Systems and methods for pest pressure heat maps that convey information about resistance genetic markers to pest control products

Abstract

A system and method for generating and displaying a heat map is provided, comprising: a heat map generating computing device including a processor programmed to receive trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, the trap data including current and past pest pressure values ​​for each of the plurality of pest traps; receive weather data for the geographic location; receive image data for the geographic location; apply a machine learning algorithm to generate predicted future pest pressure values ​​for each of the plurality of pest traps; generate a first heat map at a first time point and a second heat map at a second time point; transmit the first and second heat maps to a mobile computing device; and display the time lapse heat map in a user interface on the mobile computing device.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 433,554, filed December 19, 2022.

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 433,554, filed December 19, 2022, the entire contents of which are incorporated herein by reference.

[0003] Reference to Electronic Sequence Listing The contents of the electronic sequence listing (Sequence Listing 38569-751(61506-WO).xml; Size: 19,668 bytes; Created: November 30, 2023) are incorporated herein by reference in their entirety.

[0004] This application relates generally to techniques that can be useful in monitoring pest pressure, and more particularly to a network-based system and method for generating and displaying pest pressure heat maps that convey information about genetic markers of resistance or susceptibility to pest control products. [Background technology]

[0005] As the world's population grows and the area of ​​cultivated land decreases, there is a need for methods and systems for increasing agricultural crop productivity. At least one factor affecting agricultural crop productivity is pest pressure. Another factor affecting agricultural crop productivity is the resistance or susceptibility of pest populations to specific pest control products. The resistance or susceptibility of pest populations to specific pest control products can be detected by the presence of specific genetic markers of resistance or susceptibility to the specific pest control product in a given pest population.

[0006] Accordingly, systems and methods have been developed for monitoring and analyzing pest pressure. For example, in at least some known systems, multiple insect traps are placed within a target field. To monitor the pest pressure in the target field, the traps are periodically inspected and the number of pests in each trap is counted. Based on the number of pests in each trap, the pest pressure level in the target field can be determined.

[0007] DNA and / or RNA can be extracted from the pests in the trap and analyzed for the presence of genetic markers of resistance or susceptibility to the pest control product. DNA and / or RNA can be extracted and analyzed from individual pests. Alternatively, DNA and / or RNA extracted from all pests of the same species in the trap can be extracted, combined, and analyzed for the presence of genetic markers of resistance or susceptibility to the pest control product.

[0008] 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 influenced by several factors. Therefore, accurately predicting future pest pressure based primarily on trap counts may be relatively inaccurate. Furthermore, at least some known systems for monitoring pest pressure focus on the individual farm level, limiting visualization and introducing significant time lags in data collection. Furthermore, at least some known systems for predicting future pest pressure rely on static logic (e.g., fixed phenology models or decision trees) and therefore are limited in their ability to accurately predict future pest pressure.

[0009] Pests may be characterized by ploidy. Generally, pests may have any suitable ploidy known in the art. In some embodiments, pests are haploid (i.e., haploid), diploid, triploid, tetraploid, pentaploid, hexaploid, heptaploid, septaploid, octaploid, polyploid, or a combination thereof. In some embodiments, pests are haploid. In some embodiments, pests are diploid.

[0010] Haploid pests do not have heterozygosity, and therefore, pesticide resistance traits can only be characterized as resistant or susceptible. When a pest is a haploid pest, the pest may be individually characterized as pesticide-susceptible and pesticide-resistant individuals. For population-level characterization, individual characterization can be applied to populations of individual pests. Individual characterization includes monitoring the frequency of resistance alleles and / or susceptible alleles within individual pests and / or pest populations. Alternatively, individual characterization includes monitoring the proportion of individuals within a pest population that have susceptible alleles.

[0011] In contrast, non-haploid pests, such as diploid pests, exhibit heterozygosity, and as a result, pesticide resistance traits can be characterized as pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. When a pest is a non-haploid pest, it may be individually characterized as pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. For population-level characterization, individual characterization can be applied to individual pest populations. Without intending to be limited to a particular theory, the occurrence of one or more genetic populations may result in resistance to a particular insect control product. Consequently, predicting the occurrence of one or more genetic populations can help growers select appropriate pest control formulations and the appropriate timing for pest treatment. Similarly, non-insect pests (e.g., fungi, weeds) may also possess genetic markers that convey resistance to pesticide control agents, and such genetic populations can be characterized. However, characterizing genetic populations and their corresponding responses to pesticides is complex. It is also complicated to ascertain how a particular pest population with a particular genetic population will respond to a pesticide treatment.

[0012] The number of pesticide-resistant homozygous or pesticide-resistant heterozygous pests monitored in each trap can be used to predict future pest pressure that will be resistant to a particular pest control product. Alternatively, the number of pesticide-susceptible homozygous or pesticide-resistant heterozygous pests monitored in each trap can be used to predict future pest pressure that will be susceptible to a particular pest control product.

[0013] The number of traps containing pests with pesticide resistance alleles or pests that are homozygous or heterozygous for pesticide resistance can be used to predict future pest pressure that will become resistant to a particular pest control product. Alternatively, the number of traps containing pests with pesticide resistance alleles or pesticide-susceptible homozygous or heterozygous for pesticide resistance can be used to predict future pest pressure that will become susceptible to a particular pest control product.

[0014] It is therefore desirable to provide a system that collects and intelligently analyzes multiple different types of information, including genetic information of pests, to quickly and accurately predict future pest pressure and / or the pest's likely response to pesticide treatments. Furthermore, it is desirable to present predicted future pest pressure and a genetic profile of the predicted future pest pressure to assist a user in performing the technical task of monitoring pest pressure and, optionally, optimizing or selecting a pest treatment system to minimize pest resistance to pest control products. Summary of the Invention [Means for solving the problem]

[0015] In one aspect, a heat map generating computing device is provided, the heat map generating computing device including a memory and a processor communicatively coupled to the memory, the processor being programmed to receive trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data and including current and past pest pressure values ​​for 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, weather data, and image data to generate predicted future pest pressure values ​​for each of the plurality of pest traps. The processor is further programmed to generate a first heat map at a first time point and a second heat map at a second time point, the second heat map being generated using the predicted future pest pressure values, the first and second heat maps each being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at an associated time point, and coloring at least some remaining portions of the map of geographic locations to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between the pest pressure values ​​associated with the plurality of nodes at the associated time points. The processor is further programmed to transmit the first and second heat maps to a mobile computing device and cause a user interface on the mobile computing device to display 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.

[0016] In one embodiment, the first and second heat maps also include pest pressures corresponding to the pesticide-susceptible and pesticide-resistant populations.

[0017] In one aspect, the processor is further programmed to generate a first heat map of pesticide-susceptible and pesticide-resistant populations at a first time point and a second heat map at a second time point, the second heat map being generated using the predicted future pesticide-susceptible and pesticide-resistant populations, each of the first and second heat maps being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pesticide-susceptible and pesticide-resistant population of the corresponding pest trap at an associated time point; and coloring at least some remaining portions of the map of geographic locations, generating a continuous map of pesticide-susceptible and pesticide-resistant populations for the geographic locations by interpolating between the pesticide-susceptible and pesticide-resistant population values ​​associated with the plurality of nodes at the associated time points. The processor is further programmed to transmit the first and second heat maps to a mobile computing device and cause a user interface on the mobile computing device to display 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 another aspect, a method for generating a heat map is provided, the method being implemented using a heat map generating computing device including a memory communicatively coupled to a processor, the method including receiving trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data and including current and past pest pressure values ​​for 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, weather data, and image data to generate predicted future pest pressure values ​​for each of the plurality of pest traps. The method further includes generating a first heat map at a first time point and a second heat map at a second time point, the second heat map being generated using predicted future pest pressure values, the first and second heat maps each being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at an associated time point, and coloring at least some remaining portions of the map of geographic locations to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between the pest pressure values ​​associated with the plurality of nodes at the associated time points. The method further includes transmitting the first and second heat maps to a mobile computing device and causing a user interface on the mobile computing device, the user interface implemented via an application installed on the mobile computing device, to display a time lapse heat map that dynamically transitions between the first heat map and the second heat map over time.

[0019] In another aspect, a method for generating a heat map is provided, the method being implemented using a heat map generating computing device including a memory communicatively coupled to a processor, the method including receiving trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data and including current and past pest pressure values ​​for 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, weather data, and image data to generate predicted future pest pressure values ​​for each of the plurality of pest traps. The method further includes generating a first heat map of the pesticide-susceptible and pesticide-resistant populations at a first time point and a second heat map of the pesticide-susceptible and pesticide-resistant populations at a second time point, the second heat map being generated using predicted future pest pressure values ​​of the pesticide-susceptible and pesticide-resistant populations, each of the first and second heat maps being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the pesticide-susceptible and pesticide-resistant populations for the corresponding pest trap at an associated time point; and coloring at least some remaining portions of the map of geographic locations, by interpolating between the pest pressure values ​​associated with the plurality of nodes at the associated time points to generate a continuous map of pest pressure values ​​of the pesticide-susceptible and pesticide-resistant populations for the geographic locations. The method further includes transmitting the first and second heat maps to a mobile computing device and causing a user interface on the mobile computing device, the user interface implemented via an application installed on the mobile computing device, to display a time lapse heat map that dynamically transitions between the first heat map and the second heat map over time.

[0020] In yet another aspect, a computer-readable storage medium having computer-executable instructions embodied thereon is provided that, when executed by a heat map generating computing device including at least one processor in communication with a memory, cause the heat map generating computing device to receive trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, the trap data including current pest pressure values ​​and past pest pressure values ​​for 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, genetic data, weather data, and image data to generate predicted future pest pressure values ​​for pesticide-susceptible and pesticide-resistant populations in each of the plurality of pest traps. The instructions further cause the heat map generation computing device to generate a first heat map of the pesticide-susceptible and pesticide-resistant populations at a first time point and a second heat map of the pesticide-susceptible and pesticide-resistant populations at a second time point, the second heat map being generated using predicted future pest pressure values ​​of the pesticide-susceptible and pesticide-resistant populations, each of the first and second heat maps being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at the relevant time point for the pesticide-susceptible and pesticide-resistant populations, and coloring at least some remaining portions of the map of geographic locations, and the instructions further cause the heat map generation computing device to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between the pest pressure values ​​associated with the plurality of nodes at the relevant time points for the pesticide-susceptible and pesticide-resistant populations.The instructions further cause the heatmap generation computing device to transmit the first and second heatmaps to a mobile computing device and cause a user interface on the mobile computing device to display a time-lapse heatmap that dynamically transitions between the first heatmap and the second heatmap over time, the user interface being implemented via an application installed on the mobile computing device. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a block diagram of a computer system used to predict pest pressure according to the present disclosure. [Figure 2] FIG. 2 is a block diagram illustrating data flow through the system shown in FIG. 1 in accordance with the present disclosure. [Figure 3] 3 illustrates an exemplary configuration of a server system such as the pest pressure prediction computing device of FIGS. 1 and 2 according to the present disclosure. [Figure 4] 3 illustrates an exemplary configuration of the client system shown in FIGS. 1 and 2 according to the present disclosure. [Figure 5] FIG. 2 is a flow diagram of an exemplary method for generating pest pressure data using the system shown in FIG. 1 according to the present disclosure. [Figure 6] FIG. 2 is a flow diagram illustrating an exemplary method for generating a heat map using the system shown in FIG. 1 according to the present disclosure. [Figure 7] 2 is a screenshot of a user interface that may be generated using the system shown in FIG. 1 in accordance with the present disclosure. [Figure 8] 2 is a screenshot of a user interface that may be generated using the system shown in FIG. 1 in accordance with the present disclosure. [Figure 9] 2 is a screenshot of a user interface that may be generated using the system shown in FIG. 1 in accordance with the present disclosure. [Figure 10]2 is a screenshot of a user interface that may be generated using the system shown in FIG. 1 in accordance with the present disclosure. [Figure 11] 1 is a predicted result of an allele discrimination plot according to the present disclosure. [Figure 12] 1 is a predicted result of an allele discrimination plot according to the present disclosure. [Figure 13] 1 is a graph showing the relationship between R allele frequency and bioassay at LC99 for a particular genetic population according to the present disclosure. [Figure 14] 1 is a graph showing the relationship between R allele frequency and LC99 mortality rate for a particular genetic population according to the present disclosure. [Figure 15] 1 is a graph showing the observed mortality at LC99 for specific genetic populations after treatment with the indicated diamide-containing insect control formulations according to the present disclosure. [Figure 16] 1 is a graph showing the relative resistance levels of specific genetic populations with specific mutations to various pesticides according to the present disclosure. [Figure 17] 1 is an exemplary processing window recommendation according to the present disclosure. [Figure 18] 1 is an exemplary processing window recommendation according to the present disclosure. [Figure 19] 1 is an allele discrimination plot according to the present disclosure. [Figure 20] 1 is an allele discrimination plot according to the present disclosure. [Figure 21] 1 is an allele discrimination plot according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0022] Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only, and any feature of any drawing may be referenced and / or claimed in combination with any feature of any other drawing.

[0023] The systems and methods described herein relate to a computer-implemented system for generating and displaying pest pressure heat maps of pesticide-susceptible and pesticide-resistant populations. The heat map generating computing device receives trap data (including pest genetic data) for a plurality of pest traps at a geographic location, receives weather data for the geographic location, receives image data for the geographic location, and applies machine learning algorithms to the trap data, weather data, and image data to generate predicted future pest pressure values ​​for the pesticide-susceptible and pesticide-resistant populations in each of the plurality of pest traps. The heat map generating computing device generates a first heat map of the pesticide-susceptible and pesticide-resistant populations at a first time point and a second heat map of the pesticide-susceptible and pesticide-resistant populations at a second time point, the second heat map being generated using predicted future pest pressure values ​​of the pesticide-susceptible and pesticide-resistant populations, each of the first and second heat maps being generated by plotting a plurality of nodes on a map of geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at the relevant time point for the pesticide-susceptible and pesticide-resistant populations, and coloring at least some remaining portions of the map of geographic locations, the instructions further direct the heat map generating computing device to generate a continuous map of pest pressure values ​​of the pesticide-susceptible and pesticide-resistant populations at the geographic locations by interpolating between the pest pressure values ​​associated with the plurality of nodes at the relevant time points for the pesticide-susceptible and pesticide-resistant populations. The first map and / or heat map may also include information including genetic marker populations (e.g., pesticide-sensitive homozygotes, pesticide-resistant homozygotes, or pesticide-resistant heterozygotes, as shown in FIG. 11).The heatmap generating computing device transmits the first and second heatmaps to a mobile computing device and displays a time-lapse heatmap on a user interface on the mobile computing device that dynamically transitions between the first heatmap and the second heatmap over time, the user interface being implemented by an application installed on the mobile computing device.

[0024] The systems and methods described herein facilitate accurately predicting pest pressure of pesticide-susceptible and pesticide-resistant populations in one or more geographic locations. As used herein, "geographic location" generally refers to a geographic location related to agriculture (e.g., a location including one or more fields and / or farms for producing crops). Furthermore, as used herein, "pest pressure" refers to a qualitative and / or quantitative assessment of the number of pests present in a particular location of pesticide-susceptible and pesticide-resistant populations. For example, high pest pressure indicates the presence of a relatively large number of pests (compared to the expected number) at that location. In contrast, low pest pressure indicates the presence of a relatively small number of pests at that location. In at least some of the embodiments described herein, pest pressure is analyzed for agricultural purposes; that is, pest pressure in one or more fields is monitored and predicted. However, one of ordinary skill in the art will understand that the systems and methods described herein can be used to analyze pest pressure in any suitable environment.

[0025] In some embodiments, the pest is non-haploid. In some embodiments, the pest is diploid. In some embodiments, the pest is haploid.

[0026] As used herein, the term "pest" refers to an organism whose presence is generally undesirable in a particular geographic location, particularly one related to agriculture. For example, in an implementation analyzing pest pressure in one or more fields, pests may include insects that tend to damage crops in those fields. However, one skilled in the art will understand that the systems and methods described herein can be used to analyze pest pressure for 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, a "pest trap" refers to any device capable of containing and / or monitoring the presence of a targeted pest, and "trap data" refers to data collected using such a device. For example, in the case of insects, a "pest trap" can be a conventional containment device that captures pests. Alternatively, in the case of fungi, weeds, or diseases, a "pest trap" can refer to any device capable of monitoring the presence and / or levels of fungi, weeds, and / or diseases. For example, in embodiments where the "pest" is one or more fungi, a "pest trap" may refer to a sensing device that can quantitatively measure the level of spores associated with one or more fungi in the ambient environment surrounding the sensing device. In one embodiment, the "pest" is one type of insect or multiple types of insects, and the terms "pest trap" and "pest traps" refer to an "insect trap" and "insect traps," respectively.

[0027] In some embodiments, when the pest is a haploid pest, monitoring pest pressure includes monitoring changes in the frequency of resistance alleles and / or susceptibility alleles within individual pests and / or pest populations.

[0028] 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 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% %,approximately 23%,approximately 24%,approximately 25%,approximately 26%,approximately 27%,approximately 28%,approximately 29%,approximately 30%,approximately 31%,approximately 32%,approximately 33%,approximately 34%,approximately 35%,approximately 36%,approximately 37%,approximately 38%,approximately 39%,approximately 40%,approximately 41%,approximately 42%,approximately 43%,approximately 44%,approximately 45%,approximately 46%,approximately 47%,approximately 48%,approximately 49%, approximately 50%, approximately 51%, approximately 52%, approximately 53%, approximately 54%, approximately 55%, approximately 56%, approximately 57%, approximately 58%, approximately 59%, approximately 60%, approximately 61%, approximately 62%, approximately 63%, approximately 64%, approximately 65%, approximately 66%, approximately 67%, approximately 68%, approximately 69%, approximately 70%, approximately 71%, approximately 72%, approximately 73%, approximately 74%, approximately 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%.

[0029] In some embodiments, the change in frequency of the susceptibility 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 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%, about 2%, approximately 23%, approximately 24%, approximately 25%, approximately 26%, approximately 27%, approximately 28%, approximately 29%, approximately 30%, approximately 31%, approximately 32%, approximately 33%, approximately 34%, approximately 35%, approximately 36%, approximately 37%, approximately 38%, approximately 39%, approximately 40%, approximately 41%, approximately 42%, approximately 43%, approximately 44%, approximately 45%, approximately 46%, approximately 47%, approximately 48%, Approximately 49%, approximately 50%, approximately 51%, approximately 52%, approximately 53%, approximately 54%, approximately 55%, approximately 56%, approximately 57%, approximately 58%, approximately 59%, approximately 60%, approximately 61%, approximately 62%, approximately 63%, approximately 64%, approximately 65%, approximately 66%, approximately 67%, approximately 68%, approximately 69%, approximately 70%, approximately 71%, approximately 72%, approximately 73%, approximately 74%, approximately 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%.

[0030] In some embodiments, when the pest is a haploid pest, monitoring pest pressure includes comparing the frequency of resistance and / or susceptibility alleles in individual pests and / or pest populations sampled at a first time point with the frequency of resistance and / or susceptibility alleles in individual pests and / or pest populations sampled at a second time point. In these embodiments, a threshold value of change can be used to determine significance. For example, a 10% frequency of resistance change can be used as the threshold.

[0031] In some embodiments, if the pest is a non-haploid pest, the pest may be individually characterized as pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous individuals. For population-level characterization, individual characterizations can be applied to populations of individual pests.

[0032] In some embodiments, if the pest is a polyploid pest, the pest can be individually characterized as pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous individuals. Pesticide-resistant heterozygous individuals differ from each other and may have varying degrees of pesticide resistance depending on genetic differences. For population-level characterization, individual characterization can be applied to individual pest populations.

[0033] In the following detailed description of embodiments of the present disclosure, reference is made to the accompanying drawings, in which the same reference numbers in different drawings may identify the same or similar elements, and the following detailed description does not limit the scope of the claims.

