System and method for predicting and dynamically displaying harmful biological pressures

Abstract

A system and method for predicting future harmful biopressures are provided. A harmful biopressure prediction computing device receives historical harmful biopressure data and meteorological data for geographical locations, applies machine learning algorithms to the historical harmful biopressure data and meteorological data to generate predicted future harmful biopressure data, determines the relevant predicted future harmful biopressure value, the relevant predicted peak harmful biopressure, and the relevant estimated peak pressure arrival time for each of several geographical regions from the predicted future harmful biopressure data, and is programmed to display each of the several geographical regions in a color corresponding to the relevant estimated peak pressure arrival time, and to display a specific geographical region on a user computing device in association with a graph showing the predicted pressure value and the time of the predicted peak harmful biopressure for that particular geographical region.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Provisional Patent Application No. 63 / 464,000, filed May 4, 2023, which is hereby incorporated by reference in its entirety.

Background Art

[0002] This application generally relates to techniques that can be used to assist in predicting pest pressure, and more specifically, to network - based systems and methods for predicting and dynamically displaying pest pressure information.

[0003] Worldwide, the population is increasing while the area of arable land is decreasing, so there is a need for methods and systems to increase the productivity of agricultural crops. At least one factor affecting the productivity of agricultural crops is pest pressure.

[0004] Therefore, systems and methods for monitoring and analyzing pest pressure have been developed. For example, in at least some known systems, multiple insect traps are placed in the target field. To monitor the pest pressure in the target field, the traps are inspected regularly and the number of pests in each trap is counted. Based on the number of pests in each trap, the pest pressure level of the target field can be determined.

[0005] It is also possible to predict future pest pressure using the number of pests monitored in each trap. However, pest pressure is a relatively complex phenomenon influenced by several factors. Therefore, accurately predicting future pest pressure based primarily on the number of traps can be relatively inaccurate. Furthermore, at least some known systems for monitoring pest pressure focus on the individual farm level, which limits visualization and results in significant time lags in data collection. Moreover, at least some known systems for predicting future pest pressure rely on static logic (such as fixed phenology models or decision trees), and therefore have limitations in their ability to accurately predict future pest pressure.

[0006] Therefore, it is desirable to provide a system that collects and intelligently analyzes multiple different types of information to rapidly and accurately predict future pest pressure. Furthermore, it is desirable to dynamically present the predicted future pest pressure to the user in a clear and intuitive manner, enabling the user to perform the technical tasks of monitoring pest pressure and optionally controlling pest trapping and / or pest treatment systems. [Overview of the Initiative] [Means for solving the problem]

[0007] In one embodiment, a computing device for predicting harmful biological pressures is provided. The hazardous biopressure prediction computing device includes memory and a processor communicatively coupled to the memory, the processor receiving historical hazardous biopressure data for a geographic location, including current and past hazardous biopressure data for that geographic location, and weather data for that geographic location, including current and historical weather conditions for that geographic location, the processor applying machine learning algorithms to the historical hazardous biopressure data and weather data to generate predicted future hazardous biopressure data for that geographic location, the processor determining, from the predicted future hazardous biopressure data, the associated predicted hazardous biopressure value, the associated predicted peak hazardous biopressure, and the associated estimated peak pressure arrival time for each of several geographic regions within the geographic location, the computing device being programmed to display the several geographic regions, with each geographic region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region, and in response to user input on the computing device to select a specific geographic region from among the several geographic regions, the user computing device being programmed to display the specific geographic region in association with a graph showing the predicted pressure value for that geographic region over time and when the predicted peak hazardous biopressure for that geographic region is expected to occur.

[0008] In another embodiment, a method for generating and displaying hazardous biopressure prediction data is provided. This method is implemented using a hazardous biopressure prediction computing device that includes memory communicably coupled to a processor. The method includes receiving historical pest pressure data for a geographic location, which includes current and past pest pressure data for that geographic location; receiving meteorological data for a geographic location, which includes current and historical meteorological conditions for that geographic location; applying a machine learning algorithm to the historical pest pressure data and meteorological data to generate predicted future pest pressure data for the geographic location; determining, from the predicted future pest pressure data, the associated predicted pest pressure value, the associated predicted peak pest pressure, and the associated estimated peak pressure arrival time for each of several geographic regions within the geographic location, for each of several geographic regions within the geographic location; displaying the several geographic regions on a computing device, with each geographic region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region; and, in response to user input on the computing device to select a specific geographic region from among several geographic regions, displaying the specific geographic region on the user computing device in association with a graph showing the predicted pressure value for that specific geographic region over time and when the predicted peak pest pressure for that specific geographic region is expected to occur.

