozygous sensitive SS genotype, and X represents undetermined genotype.

[0227] Example 6. qPCR assay.

[0228] qPCR assay was performed using Taqman probes for the I4790M SNP (Plutella xylostella RyR numbering) in Spodoptera frugiperda. The oligomers used were as follows.

[0229]

[0230] The results are shown in Figure 20 In. Figure 20 In the figure, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, red represents the homozygous sensitive SS genotype, and X represents the undetermined genotype.

[0231] Example 7. qPCR assay.

[0232] qPCR assay was performed using Taqman probes for the I4790K SNP (Plutella xylostella RyR numbering) in Spodoptera frugiperda. The oligomers used were as follows.

[0233]

[0234] The results are shown in Figure 20 In. Figure 20 In the figure, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, red represents the homozygous sensitive SS genotype, and X represents the undetermined genotype.

[0235] This written description uses examples to disclose the present disclosure, including its best mode, and also to enable any person skilled in the art to practice these embodiments, including making and using any devices or systems and performing any methods incorporated therein. The patentable scope of the present disclosure is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

[0236] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," "contains," "containing," "characterized by," or any other variations thereof, are intended to cover a non-exclusive inclusion, subject to any limitation expressly stated. For example, a composition, mixture, process, or method that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such composition, mixture, process, or method.

[0237] The transitional phrase "consisting of" excludes any unrecited element, step, or ingredient. If included in a claim, such a phrase renders the claim closed, excluding materials other than those recited, except for conventional impurities associated therewith. When the phrase "consisting of" appears in a clause of the body of a claim rather than immediately following the preamble, the phrase limits only the elements recited in that clause; other elements are not excluded from the claim as a whole.

[0238] The transitional phrase "consisting essentially of is used to qualify compositions or methods as including materials, steps, features, components, or elements in addition to those literally disclosed, provided that such additional materials, steps, features, components, or elements do not materially affect the basic and novel characteristics of the claimed disclosure. The term "consisting essentially of is intermediate between "comprising" and "consisting of."

[0239] When a disclosure or a portion thereof is defined by an open-ended term such as "comprising," it should be readily understood that (unless otherwise indicated) the description should be interpreted as also using the term "consisting essentially of" or "consisting of" to describe such disclosure.

[0240] In addition, unless expressly stated to the contrary, "or" refers to an inclusive or rather than an exclusive or. For example, condition A or B is satisfied by any of the following conditions: A is true (or exists) and B is false (or does not exist), A is false (or does not exist) and B is true (or exists), and both A and B are true (or exist).

[0241] Likewise, the indefinite article "a / an" preceding an element or component of the present disclosure is intended to be non-limiting with respect to the number of instances (i.e., occurrences) of the element or component. Thus, "a" or "an" should be understood to include one or at least one, and singular word forms of an element or component also include the plural unless the number clearly dictates the singular.

[0242] It should also be understood that any numerical ranges recited herein include all values from the lower limit to the upper limit. For example, if a weight ratio range is specified as 1:50, it is expected that values such as 2:40, 10:30, or 1:3 are explicitly recited in this specification. These are merely examples of specific intent, and all possible combinations of values between (and including) the lowest and highest values recited are considered to be explicitly stated in this application.

[0243] As used herein, the term "about" means plus or minus 10% of the value.

Claims

1. A heat map generation computing device, comprising: Memory; as well as a processor communicatively coupled to the memory, the processor being programmed to: receiving trap data for a plurality of pest traps in a geographic location, the trap data comprising pest genetic data, the trap data comprising a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving at least one of: i) weather data for the geographic location; ii) image data for the geographic location; applying a machine learning algorithm to the trap data and at least one of the weather data and the image data to generate predicted future pest pressure values for the pesticide-susceptible population and the pesticide-resistant population at each of the plurality of pest traps; generating a first heat map for a first point in time for the pesticide-susceptible population and the pesticide-resistant population, and a second heat map for a second point in time for the pesticide-susceptible population and the pesticide-resistant population, the second heat map being generated using the predicted future pest pressure values, the first heat map and the second heat map each being generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node indicating a pest pressure value for the corresponding pest trap for a pesticide-susceptible population and a pesticide-resistant population at a relevant point in time; and annotating at least some remaining portion of the map for the geographic location to generate a continuous map of pest pressure values for the geographic location by interpolating between pest pressure values associated with the plurality of nodes at the relevant point in time for the pesticide-susceptible population and the pesticide-resistant population; as well as The first heat map and the second heat map are transmitted to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

2. The heat map generating computing device according to claim 1, wherein: At least one of the first time point and the second time point is a future time point.

3. The heat map generating computing device according to claim 1, wherein: The processor is further programmed to: Treatment recommendations for that geographic location are generated based on these predicted future pest pressure values.

4. The heat map generating computing device according to claim 1, wherein: The processor is further programmed to: receiving, from the mobile computing device, a user selection of a selected node in the heat map, the user selection made using the user interface; as well as The user interface is caused to display the pest pressure value for the selected node plotted over time in response to the received user selection.

5. The heat map generating computing device according to claim 1, wherein: The processor is programmed to annotate at least some remaining portions of the map by interpolating based on distances to nearby pest traps of the plurality of pest traps.

6. The heat map generating computing device according to claim 1, wherein: To generate the first heat map and the second heat map, the processor is further programmed to draw farm boundaries on a map of the geographic location.

7. The heat map generating computing device according to claim 1, wherein: The first thermal map and the second thermal map are associated with a first pest, and wherein the processor is further programmed to: generating a third heat map associated with the second pest; receiving a user selection of the second pest using the user interface; and The user interface is caused to display the third heat map in response to the received user selection.