[0034] This specification describes computer systems, such as pest pressure prediction computing devices. As described herein, all such computer systems include a processor and a memory. However, any processor in a computing device referred to herein may also refer to one or more processors, which may be in a single computing device or in multiple computing devices operating in parallel. Furthermore, any memory in a computing device referred to herein may also refer to one or more memories, which may be in a single computing device or in multiple computing devices operating in parallel.

[0035] As used herein, a processor may include any programmable system, including systems that use microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), logic circuits, and other circuits or processors capable of performing the functions described herein. The above examples are illustrative only and are not intended to limit the definition or meaning of the term "processor."

[0036] As used herein, the term "database" may refer to either 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 other structured collections of records or data stored in a computer system. The above examples are merely illustrative and do not limit the definition or meaning of the term database. Examples of RDBMSs include, but are not limited to, Oracle® Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, any database that enables the systems and methods described herein may be used. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, California. IBM is a registered trademark of International Business Machines Corporation, Armonk, New York. Microsoft is a registered trademark of Microsoft Corporation, Redmond, Washington. Sybase is a registered trademark of Sybase, Dublin, California.)

[0037] In one embodiment, a computer program is provided, the program embodied on a computer-readable medium. In one embodiment, the system runs on a single computer system without requiring connection to a server computer. In a further embodiment, the system runs in a Windows environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Washington). In yet another embodiment, the system runs in a mainframe environment and a UNIX server environment (UNIX is a registered trademark of X / Open Company Limited, Reading, Berkshire, UK). The application is designed to be flexible and to run in a variety of environments without loss of primary functionality. In some embodiments, the system includes multiple components distributed across multiple computing devices. One or more components may be in the form of computer-executable instructions embodied on a computer-readable medium.

[0038] As used herein, the terms "software" and "firmware" may be used interchangeably 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 are thus not limiting of the types of memory that may be used to store computer programs.

[0039] The systems and processes are not limited to the specific embodiments described herein. In addition, each system component and each process can be implemented separately and independently of other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes.

[0040] The following detailed description illustrates embodiments of the present disclosure by way of example, not limitation. The present disclosure is believed to have general applicability to predicting pest pressure.

[0041] 1 is a block diagram of an embodiment of a computer system 100 for use in predicting pest pressure, including a pest pressure prediction (PPP) computing device 112 according to an embodiment of the present disclosure. The PPP computing device 112 is also referred to herein as a heat map generation computing device, as described herein. In an exemplary embodiment, the system 100 is used to predict pest pressure and generate a pest pressure heat map, as described herein.

[0042] More specifically, in this exemplary embodiment, system 100 includes a pest pressure prediction (PPP) computing device 112 and multiple client subsystems, also referred to as client systems 114, connected to PPP computing device 112. In one embodiment, client system 114 is a computer including a web browser, allowing PPP computing device 112 to access client system 114 using the Internet and / or using network 115. Client system 114 may be interconnected to the Internet through a number of interfaces, including a local area network (LAN), a wide area network (WAN), a dial-in connection, a cable modem, a specialized high-speed integrated services digital network (ISDN) line, an RDT network, or other network 115. Client systems 114 may include systems associated with farmers, growers, scouts, etc., as well as external systems used to store data. PPP computing device 112 also communicates with one or more data sources 130 using network 115. Furthermore, client system 114 may also communicate with additional data sources 130 using network 115. Additionally, in some embodiments, as described herein, one or more client systems 114 can function as data sources 130. A client system 114 is any device capable of interconnecting to the Internet, including a web-based phone, a PDA, or other web-based connectable appliance.

[0043] The database server 116 is connected to a database 120 containing information on various matters, as described in more detail below. In one embodiment, the centralized database 120 is stored on the PPP device 112 and is accessible to potential users at one of the client systems 114 by logging on to the PPP computing device 112 through one of the client systems 114. In another embodiment, the database 120 may be stored remotely from the PPP device 112 and may be decentralized. The database 120 may be a database configured to store information used by the PPP computing device 112, including, for example, transaction records, as described herein.

[0044] Database 120 may include a single database with separate sections or partitions, or may include multiple databases, each separate from one another. Database 120 may store data received from data sources 130 and generated by PPP computing device 112. For example, database 120 may store weather data, imaging data, trap data, scouting data, grower data, pest pressure forecast data, and / or heat map data, as described in more detail herein.

[0045] In an exemplary embodiment, client systems 114 may be associated with, for example, growers, scouting agencies, pest management agencies, and / or any other parties that may use system 100 as described herein. In an exemplary embodiment, at least one of client systems 114 includes a user interface 118. For example, user interface 118 may include a graphical user interface with interactive capabilities, whereby pest pressure forecasts and / or heat maps transmitted from PPP computing device 112 to client system 114 may be displayed in graphical form. A user of client system 114 may interact with user interface 118 to view, explore, and otherwise interact with the displayed information.

[0046] In an exemplary embodiment, the PPP computing device 112 receives data from multiple data sources 130 and aggregates and analyzes the received data (e.g., using machine learning) to generate pest pressure predictions and / or heat maps, as described in detail herein.

[0047] Figure 2 is a block diagram illustrating the flow of data through system 100. In the embodiment illustrated in Figure 2, data sources 130 include a weather data source 202, an imaging data source 204, a trap data source 206, a scout data source, a grower data source 210, and another data source 212. Those skilled in the art will appreciate that the data sources 130 illustrated in Figure 2 are merely examples, and that any suitable number and types of data sources may be included in system 100.

[0048] The weather data source 202 provides weather data to the PPP computing device 112 for use in generating a pest pressure forecast. The weather data may include, for example, temperature data (e.g., indicative of current and / or past temperatures measured at one or more geographic locations), humidity data (e.g., indicative of current and / or past humidity measured at one or more geographic locations), wind data (e.g., indicative of current and / or past wind speed and direction measured at one or more geographic locations), precipitation data (e.g., indicative of current and / or past rainfall amounts measured at one or more geographic locations), and forecast data (e.g., indicative of predicted future weather conditions for one or more geographic locations).

[0049] The imaging data source 204 provides image data to the PPP computing device 112 for use in generating the pest pressure forecast. The image data may include, for example, satellite and / or drone imagery obtained from one or more geographic locations.

[0050] The trap data source 206 provides trap data to the PPP computing device 112 for use in generating the pest pressure forecast. The trap data may include, for example, a pest count (e.g., expressed as a number of pest species, a density of the pest species, etc.) from at least one pest trap at a geographic location. Additionally, the trap data may include, for example, in the case of insects, the type of pest (taxonomic genus, species, variety, etc.) and / or the developmental stage and sex of the pest (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 a pest 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 ambient levels of spores associated with one or more fungal species. In such embodiments, the trap data may include, for example, the number of spores (representing the number of pests), the type of fungus, the developmental stage of the fungus, etc.

[0051] In some embodiments, trap data source 206 is a pest trap communicatively coupled (e.g., via a wireless communication link) to PPP computing device 112. Thus, in such embodiments, trap data source 206 may be able to automatically determine the number of pests in the pest trap (e.g., using image processing algorithms) and transmit the determined number of pests to the PPP computing device.

[0052] 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 markers of pesticide resistance within a population. Such markers of pesticide resistance can be monitored pre-emergence, emergence, and / or post-emergence.

[0053] In one embodiment, population dynamics can be integrated with pest genetic data (e.g., R allele detection). In this embodiment, mapping can yield accurate treatment recommendations.

[0054] Generally, genetic data can be collected according to any suitable method known in the art. For example, collecting genetic data may include manually collecting genetic data and / or automatically collecting genetic data. In some examples, collecting genetic data may include collecting genetic data selected from phenotypes, genotypes, epigenotypes, allele distributions, and combinations thereof. In some examples, genetic data can be collected using a technique 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.

[0055] Generally, the genetic data may include any suitable genetic markers known in the art, hi some embodiments, the genetic data includes genetic markers selected from single nucleotide polymorphisms, and combinations thereof.

[0056] In some embodiments, the genetic data informs treatment recommendations, which in many embodiments include compositions for treating pest populations.

[0057] As used herein, a composition according to the present disclosure is a composition that is applied to a pest population and / or is recommended by a treatment program.

[0058] 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 having at least one different mode of action (MoA).

[0059] In many embodiments, the treatment recommendations recommend applying one or more compositions in one or more treatment windows, hi some embodiments, one or more compositions in one or more treatment windows comprise active ingredients having at least one different mechanism of action.

[0060] In many embodiments, the active ingredient is selected from the group consisting of insecticides, herbicides, biopesticides, nematicides, fungicides, and fungicides. General references for these active ingredients (i.e., insecticides, fungicides, nematicides, acaricides, herbicides, and biologicals) include The Pesticide Manual, 13th Edition, CDS Tomlin, Ed., British Crop Protection Council, Farnham, Surrey, UK, 2003, and The BioPesticide Manual, 2004. ndEdition, L. G. Copping, Ed., British Crop Protection Council, Farnham, Surrey, UK, 2001.

[0061] Non-limiting examples of insecticides include abamectin, acephate, acequinocyl, acetamiprid, acrinathrin, acinonapyr, afidopyropen ([(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-pyridinyl)-2H,11H-naphtho[2,1-b]pyrano[3,4-e]pyran-4-yl]methylcyclohexyl, lopropanecarboxylate), amidoflumet, amitraz, avermectin, azadirachtin, azinphos-methyl, benfuracarb, bensultap, benzpyrimoxan, bifenthrin, kappa-bifenthrin, bifenazate, bistrifluron, borate, brofuranilide, buprofezin, cadusafos, carbaryl, carbofuran, cartap, carsol, chlorfenapyr, chlorfluazuron, chlorprallethrin, chlorpyrifos, chlorpyrifos-e, chlorpyrifos-methyl, chromafenozide, clofen Tedin, chlorprallethrin, clothianidin, cycloprothrin, cycloxapride ((5S,8R)-1-[(6-chloro-3-pyridinyl)methyl]-2,3,5,6,7,8-hexahydro-9-nitro-5,8-epoxy-1H-imidazo[1,2-a]azepine), cyenopyrafen, cyflumetofen, cyfluthrin, beta-cyfluthrin, cyhalothrin, gamma-cyhalothrin, lambda-cyhalothrin, cypermethrin, alpha-cypermethrin, zeta-cypermethrin, cyromazine, deltamethrin, diafen Thiuron, diazinon, dichloromezothiaz, dieldrin, diflubenzuron, dimefluthrin, dimehypo, dimethoate, dinpropylidaz, dinotefuran, diofenolan, emamectin, emamectin benzoate, endosulfan, esfenvalerate, ethiprole, etofenprox, epsilon-metofluthrin, etoxazole, fenbutatin oxide, fenitrothion, fenothiocarb, fenoxycarb, fenpropathrin, fenvalerate, fipronil, flometoquin (2-ethyl-3,7-dimethyl-6-[4-(trifluoromethoxy)phenoxy]-4-quinolinylmethyl carbonate), flonicamid, fluazaindolizine, flucythrinate, flufenerim, flufenoxuron, flufenoxystrobin (methyl(αE)-2-[[2-chloro-4-(trifluoromethyl)phenoxy]methyl]-α-(methoxymethylene)benzeneacetate), fluensulfone (5-chloro-2-[(3,4,4-trifluoro-3-buten-1-yl)sulfonyl]thiazole), fluhexafon, fluopyram, Flupiprole (1-[2,6-dichloro-4-(trifluoromethyl)phenyl]-5-[(2-methyl-2-propen-1-yl)amino]-4-[(trifluoromethyl)sulfinyl]-1H-pyrazole-3-carbonitrile), flupyradifurone (4-[[(6-chloro-3-pyridinyl)methyl](2,2-difluoroethyl)amino]-2(5H)-furanone), flupirimine, fluvalinate, tau-fluvalinate, fluxametamide, fonofos, formetanate, fosthiazate, gamma-cyhalothrin, halofenozide , heptafluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 2,2-dimethyl-3-[(1Z)-3,3,3-trifluoro-1-propen-1-yl]cyclopropanecarboxylate), hexaflumuron, hexythiazox, hydramethylnon, imidacloprid, indoxacarb, insecticidal soap, isofenphos, isocycloceram, kappa-tefluthrin, lambda-cyhalothrin, lufenuron, malathion, meperfluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 2,2-dimethyl-3-[(1Z)-3,3,3-trifluoro-1-propen-1-yl]cyclopropanecarboxylate] methyl)phenyl]methyl (1R,3S)-3-(2,2-dichloroethenyl)-2,2-dimethylcyclopropanecarboxylate), metaflumizone, metaldehyde, methamidophos, methidathion, methiocarb, methomyl, methoprene, methoxychlor, metofluthrin, methoxyfenozide, epsilon-metofluthrin, epsilon-monfluorothrin, monocrotophos, monofluorothrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 3-(2-cyano-1-propen-1-yl)-2,2-Dimethylcyclopropanecarboxylate), nicotine, nitenpyram, nithiazine, novaluron, noviflumuron, N-[1,1-dimethyl-2-(methylthio)ethyl]-7-fluoro-2-(3-pyridinyl)-2H-indazole-4-carboxamide, N-[1,1-dimethyl-2-(methylsulfinyl)ethyl]-7-fluoro-2-(3-pyridinyl)-2H-indazole-4-carboxamide, N-[1,1-dimethyl-2-(methylsulfonyl)ethyl]- 7-Fluoro-2-(3-pyridinyl)-2H-indazole-4-carboxamide, N-(1-methylcyclopropyl)-2-(3-pyridinyl)-2H-indazole-4-carboxamide, N-[1-(difluoromethyl)cyclopropyl]-2-(3-pyridinyl)-2H-indazole-4-carboxamide, oxamyl, oxazosulfil, parathion, parathion methyl, permethrin, phorate, phosalone, phosmet, phosphamidon, pirimicarb, profenofos , profluthrin, propargite, protrifenbute, piflubumid (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, pyrafluprole, pyrethrins, pyridaben, pyridalyl, pyrifluquinazone, pyriminostrobin (methyl(αE)-2-[[[2-[ (2,4-dichlorophenyl)amino]-6-(trifluoromethyl)-4-pyrimidinyl]oxy]methyl]-α-(methoxymethylene)benzeneacetate), pyriprole, pyriproxyfen, rotenone, ryanodine, silafluofen, spinetoram, spinosad, spirodiclofen, spiromesifen, spiropydione, spirotetramat, sulprofos, sulfoxaflor (N-[methyloxide[1-[6-(trifluoromethyl)-3-pyridinyl]ethyl]-λ, 4-sulfanylidene]cyanamide), tebufenozide, tebufenpyrad, teflubenzuron, tefluthrin, kappa-tefluthrin, terbufos, tetrachlorvinphos, tetramethrin, tetramethylfluthrin ([2,3,5,6-tetrafluoro-4-(methoxymethyl)phenyl]methyl 2,2,3,3-tetramethylcyclopropanecarboxylate), thiacloprid, thiamethoxam, thiodicarb, thiosultap-sodium, thioxazam These include phen (3-phenyl-5-(2-thienyl)-1,2,4-oxadiazole), tolfenpyrad, tralomethrin, triazameate, trichlorfon, triflumezopyrim (2,4-dioxo-1-(5-pyrimidinylmethyl)-3-[3-(trifluoromethyl)phenyl]-2H-pyrido[1,2-a]pyrimidinium inner salt), triflumuron, cyclopyrazoflor, zeta-cypermethrin, Bacillus thuringiensis delta-endotoxin, entomopathogenic fungi, entomopathogenic viruses, or entomopathogenic fungi, and combinations thereof.

[0062] Non-limiting examples of insecticides also include diamides, such as chlorantraniliprole, cyantraniliprole, tetrachlorantraniliprole, bromoantraniliprole, dichlorantraniliprole, tetraniliprole, cyclaniliprole, cyhalodiamide, and flubendiamide.

[0063] Non-limiting examples of fungicides include fungicides such as acibenzolar-S-methyl, aldimorph, ametoctrazine, aminopyrifen, amisulbrom, allilazine, azaconazole, azoxystrobin, benalaxyl (including benalaxyl-M), bendanil, benomyl, benthiavalicarb (including benthiavalicarb-isopropyl), benzovindiflupyr, bethoxadin, binapropacryl, biphenyl, bitertanol, bixafen, blasticidin-S, boscalid, bromuconazole, bupirimate, buthiobate, carboxin, , carpropamid, captafol, captan, carbendazim, chloroneb, chlorothalonil, chlozolinate, copper hydroxide, copper oxychloride, copper sulfate, comoxystrobin, cyazofamid, cyflufenamid, cymoxanil, cyproconazole, cyprodinil, diclobenthiazox, dichlofluanid, diclocymet, diclomedine, dicloran, diethofencarb, difenoconazole, diflumetrim, dimethirimol, dimethomorph, dimoxystrobin, diniconazole (including diniconazole-M), dinocap, dipimethitron, Dithianon, dithiolane, dodemorph, dodine, econazole, etaconazole, edifenphos, enoxastrobin (also known as enestrobrin), epoxiconazole, ethaboxam, ethirimol, etridiazole, famoxadone, fenamidone, phenaminestrobin, fenarimol, fenbuconazole, fenfuram, fenhexamid, fenoxanil, fenpiclonil, fenpicoxamide, fenpropidin, fenpropimorph, fenpyrazamine, fentin acetate, fentin hydroxide, faba fluoxalamide, ferimzone, flometoquin, florylpicoxamide, fluopimomide, fluazinam, fludioxonil, flufenoxystrobin, fluindapyr, flumorph, fluopicolide, fluopyram, fluoxapiprolin, fluoxastrobin, fluquinconazole, flusilazole, flusulfamide, flutianil, flutolanil, flutriafol, fluxapyroxad, folpet, fthalide (also known as phthalide), fuberidazole, furalaxyl, furametpyr, hexaconazole, hymexazole, guazatine,Imazalil, imibenconazole, imioctadine albesilate, imioctadine triacetate, impilfluxam, iodicarb, ipconazole, ipfentrifluconazole, ipflufenoquine, isofetamide, iprobenfos, iprodione, iprovalicarb, isoflucipram, isoprothiolane, isopyrazam, isotianil, kasugamycin, kresoxim methyl, lancotrione, mancozeb, mandipropamide, mandestrobin, maneb, magampine, mefentrifluconazole, mepronil, meptyldinocap, mefentrifluconazole Taraxyl (including metalaxyl-M / mefenoxam), metconazole, metasulfocarb, metiram, metominostrobin, methyltetraprole, metrafenone, myclobutanil, naphthitine, neo-asozin (ferric methanearsonate), nuarimol, octhilinone, ofrace, orysastrobin, oxadixyl, oxathiapiprolin, oxolinic acid, oxpoconazole, oxycarboxin, oxytetracycline, penconazole, pencycuron, penflufen, penthiopyrad, perfluazolate, phosphorous acid (and its salts) , including, for example, fosetyl-aluminum), picoxystrobin, piperalin, polyoxins, probenazole, prochloraz, procymidone, propamocarb, propiconazole, probineb, proquinazide, prothiocarb, prothioconazole, pydiflumetofen (Adepidyn®), pyraclostrobin, pyrametostrobin, pyrapropoin, pyroxystrobin, pyraziflumid, pyrazophos, pyribencarb, pyributacarb, pyridaclomethyl, pyrifenox, pyriophenone, perisoxazole, pyrimethani ru, pyrifenox, pyrrolnitrin, pyroquilon, quinconazole, quinmethionate, quinofumelin, quinoxyfen, quintozene, silthiofam, sedaxane, simeconazole, spiroxamine, streptomycin, sulfur, tebuconazole, tebufloquine, teleclosalam, tecloftalam, tecnazene, terbinafine, tetraconazole, thiabendazole, thifluzamide, thiophanate, thiophanate-methyl, thiram, tiadinil, tolclofos-methyl, tolprocarb, tolifluanid, triadimefon, triadimenol,These include triarimol, triazoxide, tribasic copper sulfate, triclopiricarb, tridemorph, trifloxystrobin, triflumizole, trimofuramide tricyclazole, trifloxystrobin, triforine, triticonazole, uniconazole, validamycin, valifenalate (also known as valifenal), vinclozolin, zineb, 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.