[0009] In yet another embodiment, a computer-readable storage medium is provided in which a computer-executable instruction is embodied. When executed by a pest pressure prediction computing device including at least one processor communicating with memory, the computer-readable instruction causes the pest pressure prediction computing device to receive historical pest pressure data for a geographic location, which includes current and past pest pressure data for the geographic location; to receive meteorological data for the geographic location, which includes current and historical meteorological conditions for the geographic location; to apply a machine learning algorithm to the historical pest pressure data and meteorological data to generate predicted future pest pressure data for the geographic location; and from the predicted future pest pressure data, for each of a plurality of geographic regions within the geographic location, related The system determines the predicted hazardous biopressure value, the associated predicted peak hazardous biopressure, and the associated estimated time to reach the peak pressure; the computing device displays multiple geographical regions, each geographical region displayed in a color corresponding to the estimated time to reach the peak pressure associated with that region; and, in response to user input on the computing device to select a specific geographical region from among the multiple geographical regions, the user computing device displays the specific geographical region associated with a graph showing the predicted pressure value for that geographical region over time and when the predicted peak hazardous biopressure for that geographical region is expected to occur.

[0010] Figures 1-20B illustrate exemplary embodiments of the methods and systems described herein. [Brief explanation of the drawing]

[0011] [Figure 1] This is a block diagram of a computer system used to predict harmful biopressures according to one embodiment of the present disclosure. [Figure 2] Figure 1 is a block diagram showing the data flow through the system. [Figure 3]Figures 1 and 2 show an example configuration of a server system, such as a harmful biopressure prediction computing device. [Figure 4] Figures 1 and 2 show an example configuration of the client system. [Figure 5] Figure 1 is a flowchart illustrating an exemplary method for generating harmful biopressure data using the system shown. [Figure 6] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 7] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 8] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 9] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 10A] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 10B] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 11A] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 11B] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 12] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 13A] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 13B] This is a screenshot of a user interface that can be generated using the system shown in Figure 1. [Figure 14]A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 15A] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 15B] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 16] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 17A] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 17B] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 18] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 19A] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 19B] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 20A] A screenshot of a user interface that can be generated using the system shown in FIG. 1. [Figure 20B] A screenshot of a user interface that can be generated using the system shown in FIG. 1.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Specific features of various embodiments may be shown in some drawings and not in others, but this is for convenience only. Any feature in any drawing can be referred to and / or claimed in combination with any feature in any other drawing.

[0013] The systems and methods described herein relate to computer implementation systems for predicting future harmful biopressures. The harmful biopressure prediction computing device includes memory and a processor communicatively connected to the memory. The processor is programmed to receive historical pest pressure data for a geographic location, including current and past pest pressure data for that geographic location, and weather data for that geographic location, including current and historical weather conditions for that geographic location. It applies a machine learning algorithm to the historical pest pressure data and weather data to generate predicted future pest pressure data for the geographic location. From the predicted future pest pressure data, it determines, for each of several geographic regions within the geographic location, the associated predicted pest pressure value, the associated predicted peak pest pressure, and the associated estimated peak pressure arrival time. The processor is programmed to display the multiple geographic regions on a computing device, with each geographic region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region. In response to user input on the computing device, which allows the user to select a specific geographic region from among the multiple geographic regions, the processor displays the specific geographic region in association with a graph showing the predicted pressure value for that geographic region over time and when the predicted peak pest pressure for that geographic region is expected to occur.

[0014] The systems and methods described herein facilitate the accurate prediction of pest pressure at one or more geographical locations. As used herein, “geographical location” generally refers to a geographical location relevant to agriculture (e.g., a location including one or more fields and / or farms for crop production). Furthermore, as used herein, “pest pressure” refers to a qualitative and / or quantitative assessment of the number of pests present at a particular location. For example, high pest pressure indicates the presence of a relatively large number of pests (compared to expected numbers) at that location. In contrast, low pest pressure indicates a relatively small number of pests present at that location. In at least some of the embodiments described herein, pest pressure is analyzed for agricultural purposes; that is, the pest pressure of one or more fields is monitored and predicted. However, those skilled in the art will understand that the systems and methods described herein can be used to analyze pest pressure in any suitable environment.

[0015] As used herein, the term “pest” refers to an organism whose presence is generally undesirable in a particular geographical location, especially in a geographical location related to agriculture. For example, in an implementation to analyze pest pressure in one or more fields, pests may include insects that tend to damage crops in those fields. However, those 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 trap” and “trap data.” As used herein, “pest trap” refers to any device capable of containing and / or monitoring the presence of a target pest, and “trap data” refers to data collected using such a device. For example, in the case of insects, a “pest trap” may be a conventional containment device for capturing pests. Or, in the case of fungi, weeds, or diseases, a “pest trap” may refer to any device capable of monitoring the presence and / or levels of fungi, weeds, and / or diseases. For example, in an embodiment where "pest" refers to one or more species of fungi, "pest trap" may refer to a sensing device capable of quantitatively measuring the level of spores associated with one or more species of fungi in the surrounding environment. In one embodiment, "pest" refers to one or more types of insects, and the terms "pest trap" and "multiple pest traps" refer to "insect trap" and "multiple insect traps," respectively.