8. The heat map generating computing device according to claim 1, wherein: The pest genetic data are used to characterize the relative proportions of pest genetic populations and generate treatment recommendations, wherein the pest genetic populations preferably include individuals selected from pesticide-susceptible homozygous individuals, pesticide-resistant homozygous individuals, and pesticide-resistant heterozygous individuals.

9. The heat map generating computing device according to claim 1, wherein: Integrate genetic population dynamics with pest genetic data to generate treatment recommendations.

10. A method for generating a heat map, the method being implemented using a heat map generating computing device comprising a memory communicatively coupled to a processor, the method comprising: receiving trap data for a plurality of pest traps in a geographic location, the trap data comprising pest genetic data, the trap data comprising a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving at least one of: i) weather data for the geographic location; ii) image data for the geographic location; applying a machine learning algorithm to the trap data and at least one of the weather data and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps; Generating a first heat map at a first time point and a second heat map at a second time point, wherein the second heat map is generated using the predicted future pest pressure values, wherein the first heat map and the second heat map are each generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node indicating a pest pressure value for the corresponding pest trap at a relevant point in time; and annotating at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the relevant points in time to generate a continuous map of pest pressure values for the geographic location; as well as The first heat map and the second heat map are transmitted to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

11. The method according to claim 10, wherein: At least one of the first time point and the second time point is a future time point.

12. The method of claim 10, further comprising: Treatment recommendations for that geographic location are generated based on these predicted future pest pressure values.

13. The method of claim 10, further comprising: receiving, from the mobile computing device, a user selection of a selected node in the heat map, the user selection made using the user interface; as well as The user interface is caused to display the pest pressure value for the selected node plotted over time in response to the received user selection.

14. The method of claim 10, wherein: Annotating at least some remaining portions of the map includes annotating by interpolating based on distances to nearby pest traps in the plurality of pest traps.

15. The method of claim 10, wherein: Generating the first heat map and the second heat map further includes drawing farm boundaries on a map of the geographic location.

16. The method of claim 10, wherein: The first heat map and the second heat map are associated with a first pest, and wherein the method further comprises: generating a third heat map associated with the second pest; receiving a user selection of the second pest using the user interface; and The user interface is caused to display the third heat map in response to the received user selection.

17. The method of claim 10, further comprising using the pest genetic data to characterize relative proportions of pest genetic populations and generate treatment recommendations, wherein: These pest genetic populations preferably include individuals selected from susceptible homozygous individuals, resistant homozygous individuals, and heterozygous individuals.

18. The method of claim 10, further comprising combining genetic population dynamics with pest genetic data to generate treatment recommendations.

19. A computer-readable storage medium having computer-executable instructions embodied thereon, wherein: These computer readable instructions, when executed by a heat map generating computing device comprising at least one processor in communication with a memory, cause the heat map generating computing device to: receiving trap data for a plurality of pest traps in a geographic location, the trap data comprising pest genetic data, the trap data comprising a current pest pressure value and a historical pest pressure value at each of the plurality of pest traps; receiving at least one of: i) weather data for the geographic location; ii) image data for the geographic location; applying a machine learning algorithm to the trap data and at least one of the weather data and the image data to generate a predicted future pest pressure value at each of the plurality of pest traps; Generating a first heat map at a first time point and a second heat map at a second time point, wherein the second heat map is generated using the predicted future pest pressure values, wherein the first heat map and the second heat map are each generated by: drawing a plurality of nodes on a map of the geographic location, each node corresponding to one of the plurality of pest traps, each node indicating a pest pressure value for the corresponding pest trap at a relevant point in time; and annotating at least some remaining portion of the map of the geographic location by interpolating between the pest pressure values associated with the plurality of nodes at the relevant points in time to generate a continuous map of pest pressure values for the geographic location; as well as The first heat map and the second heat map are transmitted to a mobile computing device so that a user interface on the mobile computing device displays a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time, the user interface being implemented via an application installed on the mobile computing device.

20. The computer-readable storage medium of claim 19, wherein: At least one of the first time point and the second time point is a future time point.

21. The computer-readable storage medium of claim 19, wherein: These instructions further cause the heat map generating computing device to: Treatment recommendations for that geographic location are generated based on these predicted future pest pressure values.

22. The computer-readable storage medium of claim 19, wherein: These instructions further cause the heat map generating computing device to: receiving, from the mobile computing device, a user selection of a selected node in the heat map, the user selection made using the user interface; as well as The user interface is caused to display the pest pressure value for the selected node plotted over time in response to the received user selection.

23. The computer-readable storage medium of claim 19, wherein: To annotate at least some remaining portions of the map, the instructions cause the heat map generating computing device to interpolate based on distances to nearby pest traps in the plurality of pest traps.

24. The computer-readable storage medium of claim 19, wherein: To generate the first heat map and the second heat map, the instructions cause the heat map generating computing device to draw farm boundaries on a map of the geographic location.

25. The computer-readable storage medium of claim 19, wherein: The instructions cause the heat map generating computing device to use the pest genetic data to characterize the relative proportions of pest genetic populations and generate treatment recommendations, wherein the pest genetic populations preferably include individuals selected from susceptible homozygous individuals, resistant homozygous individuals, and heterozygous individuals.

26. The computer-readable storage medium of claim 19, wherein: The instructions cause the heat map generating computing device to combine genetic population dynamics with pest genetic data to generate treatment recommendations.

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