[0064] Non-limiting examples of nematicides include fluopyram, spirotetramat, thiodicarb, fosthiazate, abamectin, iprodione, fluensulfone, dimethyl disulfide, thioxazaphen, 1,3-dichloropropene (1,3-D), metam (sodium and potassium), dazomet, chloropicrin, fenamiphos, ethoprophos, cadusafos, terbufos, imicyafos, oxamyl, carbofuran, thioxazaphen, Bacillus firmus, Pasteuria nishizawae, and combinations thereof. A non-limiting example of a fungicide is streptomycin. Non-limiting examples of acaricides include amitraz, thinomethionate, chlorobenzilate, cyhexatin, dicofol, dienochlor, etoxazole, fenazaquin, fenbutatin oxide, fenpropathrin, fenpyroximate, hexythiazox, propargite, pyridaben, tebufenpyrad, and combinations thereof.

[0065] Non-limiting examples of herbicides include acetochlor, acifluorfen and its sodium salt, aclonifen, acrolein (2-propenal), alachlor, alloxydim, ametryn, amicarbazone, amidosulfuron, aminocyclopyrachlor and its esters (e.g., methyl, ethyl) and salts (e.g., sodium, potassium), aminopyralid, amitrole, ammonium sulfamate, anilofos, asulam, atrazine, azimsulfuron, bixlozone, beflubutamid, beflubutamid-M, benazolin, -ethyl, bencarbazone, benfluralin, benfuresate, benquinotrion, bensulfuron-methyl, bensulfide, bentazone, benzobicyclon, benzofenap, bicyclopyrone, bifenox, viranaphos, bipyrazone, bispyribac and its sodium salt, bromacil, bromobutide, bromofenoxime, bromoxynil, bromoxynil octanoate, butachlor, butafenacil, butamifos, butralin, butroxydim, butyrate, cafenstrole, carbetamide, carfentrazone-ethyl, catechin, Chlomethoxyfen, chloramben, chlorbromuron, chlorflurenol-methyl, chloridazon, chlorimuron-ethyl, chlorotoluron, chlorpropham, chlorsulfuron, chlorsal-dimethyl, chlorthiamid, cinidon-ethyl, cinmethylin, cinosulfuron, clasifos, clefoxydim, clethodim, clodinafop-propargyl, clomazone, clomeprop, clopyralid, clopyralid-olamine, chloransulam-methyl, cumyluron, cyanazine, cycloate, cyclopyranyl, cyclopyrimorate, cyclosulfam ron, cycloxydim, cyhalofop-butyl, sipirafluon, 2,4-D and its butotyl, butyl, isoctyl and isopropyl esters, and its dimethylammonium, diolamine and trolamine salts, dymron, dalapon, dalapon-sodium, dazomet, 2,4-DB and its dimethylammonium, potassium and sodium salts, desmedipham, desmetrin, dicamba and its diglycolammonium, dimethylammonium, potassium and sodium salts, dichlobenil, dichlorprop, diclofop-methyl, diclosulam,Difenzoquat methyl sulfate, diflufenican, diflufenzopyr, dimefron, dimesulfazate, dimepiperate, dimesulfazate, dimethachlor, dimethamethrin, dimethenamid, dimethenamid P, dimethipine, dimethylarsinic acid and its sodium salt, dinitramine, dinoterb, dioxopyritrion, diphenamide, diquat dibromide, dithiopyr, diuron, DNOC, endosal, EPTC, epirifenacil, esprocarb, ethalfluralin, ethametsulfuron-methyl, etiodin, ethofumesate , ethoxyphene, ethoxysulfuron, etobenzanide, fenoxaprop-ethyl, fenoxaprop-P-ethyl, fenoxasulfone, fenpyrazone, fenquinotrione, fentrazamide, fenuron, fenuron-TCA, flamprop-methyl, flamprop-M-isopropyl, flamprop-M-methyl, flazasulfuron, florasulam, fluazifop-butyl, fluazifop-P-butyl, fluazolate, flucarbazone, flucetosulfuron, fluchloralin, fluchloraminopyr, flufenacet, flu Fenoximacil, flufenpyr, flufenpyr ethyl, flumetsulam, flumiclorac-pentyl, flumioxazin, fluometuron, fluoroglycofen-ethyl, flupoxam, flupyrsulfuron-methyl and its sodium salt, flurenol, flurenol butyl, fluridone, fluorochloridone, fluroxypyr, flurtamone, flusulfinam, fluthiacet-methyl, fomesafen, foramsulfuron, fosamine-ammonium, glufosinate, glufosinate-ammonium, L-glufosinate ammonium, glufosinate Fosinate-P, glyphosate and its salts (ammonium, isopropylammonium, potassium, sodium (including sesquisodium), trimesium (also known as sulfosate), halaxifen, halaxifen-methyl, halosulfuron-methyl, haloxyfop-ethotyl, haloxyfop-methyl, hexazinone, hydantocidin, imazamethabenz-methyl, imazamox, imazapic, imazapyr, imazaquin, imazaquin-ammonium, imazethapyr, imazethapyr-ammonium, imazosulfuron, indanofan, indaziflam,Iofensulfuron, iodosulfuron methyl, ioxynil, ioxynil octanoate, ioxynil-sodium, ipfencarbazone, iptriazopyride, isoproturon, isouron, isoxaben, isoxaflutole, isoxachlortole, lactofen, lancotrione, lenacil, linuron, maleic hydrazide, MCPA and its salts (e.g., MCPA-dimethylammonium, MCPA-potassium, MCPA-sodium), esters (e.g., MCPA-2-ethylhexyl, MCPA-butotyl), and thioesters esters (e.g., MCPA-thioethyl), MCPB and its salts (e.g., MCPB sodium) and esters (e.g., MCPB ethyl), mecoprop, mecoprop-P, mefenacet, mefluidide, mesosulfuron methyl, mesotrione, metam-sodium, metamifop, metamitron, metazachlor, metazosulfuron, methabenzthiazuron, methylarsonic acid and its calcium, monoammonium, monosodium and disodium salts, methyldimron, metobenzuron, metobromuron, metolachlor, S-metolachlor , metsulam, metoxuron, metribuzin, metsulfuron-methyl, molinate, monolinuron, naproanilide, napropamide, napropamide M, naptalam, nebron, nicosulfuron, norflurazon, orbencarb, orthosulfamuron, oryzalin, oxadiargyl, oxadiazon, oxasulfuron, oxaziclomefone, oxyfluorfen, paraquat chloride, pebulate, pelargonic acid, pendimethalin, penoxsulam, pentanochlor, pentoxazone, perfluidon, petoxamide, phenoxamide, phenoxamic acid ... Nmedipham, picloram, picloram-potassium, picolinafen, pinoxaden, piperophos, pretilachlor, primisulfuron-methyl, prodiamine, profoxydim, prometon, prometryn, propachlor, propanil, propaquizafop, propazine, propham, propisochlor, propoxycarbazone, propyrisulfuron, propyzamide, prosulfocarb, prosulfuron, pyraclonil, pyraflufen-ethyl, pyrasulfotole, pyrazogyl, pyrazolinate, pyrazoxyfen, pyrazosulfuron-ethyl,Pyribenzoxim, pyributicarb, pyridate, pyriflubenzoxim, pyriftalid, pyriminobac-methyl, pyrimisulfan, pyrithiobac, pyrithiobac-sodium, pyroxasulfone, pyroxulam, quinclorac, quinmerac, quinoclamine, quizalofop-ethyl, quizalofop-p-ethyl, quizalofop-p-tefuryl, rimsulfuron, saflufenacil, sethoxydim, siduron, simazine, simetryn, sulcotrione, sulfentrazone, sulfometuron-methyl, sulfosulfuron, 2,3,6- TBA, TCA, TCA-sodium, tebutam, tebuthiuron, tefuryltrione, tembotrione, tepraloxydim, terbacil, terbumeton, terbuthylazine, terbutryn, tetflupyrolimet, thenylchlor, thiazopyr, thiencarbazone, thifensulfuron-methyl, thiobencarb, thiafenacil, thiocarbazil, tolpyralate, topramezone, tralkoxydim, tri-allate, triafamone, triasulfuron, triaziflam, tribenuron-methyl, triclopyr, triclopyr-butotyl, triclopyr-butotyl, triclopyr-butotyl propyltriethylammonium, tridiphane, trietazine, trifloxysulfuron, trifludimoxazine, trifluralin, triflusulfuron-methyl, tripyrasulfone, tritosulfuron, vernolate, 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-pyridinyl)-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(2)-pyridazinone), 5-[[(2,6-difluorophenyl)methoxy]methyl]-4,5-dihydro-5-methyl-3-(3-methyl-2-thienyl)isoxazole (formerly methioxoline),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-pyridinecarboxylate, 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 environmentally compatible salts, "acids," esters, and amides thereof. Other herbicides also include biological herbicides such as Alternaria destruens Simmons, Colletotrichum gloeosporiodes (Penz.) Penz. & Sacc., Drechsiera monoceras (MTB-951), Myrothecium verrucaria (Albertini & Schweinitz) Ditmar:Fries, Phytophthora palmivora (Butl.) Butl., Puccinia thlaspeos Schub, or environmentally compatible salts, acids, esters, and amides thereof.

[0066] Non-limiting examples of herbicides also include acetyl-CoA carboxylase inhibitors (ACC), for example, cyclohexenone oxime ethers such as alloxydim, clethodim, cloproxidim, cycloxydim, sethoxydim, tralkoxydim, butroxydim, clefoxydim, or tepraloxydim; phenoxyphenoxypropionic acid esters such as clodinafop propargyl, cyhalofop butyl, diclofop methyl, fenoxaprop ethyl, fenoxaprop-P-ethyl, fentiapropetyl, fluazifop butyl, fluazifop-P-butyl, haloxyfop ethoxyethyl, haloxyfop methyl, haloxyfop-P-methyl, isoxapyrifop, propaquizafop, quizalofop ethyl, quizalofop-P-ethyl, or quizalofop tefuryl; or Arylaminopropionic acids, such as flammprop-methyl and flammprop-isopropyl; p-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors, such as pyrazolinates, pyrazoxyfen, benzofenap, sulcotrione, isoxaflutole, mesotrione, isoxachlortole, ketospiradox, and tembotrione; acetolactate synthase inhibitors (ALS), such as imidazolinones, such as imazapyr, imazaquin, imazamethabenzmethyl (imazame), imazamox, imazapic, or imazethapyr; pyrimidyl ethers, such as pyrithiobac acid, pyrithiobac sodium, bispyribac sodium, or pyribenzoxim; sulfonamides, such as cloransulam, diclosulam, florasulam, flumetsulam, metosulam, or penoxsulam;Or amidosulfuron, azimsulfuron, bensulfuron methyl, chlorimuronethyl, chlorsulfuron, cinosulfuron, cyclosulfamuron, ethametsulfuron methyl, ethoxysulfuron, flazasulfuron, foramsulfuron, halosulfuron methyl, imazosulfuron, iodosulfuron, metsulfuron methyl, nicosulfuron, primisulfuron methyl, prosulfuron, pyrazosulfuron ethyl, rimsulfuron, sulfometuron methyl or 3-oxetanyl, sulfosulfuron, thifensulfuron sulfonylureas, such as benzoylureas, triflusulfuron methyl, triasulfuron, tribenuron methyl, triflusulfuron methyl, or tritosulfuron; amides, such as alidoclor, benzoylpropethyl, bromobutide, chlorthiamid, diphenamide, etobenzanide (benzchromet), fluthiamid, fosamine, or monalid; auxinic herbicides, such as pyridine carboxylic acids, such as clopyralid or picloram; 2,4-D or benazolin; auxin transport inhibitors, such as naptalam or diflufenzopyr; carotenoid biosynthetics. Synthesis inhibitors such as amitrole, diflufenican, fluorochloridone, fluridone, flurtamone, norflurazon, or picolinafen; enolpyruvylshikimate-3-phosphate synthase inhibitors (EPSPS), such as glyphosate or sulfosate; glutamine synthetase inhibitors, such as viranaphos (bialaphos) or glufosinate ammonium; lipid biosynthesis inhibitors, such as anilides, such as anilophos or mefenacet; dimethenamid, S-dimethenamid, acetochlor, alachlor, butachlor chloroacetanilides such as butenachlor, diethathylethyl, dimethachlor, metazachlor, metolachlor, S-metolachlor, pretilachlor, propachlor, prinachlor, terbuchlor, thenylchlor or xylaclor; thioureas such as butyrate, cycloate, diallate, dimepiperate, EPTC, esprocarb, molinate, pebulate, prosulfocarb, thiobencarb (benthiocarb), triallate or vernolate; or benfresate or perfluidone;Mitotic inhibitors, for example, carbamates such as asulam, carbetamide, chlorpropham, orbencarb, propyzamide, propham, or thiocarbazyl; dinitroanilines such as benefin, butralin, dinitramine, ethalfluralin, fluchloralin, oryzalin, pendimethalin, prodiamine, or trifluralin; pyridines such as dithiopyr or thiazopyr; or butamiphos, chlorthaldimethyl (DCPA), or maleic hydrazide; protoporphyrinogen IX acid enzyme inhibitors, for example, diphenyl ethers such as acifluorfen, acifluorfen sodium, aclonifen, bifenox, chlornitrofen (CNP), ethoxyfen, fluorodifen, fluoroglycofen ethyl, fomesafen, furyloxyfen, lactofen, nitrofen, nitrofluorfen, or oxyfluorfen; oxadiazoles such as oxadiargyl or oxadiazone; azafenidin, butafenacil, carfentrazone ethyl, cini Cyclic imides such as thiazolinone, thiazolinone, thiazolinone, thiazolinone-3, thiazolinone-4, thiazolinone-5, thiazolinone-6, thiazolinone-7, thiazolinone-8, thiazolinone-9, thiazolinone-10, thiazolinone-20, thiazolinone-30, thiazolinone-40, thiazolinone-50, thiazolinone-60, thiazolinone-70, thiazolinone-80, thiazolinone-90, thiazolinone-11, thiazolinone-12, thiazolinone-13, thiazolinone-14, thiazolinone-15, thiazolinone-16, thiazolinone-17, thiazolinone-18, thiazolinone-19, thiazolinone-21, thiazolinone-22, thiazolinone-23, thiazolinone-24, thiazolinone-25, thiazolinone-26, thiazolinone-27, thiazolinone-28, thiazolinone-29, thiazolinone-30, thiazolinone-31, thiazolinone-32, thiazolinone-33, thiazolinone-34, thiazolinone-35, thiazolinone-36, thiazolinone-37, thiazolinone-48, thiazolinone-51, thiazolinone-62, thiazolinone-73, thiazolinone-84, thiazolinone-94, thiazolinone-19, dipyridylenes such as diquat chloride, difenzoquat methyl sulfate, diquat, or paraquat dichloride; ureas such as chlorbromuron, chlorotoluron, difenoxuron, dimefuron, diuron, ethidimuron, fenuron, fluometuron, isoproturon, isouron, linuron, methabenzthiazuron, methazole, metobenzuron, metoxuron, monolinuron, nebron, siduron, or tebuthiuron; phenols such as bromoxynil or ioxynil; chloridazon;Triazines, such as ametryn, atrazine, cyanazine, desmetryn, dimethametryn, hexazinone, prometon, prometryn, propazine, simazine, simetryn, terbumeton, terbutryn, terbuthylazine, or trietazine; triazinones, such as metamitron or metribuzin; uracils, such as bromacil, lenacil, or terbacil; or biscarbamates, such as desmedipham or phenmedipham; growth substances, for example, 2,4-DB, chloramphenicol ... Aryloxyalkanoic acids such as lomeprop, dichlorprop, dichlorprop-P (2,4-DP-P), fluroxypyr, MCPA, MCPB, mecoprop, mecoprop-P, or triclopyr; benzoic acids such as chloramben or dicamba; or quinolinecarboxylic acids such as quinclorac or quinmerac; cell wall synthesis inhibitors such as isoxaben or dichlobenil; various other herbicides such as dichloropropionic acids such as dalapon; ethofumes dihydrobenzofurans such as benzophenone; phenylacetic acids such as chlorfenac (fenac); or adiprothrin, barban, bensulide, benzthiazuron, benzofluor, buminaphos, butidazole, buturon, cafenstrole, chlorbufam, chlorfenprop methyl, chloroxuron, cinmethylin, cumyluron, cycluron, ciprazine, ciprazole, dibenzyluron, dipropetrin, dymron, eglinadin ethyl, endothal, etiodin, Also included are flucabazone, fluorbentranil, flupoxam, isocarbamide, isopropanil, carbutilate, mefluidide, monuron, napropamide, napropanilide, nitralin, oxacyclomefone, phenisopham, piperophos, procyazine, profluralin, pyributicarb, secbumeton, sulfarate (CDEC), terbucarb, triaziflam, triazofenamide, or trimeturon; or compatible salts, acids, esters, or amides thereof;

[0067] insect In some embodiments, the insect is a herbivorous insect. A herbivorous insect refers to an invertebrate pest that feeds on plants, causing damage to the plant, such as by eating leaves, stems, roots, foliage, flowers, pods, fruits, seed tissue, or other vegetative or reproductive plant structures, or by sucking the plant's vascular sap. Leaf-feeding insects can be external (epiparasitic), or they can sometimes specialize in specific cell types and drill holes into the tissue. There are herbivorous insect species in most insect orders, including Hemiptera, Thysanoptera, Orthoptera, Lepidoptera, Coleoptera, Heteroptera, Hymenoptera, and Diptera.