[0016] In the following detailed description of embodiments of the present disclosure, reference will be made to the accompanying drawings. The same reference numerals in different drawings may identify the same or similar elements. Furthermore, the following detailed description will not limit the scope of the claims.

[0017] This specification describes computer systems such as hazardous biopressure prediction computing devices. As described herein, all such computer systems include a processor and memory. However, any processor within a computer device referred to herein may also refer to one or more processors, which may reside in a single computing device or in multiple computing devices operating in parallel. Furthermore, memory within a computer device referred to herein may also refer to one or more memory locations, which may reside in a single computing device or in multiple computing devices operating in parallel.

[0018] As used herein, a processor may include any programmable system, including microcontrollers, reduced instruction set circuits (RISC), application-specific integrated circuits (ASICs), logic circuits, and other circuits or processors capable of performing the functions described herein. The above examples are illustrative and do not limit the definition or meaning of the term "processor."

[0019] As used herein, the term “database” may refer to the 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 illustrative and do not limit the definition or meaning of the term database. Examples of RDBMS 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.)

[0020] In one embodiment, a computer program is provided, which is embodied on a computer-readable medium. In one embodiment, the system runs on a single computer system without requiring a connection to a server computer. In a further embodiment, the system runs in a Windows® environment (Windows is a registered trademark of Microsoft Corporation in 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 in Reading, Berkshire, UK). The application is flexible and designed to run in various environments without compromising its core 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.

[0021] When used herein, elements or steps listed in the singular and followed by the word "a" or "an" should be understood not to exclude multiple elements or steps unless such exclusions are explicitly listed. Furthermore, references to “exemplary embodiments” or “one embodiment” in this disclosure are not intended to be construed as excluding the existence of additional embodiments that also incorporate the listed mechanisms.

[0022] As used herein, the terms “software” and “firmware” are interchangeable and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The memory types described above are merely examples and therefore do not limit the types of memory that can be used to store computer programs.

[0023] 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.

[0024] The following detailed description illustrates, but is not limited to, embodiments of the present disclosure. The present disclosure is generally applicable to predicting harmful biopressures.

[0025] Figure 1 is a block diagram of one embodiment of a computer system 100 used for predicting pest pressure, including a pest pressure prediction (PPP) computing device 112 according to one embodiment of the present disclosure. The PPP computing device 112 is also referred to herein as a heatmap generation computing device. In an exemplary embodiment, the system 100 is used to predict pest pressure and to dynamically display pest pressure information to a user (e.g., via a graphical user interface), as described herein.

[0026] More specifically, in this exemplary embodiment, System 100 includes a Pest Pressure Prediction (PPP) computing device 112 and several client subsystems, also called client systems 114, connected to the PPP computing device 112. In one embodiment, a client system 114 is a computer including a web browser, thereby enabling the PPP computing device 112 to access the client system 114 using the Internet and / or a network 115. The client systems 114 are interconnected to the Internet via many interfaces, including a network 115 such as a local area network (LAN), a wide area network (WAN), dial-in connections, cable modems, special high-speed integrated digital service network (ISDN) lines, and RDT networks. The client systems 114 may include systems associated with farmers, growers, scouts, etc., and external systems used to store data. The PPP computing device 112 also communicates with one or more data sources 130 using the network 115. Furthermore, the client systems 114 can also communicate additionally with the data sources 130 using the network 115. Furthermore, in some embodiments, one or more client systems 114 can function as data sources 130, as described herein. A client system 114 is any device that can interconnect to the Internet, including a web-based telephone, PDA, or other web-based connectable device.

[0027] The database server 116 is connected to a database 120 that contains information on various matters, as will be described in more detail below. In one embodiment, the centralized database 120 is stored in a PPP device 112 and can be accessed by a potential user in one of the client systems 114 by logging on to the PPP computing device 112 via one of the client systems 114. In another embodiment, the database 120 may be stored remotely from the PPP device 112 and 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 will be described herein.

[0028] Database 120 may include a single database having separate sections or partitions, or it may include multiple databases, each separated from the others. Database 120 can store data received from data source 130 and generated by PPP computing device 112. For example, database 120 may store weather data, imaging data, trap data, reconnaissance data, grower data, pest pressure prediction data, and / or heatmap data, as described in detail herein.

[0029] In exemplary embodiments, the client system 114 may be associated with, for example, a grower, a reconnaissance agency, a pest control agency, and / or any other party that can use the system 100 described herein. In exemplary embodiments, at least one of the client systems 114 includes a user interface 118. For example, the user interface 118 may include a graphical user interface with interactive capabilities, thereby displaying pest pressure predictions and / or heatmaps transmitted from the PPP computing device 112 to the client system 114 in a graphical format. Users of the client system 114 can interact with the user interface 118 to view, explore, and otherwise interact with the displayed information.

[0030] In an exemplary embodiment, the PPP computing device 112 receives data from multiple data sources 130, aggregates and analyzes the received data (e.g., using machine learning) to generate a harmful biopressure forecast and / or heatmap, as described in detail herein.

[0031] Figure 2 is a block diagram showing the data flow through system 100. In the embodiment shown in Figure 2, data source 130 includes a weather data source 202, an imaging data source 204, a trap data source 206, a reconnaissance data source 208, a grower data source 210, and another data source 212. Those skilled in the art will understand that data source 130 shown in Figure 2 is merely an example, and system 100 can include any appropriate number and types of data sources. Furthermore, imaging data source 204, trap data source 206, reconnaissance data source 208, and grower data source 210 are examples of sources for historical pest pressure data (as these can include data showing current or past pest pressure).

[0032] The meteorological data source 202 provides meteorological data to the PPP computing device 112 for use in generating harmful biopressure forecasts. The meteorological data may include, for example, temperature data (e.g., current and / or past temperatures measured at one or more geographic locations), humidity data (e.g., current and / or past humidity measured at one or more geographic locations), wind data (e.g., current and / or past wind speed and direction measured at one or more geographic locations), precipitation data (e.g., current and / or past rainfall amounts measured at one or more geographic locations), and forecast data (e.g., predicted future weather conditions for one or more geographic locations).

[0033] The imaging data source 204 provides image data to the PPP computing device 112 for use in generating harmful biopressure predictions. The image data may include, for example, satellite imagery and / or drone imagery acquired from one or more geographic locations.

[0034] The trap data source 206 provides trap data to the PPP computing device 112 for use in generating pest pressure predictions. The trap data may include, for example, the number of pests from at least one pest trap at a geographical location (expressed as, for example, the number of pest species, the density of pest species, etc.). Furthermore, the trap data may include, for example, the type of pest (taxonomic genus, species, variety, etc.) and / or the developmental stage and sex of the pest (larva, juvenile, adult, male, female, etc.). The pest trap may be, for example, an insect trap. Alternatively, the pest trap may be any device capable of determining the presence of pests and providing trap data to the PPP computing device 112, as described herein. For example, in some embodiments, the pest trap is a sensing device capable of sensing the ambient level 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.

[0035] In some embodiments, the trap data source 206 is a pest trap communicatively coupled (e.g., via a wireless communication link) to the PPP computing device 112. Thus, in such embodiments, the trap data source 206 may automatically determine the number of pests in the pest trap (e.g., using an image processing algorithm) and transmit the determined number of pests to the PPP computing device.

[0036] The reconnaissance data source 208 provides reconnaissance data to the PPP computing device 112 for use in generating pest pressure predictions. The reconnaissance data may include data provided by human scouts monitoring one or more geographic locations. For example, the reconnaissance data may include crop status, the number of pests (e.g., the number manually counted by human scouts in pest traps), etc. In some embodiments, the reconnaissance data source 208 is one of the client systems 114. That is, the scouts can use the same computing device (e.g., a mobile computing device) to provide reconnaissance data to the PPP computing device 112 while simultaneously displaying pest pressure prediction data and / or heatmap data.

[0037] The grower data source 210 provides grower data to the PPP computing device 112 for use when generating pest pressure predictions. The grower data may include, for example, field boundary data, crop status data, etc. Furthermore, similar to the reconnaissance data source 208, in some embodiments, the grower data source 210 is one of the client systems 115. That is, the grower can use the same computing device (e.g., a mobile computing device) to provide reconnaissance data to the PPP computing device 112 while simultaneously viewing pest pressure prediction data and / or heatmap data.

[0038] Other data sources 212 can provide the PPP computing device 112 with other types of data that are not available from data sources 202-210. For example, in some embodiments, other data sources 212 include a mapping database that provides the PPP computing device 112 with mapping data (e.g., topographic maps of one or more geographic locations).

[0039] In an example of the 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 prediction data as described herein. Furthermore, the PPP computing device 112 may also aggregate and analyze the data to generate heatmap data, as described herein. The pest pressure prediction data and / or heatmap data may be transmitted to the client system 114 (e.g., for display to a user of the client system 114).

[0040] 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). Furthermore, in some embodiments, the PPP computing device 112 polls or queries at least one of the data sources 202-210 (e.g., periodically or continuously) to retrieve associated data.

[0041] Figure 3 shows an exemplary configuration of a server system 301, such as a PPP computing device 112 (shown in Figures 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 harmful biopressure prediction data and heatmap data, as described herein.

[0042] The server system 301 includes a processor 305 for executing instructions. 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 instructions. Instructions can be executed within various different operating systems on the server system 301, such as UNIX, LINUX, and Microsoft Windows®. It should also be understood that, as the computer-based method is initiated, various instructions may be executed during initialization. Some operations may be necessary to run one or more processes described herein, while others may be more general and / or specific to a particular programming language (such as C, C#, C++, Java, or other suitable programming language).

[0043] The processor 305 is operably coupled to the communication interface 315, thereby enabling the server system 301 to communicate with user systems and other remote devices such as other server systems 301. For example, the communication interface 315 can receive requests from the client system 114 via the internet, as shown in Figure 2.