[0068] Examples of agronomic or non-agronomic invertebrate pests include the noctuid moths, armyworms, loopers, and tobacco budworms (e.g., pink stem borer (Sesamia inferens Walker), corn stalk borer (Sesamia nonagrioides Lefebvre), armyworm moth (Spodoptera eridania Cramer), fall armyworm (Spodoptera frugiperda J.E. Smith), beet armyworm (Spodoptera exigua Huebner), cotton leafworm (Spodoptera littoralis littoralis Boisduval), Yellow Stripped Armyworm (Spodoptera ornithogalli Guenee), Cutthroat Moth (Agrotis ipsilon Hufnagel), Mucuna Caterpillar (Anticarsia gemmatalis Huebner), Green Fruit Worm (Lithophane antennata Walker), Armyworm Moth (Barathra brassicae Linnaeus), Soybean Looper (Pseudoplusia includens Walker), Cabbage Looper (Trichoplusia ni Huebner), False Tobacco Moth (Heliothis virescens eggs, larvae and adults of Lepidoptera, such as the European corn borer (Ostrinia nubilis) Fabricius; borers from the family Pyralidae, case bearers, webworms, corn caterpillars, cabbage worms and skeletonizers (e.g. the European corn borer (Ostrinia nubilis)nubilalis Huebner), navel orangeworm (Amyelois transitella Walker), corn root webworm (Crambus caliginosellus Clemens), turf worm (Herpetogramma licarsisalis Walker), sugarcane stem borer (Chilo infuscatellus Snellen), tomato small borer (Neoleucinodes elegantalis Guenee), green leafholder (Cnaphalocrocis medinalis), grape leafholder (Desmia funeralis Huebner), melon worm (Diaphania nitidaris) nitidalis Stoll), Cabbage center grub (Helluala hydralis Guenee), Yellow stem borer (Scirpophaga incertulas Walker), Early shoot borer (Scirpophaga infuscatellus Snellen), White stem borer (Scirpophaga innotata Walker), Top shoot borer (Scirpophaga nivella Fabricius), Dark-headed rice borer (Chilo polychrysus Meyrick), Striped rice borer (Chilo suppressalis Walker), Cabbage cluster caterpillar (Crocidolomiasodwebworms (Crambinae) such as the sodwebworms (Pyralidae: Crambinae); leafrollers, budworms (caterpillars that feed on plant buds), seedworms, and fruitworms (e.g., codling moth (Cydia pomonella Linnaeus), grapeberry moth (Endopiza viteana Clemens), pear fruit moth (Grapholita molesta Busck), citrus second codling moth (Cryptophlebia leucotreta Meyrick), citrus borer (Ecdytolopha aurantiana Lima), red-striped leafroller (Argyrotaenia velutina) velutinana Walker), Oblique-striped Leaf Roller (Choristoneura rosaceana Harris), Light Brown Apple Moth (Epiphyas postvittana Walker), European Grapeberry Moth (Eupoecilia ambiguella Huebner), Apple Bud Moth (Pandemis pyrusana Kearfott), Omnivorous Leaf Roller (Platynota stultana Walsingham), Striped Fruit Tree Tortricid Moth (Pandemis cerasana Huebner), Apple Brown Tortricid Moth (Pandemis heparana Denis & Schiffermueller); as well as many other economically important Lepidoptera (e.g., diamondback moth (Plutella xylostella Linnaeus), pink bollworm moth larvae (Pectinophora gossypiella Saunders), gypsy moth (Lymantria dispar Linnaeus), peach fruit borer (Carposina niponensis Walsingham), peach twig borer (Anarsia lineatella Zeller), potato leaf moth larvae (Phthorimaea operculella Zeller), spotted teniform leafminer (Lithocolletis blancardella)Fabricius), Asian apple leafminer (Lithocolletis ringoniella)Matsumura), rice leaf folder (Lerodea eufala)Edwards), apple leafminer (Leucoptera scitella)Zeller);Cockroaches from the families Blattidae and Blattidae (e.g., the Asian cockroach (Blatta orientalis Linnaeus), the Asian cockroach (Blatella asahinai Mizukubo), the German cockroach (Blattella germanica Linnaeus), the brown-striped cockroach (Supella longipalpa Fabricius), the American cockroach (Periplaneta americana Linnaeus), the brown brown cockroach (Periplaneta brunnea Burmeister), the Madeira cockroach (Leucophaea maderae) eggs, nymphs and adults of the order Blattella, including the brown cockroach (Periplaneta fuliginosa, Service), the Australian cockroach (Periplaneta australasiae, Fabr.), the lobster cockroach (Nauphoeta cinerea, Olivier) and the smooth cockroach (Symploce pallens, Stephens);Weevils from the families Pholioideae, Bruchidae, and Curculionidae (e.g., boll weevil (Anthonomus grandis Boheman), rice water weevil (Lissorhoptrus oryzophilus Kuschel), granary weevil (Sitophilus granarius Linnaeus), grain weevil (Sitophilus oryzae Linnaeus)), annual bluegrass weevil (Listronotus maculicollis Dietz), bluegrass weevil (Sphenophorus palustris), Eggs, foliar-feeding, fruit-feeding, root-feeding, seed-feeding and vesicle-feeding larvae and adults of Coleoptera, including the grass weevil (Sphenophorus parvulus Gyllenhal), the grass weevil (Sphenophorus venatus vestitus), and the Denverville bug (Sphenophorus cicatristriatus Fahraeus); bag beetles, cucumber beetles, root cut beetles, potato beetles, and leaf miners from the family Chrysomelidae (e.g., Colorado potato beetle (Leptinotarsa ​​decemlineata) Say), western corn rootworm (Diabrotica virgifera LeConte)); scarab beetles and other beetles from the family Scarabaeidae (e.g., Japanese beetle (Popillia japonica) Newman), Japanese scarab beetle (Anomala orientalis Waterhouse, Exomala orientalis (Waterhouse) Baraud), northern scarab beetle (Cyclocephala borealis) borealis (Arrow), Southern scarab beetle (Cyclocephala immaculata Olivier or C. lurida Bland), dung beetles and grubs (Scarabaeida spp.), Dung beetle (Ataenius spretulus Haldeman), Greenish cockroach beetle (Cotinis nitida Linnaeus), Red-bellied scarab beetle (Maladera castanea (Arrow), May / June beetle (Phyllophaga spp.) and European scarab beetle (Rhizotrogus majalis majalis) Razoumowsky); dermestid beetles from the Dermestidae family; click beetle larvae from the Elateridae family; bark beetles from the Scolytidae family and red flour beetles from the Tenebrionidae family.

[0069] Further agronomic and non-agronomic pests include eggs, adults and larvae of Dermoptera, including earwigs from the family Dermoptera (e.g., European earwig (Forficula auricularia Linnaeus), black earwig (Chelisoches morio Fabricius)); mirid bugs from the family Miridae, cicadas from the family Cicadidae, leafhoppers from the family Cicadidae (e.g., Empoasca spp.), potato leafhoppers from the family Cimexidae, bedbugs (e.g., Cimex lectularius), lectularius Linnaeus), planthoppers from the superfamily Delphadoidea and Delphacidae, treehoppers from the family Cerambycidae, psyllids from the family Psyllidae, whiteflies from the family Aleyrodidae, aphids from the family Aphididae, mealybugs from the family Pseudococcidae, scale insects from the families Coccidae, Diaspididae and Diaspididae, finches from the family Tingidae, stink bugs from the family Hemiptera, American long-horned stink bugs from the family Lygaeidae (e.g., the hairy American long-horned stink bug (Blissus leucopterus hirtus) Montandon) and the southern American long-horned stink bug (Blissus insularis) Insularis (Barber) and other seed pests from the Lygaeidae family, the foxtail bug from the Scypholidae family, the core bug from the Coreidae family, and the eggs, immatures, adults and larvae of the Hemiptera and Scypholidae suborders such as chigger larvae and cotton stainers from the Pyrrhocoridae family.

[0070] 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., apple red mite (Panonychus ulmi Koch), two-spotted spider mite (Tetranychus urticae Koch), McDaniel mite (Tetranychus mcdanieli McGregor)); flat mites of the family Ornithine (e.g., citrus flat mite (Brevipalpus luisi)); lewisi McGregor); rust and bud mites and other foliar-feeding mites of the family Eriophyidae, and mites important to human and animal health, namely house mites of the family Dermatophagoides, Demodex mites of the family Demodex mites, and acarid mites of the family Sarcophagidae; ticks of the family Ixodidae, commonly known as hard mites (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 Ardisidae, commonly known as soft mites (e.g., relapsing fever tick (Ornithodoros tsurikata)). turicata), common poultry ticks (Argas radiatus); cricket mites, lice mites, and scabies and mange mites of the Sarcoptes scabra family;Grasshoppers, grasshoppers, and crickets (e.g., migratory grasshoppers (e.g., Melanoplus sanguinipes Fabricius, M. differentialis Thomas), American grasshoppers (e.g., Schistocerca americana Drury), desert locusts (Schistocerca gregaria Forskal), migratory locusts (Locusta migratoria Linnaeus), bush locusts (Zonocerus spp.), house crickets (Acheta domesticus Linnaeus), grasshoppers (e.g., tawny grasshoppers (Scapteriscus vicinae)), grasshoppers (e.g., brown ... Eggs, adults and nymphs of Orthoptera, including the Southern Mole Cricket (Scapteriscus borellii Giglio-Tos);Leafminers (e.g., Liriomyza spp., such as the bean leafminer (Liriomyza sativae Blanchard)), small insects, fruit flies (Tephritidae), frit flies (e.g., Oscinella frit Linnaeus), soil maggots, house flies (e.g., Musca domestica Linnaeus), small 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., Chrysomya spp.), spp., Phormia spp.), and other blowfly pests, bot flies (e.g., Tabanus spp.), bot fly larvae (e.g., Gastrophilus spp., Oestrus spp.), cow flies (e.g., Hypoderma spp.), deer flies (e.g., Chrysops spp.), sheep lice (e.g., Melophagus ovinus Linnaeus) and other brachyhorns, mosquitoes (e.g., Aedes spp., Anopheles spp., Culex spp.), and other insect pests. eggs, adults and nymphs of Diptera, including blackflies (e.g., Prosimulium spp.), gnats (e.g., Prosimulium spp.), Simulium spp.), midges, sand flies, sciarid gnats, and other macroceratopsians; eggs, adults and nymphs of Thysanoptera, including onion thrips (Thrips tabaci Lindeman), broad-leaved flower thrips (Frankliniella spp.), and other foliar-feeding thrips;Florida carpenter ant (Camponotus floridanus Buckley), red carpenter ant (Camponotus ferrugineus Fabricius), black carpenter ant (Camponotus pennsylvanicus De Geer), white-legged ant (Technomyrmex albipes fr. Smith), large-headed ant (Pheidole sp.), ghost ant (Tapinoma melanocephalum Fabricius); Pharaoh ant (Monomorium pharaonis Linnaeus), small fire ant (Wasmannia auropunctata) These include pests of the Hymenoptera order, including ants of the family Formicidae, which include the fire ant (Solenopsis auropunctata Roger), fire ant (Solenopsis geminata Fabricius), red fire ant (Solenopsis invicta Buren), Argentine ant (Iridomyrmex humilis Mayr), crazy ant (Paratrechina longicornis Latreille), pavement ant (Tetramorium caespitum Linnaeus), cornfield ant (Lasius alienus Foerster), and odorous house ant (Tapinoma sessile Say). Other Hymenoptera, including bees (including carpenter bees), hornets, yellow jackets, wasps, and sawflies (Neodiprion spp.; Cephus spp.);Termites of the families Termitidae (e.g., Macrotermes sp., Odontotermes obesus Rambur), Isotermitidae (e.g., Cryptotermes sp.), and Rhinotermitidae (e.g., Reticulitermes sp., Coptotermes sp., Heterotermes tenuis Hagen), eastern subterranean termites (Reticulitermes flavipes Kollar), western subterranean termites (Reticulitermes hesperus Banks), Formosan termites (Coptotermes formosanus), formosanus Shiraki), West Indian drywood termite (Incisitermes immigrans Snyder), powder post termite (Cryptotermes brevis Walker), drywood termite (Incisitermes snyderi Light), southeastern subterranean termite (Reticulitermes virginicus Banks), western drywood termite (Incisitermes minor Hagen), arboreal termites such as Nasutitermes sp., as well as other isopteran pests including other termites of economic importance; saccharina Linnaeus) and spotted silverfish (Thermobia domestica Packard);From the order Ciliophaga, and head lice (Pediculus humanus capitis De Geer), human body lice (Pediculus humanus Linnaeus), chicken lice (Menacanthus stramineus Nitszch), dog biting lice (Trichodectes canis De Geer), downy lice (Goniocotes gallinae De Geer), sheep lice (Bovicola ovis Schrank), cattle lice (Haematopinus eurysternus pests including the oriental rat flea (Xenopsylla cheopis Rothschild), the cat flea (Ctenocephalides felis Bouche), the dog flea (Ctenocephalides canis Curtis), the chicken flea (Ceratophyllus gallinae Schrank), the sucking flea (Echidnophaga gallinacea Westwood), the human flea (Pulex irritans), Pests of the order Siphonaptera, including fleas that affect mammals and birds (Loxosceles reclusa Gertsch & Mulaik) and other fleas that affect 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 Scutigera, such as the house centipede (Scutigera coleoptrata Linnaeus);

[0071] Examples of invertebrate pests of stored grain include the boston beetle (Prostephanus truncatus), the mealybug (Rhyzopertha dominica), the grain weevil (Stiophilus oryzae), the maize weevil (Stiophilus zeamais), the cowpea weevil (Callosobruchus maculatus), the red flour beetle (Tribolium castaneum), the granary weevil (Stiophilus granarius), and the Indian meal moth (Plodia interpunctella). interpunctella), the Mediterranean red flour beetle (Ephestia kuhniella) and the rusty grain beetle (Cryptolestis ferrugineus).

[0072] The compositions of the present disclosure are effective against economically important agricultural pests (i.e., root-knot nematodes of the genus Meloidogyne, lesion nematodes of the genus Pratylenchus, stubby root nematodes of the genus Trichodorus, etc.) and animal and human health pests (i.e., Strongylus vulgaris in horses, Toxocara canis in dogs, Haemonchus contortus in sheep, Dirofilaria immitis Leidy in dogs, parasitic helminths Anoplocephala perfoliata in horses, Fasciola hepatica in ruminants, and the like). It may have activity against members of the classes Nematoda, Cestoda, Trematoda, and Acanthocephala, including commercially important members of the orders Strongyloides, Ascarida, Oxyzoida, Rhabdochida, Spiruura, and Enoplus, such as, but not limited to, all economically important trematodes, cestodes, and roundworms (such as, but not limited to, Staphylococcus aureus ...

[0073] The compositions of the present disclosure are effective against Lepidoptera pests (e.g., Alabama argillacea Huebner (cotton leafworm), Archips argyrospila Walker (fruit tree leafroller), A. rosan Linnaeus (European leafroller) and other Archips species, Chilo suppressalis Walker (rice stem borer), Cnaphalocrosis medinalis Guenee (rice leafroller), Crambus caliginosellus Clemens (corn root webworm), Crambus teterrellus Zincken (bluegrass webworm), Cydia pomonella pomonella Linnaeus (Codling Moth), Earias insulana Boisduval (Spiny Bowlworm), Earias vittella Fabricius (Spotted Bowlworm), Helicoverpa armigera Huebner (American Bowlworm), Helicoverpa zea Boddie (Tobacco Moth Larva), Heliothis virescens Fabricius (False Tobacco Moth), Herpetogramma licarsisalis Walker (Sod Webworm), Lobesia botrana Denis & Schiffermueller (Grapeberry Moth), Pectinophora gossypiera gossypiella Saunders (larvae of the pink bollworm moth), Phyllocnistis citrella Stainton (orange leafminer), Pieris brassicaeIt may be active against Spodoptera exigua Huebner (beetle armyworm), Spodoptera litura Fabricius (common cutworm, cluster caterpillar), Spodoptera frugiperda J. E. Smith (stalk fall armyworm), Trichoplusia ni Huebner (cabbage looper), and Tuta absoluta Meyrick (tomato leafminer).

[0074] The compositions of the present disclosure are effective against 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 De Geer (apple aphid), Aphis spiraecola Patch (Spirea aphid), Aulacorthum solani Kaltenbach (foxglove aphid), Chaetosiphon fragaehorii (spider aphid), Aphis cristatus var. ... fragaefolii Cockerell (strawberry aphid), Diuraphis noxia Kurdjumov / Mordvilko (Russian wheat aphid), Dysaphis plantaginea Paaserini (rosy apple aphid), Eriosoma lanigerum Hausmann (apple aphid), Hyalopterus pruni Geoffroy (mealie plum aphid), Lipaphis erysimi Kaltenbach (false radish aphid), Metopolophium dirrhodum Walker (cereal aphid), Macrosiphum euphorbiae euphorbiae Thomas (potato aphid), Myzus persicae Sulzer (peach-potato aphid, green peach aphid), Nasonovia ribisnigri Mosley (lettuce aphid), Pemphigus spp. (root aphids and gall aphids), Rhopalosiphum myzismaidis Fitch (corn aphid), Rhopalosiphum padi Linnaeus (bird cherry oat aphid), Schizaphis graminum Rondani (greengrass aphid), Sitobion avenae Fabricius (English grain aphid), Therioaphis maculata Buckton (spotted alfalfa aphid), Toxoptera aurantii Boyer de Fonscolombe (black citrus aphid), and Toxoptera citricida citricida) Kirkaldy (brown citrus aphid); Adelges spp. (pillar aphid); Phylloxera devastatrix) Pergande (pecan phylloxera); Bemisia tabaci) Gennadius (tobacco whitefly, cotton whitefly), Bemisia argentifolii) Bellows & Perring (silverleaf whitefly), Dialeurodes citri) Ashmead (citrus whitefly) and Trialeurodes vaporariorum) Westwood (greenhouse whitefly); Empoasca fabae fabae) Harris (Potato Leafhopper), Laodelphax striatellus) Fallen (Small Brown Planthopper), Macrolestes quadrilineatus) Forbes (Aster Leafhopper), Nephotettix cinticeps) Uhler (Green Leafhopper), Nephotettix nigropictusnigropictus Stall (Rice Leafhopper), Nilaparvata lugens Stall (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 spp. (Grape Leafhopper); Magicidada septendecim Linnaeus (Periodic Cicada); Icerya purchasii It may have significant activity against members of the Homoptera, including (A. purchasi) Maskell (cotton scale), Quadraspidiotus perniciosus (Comstock) (San Jose scale); Planococcus citri (Risso) (citrus mealybug); Pseudococcus species (other mealybug complex); Cacopsylla pyricola (Foerster) (sunflower psylla), and Trioza diospyri (Ashmead) (persimmon psylla).

[0075] The compositions of the present disclosure also have utility in preventing and treating Acrosternum hilare Say (southern green stink bug), Anasa tristis De Geer (squash bug), Blissus leucopterus Say (American rice bug), Cimex lectularius Linnaeus (bed bug), Corythuca gossypii Fabricius (cotton lace bug), Cyrtopeltis modesta Distant (tomato bug), Dysdercus suturellus Herrich-Schaeffer (cotton stainer), Euchistus cervus (squash bug), Corythuca gossypii Fabricius (cotton lace bug), Corythuca gossypii Fabricius (tomato bug), Corythuca cervus (squash ... servus) Say (Brown Stink Bug), Euchistus variolarius) Palisot de Beauvois (One-Spotted Stink Bug), Graptosthetus spp. (Seed Bug Complex), Halymorpha halys) Stal (Brown Marbled Stink Bug), Leptoglossus corculus) Say (Leaf-Footed Pine Seed Bug), Lygus lineolaris) Palisot de Beauvois (Green Mirid Bug), Nezara viridula) Linnaeus (Southern Green Stink Bug), Oebalus pugnax May have activity against members of the Hemiptera order, including Oncopeltus fasciatus Dallas (large milkweed bug), Pseudatomoscelis seriatus Reuter (cotton freehopper), and Oncopeltus pugnax Fabricius (rice stink bug).Other insect orders controlled by the compounds of the present disclosure include Thysanoptera (e.g., Frankliniella occidentalis Pergande) (Occitan flower thrips), Scirthothrips citri Moulton (Cigar thrips), Sericothrips variabilis Beach (Soybean thrips), and Thrips tabaci Lindeman (Onion thrips); and Coleoptera (e.g., Leptinotarsa ​​decemlineata Say (Colorado potato beetle), Epilachna varivestis (Leptinotarsa ​​decemlineata Say (Colorado potato beetle), Epilachna varivestis (Leptinotarsa ​​varivestis), and Coleoptera (Cigar thrips ... varivestis) Mulsant (heart beetle) and wireworms of the genera Agriotes, Athous or Limonius.

[0076] In some embodiments, the compositions of the present disclosure are useful for controlling western flower thrips (Frankliniella occidentalis). In some embodiments, the compositions of the present disclosure are useful for controlling potato leaf hoppers (Empoasca fabae). In some embodiments, the compositions of the present disclosure are useful for controlling cotton aphids (Aphis gossypii). In some embodiments, the compositions of the present disclosure are useful for controlling diamondback moths (Plutella xylostella L.). In some embodiments, the compositions of the present disclosure are useful for controlling silverleaf whiteflies (Bemisia argentifolii Bellows & Perring).