[0044] The processor 305 can also be operably coupled to the storage device 134. The storage device 134 is computer operation 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 can be accessed from 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 RAID (redundant array of inexpensive disks) configuration. The storage device 134 may include a storage area network (SAN) and / or network-attached storage (NAS) system.

[0045] In some embodiments, the processor 305 is operably coupled to the storage device 134 via a storage interface 320. The storage interface 320 is any component that can provide the processor 305 with access to the storage device 134. The storage interface 320 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any other component that provides the processor 305 with access to the storage device 134.

[0046] The memory area 310 may include, but is not limited to, random access memory (RAM) such as dynamic RAM (DRAM) and static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are merely examples and do not limit the types of memory that can be used to store computer programs.

[0047] Figure 4 shows an exemplary configuration of a client computing device 402. The client computing device 402 may, but is not limited to, include 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.

[0048] The client computing device 402 also includes at least one media output component 408 for presenting information to the user 400. The media output component 408 is any component capable of conveying information to the user 400. In some embodiments, the media output component 408 includes output adapters such as a video adapter and / or an audio adapter. The output adapter is operably coupled to the processor 404 and can be operably coupled to output devices such as a display device (e.g., a liquid crystal display (LCD), an organic light-emitting diode (OLED) display, a cathode ray tube (CRT), or an "electronic ink" display) or an audio output device (e.g., a speaker or headphones).

[0049] 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, pointing device, mouse, stylus, touch-sensitive panel (e.g., touchpad or touchscreen), camera, gyroscope, accelerometer, position detector, and / or audio input device. A single component, such as a touchscreen, can function as both an output device and an input device 410 for the media output component 408.

[0050] The client computing device 402 also includes a communication interface 412 that can be connected to a remote device such as a server system 301 or a web server. The communication interface 412 may include, for example, wired or wireless network adapters or wireless data transceivers for use with cellular networks (e.g., Global System for Mobile communications (GSM), 3G, 4G, 5G, or Bluetooth) or other mobile data networks (e.g., Worldwide Interoperability for Microwave Access (WiMAX)).

[0051] Memory area 406 stores computer-readable instructions for, for example, providing a user interface to user 400 via media output component 408, and optionally for receiving and processing input from input device 410. The user interface may include, among many possibilities, a web browser and a client application. Using the web browser, user 400 can 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 a server application. The user interface facilitates the display of harmful biopressure information provided by the PPP computing device 112 via either the web browser or the client application, or both. The client application may be able to operate in both online mode (a mode in which the client application communicates with the PPP computing device 112) and offline mode (a mode in which the client application does not communicate with the PPP computing device 112).

[0052] Figure 5 is a flowchart illustrating an exemplary method 500 for generating harmful biopressure data. Method 500 can be implemented, for example, using a PPP computing device 112.

[0053] Method 500 includes receiving historical pest pressure data at geographical locations (502). Historical pest pressure data may be received from, for example, an imaging data source 204, a trap data source 206, a reconnaissance data source 208, and / or a grower data source 210 (all shown in Figure 2). In this exemplary embodiment, the historical pest pressure data includes current and past pest pressure data. Furthermore, the PPP computing device 112 can analyze the received historical pest pressure data to generate additional data. For example, from the received historical pest pressure data, the PPP computing device 112 can determine the number of traps for each level for many different pest pressure levels (defined, for example, by appropriate upper and lower thresholds). Furthermore, the PPP computing device 112 can determine the average pest pressure over a large number of traps and / or at least a portion of the geographical locations. This additional data can be used to identify correlations and predict future pest pressures, as described herein.

[0054] Method 500 further includes receiving (504) meteorological data 502 for a geographic location. In the exemplary embodiment, the meteorological data includes both current and past meteorological conditions for the geographic location. Furthermore, in some embodiments, the meteorological data may include predicted future meteorological conditions for the geographic location. The meteorological data may be received from, for example, a meteorological data source 202 (shown in Figure 2) (504).

[0055] In some embodiments, the method includes identifying at least one geospatial feature located within or near a geographic location.

[0056] As used herein, “geospatial features” means geographical features or structures that may affect harmful biopressure. For example, geographical features may include bodies of water (e.g., rivers, streams, lakes), elevation features (e.g., mountains, hills, valleys), transport routes (e.g., roads, railway lines), farm locations, and factories (e.g., cotton mills).

[0057] In one embodiment, at least geospatial features are identified from existing map data. For example, the PPP computing device 112 can obtain previously generated maps (e.g., topographic maps, elevation maps, road maps, survey maps, etc.) from a map data source (such as another data source 212 (shown in Figure 2)), and the previously generated maps demarcate one or more geospatial features.

[0058] In another embodiment, the PPP computing device 112 identifies one or more geospatial features by analyzing the received image data. 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 one or more geospatial features from the digital elevation map based on the elevation values. For example, such a technique can be used to identify elevation features or bodies of water.