[0077] In the cyantraniliprole embodiment of the present disclosure, the composition of the present disclosure is effective against insects from the Coleoptera, Chrysomelidae family, Cerotoma trifurcata bean leaf beetle, Chaetocnema concinna beetle, Epilachna varivestis common ladybird beetle, Epitrix cucumeris potato leaf beetle, Leptinotarsa ​​decemlineata Colorado potato beetle, Oulema melanopus cereal leaf beetle, Oulema oryzae rice leaf beetle, Phyllotreta cruciiferae cabbage flea beetle, Phyllotreta striolata flea beetle, Psylliodes flea beetle, family Curculionidae, Anthonomus eugenii pepper weevil, Ceutorhynchus napi cabbage stem weevil, Ceutorhynchus quadridens cabbage root weevil, Conotrachelus nenuphar plum weevil, Hypera bruneipennis Egyptian alfalfa weevil, Hypera postica alfalfa weevil, Lissorhoptrus oryzophilus rice water weevils, Nitidulidae, Meligethes aeneus pollen beetles, blossom beetles, Scarabaeidae, Cotinis nitida blue cockroach beetles, Phyllophaga species cockroach beetles, maggots, Popilliajaponica) Japanese beetle, Diptera, Agromyzidae, Liromyza chinensis) Winter onion leaf miner, Liromyza huidobrensis) Pea leaf miner, Liriomyza sativae) Serpentine / vegetable leaf miner, Liromyza trifolii) American serpentine leaf miner, Anthomyiidae, Delia antiqua) Onion fly, Delia platura) Seed corn maggot, Muscidae, Atherigona oryzae oryzae rice seedling fly, Psilidae family, Psila rosae carrot fly, Tephritidae family, 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 leaf miner, Rhagoletis cerasi) Cherry fruit fly, Rhagoletis cingulata) Cherry fruit fly, Rhagoletis indifferens) Western cherry fruit fly, Rhagoletis pomonellapomonella (apple maggot), Hemiptera, Aleyrodidae, Aleyrodes proletella (cabbage whitefly), Bemisia tabaci (sweet potato whitefly), cotton whitefly, Dialeurodes citri (citrus whitefly), Trialeurodes vaporariorum (greenhouse whitefly), Aphididae, Acyrthosiphon pisum (pea aphid), Aphis craccivora (cowpea aphid), Aphis fabae (black bean aphid), Aphis soybean aphid Soybean aphid (Aphis gossypii), cotton aphid (Aphis gossypii), melon aphid (Aphis nasturtii), buckthorn aphid (Aphis nasturtii), green apple aphid (Aphis pomi), spiraea aphid (Aphis spiraceola), foxglove aphid (Aulacorthum solani), black peach aphid (Brachycaudus persicae), cabbage aphid (Brevicoryne brassicae), European walnut aphid (Chromaphis juglandicola), Dysaphis plantaginea plantaginea) Rosie apple aphid, peach butterbur aphid (Hyalopterus pruni) Mealy plum aphid, false radish aphid (Lipaphis erysimi) Mustard aphid, turnip aphid, tulip long-horn aphid (Macrosiphum euphorbiae) Potato aphid, green peach aphid (Myzus persicae) Green peach aphid, peach and potato aphid, wheat neck aphid (Rhopalosiphumpadi) wild cherry oat aphid, Rhopalosiphum nymphaeae plum aphid, Schizaphis graminum greengrass aphid, Sitobion avenae English grain aphid, Therioaphis maculata spotted alfalfa aphid, Toxoptera citricida brown citrus aphid, Oriental citrus aphid, Cicadellidae, Empoasca fabae leafhopper / leafhopper complex, Empoasca vitis (Green Frogfly), Hortensia similis (Common Green Leafhopper), Idioscopus (Mango Leafhopper), Jacobiasca lybica (Leafhopper), Nephotettix (Rice Green Leafhopper Complex), Typhlocyba rosae (Rose Leafhopper), Typhlocyba pomaria (White Apple Leafhopper), Leptocorisa oratorius (Rice Bug), Rice Ear Bug, Paddy Bug, Delphacidae (Planthoppers), Nilaparvata lugens rice brown planthopper, Diaspididae, Aonidiella aurantii citrus scale bug, Flatidae, Metcalfa pruinosa citrus leafhopper, Pentatomidae, Euschistus brown stink bug, Edessa stink bug, Psyllidae, Diaphorina citri Asian citrus psyllid, Paratrioza cockerelli potato psyllid, Tomato psyllid, Trioza eugenia eugeniae Eugenia psyllid, Lilly pilly psyllid, Hymenoptera Tenthredinidae, Hoplocampa testudinea European apple sawfly, Lepidoptera Crambidae, Scirpophaga incertulas Yellow (rice) stem borer, Gelechiidae, Anarsia lineatellalineatella) peach twig borer, Keiferia lycopersicella tomato pinworm, Pectinophora gossypiella pink bowl worm, Tuta absoluta tomato leaf miner, Gracilaria theivora tea leaf roll moth, Phyllonorycter blancardella spotted tenchform leaf miner, Phyllonorycter coryfoliella nut leaf blister moth, Phyllonorycter crataegella apple blotch leaf miner, Phyllonorycter ringoniella apple leaf miner, Phyllonorycter elmaella western tenchform leaf miner, Hesperiidae family, Borbo cinara rice leafminer, Lyonetiidae family, Leucoptera coffeella white coffee leaf miner, Leucoptera scitella pear leafminer, Lyonetiidae peach leafminer, Noctuidae family, Agrotis segetum common cutworm, Alabama argillacea cotton leafworm, Autographa california alfalfa looper, Barathra brassica brassicae) Cabbage cutworm, Chrysodeixis chalcites Green garden looper, Chrysodeixis eriosoma Green semilooper, Earias insuranainsulana Egyptian bowlworm, Earias vittella Northern rough bowlworm, Feltia subterranea Granular cutworm, Helicoverpa armigera American bowlworm, cotton bollworm, Helicoverpa punctigera Climbing cutworm, Heliothis virescens Tobacco budworm, Helicoverpa zea Tobacco budworm larvae, Prodenia ornithogalli Yellowstriped armyworm, Pseudaletia unipuncta True armyworm, Pseudoplusia includens Soybean looper, Sesamia inferens pink (rice) stem borer, Spodoptera eridania southern armyworm, Spodoptera exigua beet armyworm, Spodoptera frugiperda rice armyworm, Spodoptera littoralis Egyptian armyworm, Spodoptera litura cluster caterpillar, Thermesia gemmatalis peanut moth, Trichoplusia ni nettle looper, Phyllocnistidae, citrus leafminer Phyllocnistis citrella (citrus leaf miner), Pieridae (Pieridae), Colias eurytheme (alfalfa leaf miner), Leptophobia aripa (green-eyed white), Pieris brassicae (cabbage butterfly), Pieris brassicae (large white butterfly), Pierisrapae) nano green caterpillar, cabbage white butterfly, Plutellidae, Plutella xylostella diamondback moth, Pyralidae, Chilo suppressalis (rice suppressor moth), Cnaphalocerus medinalis (rice leaf folder), Crocidolomia binotalis (cabbage caterpillar), Desmia funeralis (grape leaf holder), Diaphania indica (bollworm), Diaphania nitidaltis (melon worm), Hellula hydralis (cabbage center maggot), Hellula undalis (cabbage webworm), Lerodea eufala (rice leaf folder), Leucinodes orbonalis (leucinodes orbonalis eggplant fruit borer, Maruca testulalis bean pod borer, Neoleucinodes elegantalis small tomato borer, Nymphula depunctalis rice trichopteran larvae, Ostrinia furnicalis Asian corn borer, Ostrinia nubilalis European corn borer, Sphingidae family, Manduca sexta tomato caterpillar, tobacco caterpillar, Smerinthus species of hawkmoth, Tortricidae family, Adoxophyes orana summer fruit tortrix, Argyrotaenia pulcherrima pulchellana) grape tortrix moth, Argyrotaenia velutinana) red-striped leafroller, Choristoneura rosaceana) diagonal-striped leafroller, Eupoecilia ambiguella) grapeberry moth, Cydia pomonellapomonella codling moth, Cydia prunivora small appleworm, Grapholita molesta pear fruit moth, Lobesia botrana grapevine moth, Pandemis heparana apple brown tortrix moth, Pandemis limitata three-lined leafroller, Paramyeloi transitella navel orangeworm, Platynota idaeusalis tufted applebud moth, Platynota stultana omnivorous leafroller, Thysanoptera, Thripidae, Enneothrips flavens), Frankliniella fusca (tobacco thrips), Frankliniella intonsa (European flower thrips), Frankliniella occidentalis (Occidental flower thrips), Frankliniella schultzei (common blossom thrips), Frankliniella tritici (Eastern flower thrips), Megalurothrips sjostedti (cowpea thrips), Megalurothrips usitatus (bean blossom thrips), Scirthothrips citri) Citrus thrips, Scirthothrips dorsalis) Yellow tea thrips, Chili thrips, Sericothrips variabilis) Soybean thrips, Stenchaetothrips biformis) Oriental rice thrips, Thrips arizonensisIt is effective against cotton thrips (Thrips arizonensis), peach thrips (Thrips meridionalis), melon thrips (Thrips palmi), and onion thrips (Thrips tabaci), as well as common cotton thrips.

[0078] In some cyantraniliprole embodiments of the present disclosure, the compositions of the present disclosure are effective against insects such as: Colorado potato beetle (Leptinotarsa ​​decemlineata), rice leaf beetle (Oulema oryzae), cabbage flea beetle (Phyllotreta cruciiferae), cabbage flea beetle (Phyllotreta striolata), flea beetles of the genus Psylliodes, pepper weevil (Anthonomus eugenii), plum weevil (Conotrachelus nenuphar), rice water weevil (Lissorhoptrus oryzophilus), Meligethes aenois, and the like. aeneus pollen beetle, blossom beetle, Liromyza chinensis winter onion leaf miner, Liromyza huidobrensis pea leaf miner, Liriomyza sativae serpentine / vegetable leaf miner, Liromyza trifolii American serpentine leaf miner, Delia antiqua onion fly, Delia platura seed corn maggot, Psila rosae carrot fly, Bactrocera dorsalis oriental fruit fly, Bactrocera oleae olive fly, Ceratitis capitata capitata) Mediterranean fruit fly, Rhagoletis indifferens) Western cherry fruit fly, Rhagoletis pomonella) Apple maggot, Bemisia tabaci) Sweet potato whitefly, cotton whitefly, Trialeurodesvaporariorum), greenhouse whitefly, pea aphid (Acyrthosiphon pisum), pea aphid, bean aphid (Aphis craccivora), cowpea aphid, Aphis fabae, black bean aphid, Aphis gossypii, cotton aphid, melon aphid, Aphis pomi, green apple aphid, Aphis spiraceola, potato aphid (Aulacorthum solani), foxglove aphid, radish aphid (Brevicoryne brassicae), cabbage aphid, Dysaphis plantaginea plantaginea) Rosie apple aphid, False radish aphid (Lipaphis erysimi) Mustard aphid, Turnip aphid, Tulip long-horn aphid (Macrosiphum euphorbiae) Potato aphid, Green peach aphid (Myzus persicae) Peach aphid, Peach potato aphid, Wheat neck aphid (Rhopalosiphum padi) Cherry oat aphid, Schizaphis graminum Green wheat aphid, Sitobion avenae English grain aphid, Toxoptera citricida Brown citrus aphid, Oriental citrus aphid, Empoasca vitis vitis Green Frogfly, Idioscopus species of Mango Leafhopper, Nilaparvata lugens Rice Brown Planthopper, Aonidiella aurantii Citrus Sky Bug, Euschistus species of Brown Stink Bug, Diaphorina citri Asian Citrus Sky Bug, Paratrioza cockerelliicockerelli) potato psyllid, tomato psyllid, Scirpophaga incertulas) yellow (rice) stem borer, Anarsia lineatella) peach twig borer, Tuta absoluta) tomato leaf miner, Leucoptera coffeella) white coffee leaf miner, Alabama argillacea) cotton leafworm, Helicoverpa armigera) American bowl worm, cotton bowl worm, Helicoverpa punctigera) climbing cutworm, Heliothis virescens) tobacco budworm, Helicoverpa zea) tobacco budworm larvae, Pseudoplusia includens) soybean looper, Sesamia inferens) pink (rice) stem borer, Spodoptera eridania) southern armyworm, Spodoptera exigua) beet cutworm, Spodoptera frugiperda) rice armyworm, Spodoptera littoralis) Egyptian armyworm, Spodoptera litura) cluster caterpillar, Thermesia gemmatalis) vermiculata beetworm, Trichoplusia ni) nettle looper, citrus leafminer, Phyllocnistis citrella) Citrus leaf miner, Pieris brassicae (cabbage butterfly), large cabbage white butterfly, Pieris rapae (nano green caterpillar), cabbage white butterfly, Plutella xylostella (diamond moth), Chilo suppressalis (rice stem borer)Rice stem borer (Chloro suppressalis), rice leaf folder (Cnaphalocerus medinalis), eggplant fruit borer (Leucinodes orbonalis), Asian corn borer (Ostrinia furnicalis), European corn borer (Ostrinia nubilalis), diagonal striped leafroller (Choristoneura rosaceana), grapeberry moth (Eupoecilia ambiguella), codling moth (Cydia pomonella), pear fruit moth (Grapholita molesta), grapevine moth (Lobesia botrana), Frankliniella fusca It is effective against tobacco thrips (Frankliniella fusca), European flower thrips (Frankliniella intonsa), western flower thrips (Frankliniella occidentalis), citrus thrips (Scirthothrips citri), yellow tea thrips (Scirthothrips dorsalis), chili thrips, melon thrips (Thrips palmi), onion thrips (Thrips tabaci), and common cotton thrips.

[0079] In some cyantraniliprole embodiments of the present disclosure, the compositions of the present disclosure are effective against Conotrachelus nenuphar plum weevil, Liromyza huidobrensis pea leafminer, Liriomyza sativae serpentine / vegetable leafminer, Liromyza trifolii American serpentine leafminer, Bemisia tabaci sweet potato whitefly, cotton whitefly, Trialeurodes vaporariorum greenhouse whitefly, Acyrthosiphon pisum pea aphid, Aphis craccivora cowpea aphid, Aphis gossypii gossypii (cotton aphid), melon aphid, radish aphid (Brevicoryne brassicae) cabbage aphid, Dysaphis plantaginea (rosy apple aphid), green peach aphid (Myzus persicae), peach and potato aphid, Diaphorina citri (Asian citrus skin fly), Paratrioza cockerelli (potato psyllid), tomato psyllid, Scirpophaga incertulas (yellow rice stem borer), Anarsia lineatella (peach twig borer), tomato leafminer (Tuta absoluta), Leucoptera copheera (Leucoptera coffeella) White coffee leaf miner, Alabama argillacea) Cotton leafworm, Helicoverpa armigera) American bowl worm, cotton bowl worm, Helicoverpa punctigera) Climbing cutworm, Heliothisvirescens tobacco budworm, Helicoverpa zea tobacco budworm larvae, Pseudoplusia includens soybean looper, Sesamia inferens pink (rice) stem borer, Spodoptera eridania southern armyworm, Spodoptera exigua beet armyworm, Spodoptera frugiperda leafworm, Spodoptera littoralis Egyptian armyworm, Spodoptera litura cluster caterpillar, Phyllocnistis citrella citrus leafminer, Plutella xylostella xylostella diamondback moth, Chilo suppressalis rice stem borer, Cnaphalocerus medinalis rice leaf folder, Choristoneura rosaceana diagonal striped leafroller, Eupoecilia ambiguella grapeberry moth, Cydia pomonella codling moth, Grapholita molesta pear fruit moth, Lobesia botrana grapevine moth, Frankliniella fusca tobacco thrips, Frankliniella occidentalis western flower thrips, Scirthothrips dorsalis It is effective against yellow tea thrips (Thrips dorsalis), chili thrips, melon thrips (Thrips palmi), and onion thrips (Thrips tabaci), as well as common cotton thrips.

[0080] In the chlorantraniliprole embodiment of the present disclosure, the composition of the present disclosure is effective against: Coleoptera (Family: Chrysomelida, Leptinotarsa ​​decemlineata Colorado potato beetle, Family: Curculionidae, Lissorhoptrus oryzophilus rice water weevil, Listronotus maculicollis annual blue weevil, Oryzophagus oryzae rice water weevil, Sphenophorus species weevils, Family: Scarabaeidae Ataenius spurturs spretulus, a species of scarab beetle, Aphodius, Cotinis nitida, Cyclocephala, Exomala orientalis, Maladera castanea, Phyllophaga, Popillia japonica, and Rhizotrogus majalis; Diptera (Family Agromyzidae, Chromatomyia horticola garden pea leaf miner, and Liriomyza species leaf miner; Hemiptera (Aleyrodidae, Bemisia species whitefly, Trialeurodes abutiloneus banded-winged whitefly, Cicadellidae, and Typhlocyba pomariipomaria white apple leafhopper); Isoptera (Rhinotermitidae, Heterotermes tenuis sugarcane termite, Termitidae, Microtermes obesi sugarcane termite, and Odontotermes obesus sugarcane termite); and Lepidoptera (Arctiidae, Estigmene acrea American tiger moth larvae, Crambidae, Achyra rantalis garden webworm, Desmia funeralis grape leafholder, Ostrinia nubilalis nubilalis (European corn borer), Gelechiidae (Gelechiidae), Anarsia lineatella (Peach twig borer), Keiferia lycopersicella (Tomato pinworm), Phthorimaea operculella (Potato tooth moth larvae), Tuta absoluta S. (American tomato pinworm), Geometridae (Geometridae), Operophthera brumata (Winter geometrid), Gracilaridae (Gracilaridae), Phyllocnistis citrella (Citrus leafminer), Lithocolletis ringoniella (Apple leafminer), Phyllonorycter blancardella Spotted Tenchform Leafminer, Leucoptera species of the family Lyonetidae (i.e., malifoliella, coffeella) Coffee Leafminer, Pear Moth, Noctuidae, and Agrotisipsilon black cutworm, Alabama argillacea Egyptian armyworm, Amphipyra pyramidoides knob green fruitworm, Anticarsia gemmatalis lizard beetle, Autographa gamma common goldfly, Barathra brassica brassicae cabbage armyworm, Earias species (i.e., huegeliana, insulana, vitella) rough, spiny, and northern rough bowlworms, Helicoverpa species (i.e., armigera, punctigera, zea) bowlworms / budworms / fruitworms, Heliothis virescens tobacco budworm, Lithophane antennata green fruitworm, Mamestra brassicae cabbage moth, Orthosia hibisci green fruitworm, Phalaenoides glycinae grapevine moth, Phytometra acta acuta tomato semilooper, Pseudoplusia includens soybean looper, Spodoptera species (i.e., Exigua, Frugiperda, and Littoralis) beet armyworm, grass fall armyworm, Egyptian armyworm, Trichoplusia ni nettle looper, Pieridae, Pieris species (i.e., Brassica and Rapae) large white butterfly, nano caterpillar, Plutellidae, Plutella xylostellaxylostella diamondback moth, Pyralidae family, Amyelois transitella navel orangeworm, Chilo species (i.e., infuscatellus, polychrysus, and suppressalis) sugarcane / rice stem borer, Cnaphalocrocis medinalis rice leafroller, Crambus species cut grass webworm, Crocidolomia binotalis cabbage cluster caterpillar, Diaphania species (i.e., hyalinata and nitidalis) melon worm, pickle worm, Diatraea saccharalis saccharalis, Brazilian sugarcane borer, Elasmopalpus lignosellus small stalk borer, Evergestis rimosalis cross-striped leaf beetle, Hedylepta indicata soybean leaf folder, Hellula species (i.e., hydralis, undalis) cabbage center maggot, cabbage webworm, Leucinodes orbonalis eggplant shoot and fruit borer, Maruca species pod borer, Neoleucinodes elegantaliselegantalis tomato small borer, sugarcane / rice stem borer, which is a species of Scirpophaga, pink stem borer / cornstalk borer, which is a species of Sesamia (i.e., inferens, nonagrioides), tomato / tobacco caterpillar, which is a species of Sphingidae, Manduca (i.e., quinquemaculata, sexta), Tortricidae, and smaller apple tortrix, Adoxophyes orana summer fruit tortrix, Argyrotaenia species (i.e., Pulchellana, Velutinana) grape tortrix, red-striped leafroller, Bonagota cranaodes Brazilian apple leafroller, Carposina species (i.e., Niponensis, Sasaki) peach fruit borer, peach fruit moth, Choristoneura rosaceana diagonal-striped leafroller, Cryptophlebia leucotreta false codling moth, Cydia pomonella codling moth, Ecdytolopha aurantiana aurantiana Citrus Borer, Endopiza vitana Grapeberry Moth, Epiphyas postvittana Light Brown Apple Moth, Eupoecilia ambiguella European Grapeberry Moth, Grapholita molesta Pear Fruit Moth, Lobesia botolanaIt is effective against the European grapevine moth (Ae. botrana), the apple brown tortrix moth (Ae. cerasana, Ae. heparana, the striped fruit tree tortrix moth, Ae. limitata, Ae. pyrusana), the three-striped leafroller, the apple pandemic moth (Ae. cerasana, Ae. heparana), the tufted applebud moth (Ae. idaeusalis, Ae. stultana), the omnivorous leafrollers, the Zygaenidae, and the grapeleaf / western grapevine leaf-feeding larvae (Ae. brillians, Ae. americana, Ae. americana).

[0081] In some chlorantraniliprole embodiments of the present disclosure, the compositions of the present disclosure are effective against Leptinotarsa ​​decemlineata Colorado potato beetle, Liriomyza spp. leaf miner, Bemisia spp. whitefly, Trialeurodes abutiloneus banded-winged whitefly, Heterotermes tenuis sugarcane termite, Microtermes obesi sugarcane termite, and Odontotermes obesus sugarcane termite, Ostrinia nubilalis European corn borer, Anarsia lineatella peach twig borer, Phthorimaea operculella potato leafminer larvae, tomato leafminer (Tuta absoluta S.) American tomato pinworm, citrus leafminer (Phyllocnistis citrella) citrus leafminer, Phyllonorycter blancardella spotted tenchform leafminer, Leucoptera species (i.e., malifoliella, coffeella) coffee leafminer, Agrotis ipsilon black cutworm, Alabama argillacea Egyptian armyworm, and Anticarsia gemmatalis gemmatalis worms, Helicoverpa species (i.e., armigera, punctigera, and zea) bowlworms / budworms / fruitworms, Heliothis virescens tobacco budworms, and Pseudoplusia includens.includens soybean loopers, Spodoptera species (i.e., Exigua, Frugiperda, and Littoralis) beet armyworms, grass fall armyworms, Egyptian armyworms, Trichoplusia ni nettle loopers, Pieris species (i.e., Brassica and Rapa) large white butterfly, nano caterpillar, Plutella xylostella diamondback moth, Amyelois transitella transitella navel orangeworm, Chilo species (i.e., infuscatellus, polychrysus, and suppressalis) sugarcane / rice stem borers, Cnaphalocrocis medinalis rice leafroller, Diatraea saccharalis Brazilian sugarcane borer, and Leucinodes orbonalis orbonalis eggplant shoot and fruit borer, sugarcane / rice stem borer, which is a species of Scirpophaga, pink stem borer / cornstalk borer, which is a species of Sesamia (i.e., inferens, nonagrioides), peach fruit borer, which is a species of Carposina (i.e., niponensis, sasaki), peach fruit moth, Choristoneura rosaceana diagonal striped leafroller, Cydia pomonella codling moth, Eupoecilia ambiguella European grapeberry moth, Grapholita molesta pear fruit moth, and Lobesia botolana botrana) is effective against the European grapevine moth.

[0082] In some chlorantraniliprole embodiments of the present disclosure, the compositions of the present disclosure are effective against Liriomyza species leaf miners, Bemisia species whiteflies, Trialeurodes abutiloneus banded-winged whitefly, Heterotermes tenuis sugarcane termites, Microtermes obesi sugarcane termites, and Odontotermes obesus sugarcane termites, Ostrinia nubilalis European corn borer, Anarsia lineatella peach twig borer, Tuta absoluta S.) American tomato pinworm, Anticarsia gemmatalis (a type of peanut moth), Helicoverpa species (i.e., armigera, punctigera, and zea) bowlworms / budworms / fruitworms, Heliothis virescens (a type of tobacco budworm), Pseudoplusia includens (a type of soybean looper), Spodoptera species (i.e., exigua, frugiperda, and littoralis) beet armyworm, Japanese armyworm, Egyptian armyworm, Plutella xylostella (a type of diamondback moth), Amyelois transitella (a type of beetworm), transitella navel orangeworm, sugarcane / rice stem borers that are species of the genus Chilo (i.e., infuscatellus, polychrysus, and suppressalis), rice leafroller Cnaphalocrocis medinalis, Brazilian sugarcane borer Diatraea saccharalis, sugarcane / rice stem borers that are species of the genus Scirpophaga, pinkstem borers / cornstalk borers that are species of the genus Sesamia (i.e., inferens and nonagrioides), codling moth Cydia pomonella, and pear fruit moth Grapholita It is effective against the pear fruit moth (Pear fruit moth) and the European grapevine moth (Lobesia botrana).

[0083] plant These compositions are useful for protecting agricultural crops and other non-agricultural horticultural crops and plants from herbivorous invertebrate pests. This utility includes protecting crops and other plants (i.e., both agronomic and non-agronomic) that contain genetic material introduced by genetic engineering (i.e., transgenic) or modified by mutagenesis to provide advantageous traits. Examples of such traits include herbicide tolerance, 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 growing conditions such as high or low temperatures, low or high soil moisture, and high salinity, increased flowering or fruiting, increased harvest yield, more rapid maturation, higher quality and / or nutritional value of the harvested product, or improved storage or processing characteristics of the harvested product. Transgenic plants can be engineered to express multiple traits. Examples of plants containing traits achieved by genetic engineering or mutagenesis include corn, cotton, soybean, and potato varieties that express insecticidal Bacillus thuringiensis toxins, such as YIELD GARD®, KNOCKOUT®, STARLINK®, BOLLGARD®, NuCOTN®, and NEWLEAF®, INVICTA RR2PRO™, and herbicide-tolerant varieties of corn, cotton, soybean, and rapeseed, such as ROUNDUP READY®, LIBERTY LINK®, IMI®, STS®, and CLEARFIELD®, as well as crops expressing N-acetyltransferase (GAT) genes that confer resistance to glyphosate herbicides or containing HRA genes that confer resistance to herbicides that inhibit acetolactate synthase (ALS). The present compositions may interact synergistically with traits introduced by genetic engineering or modified by mutagenesis, thus enhancing the phenotypic expression or efficacy of the trait or increasing the invertebrate pest control efficacy of the present compounds and compositions.In particular, the compositions may act synergistically with the expression of proteins or other natural products that are toxic to invertebrate pests to achieve greater than additive control of these pests, i.e., produce a combined effect that is greater than the sum of their individual effects.

[0084] Plants within the scope of this disclosure include crops, vegetables, fruits, non-fruit trees, turf, and other uses (flowers, biofuel plants, and ornamental foliage). Crops include corn, rice, wheat, barley, rye, oats, sorghum, cotton, soybeans, peanuts, buckwheat, beets, rapeseed, sunflowers, sugarcane, tobacco, and others 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 melon); cruciferous vegetables (e.g., radish, turnip, horseradish, kohlrabi, Chinese cabbage, cabbage, mustard greens, broccoli, and cauliflower); Asteraceae vegetables (e.g., burdock, garland chrysanthemum, artichoke, and lettuce); liliaceae vegetables (e.g., leeks, onions, garlic, and asparagus); Apiaceae vegetables (e.g., carrots, parsley, celery, and parsnips); Chenopodiaceae vegetables (e.g., spinach and Swiss chard); and Lamiaceae vegetables (e.g., perilla (Perilla frutescens), mint, and basil). Fruits include pome fruits (e.g., apples, pears, Japanese pears, quince, and quince); stone fruits (e.g., peaches, plums, nectarines, Prunus mume, cherries, apricots, and prunes); citrus fruits (e.g., Citrus unshiu, oranges, lemons, limes, and grapefruit); tree nuts (e.g., chestnuts, walnuts, hazelnuts, almonds, pecans, pistachios, cashews, and macadamia nuts); berries (e.g., blueberries, cranberries, blackberries, strawberries, and raspberries); grapes; persimmons; persimmon trees; olives; plums; bananas; coffee; dates; coconuts; and oil palms.Non-fruit trees include tea; mulberry; and other trees (e.g., ash, birch, dogwood, eucalyptus, ginkgo (Ginkgo biloba), lilac, maple, oak (Quercus), poplar, Judas tree, liquidambar (Liquidambar formosana), plane tree, zelkova, arborvitae, fir tree, hemlock, juniper, pine (Pinus), spruce (Picea), yew (Taxus cuspidate), elm, and horse chestnut), coral tree, dogwood (Podocarpus macrophyllus), cedar, cypress, croton, Japanese einkorn, and Photinia glabra). Turf uses include grasses (e.g., Zoysia japonica, Zoysia matrella); barley; bentgrass; festuca; and ryegrass. Flower uses include roses, carnations, chrysanthemums, eustomas, gypsophila, gerberas, marigolds, salvia, petunias, verbena, tulips, asters, gentiana, lilies, pansies, cyclamen, orchids, lily of the valley, lavender, stocks, snapdragons, primulas, poinsettias, gladioli, cattleyas, daisies, cymbidiums, and begonias. Biofuel plants include jatropha, safflower, camelina, switchgrass, Miscanthus giganteus, Phalaris arundinacea, Arundo donax, kenaf, cassava, and willow.

[0085] Non-agricultural uses Non-agricultural uses refer to the control of invertebrate pests in areas other than crop fields. Non-agricultural uses of the present composition include the control of invertebrate pests in stored grains, beans, and other foodstuffs, and in textile products, such as clothing and carpets. Non-agricultural uses of the present composition also include the control of invertebrate pests in ornamental plants, forests, gardens, roadsides and railroads, and on lawns, such as lawns, golf courses, and pastures. Non-agricultural uses of the present composition also refer to the control of invertebrate pests in homes and other buildings that may be occupied by humans and / or pets, livestock, farm animals, zoo animals, or other animals. Non-agricultural uses of the present composition also include the control of pests that may damage wood or other structural materials used in buildings, such as termites.

[0086] Non-agricultural uses of the present compositions also include protecting human and animal health by controlling invertebrate pests that are parasitic or transmit infectious diseases. Control of animal parasites includes controlling ectoparasites that infest the surface of a host animal's body (e.g., shoulders, axillae, abdomen, inner thighs) and endoparasites that infest the interior of a host animal's body (e.g., stomach, intestines, lungs, veins, subcutaneous tissue, lymphatic tissue). Ectoparasitic or disease-vector pests include, for example, chiggers, ticks, lice, mosquitoes, flies, mites, and fleas. Endoparasites include heartworms, duodenal worms, and helminths. The compositions of the present disclosure are suitable for systemic and / or non-systemic control of parasitic infestation or infection on animals. The compositions of the present disclosure are particularly suitable for combating ectoparasitic or disease-vector pests. The compositions of the present disclosure are suitable for combating parasites in farm animals, such as cattle, sheep, goats, horses, pigs, donkeys, camels, buffalo, rabbits, hens, turkeys, ducks, geese, and bees; pet animals and livestock, such as dogs, cats, pet birds, and ornamental fish; and so-called laboratory animals, such as hamsters, guinea pigs, rats, and mice. Combating these parasites reduces mortality and performance losses (in terms of meat, milk, wool, skin, eggs, honey, etc.), and as a result, applying the compositions of the present disclosure allows for more economical and simple animal husbandry.

[0087] All or any part of a plant can be treated according to the present disclosure. As used herein, the term "plant" is understood to mean all plants and plant populations, for example, desirable and undesirable wild plants or crops (including naturally occurring crops). Crops may be plants obtained by conventional breeding and optimization methods, or by biotechnology and genetic engineering methods, or by a combination of these methods, including transgenic plants and plant cultivars that may or may not be protected by plant breeder's rights. Plant parts are understood to mean all parts and organs of plants above and below ground level, such as shoots, leaves, flowers, and roots. Examples that may be mentioned are leaves, needles, stalks, stems, flowers, fruiting bodies, fruits, and seeds, as well as roots, tubers, and rhizomes. Plant parts also include collected materials, as well as vegetative and generative propagation materials, such as cuttings, tubers, rhizomes, lateral shoots, and seeds.

[0088] 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, treatment as described herein may be by immersion, spraying, evaporation, atomization, dusting, spreading, pouring, and in the case of propagation material (especially in the case of seeds), by applying a layer of coating comprising the composition, optionally with further layers.

[0089] In one embodiment, the compositions of the present disclosure are delivered to plants aerial hi another embodiment, the compositions of the present disclosure are delivered by unmanned aerial vehicle (UAV).

[0090] Wild plant species and plant cultivars, or those obtained by conventional biological breeding methods, for example, crossing or protoplast fusion, and parts thereof, may be treated. Also treated are transgenic plants and plant cultivars (genetically modified organisms), obtained, where appropriate, by genetic engineering methods combined with conventional methods, and parts thereof. In any case, plants of plant cultivars that are commercially available or in use are treated according to the present disclosure. Plant cultivars are understood to mean plants with novel properties ("traits") that have been obtained by conventional breeding, by mutagenesis, or by recombinant DNA techniques. These may be cultivars, biotypes, or genotypes.

[0091] Transgenic plants or plant cultivars (obtained by genetic engineering) that can be treated according to the present disclosure include all plants that have received genetic material by genetic modification that gives these plants particularly advantageous and useful traits.Examples of such traits are better plant growth, increased resistance to high or low temperatures, increased resistance to drought or water or soil salt content, increased flowering performance, easier harvesting, accelerated maturation, higher yields, higher quality and / or higher nutritional value of collected products, better storage stability and / or processability of collected products.Further particularly highlighted examples of such traits are better plant defense against animal and microbial pests, such as insects, mites, plant pathogenic fungi, bacteria and / or viruses, and increased plant resistance to certain herbicidal compounds. Examples of transgenic plants include important crops such as cereals (wheat, rice), corn, soybean, potato, sugar beet, tomato, pea and other vegetable varieties, cotton, tobacco, oilseed rape and fruit plants (with the fruits apple, pear, citrus fruits and grapes), with an emphasis on corn, soybean, potato, cotton, tobacco and oilseed rape. Traits include increased plant defense against insects, arachnids, nematodes, and slugs and snails due to toxins formed in plants, particularly those formed in plants by genetic material from Bacillus thuringiensis (e.g., by the genes CryIA(a), CryIA(b), CryIA(c), CryIIA, CryIIIA, CryIIIB2, Cry9c, Cry2Ab, Cry3Bb, CryIF, Vip3A, and combinations thereof) ("Bt plants"). Other traits are increased plant defense against fungi, bacteria, and viruses due to systemic acquired resistance (SAR), systemins, phytoalexins, elicitors, and resistance genes and the corresponding expressed proteins and toxins. Traits also include increased plant tolerance to particular herbicidally active compounds, such as imidazolinones, sulfonylureas, glyphosate, or phosphinothricin (eg, the "PAT" gene).Genes conferring the desired trait in question can also be present in combination with each other in transgenic plants. Examples of "Bt plants" include corn, cotton, soybean, and potato varieties sold under the trade names YIELD GARD® (e.g., corn, cotton, soybean), KnockOut® (e.g., corn), StarLink® (e.g., corn), Bollgard® (cotton), Nucotn® (cotton), and NewLeaf® (potato). Examples of herbicide-tolerant plants are corn, cotton, and soybean varieties sold under the trade name Roundup Ready® (tolerant to glyphosate, e.g., corn, cotton, soybean). Liberty Link® (tolerant to phosphinothricin, e.g., rapeseed), IMI® (tolerant to imidazolinones), and STS® (tolerant to sulfonylureas, e.g., corn). Herbicide-resistant plants (plants conventionally bred for herbicide tolerance) include varieties sold under the name Clearfield® (e.g., corn). Agricultural crops are selected from the group consisting of cereals, fruit trees, citrus fruits, legumes, horticultural crops, cucurbits, oil plants, tobacco, coffee, tea, cocoa, sugar beet, sugar cane, and cotton.

[0092] Depending on the plant species or plant cultivars, their location and growing conditions (soil, climate, growing season, diet), the treatment according to the present disclosure may also produce superadditive ("synergistic") effects. Thus, for example, it is possible to reduce the application rate and / or widen the activity spectrum and / or increase the activity of the substances and compositions that can be used according to the present disclosure, improve plant growth, increase resistance to high or low temperatures, increase resistance to drought or water or soil salt content, increase flowering performance, easier harvesting, accelerated maturation, higher yield, higher quality and / or higher nutritional value of the collected product, better storage stability and / or processability of the collected product, which actually exceed the expected effects.

[0093] Crops that can be protected with the compositions of the present disclosure include, for example, cereals (wheat, barley, rye, oats, rice, corn, sorghum, etc.), fruit trees (apples, pears, plums, peaches, almonds, cherries, bananas, grapes, strawberries, raspberries, blackberries, etc.), citrus trees (oranges, lemons, mandarins, grapefruit, etc.), legumes (green beans, peaches, etc.), and the like. These include: beans, lentils, soybeans, etc.), vegetables (spinach, lettuce, asparagus, cabbage, carrots, onions, tomatoes, potatoes, eggplant, peppers, etc.), cucurbitaceae (pumpkin, zucchini, cucumber, melon, watermelon, etc.), oleaginous plants (sunflower, rapeseed, peanuts, castor, coconut, etc.), tobacco, coffee, tea, cocoa, sugar beet, sugarcane, and cotton.

[0094] For the protection of agricultural crops, the compositions of the present disclosure can be applied to any part of the plant, or onto the seed before sowing, or onto the soil in which the plants are growing.

[0095] 2 , the reconnaissance data source 208 provides reconnaissance data to the PPP computing device 112 for use in generating the pest pressure forecast. The reconnaissance data may include data provided by a human scout monitoring one or more geographic locations. For example, the reconnaissance data may include crop conditions, pest populations (e.g., manual counts by the human scout at pest traps), etc. In some embodiments, the reconnaissance data source 208 is one of the client systems 114. That is, the scout can use the same computing device (e.g., a mobile computing device) to provide reconnaissance data to the PPP computing device 112 and simultaneously display the pest pressure forecast data and / or heat map data.

[0096] The grower data source 210 provides grower data to the PPP computing device 112 for use in generating the pest pressure forecast. The grower data may include, for example, field boundary data, crop condition data, etc. Additionally, like the reconnaissance data source 208, in some embodiments, the grower data source 210 is one of the client systems 115. That is, a grower can use the same computing device (e.g., a mobile computing device) to provide reconnaissance data to the PPP computing device 112 and simultaneously display pest pressure forecast data and / or heat map data.

[0097] Other data sources 212 may provide other types of data to PPP computing device 112 that are not available from data sources 202-210. For example, in some embodiments, other data sources 212 include a mapping database that provides mapping data (e.g., topographical maps of one or more geographic locations) to PPP computing device 112.

[0098] In an example embodiment, the PPP computing device 112 receives data from at least one of the data sources 202-212, aggregates and analyzes the data (e.g., using machine learning) to generate pest pressure forecast data as described herein. Additionally, the PPP computing device 112 may aggregate and analyze the data to generate heat map data as described herein. The pest pressure forecast 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).

[0099] In some embodiments, data from at least one of the data sources 202-210 is automatically pushed to the PPP computing device 112 (e.g., without the PPP computing device 112 polling or querying the data sources 202-210). Additionally, in some embodiments, the PPP computing device 112 polls or queries (e.g., periodically or continuously) at least one of the data sources 202-210 to obtain associated data.

[0100] 3 illustrates an exemplary configuration of a server system 301, such as a PPP computing device 112 (shown in FIGS. 1 and 2), according to one exemplary embodiment of the present disclosure. The server system 301 also includes, but is not limited to, a database server 116. In the exemplary embodiment, the server system 301 generates pest pressure forecast data and heat map data as described herein.