[0059] Method 500 further includes applying machine learning algorithms to historical pest pressure data and meteorological data to generate predicted future pest pressure at geographic locations (506). In some embodiments, predicted future pest pressure may be generated by identifying a correlation between pest pressure and at least one geospatial feature. For example, in some embodiments, the PPP computing device 112 can determine, by applying machine learning algorithms (506), that pest pressure (e.g., at the location of a pest trap) varies based on distance from at least one identified geospatial feature. For example, the PPP computing device 112 may determine that pest pressure is higher near bodies of water (e.g., due to increased pest levels in bodies of water). In another example, the PPP computing device 112 may determine that pest pressure is higher near transport routes (e.g., due to increased pest levels resulting from materials transported along the transport route). In yet another example, the PPP computing device 112 may determine that hazardous biopressure is higher near a factory (for example, due to increased hazardous biopressure levels resulting from materials processed at the factory). In yet another example, the PPP computing device 112 may determine that hazardous biopressure is reduced near at least one identified geospatial feature.

[0060] Those skilled in the art will understand that machine learning algorithms can detect complex interactions between different types of data that may be undetectable by human analysts. For example, in some embodiments, a distance-independent correlation between at least one identified geospatial feature and harmful biopressure can be identified.

[0061] In one or more exemplary embodiments, applying a machine learning algorithm (506) may include determining one or more predicted future pest pressures based on a model (e.g., a machine learning model, a pest lifecycle model). Furthermore, in some embodiments, the pest pressure of a first pest may correlate with the pest pressure of a second different pest, and this correlation may be detected using the PPP computing device 112. For example, at least one geospatial feature is a particular field with a known high pest pressure of a second pest. Using the systems and methods described herein, the PPP computing device 112 can determine that the pest pressure of the first pest is typically high near a particular field, and that this correlates with the pest pressure level of the second pest in that particular field. These “pest” correlations may be complex relationships that can be identified by the PPP computing device 112 but not by a human analyst. Similarly, “crop” correlations between nearby geographic locations may be identified by the PPP computing device 112.

[0062] In some embodiments, the PPP computing device 112 can generate predicted future pest pressures using a spray timer model, a pest lifecycle model, and the like. Those skilled in the art will understand that any suitable type of data can be incorporated to generate predicted future pest pressure values. For example, when predicting future pest pressures, data on previously planted crops, data on neighboring farms, field water level data, soil type data, etc., may be considered.

[0063] In exemplary embodiments, predicted future pest pressure is generated using an enhanced growth day (GDD) model. The enhanced GDD model leverages high-precision weather forecast data, historical pest pressure data, and advanced modeling techniques to determine predicted future pest pressure. For example, the developmental stage of a target pest (e.g., insect or fungus) is dependent on ambient temperature, and this model can predict the developmental stage of the pest based on heat accumulation (e.g., determined from temperature data). In particular, in embodiments described herein, predicted future pest pressure includes a predicted peak pressure and an estimated time to reach the predicted peak pressure (also referred to herein as estimated time to reach peak pressure).

[0064] Method 500 further includes making the predicted future hazardous biopressures available on a user computing device such as a client system 114 (shown in Figures 1 and 2) (508). For example, the predicted future hazardous biopressures are transmitted to the user computing device, which can then present the predicted future hazardous biopressures in text, graphic, and / or audio format, or other appropriate format. In some embodiments, as will be described in detail below, the estimated time to reach peak pressure is available on the user computing device.

[0065] From the generated predicted future pest pressures, 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 pressures. For example, by accurately predicting future pest pressures, the PPP computing device 112 can automatically generate treatment plans for geographic locations to mitigate future high pest pressure levels. The treatment plan may specify, for example, one or more substances (e.g., pesticides, fertilizers) and specific times (e.g., daily, weekly) when those substances should be applied. Alternatively, the treatment plan may also include other data to facilitate improved agricultural performance in consideration of the predicted future pest pressures. The treatment plan may be generated, for example, based on predicted peak pressures and estimated time to peak pressure.

[0066] Furthermore, in some embodiments, predicted future pest pressure (e.g., by a PPP computing device 112) is used to control additional systems. In one embodiment, a system that monitors pest pressure (e.g., a system including pest traps) may be controlled based on predicted future pest pressure. For example, the reporting frequency and / or type of trap data reported by one or more pest traps may be changed based on predicted future pest pressure. In another example, spraying equipment (e.g., for pesticide application) or other agricultural equipment may be controlled based on predicted future pest pressure. These systems may be controlled based, for example, on predicted peak pressure and estimated time to reach peak pressure.

[0067] Figure 6 is a first screenshot 600 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The computing device may be, for example, a mobile computing device.

[0068] The first screenshot 600 shows one embodiment of the home screen 602. The home screen 602 includes current weather information 604, as well as pest pressure information 606 for one or more areas associated with a given farm (e.g., here "Smith Farm"). The pest pressure information 606 may include past, present, and / or predicted future pest pressure information. Furthermore, as shown in Figure 6, the pest pressure information 606 is shown for different crops (e.g., soybeans and cotton) and for different pests (e.g., fall armyworms, leafhoppers, and cotton weevils).

[0069] In particular, the pest pressure information 606 includes peak pressure estimated arrival time information 608. For example, the peak pressure estimated arrival time information 608 is displayed as a text box indicating that the peak pest pressure of fall armyworm in soybean crops in area 1 is expected to occur within one week. By selecting the link 610 included in the peak pressure estimated arrival time information 608, the user can view additional information associated with the peak pressure estimated arrival time 608, as described in detail herein.

[0070] Figure 7 is a second screenshot 700 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The second screenshot 700 shows one embodiment of a menu screen 702. The menu screen 702 includes several selectable tabs 704 (e.g., Account Date, Farm, Growth Days, and Laws). For example, selecting the Farm tab may display the Home screen 602 (shown in Figure 6) in the user interface. Selecting the Growth Days tab may display the Growth Days screen overview screen (shown in Figure 8) in the user interface.

[0071] Figure 8 is a third screenshot 800 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The third screenshot 800 shows one embodiment of the GDD overview screen 802. The GDD overview screen 802 shows several different geographical regions 804, which may be displayed to a user responsible for a particular geographical region 804. In this embodiment, each geographical region 804 is a state. However, a person skilled in the art will understand that each geographical region 804 may be a country, city, county, farm, field, and / or other appropriate location.

[0072] In some embodiments, a particular entity (e.g., a company or grower) may be responsible for only one region and therefore only have access to data within that region. For that type of entity, the user interface may initially display a GDD region-specific screen (details below) instead of the GDD overview screen 802.

[0073] The GDD overview screen 802 includes estimated peak pressure arrival time 806. In this embodiment, the estimated peak pressure arrival time information 806 is a text box indicating when peak pressure is expected in at least some geographical regions 804. The GDD overview screen 802 also includes a region selection section 808. As shown in Figure 8, all geographical regions 804 are currently selected. If the user wants to display a specific geographical region 804, they can select that region 804 in the region selection section.

[0074] Figure 9 is a fourth screenshot 900 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The fourth screenshot 900 shows a GDD overview screen 802 with a peak pressure estimated arrival time scale 902. Specifically, as shown in Figures 8 and 9, geographical regions 804 are color-coded based on their associated peak pressure estimated arrival times. The peak pressure estimated arrival time scale 902 provides the user with an explanation of the color coding.

[0075] For example, the Peak Pressure Estimated Time to Arrival Scale 902 is a continuous scale that shows how the displayed colors change based on the number of weeks until the expected peak pest pressure is reached. A decrease in the number of weeks indicates that the expected peak pest pressure has already occurred. Those skilled in the art will understand that the Peak Pressure Estimated Time to Arrival Scale 902 is just one example, and that many different appropriate scales and / or legends can be used to provide users with an explanation of the color coding.

[0076] Figures 10A and 10B are fifth screenshots 1000 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The fifth screenshot 1000 shows a GDD region-specific screen 1002. The GDD region-specific screen 1002 is displayed in response to the user selecting a specific geographic region 804 on the GDD overview screen 802 (e.g., using the region selection section 808). Thus, the GDD region-specific screen 1002 displays the selected geographic region 1004, as well as peak pressure estimated arrival time information 1006 for the selected geographic region 1004 (e.g., as a text box).

[0077] In particular, the GDD region-specific screen 1002 also displays more detailed information (compared to the GDD overview screen 802) regarding the predicted future harmful biopressure (including predicted peak harmful biopressure) for the selected geographic region 1004.

[0078] For example, the GDD region-specific screen 1002 includes a graph 1010 showing predicted harmful biopressures over time, including the predicted peak harmful biopressure 1012. Furthermore, the graph 1010 includes a slider 1014. By moving the slider 1012 from left to right (for example, by clicking and dragging the slider 1012), the user can select a specific day on the graph 1010. Once the user selects a specific day, detailed day-to-day information for that day is dynamically displayed.

[0079] The GDD region-specific screen 1002 also includes a sub-region selection 1020, which the user can use to select a specific sub-region within the selected geographic region 1004 (and to view additional hazardous biopressure information for those sub-regions). Here, a sub-region refers to a county within the selected state. Again, a person skilled in the art will understand that a sub-region can be any suitable location.

[0080] Figures 11A and 11B are a sixth screenshot 1100 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The sixth screenshot 1100 shows the GDD region-specific screen 1002 when the user operates the slider 1014 to specify a different day (compared to Figure 10). Furthermore, in the sixth screenshot 1100, the user has selected a specific city ("Bailey") within a specific county ("Kent"), and the GDD region-specific screen 1002 displays hazardous biopressure information (including hazardous biopressure information) for the selected city and county. Thus, the GDD region-specific screen 1002 allows the user to quickly and easily drill down into relevant hazardous biopressure information. Moreover, the information is presented to the user in a clear and intuitive manner.