[0101] The server system 301 includes a processor 305 for executing instructions. The instructions may be stored, for example, in a memory area 310. The processor 305 may include one or more processing units (e.g., a multi-core configuration) for executing the instructions. The instructions may be executed within a variety of different operating systems on the server system 301, such as UNIX, LINUX, Microsoft Windows, etc. It should also be understood that various instructions may be executed during initialization when a computer-based method begins. Some operations may be required to perform one or more 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 language).

[0102] The processor 305 is operatively coupled to a communications interface 315, which enables the server system 301 to communicate with remote devices, such as user systems or other server systems 301. For example, the communications interface 315 can receive requests from the client systems 114 over the Internet, as shown in FIG.

[0103] The processor 305 may also be operatively coupled to a storage device 134. The storage device 134 is 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.

[0104] In some embodiments, processor 305 is operably coupled to storage device 134 via storage interface 320. Storage interface 320 is any component capable of providing processor 305 with access to storage device 134. 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 processor 305 with access to storage device 134.

[0105] The 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 examples only and are not intended to limit the types of memory that may be used to store computer programs.

[0106] 4 illustrates an exemplary configuration of a client computing device 402. The client computing device 402 may include, but is not limited to, a client system ("client computing device") 114. The client computing device 402 includes a processor 404 for executing instructions. In some embodiments, executable instructions are stored in a memory area 406. The processor 404 may include one or more processing units (e.g., a multi-core configuration). The memory area 406 is a device that can store and retrieve information, such as executable instructions and other data. The memory area 406 may include one or more computer-readable media.

[0107] 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 communicating 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 operably coupled to the processor 404 and is operably coupleable 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., speakers or headphones).

[0108] 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 touchscreen), a camera, a gyroscope, an accelerometer, a position detector, and / or an audio input device. A single component, such as a touchscreen, can function as both an output device for the media output component 408 and as an input device 410.

[0109] The client computing device 402 also includes a communication interface 412 that can be communicatively coupled to a remote device, such as the server system 301 or a web server. The communication interface 412 can include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a cellular 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)).

[0110] Memory area 406 stores 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 allows user 400 to view and manipulate media and other information typically embedded in web pages or websites from a web server. The client application allows user 400 to interact with server applications. The user interface facilitates the display of pest pressure information provided by PPP computing device 112 via either or both the web browser and the client application. The client application may be capable of operating in both an online mode (where the client application is in communication with PPP computing device 112) and an offline mode (where the client application is not in communication with PPP computing device 112).

[0111] 5 is a flow diagram illustrating an example method 500 for generating pest pressure data. The method 500 can be implemented using, for example, the PPP computing device 112.

[0112] Method 500 includes receiving 502 trap data for a plurality of pest traps at a geographic location. In an exemplary embodiment, the trap data includes both current pest pressure in the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes in each of the plurality of traps, and historical pest pressure in the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. The trap data may be received (502), for example, from trap data source 206 (shown in FIG. 2). Additionally, PPP computing device 112 can analyze the received (502) trap data to generate additional data. For example, from the received (502) trap data, PPP computing device 112 can determine the number of traps at each level for a number of different pest pressure levels in the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes (e.g., defined by appropriate upper and lower thresholds). Additionally, the PPP computing device 112 may determine the average pest pressure across multiple traps and / or at least a portion of a geographic location. This additional data can be used to identify correlations and predict future pest pressure, as described herein.

[0113] The method 500 further includes receiving 504 weather data 502 for the geographic location. In an example embodiment, the weather data includes both current and past weather conditions for the geographic location. Additionally, in some embodiments, the weather data may include predicted future weather conditions for the geographic location. The weather data may be received 504 from, for example, weather data source 202 (shown in FIG. 2 ).

[0114] In an exemplary embodiment, method 500 further includes receiving 506 image data for the geographic location. The image data may include, for example, satellite image data and / or drone image data. The image data may be received 506 from, for example, imaging data source 204 (shown in FIG. 2 ).

[0115] Additionally, the method 500 includes identifying (508) at least one geospatial feature within or near the geographic location.

[0116] As used herein, "geospatial features" refers to geographical features or structures that can affect pest pressure. For example, geographical features can include bodies of water (e.g., rivers, streams, lakes, etc.), elevation features (e.g., mountains, hills, valleys, etc.), transportation routes (e.g., roads, railroad tracks, etc.), farm locations, and factories (e.g., cotton mills).

[0117] In one embodiment, at least geospatial features are identified from existing map data 508. For example, the PPP computing device 112 may obtain a previously generated map (e.g., a topographical map, an elevation map, a road map, a survey map, etc.) from a map data source (such as other data sources 212 (shown in FIG. 2)), where the previously generated map delimits one or more geospatial features.

[0118] In another embodiment, the PPP computing device 112 identifies (508) one or more geospatial features by analyzing the image data it receives (506). For example, the PPP computing device 112 can apply raster processing to the image data to generate a digital elevation map, where each pixel (or other similar subdivision) of the digital elevation map is associated with an elevation value. The PPP computing device 112 then identifies (508) one or more geospatial features from the digital elevation map based on the elevation values. For example, such techniques can be used to identify elevation features or bodies of water.

[0119] Method 500 further includes applying 510 a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between the pest pressure and the at least one geospatial feature. Applying 510 a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature can be considered as applying 510 a machine learning-based scheme to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between the pest pressure and the at least one geospatial feature. In one or more exemplary embodiments, applying 510 a machine learning algorithm to the trap data, the weather data, the image 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.

[0120] In some embodiments, the PPP computing device 112 can apply (510) a machine learning algorithm to determine that pest pressure (e.g., at the location of the pest trap) on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes varies based on distance from at least one identified geospatial feature. For example, the PPP computing device 112 can determine that pest pressure on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is higher in locations near bodies of water (e.g., due to elevated pest levels in the bodies of water). In another example, the PPP computing device 112 can determine that pest pressure on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is higher in locations near transportation routes (e.g., due to elevated pest levels resulting from materials transported along the transportation routes). In yet another example, the PPP computing device 112 may determine that pest pressure on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is higher in locations near the factory (e.g., due to increased pest levels resulting from materials processed at the factory). In yet another example, the PPP computing device 112 may determine that pest pressure on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is reduced in locations near the at least one identified geospatial feature. The at least one identified geospatial feature may be an area treated with a pest control product.

[0121] Those skilled in the art will appreciate that applying 510 a machine learning algorithm can identify other correlations between pest pressures on populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes in at least one geospatial feature. Specifically, the machine learning algorithm can combine and consider trap data, meteorological data, image data, and at least one identified geospatial feature to detect complex interactions between different types of data that a human analyst may not be able to see. For example, in some embodiments, non-distance-based correlations between at least one identified geospatial feature and pest pressures can be identified.

[0122] For example, in one or more embodiments, applying 510 a machine learning algorithm to the trap data, weather data, image data, and the at least one identified geospatial feature may include determining pest pressure values ​​associated with the pest traps for populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes based on a model (e.g., machine learning model, pest life cycle model) characterizing a relationship (e.g., correlation) between pest pressure and the trap data (optionally, the trap data includes insect data and / or insect life stage data). Further, in one or more embodiments, applying 510 a machine learning algorithm to the trap data, weather data, image data, and the at least one identified geospatial feature may include determining pest pressure values ​​associated with the pest traps for populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes based on a model (e.g., machine learning model) characterizing a relationship (e.g., correlation) between pest pressure, the trap data, and the weather data.

[0123] Additionally, in some embodiments, pest pressure for populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes for a first pest may be correlated with pest pressure for a second, different pest, and the correlation may be detected using the PPP computing device 112. For example, at least one geospatial feature may be a particular field with known high pest pressure for a second pest. Using the systems and methods described herein, the PPP computing device 112 may determine that locations near the particular field typically have high pest pressure for the first pest, which correlates with the pest pressure level of the second pest within the particular field. These "inter-pest" correlations may be complex relationships that can be identified by the PPP computing device 112 but not by a human analyst. Similarly, the PPP computing device 112 may identify "inter-crop" correlations between nearby geographic locations producing different crops.

[0124] The method 500 then includes generating (512) predicted future pest pressure values ​​for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at the geographic location based on at least the identified correlations. Specifically, the PPP computing 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 computing 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 values ​​based on the identified patterns. Those skilled in the art will appreciate that other types of data can also be incorporated to generate (512) predicted future pest pressure values. For example, data on previously planted crops, data on neighboring farms, field water level data, soil type data, etc. may be considered when predicting future pest pressure.

[0125] As an example of a model, the developmental stage of a target pest (e.g., an insect or fungus) may be affected 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).

[0126] The predicted future pest pressure generated 512 for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is an example of pest pressure prediction data that may be transmitted to and displayed on a user computing device, such as client system 114 (shown in FIGS. 1 and 2). For example, the predicted future pest pressure for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes may be transmitted to the user computing device, which may then present the predicted future pest pressure in text, graphical, and / or audio format, or other suitable format. As described in more detail below, in some embodiments, one or more heat maps illustrating the predicted future pest pressure for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes are displayed on the user computing device.

[0127] From the predicted future pest pressure generated 512 for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes, in some embodiments, the systems and methods described herein are also used to generate treatment recommendations for geographic locations (e.g., using machine learning) to address the predicted future pest pressure. For example, by accurately predicting future pest pressure for populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes, the PPP computing device 112 can automatically generate treatment plans for the geographic locations to mitigate future high pest pressure levels. The treatment plans can specify, for example, one or more substances (e.g., pesticides, fertilizers, etc.) and specific times (e.g., daily, weekly, etc.) when the one or more substances should be applied. Alternatively, the treatment plans can also include other data to facilitate improved agricultural performance by taking into account future predicted pest pressure.

[0128] Further, in some embodiments, the predicted future pest pressure generated 512 for the populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes is used (e.g., by the PPP computing device 112) to control additional systems. In one embodiment, a system that monitors pest pressure (e.g., a system that includes pest traps) may 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 may be altered based on the predicted future pest pressure. In another example, spraying equipment (e.g., for applying pesticides) or other agricultural equipment may be controlled based on the predicted future pest pressure.

[0129] As described above, the PPP computing device 112 may also use the pest pressure forecast data to generate one or more heat maps. For purposes of this description, the PPP computing device 112 may be referred to herein as a heat map generating computing device 112.

[0130] 6 is a flow diagram of an exemplary method 600 for generating a heat map of a population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. Method 600 can be implemented, for example, using heat map generation computing device 112 (shown in FIG. 1).

[0131] Method 600 includes receiving 602 trap data for a plurality of pest traps within a geographic region. In an exemplary embodiment, the trap data includes both current pest pressure and historical pest pressure of populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes in each of the plurality of traps. The trap data may be received 602, for example, from trap data source 206 (shown in FIG. 2 ).

[0132] Further, method 600 includes receiving (604) weather data for the geographic location. In an example embodiment, the weather data includes both current and past weather conditions for the geographic location. Additionally, in some embodiments, the weather data may include predicted future weather conditions for the geographic location. The weather data may be received (604), for example, from weather data source 202 (shown in FIG. 2 ).

[0133] In an exemplary embodiment, method 600 further includes receiving 606 image data for the geographic location. The image data may include, for example, satellite image data and / or drone image data. The image data may be received 606 from, for example, imaging data source 204 (shown in FIG. 2 ).

[0134] 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 populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes in each of the plurality of pest traps.

[0135] Further, the method 600 includes generating a first heat map of the populations of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes (610) and generating a second heat map of the populations of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes (612). In an exemplary embodiment, the first heat map is associated with a first time point, and the second heat map is associated with a differential second time point. The first and second heat maps may be generated as follows (610, 612):

[0136] In an exemplary embodiment, each heat map is generated by plotting multiple nodes on a map of geographic locations. Each node corresponds to the location of a particular pest trap among the multiple pest traps. Furthermore, in this exemplary embodiment, each node is displayed in a color representing the pest pressure value of the corresponding test trap's population of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at the relevant time point. In one example, each node is displayed in green (indicating a low pest pressure value), yellow (indicating a moderate pest pressure value), or red (indicating a high pest pressure value). In Figures 7-9, green is represented by a diagonal line pattern, yellow is represented by a crosshatch pattern, and red is represented by a dot pattern. One skilled in the art will appreciate that other numbers and different colors may be used in the embodiments described herein. Depending on the time point associated with the heatmap, the color of the node may indicate past pest pressure values ​​of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations (if the time point is in the past), current pest pressure values ​​of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations (if the time point is in the present), or predicted future pest pressure values ​​of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations (if the time point is in the future). Predicted future pest pressure values ​​of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations can be generated, for example, using machine learning algorithms described herein.

[0137] To complete the heat map of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations, at least a portion of the remainder of the map containing the colored nodes is colored. Specifically, the remainder of the map is colored to generate a continuous map of pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. In an exemplary embodiment, the remainder is colored by interpolating between the pest pressure values ​​at multiple nodes.

[0138] In one embodiment, interpolation is performed using an inverse distance weighting (IDW) algorithm, and points on the remaining portion of the map are colored based on their distance from the known pest pressure values ​​of the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at the node. For example, in such an embodiment, pest pressure values ​​of pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at locations without nodes may be calculated based on a weighted average of the inverse distances of nearby nodes. This embodiment operates under the assumption that pest pressure on pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at a particular point is more strongly influenced by closer nodes (as opposed to more distant nodes). In other embodiments, interpolation can be performed based on other criteria in addition to or instead of distance from the node.

[0139] For at least a portion of the remainder of the map, pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations are generated (using interpolation as described above), and those portions are colored based on the generated pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. With respect to the nodes, in one example, green indicates low pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations, yellow indicates medium pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations, and red indicates high pest pressure values ​​for the pesticide-sensitive homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. Pest pressure may be colored similarly for pesticide-susceptible and pesticide-resistant populations.

[0140] The different color thresholds can be set based on, for example, historical pest pressure and can be adjusted over time (automatically or based on user input). Those skilled in the art will appreciate that these three colors are merely examples and that any suitable coloring scheme can be used to generate the heat maps described herein.

[0141] In an exemplary embodiment, the first and second heatmaps of the populations of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes are stored in a database, such as database 120 (shown in FIG. 1 ). Thus, in this embodiment, when a user views the heatmaps on a user device (e.g., a mobile computing device), the heatmaps have already been pre-generated and stored by heatmap generating computing device 112, as described below. Alternatively, the heatmaps can be generated and displayed in real time in response to a user request.

[0142] With the first and second heat maps generated (610, 612), in an exemplary embodiment, method 600 further includes causing a user interface to display (614) the time-lapse heat map. The user interface may be, for example, a user interface displayed on client device 114 (shown in FIGS. 1 and 2). The user interface may be implemented, for example, via an application installed on client device 114 (e.g., an application provided by an entity operating heat-generating computing device 112).

[0143] The time-lapse heat map displays an animation on the user interface. Specifically, in this exemplary embodiment, the time-lapse heat map dynamically transitions between multiple previously generated heat maps (e.g., first and second heat maps) over time, as described below. Thus, by displaying a dynamic heat map, a user can easily view and understand changes in pest pressure over time in a geographic region. The time-lapse heat map may display past, present, and / or future pest pressure values ​​for pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations in a geographic region.

[0144] It should be understood that in exemplary embodiments, the second heat map of the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at a second time point is generated using predicted pest pressure values ​​of the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes, and this second time point refers to a time point later than the time point of the latest current and past pest pressure values ​​of the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes that were incorporated into the machine learning algorithm (e.g., contained in the trap data). That is, in such embodiments, the second time point refers to a time point in the future.

[0145] Regarding the first heat map of the first time point, in exemplary embodiments, it is generated using the pest pressure values ​​of the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at a time point before the second time point.Therefore, the pest pressure values ​​used to generate the first heat map for the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes are usually either the current pest pressure values ​​or the past pest pressure values ​​of the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes.In another embodiment, the first time point is also a future time point, but is different from the second time point.Therefore, the pest pressure values ​​used to generate the first heat map for the population of pesticide-sensitive homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes are also predicted pest pressure values.

[0146] 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 "first and second heat maps" may mean that one or more (e.g., multiple) "intermediate heat maps" are generated using pest pressure values ​​(e.g., current, past, or predicted pest pressure values, as the case may be) of populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at various times between a first time point and a second time point. In such cases, the time-lapse heat map displays a dynamic transition 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 map includes one or more (e.g., multiple) intermediate heat maps generated using predicted pest pressure values. In another embodiment, the intermediate heat map includes one or more (e.g., multiple) intermediate heat maps generated using current pest pressure values ​​and / or past pest pressure values. In yet another embodiment, the intermediate heat maps include 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.

[0147] In one embodiment, to display a time-lapse heat map, each previously generated heat map is displayed briefly and then instantly switches to the next heat map (e.g., in a slideshow format). Alternatively, in some embodiments, the heat map generating computing device 112 temporally interpolates between successive heat maps to generate transition data between them (e.g., using machine learning). In such embodiments, the time-lapse heat map displays a smooth evolution of pest pressure over time instead of a series of still images.

[0148] 7 is a first screenshot 700 of a user interface that may be displayed on a computing device such as client system 114 (shown in FIGS. 1 and 2). The computing device may be, for example, a mobile computing device.

[0149] First screenshot 700 includes a pest pressure heat map 702 that displays pest pressure associated with a particular pest and crop within region 704, which includes field 706. In the example shown in first screenshot 700, the pest is the boll weevil and the crop is cotton. One skilled in the art will appreciate that the heat maps described herein can display pest pressure information for any suitable pest and crop. Additionally, in some embodiments, the heat map can display pest pressure for multiple pests on the same crop, a single pest on multiple crops, or multiple pests on multiple crops.

[0150] 7, field 706 is delimited on heat map 702 by field boundaries 708. Field boundaries 708 may be plotted on heat map 702 by heat map generating computing device 112 based on information provided by a grower associated with field 706, for example. For example, the grower may provide information to heat map generating computing device 112 from a grower computing device, such as grower data source 210 (shown in FIG. 2).

[0151] The heat map 702 includes three nodes 710 corresponding to the three pest traps in the field 706. As shown in FIG. 7 , each node 710 has an associated color (here, two red nodes and one yellow node). Additionally, in the heat map 702, locations that do not include a node 710 are colored by interpolating the pest pressure values ​​of the pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations at the node 710, generating a continuous map of pest pressure values ​​for the pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. While only three nodes 710 are shown in FIG. 7 , one skilled in the art will understand that additional pest traps can be used to color portions of the heat map 702. The node 710 can be subdivided into pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations and colored separately in the first and second heat maps 702. In this example, heat map 702 is a static heat map showing pest pressure on a population of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes at a particular point in time (e.g., either of the first or second heat maps described above).

[0152] The first screenshot 700 also includes a time lapse button 712 which, when selected by a user, displays a time lapse heatmap as described herein.

[0153] 8 is a second screenshot 800 of a user interface that may be displayed on a computing device, such as client system 114 (shown in FIGS. 1 and 2). Specifically, second screenshot 800 shows a zoomed-in view of heatmap 702 of populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. The zoomed-in view may be generated, for example, in response to a user making a selection on the user interface to change the zoom level.

[0154] As shown in FIG. 8, additional information not shown in the first screenshot 700 is shown in the expanded view. For example, additional nodes 802 (representing additional traps) are now visible. Additionally, the trap names associated with each node 710 are displayed. In an exemplary embodiment, in the expanded view, a user can select a particular node 710 to cause the user interface to display pest pressure data for the pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes populations for that node 710. This is described in more detail below in connection with FIG. 10.

[0155] 9 is a third screenshot 900 of a user interface that may be displayed on a computing device, such as client system 114 (shown in FIGS. 1 and 2). Specifically, third screenshot 900 shows a time lapse heat map 902 of a population of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. Time lapse heat map 902 may be displayed, for example, in response to a user selecting time lapse button 712 (shown in FIGS. 7 and 8).

[0156] As shown in FIG. 9 , a timeline 904 is displayed in association with a time-lapse heat map 902 of the pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. Using the timeline 904, a user can quickly determine the time period currently representing pest pressure on the pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations. The timeline 904 displays a range of dates, including past and future dates, in an exemplary embodiment. Additionally, the timeline 904 includes a current time marker 906 indicating the present (i.e., the current time) and a selected time marker 908 indicating a time associated with pest pressure on the pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous populations displayed on the time-lapse heat map 902.

[0157] For example, in Figure 9, timeline 904 extends from January 5 to February 2, the current date is January 26, and time lapse heat map 902 shows pest pressure on January 29. Notably, the pest pressure shown in Figure 9 is a predicted future pest pressure because selected time marker 908 is later than current time marker 906.

[0158] In one embodiment, a 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. Additionally, in this embodiment, if the user selects the launch icon 910, the time lapse heat map 902 is displayed as an animation, automatically switching between different static heat maps to show the evolution of pest pressure over time. A stop icon 912 is also displayed in the screenshot 900. If the user previously selected the launch icon 910, the user can select the stop icon 912 to stop the animation and freeze the time lapse heat map 902 at any point.

[0159] 10 is a fourth screenshot 1000 of a user interface that may be displayed on a computing device, such as client system 114 (shown in FIGS. 1 and 2). Specifically, fourth screenshot 1000 shows pest pressure data 1002 for a particular trap for populations of pesticide-susceptible homozygotes, pesticide-resistant homozygotes, and pesticide-resistant heterozygotes. Pest pressure data 1002 may be displayed, for example, in response to a user selecting a particular node 710 (as described above with reference to FIG. 8). In one embodiment, pest pressure data 1002 includes graphical data 1004 displaying pest pressure over time (e.g., current pest pressure and historical pest pressure) and textual data 1006 summarizing predicted future pest pressure.

[0160] Additionally, in some embodiments, the generated heat map facilitates the control of additional systems. In one embodiment, a system that monitors pest pressure (e.g., a system that includes 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 may be altered based on the heat map. In another example, spraying equipment (e.g., for applying pesticides) or other agricultural equipment can be controlled based on the heat map.

[0161] At least one of the technical problems addressed by this system includes 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 the user in a comprehensive and straightforward manner.

[0162] Technical effects provided by the embodiments described herein include at least: i) monitoring pest pressure in real time; ii) accurately predicting future pest pressure using machine learning; iii) controlling other systems or equipment based on the predicted future pest pressure; iv) generating a comprehensive heat map showing pest pressure; v) generating a time-lapse heat map that dynamically displays changes in pest pressure over time; and vi) controlling other systems or equipment based on the generated heat map.

[0163] Additionally, 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 at a geographic location, the trap data including pest genetic data, and the trap data including current and past pest pressure values ​​for 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, weather data, and image data to generate predicted future pest pressure values ​​for each of the plurality of pest traps; and (v) generating a first heat map at a first time point and a second heat map at a second time point, the second heat map being generated using the predicted future pest pressure values, each of the first and second heat maps comprising: a) plotting a plurality of nodes on a map of the geographic location; and (v) generating a time-lapse heat map of the geographic locations by: (a) plotting a map of the geographic locations, each node corresponding to one of a plurality of pest traps, each node having a color representing the pest pressure value of the corresponding pest trap at an associated time; and (b) coloring at least some remaining portions of the map of geographic locations to generate a continuous map of pest pressure values ​​of the geographic locations by interpolating between the pest pressure values ​​associated with the plurality of nodes at the associated time; and (vi) transmitting the first and second heat maps to a mobile computing device and causing a user interface on the mobile computing device, the user interface implemented via an application installed on the mobile computing device, to display a time-lapse heat map that dynamically transitions between the first heat map and the second heat map over time.

[0164] The processors or processing elements of the embodiments described herein may employ artificial intelligence and / or be trained using supervised or unsupervised machine learning, and the machine learning programs may employ convolutional neural networks, deep learning neural networks, or neural networks that are composite learning modules or programs that train in two or more fields or subject areas. Machine learning may involve identifying and recognizing patterns in existing data to facilitate prediction of subsequent data. Models may be created based on example inputs to make valid and reliable predictions for new inputs.

[0165] Additionally or alternatively, machine learning programs may be trained by inputting sample data sets or specific data, such as image data, text data, report data, and / or numerical analysis, into the program. Machine learning programs may utilize deep learning algorithms that primarily focus on pattern recognition and may be trained after processing multiple examples. Machine learning programs may include Bayesian program learning (BPL), speech recognition and synthesis, image or object recognition, optical character recognition, natural language processing, and the like, individually or in combination. Machine learning programs may also include natural language processing, semantic analysis, automated reasoning, and machine learning.

[0166] In supervised machine learning, a processing element is provided with example inputs and their associated outputs and attempts to discover general rules that map the inputs to outputs, so that when a subsequent new input is provided, the processing element can accurately predict the correct output based on the discovered rules. In unsupervised machine learning, a processing element may be required to find unique structures within unlabeled example inputs. In one embodiment, machine learning techniques can be used to extract data about computing devices, users of computing devices, computer networks that host computing devices, services running on computing devices, and / or other data.

[0167] Based on these analyses, the processing element can learn how to identify characteristics and patterns and apply them to analyses of trap data, weather data, image data, and geospatial data (e.g., using one or more models) to predict future pest pressure.

[0168] As used herein, the term "non-transitory computer-readable medium" is intended to refer to any tangible computer-based device implemented in any method or technology for short-term and long-term storage of computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Accordingly, the methods described herein may be encoded as executable instructions embodied in tangible non-transitory computer-readable media, 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, firmware, physical and virtual storage, removable and non-removable media such as CD-ROMs and DVDs), as well as networks or the Internet and any other digital sources, such as yet undeveloped digital means, excluding transitory propagating signals. [Example]

[0169] Without further elaboration, it is believed that one skilled in the art using the preceding description can utilize the present disclosure to its fullest extent. Accordingly, the following examples are to be construed as merely illustrative and not limiting of the disclosure in any way.

[0170] 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.

[0171] Example 1. qPCR assay. A qPCR assay was performed using a Taqman probe for the I4790M SNP in the ryanodine receptor gene of Fall Armyworm (FAW) (P. xylostella RyR number). The oligomers used were as follows:

[0172] [Table 1]

[0173] The PCR settings were as follows: PCR was performed on a 7500 qPCR machine under standard thermocycling and endpoint fluorescence measurement conditions. Initial denaturation was at 95°C for 5 minutes. This was followed by 35 cycles of 95°C for 15 seconds, then 60°C for 30 seconds. Endpoint analysis was performed.

[0174] [Table 2]

[0175] The predicted results are shown in Figure 11. In Figure 11, blue indicates the homozygous resistant RR genotype, green indicates the heterozygous RS genotype, and red indicates the homozygous susceptible SS genotype.

[0176] Example 2. qPCR assay. A qPCR assay was performed using a Taqman probe for the G4903E SNP in the Tuta ryanodine receptor gene (G4946E in P. xylostella RyR numbering). The oligomers used were as follows:

[0177] [Table 3]

[0178] PCR settings were as follows: PCR was performed on a 7500 qPCR machine under standard thermocycling and endpoint fluorescence measurement conditions. Initial denaturation was at 95°C for 5 minutes. This was followed by 35 cycles of 95°C for 15 seconds, followed by 58–60°C for 20 seconds. Endpoint analysis was performed.

[0179] [Table 4]

[0180] The predicted results are shown in Figure 12. In Figure 12, blue indicates the homozygous resistant RR genotype, green indicates the heterozygous RS genotype, and red indicates the homozygous susceptible SS genotype.

[0181] Example 3. Molecular surveillance of I4790M in FAW. Taqman® probe-based qPCR methods, such as those in Examples 1 and 2, can be used on a large scale for allelic discrimination. In the context of pesticide resistance, genotyping of single individuals can determine resistant, heterozygous, or susceptible genotypes. Such genotyping can be performed rapidly, with a turnover of one day or less.

[0182] According to the methods of the present disclosure, the allele frequency of I4790M in the F1 generation of fall armyworm (FAW) in crops was monitored, and the results are shown in the table below.

[0183] [Table 5]

[0184] [Table 6]

[0185] Extremely high R allele frequencies (>20%) were observed in some locations. Very high R allele frequencies (11–20%) and high R allele frequencies (5–10%) were also observed. From high to extremely high R allele frequencies, all locations had heterozygous RS frequencies of at least 10%, which are predominant carriers of the R allele.

[0186] For these populations, R allele frequencies and bioassay data at LC99 were measured. As shown in Figure 13, a moderate correlation was observed between R allele frequencies and bioassay data for 12 of the 18 FAW populations (R2 = 0.62; p = 0.023). The SNP conferring 14790M appears to be widely distributed throughout the corn-producing region. The correlation between R allele frequencies and bioassay data was accurate for approximately 70% of the FAW field populations, suggesting that bioassays are important for identifying alternative R mechanisms.

[0187] For these populations, we measured the relationship between R allele frequency and LC99 mortality. As shown in Figure 14, red circles indicate indirect correlations, blue circles indicate potential fitness costs, and green circles indicate that other single nucleotide polymorphisms (SNPs) or other resistance mechanisms may be involved.

[0188] The LC99 mortality rates of various diamide pesticides were determined for these populations. Different allele frequencies resulted in higher mortality rates for specific diamides, as shown in Figure 15. SS homozygotes showed the highest relative mortality rates, while RR homozygotes showed the lowest.

[0189] Example 4. Recommendations for insecticide resistance management (IRM). Methods according to the present disclosure can include providing recommendations for managing insecticide resistance.

[0190] One such recommendation involves treating successive generations of FAW using products with different mechanisms of action (MoA). Another such recommendation involves treating FAW using a treatment window approach, rotating the MoA in each window as needed. Figures 17-18 each show exemplary treatment window recommendations.

[0191] Example 5. qPCR assay. A qPCR assay was performed using a Taqman probe for the G4903E SNP in Tuta absoluta (G4946E according to P. xylostella RyR numbering). The oligomers used were as follows:

[0192] [Table 7]

[0193] The results are shown in Figure 19. In Figure 19, 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 susceptible SS genotype, and X represents an undetermined genotype.

[0194] Example 6. qPCR assay. A qPCR assay was performed using a Taqman probe for the I4790M SNP in Spodoptera frugiperda (P. xylostella RyR number). The oligomers used were as follows:

[0195] [Table 8]

[0196] The results are shown in Figure 20. In Figure 20, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, red represents the homozygous susceptible SS genotype, and X represents an undetermined genotype.

[0197] Example 7. qPCR assay. A qPCR assay was performed using a Taqman probe for the I4790K SNP in Spodoptera frugiperda (P. xylostella RyR number). The oligomers used were as follows:

[0198] [Table 9]

[0199] The results are shown in Figure 20. In Figure 20, blue represents the homozygous resistant RR genotype, green represents the heterozygous RS genotype, red represents the homozygous susceptible SS genotype, and X represents an undetermined genotype.

[0200] This written description presents the disclosure, including the best mode, and uses examples to enable any person skilled in the art to practice the embodiments, including making and using any device or system, and performing any incorporated methods. The patentable scope of the 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 that have insubstantial differences from the literal language of the claims.

[0201] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having," "contains," "containing," "characterized by," or any other variation thereof, are intended to cover a non-exclusive inclusion, subject to any limitations expressly stated. For example, a composition, mixture, process, or method that includes a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or that are inherent in such composition, mixture, process, or method.

[0202] The transitional phrase "consisting of" excludes any unspecified element, step, or ingredient. When included in a claim, such a phrase will limit the claim to the inclusion of materials other than those recited, except for impurities normally associated with them. When the phrase "consisting of" appears in a clause in the body of a claim rather than immediately following the preamble, it limits only the elements defined in that clause; other elements are not excluded from the claim as a whole.

[0203] The transitional phrase "consisting essentially of" is used to define compositions or methods that include materials, steps, features, ingredients, or elements in addition to those literally disclosed, provided that these additional materials, steps, features, ingredients, or elements do not materially affect the basic and novel characteristics of the claimed disclosure. The term "consisting essentially of" constitutes a middle ground between "comprising" and "consisting of."

[0204] It should be readily understood that where a disclosure or portion thereof is defined by open-ended terms such as "comprising," the statement (unless otherwise specified) should also be construed as describing such disclosure using the terms "consisting essentially of" or "consisting of."

[0205] Furthermore, unless expressly stated to the contrary, "alternative" refers to an inclusive or and not an exclusive or. For example, condition A or B is satisfied by any one of the following: 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).

[0206] Also, the indefinite articles "a" and "an" preceding an element or component of the present disclosure are intended to be open-ended regarding the number of instances (i.e., occurrences) of the element or component. Thus, "a" or "an" should be read to include one or at least one, and singular forms of elements or components also include the plural unless the number clearly indicates singularity.

[0207] Any numerical range set forth herein is understood to include all values ​​from the lower value to the upper value. For example, if a weight ratio range is stated as 1:50, values ​​such as 2:40, 10:30, or 1:3 are intended to be expressly recited in this specification. These are merely examples of what is specifically intended, and all possible combinations of numerical values ​​between and including the recited lower and upper limits are considered to be expressly recited in this application.

[0208] As used herein, the term "about" means ±10% of the value.

Claims

1. 1. A heatmap generating computing device, comprising: Memory and a processor communicatively coupled to the memory, receiving trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, and the trap data including current and historical pest pressure values ​​for each of the plurality of pest traps; receiving at least one of i) weather data for the geographic location and ii) image data for the geographic location; applying a machine learning algorithm to the trap data and the at least one of the weather data and the image data to generate predicted future pest pressure values ​​for pesticide-susceptible and pesticide-resistant populations in each of the plurality of pest traps; generating a first heat map of the pesticide-susceptible and pesticide-resistant populations at a first time point and a second heat map of the pesticide-susceptible and pesticide-resistant populations at a second time point, wherein the second heat map is generated using the predicted future pest pressure values, and wherein the first and second heat maps each comprise: plotting a plurality of nodes on the map of geographic locations, each node corresponding to one of the plurality of pest traps, each node representing the pest pressure value of the corresponding pest trap at the associated time point for the pesticide-susceptible and pesticide-resistant populations; annotating at least some remaining portions of the map of the geographic locations to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between pest pressure values ​​associated with the plurality of nodes at the relevant time points for pesticide-susceptible and pesticide-resistant populations; is generated by transmitting the first and second heat maps to a mobile computing device and causing a user interface on the mobile computing device to display 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. a processor programmed to a heat map generating computing device comprising:

2. The heatmap generating computing device of claim 1 , wherein at least one of the first time point and the second time point is a future time point.

3. The processor further comprises: generating treatment recommendations for the geographic location based on the predicted future pest pressure values; 10. The heatmap generating computing device of claim 1, programmed to:

4. The processor further comprises: receiving, from the mobile computing device, a user selection of a selected node in the heatmap, the user selection being made using the user interface; In response to the received user selection, causing the user interface to display pest pressure values ​​for the selected node plotted over time.

10. The heatmap generating computing device of claim 1, programmed to:

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

6. 2. The heat map generating computing device of claim 1, wherein the processor is further programmed to plot farm boundaries on the map of the geographic locations to generate the first and second heat maps.

7. the first and second heat maps are associated with a first pest, and the processor further: generating a third heat map associated with a second pest; receiving a user selection of the second pest made using the user interface; In response to the received user selection, causing the user interface to display a third heat map.

10. The heatmap generating computing device of claim 1, programmed to:

8. 2. The heatmap generating computing device of claim 1, wherein the pest genetic data is used to characterize relative proportions of a pest genetic population and generate treatment recommendations, the pest genetic population preferably comprising individuals selected from pesticide-susceptible homozygous, pesticide-resistant homozygous, and pesticide-resistant heterozygous individuals.

9. The heatmap generating computing device of claim 1 , wherein genetic population dynamics are integrated with pest genetic data to generate treatment recommendations.

10. 1. A method of generating a heat map, the method being implemented using a heat map generating computing device including a memory communicatively coupled to a processor, the method comprising: receiving trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, and the trap data including current and historical pest pressure values ​​for each of the plurality of pest traps; receiving at least one of i) weather data for the geographic location and ii) image data for the geographic location; applying a machine learning algorithm to the trap data and the at least one of the weather data and the image data to generate predicted future pest pressure values ​​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, the second heat map being generated using the predicted future pest pressure values, the first and second heat maps each comprising: plotting a plurality of nodes on the map of geographic locations, each node corresponding to one of the plurality of pest traps, each node indicating the pest pressure value of the corresponding pest trap at the associated time; annotating at least some remaining portions of the map of the geographic locations to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between pest pressure values ​​associated with the plurality of nodes at the relevant time points; Generated by, generating, and transmitting the first and second heat maps to a mobile computing device and causing a user interface on the mobile computing device, the user interface implemented via an application installed on the mobile computing device, to display a time lapse heat map that dynamically transitions between the first heat map and the second heat map over time; A method comprising:

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

12. generating treatment recommendations for the geographic location based on the predicted future pest pressure values; The method of claim 10 further comprising:

13. receiving, from the mobile computing device, a user selection of a selected node in the heatmap, the user selection being made using the user interface; In response to the received user selection, causing the user interface to display pest pressure values ​​for the selected node plotted over time; The method of claim 10 further comprising:

14. 11. The method of claim 10, wherein annotating at least some remaining portions of the map includes annotating by interpolating based on distance from nearby pest traps of the plurality of pest traps.

15. The method of claim 10 , wherein generating the first and second heat maps further comprises plotting farm boundaries on the map of the geographic locations.

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

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

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

19. A computer-readable storage medium having computer-executable instructions embodied therein, the computer-readable instructions, when executed by a heatmap generating computing device including at least one processor in communication with a memory, causing the heatmap generating computing device to: receiving trap data for a plurality of pest traps at a geographic location, the trap data including pest genetic data, and the trap data including current and historical pest pressure values ​​for each of the plurality of pest traps; receiving at least one of i) weather data for the geographic location and ii) image data for the geographic location; applying a machine learning algorithm to the trap data and the at least one of the weather data and the image data to generate predicted future pest pressure values ​​for 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, the second heat map being generated using the predicted future pest pressure values, the first and second heat maps each comprising: plotting a plurality of nodes on the map of geographic locations, each node corresponding to one of the plurality of pest traps, each node indicating the pest pressure value of the corresponding pest trap at the associated time; annotating at least some remaining portions of the map of the geographic locations to generate a continuous map of pest pressure values ​​for the geographic locations by interpolating between pest pressure values ​​associated with the plurality of nodes at the relevant time points; is generated by transmitting the first and second heat maps to a mobile computing device and displaying a time-lapse heat map on a user interface on the mobile computing device 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; A computer-readable storage medium.

20. 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 instructions further cause the heatmap generating computing device to: generating treatment recommendations for the geographic location based on the predicted future pest pressure values; 20. The computer-readable storage medium of claim 19.

22. The instructions further cause the heatmap generating computing device to: receiving, from the mobile computing device, a user selection of a selected node in the heatmap, the user selection being made using the user interface; and in response to the received user selection, causing the user interface to display pest pressure values ​​for the selected node plotted over time.

20. The computer-readable storage medium of claim 19.

23. 20. The computer-readable storage medium of claim 19, wherein the instructions cause the heatmap generating computing device to interpolate based on distance from nearby pest traps of the plurality of pest traps to annotate at least some remaining portions of the map.

24. 20. The computer-readable storage medium of claim 19, wherein the instructions cause the heatmap generating computing device to plot farm boundaries on the map of the geographic location to generate the first and second heatmaps.

25. 20. The computer-readable storage medium of claim 19, wherein the instructions cause the heatmap generating computing device to use the pest genetic data to characterize relative proportions of a pest genetic population and generate treatment recommendations, the pest genetic population preferably including individuals selected from susceptible homozygous, resistant homozygous, and heterozygous individuals.

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

Images