[0081] Figure 12 is a seventh screenshot 1200 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The seventh screenshot 1200 shows a GDD interval screen 1202 that may be displayed depending on whether the user has selected a GDD interval information link 1122 (shown in Figures 10 and 11). The GDD interval screen 1202 displays detailed GDD information for the hazardous organism(s) associated with the selected geographical area 1004.

[0082] Figures 13A and 13B are eighth screenshots 1300 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The eighth screenshot 1300 shows a report generation screen 1302 that may be displayed in response to user selections made, for example, on the GDD overview screen 802 and / or the GDD region-specific screen 1002. The report generation screen 1302 allows the user to generate a report containing hazardous biopressure information (including predicted hazardous biopressure) for one or more geographic regions 804. This report can be downloaded to a computing device and / or shared with other users (for example, via email).

[0083] Figure 14 is a ninth screenshot 1400 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The ninth screenshot 1400 is a city overview screen 1402 that displays several farms 1404 associated with a selected city (here, Bailey, Kent County). Selecting one of the farms 1404 displays detailed information about the selected farm (as described below). The city overview screen 1404 also includes a filter button 1406. Selecting the filter button 1406 allows the user to select specific pests (e.g., codling moths) and / or crops (e.g., apples) for which information is needed. The filter button 1406 may also be included on other screens of the user interface (e.g., GDD overview screen 802).

[0084] Figures 15A and 15B are a tenth screenshot 1500 of a user interface that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The tenth screenshot 1500 is a farm overview screen 1502 that displays pest biopressure information for a specific farm (for example, a farm selected from farms 1404 displayed on the city overview screen 1402). For a specific farm, pest biopressure information for one or more fields 1504 is displayed. The pest biopressure information for a field 1504 may include, for example, a heatmap 1506 showing the current and / or predicted pest biopressure, an estimated time to reach peak pressure 1508 (for example, as a text box), and / or GDD information 1510.

[0085] Figure 16 shows one embodiment of the predicted peak pressure map 1600. The predicted peak pressure map 1600 can be displayed as part of a user interface on a computing device such as a client system 114 (see Figures 1 and 2). For example, the predicted peak pressure map 1600 may be included in the GDD overview screen 802.

[0086] As shown in Figure 16, the predicted peak pressure map 1600 displays the geographical area 1602 divided into multiple sectors 1604. Furthermore, each sector 1604 is color-coded according to the predicted arrival time of the peak pest pressure in that sector. Key 1606 indicates that the number of weeks until the predicted peak pressure corresponds to each color.

[0087] Figures 17A and 17B show embodiments of the weather and harmful biopressure display 1700. The weather and harmful biopressure display 1700 can be displayed as part of a user interface on a computing device such as a client system 114 (shown in Figures 1 and 2).

[0088] In this embodiment, the weather and harmful biopressure display 1700 displays weather information 1702 and harmful biopressure information 1704 for a given week. The user can select the weather information to display using the weather parameter selection section 1706. In Figure 17A, the user has selected to display the probability of precipitation as weather information 1702. In Figure 17B, the user has selected to display the temperature as weather information 1702.

[0089] In this embodiment, the pest pressure information 1704 includes a quantified high pest pressure risk 1710 and a graphed high pest pressure risk 1712. The quantified high pest pressure risk 1710 is an actual percentage. In contrast, the graphed high pest pressure risk 1712 intuitively shows the high pest pressure risk to the user. For example, in this embodiment, predicted high pest pressure (e.g., a risk of over 50%) is shown by three pest icons, predicted moderate pest pressure is shown by two pests, and predicted low pest pressure is shown by one pest icon. Those skilled in the art will understand that Figures 17A and 17B are merely examples and that the weather information 1702 and pest pressure information 1704 can be displayed in any suitable way.

[0090] Figure 18 shows an alternative embodiment of the GDD overview screen 1802 that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The GDD overview screen 1802 includes a peak pressure estimated arrival legend 1804. Specifically, as shown in Figure 18, geographical regions 1806 are color-coded based on their associated peak pressure estimated arrival times. The peak pressure estimated arrival legend 1804 provides the user with an explanation of the color coding.

[0091] For example, the peak pressure estimate arrival legend 1804 includes an arrangement of colored boxes 1808 that show how the colors displayed change based on the number of weeks until the expected peak pest pressure is reached. Those skilled in the art will understand that the peak pressure estimate arrival legend 1804 is just one example, and that many different appropriate scales and legends can be used to provide users with an explanation of the color coding.

[0092] Figures 19A and 19B show alternative embodiments of the GDD overview screen 1902 that may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The GDD overview screen 1902 includes a peak pressure estimated arrival legend 1904. Specifically, as shown in Figures 19A and 19B, geographical regions 1906 are color-coded based on their associated peak pressure estimated arrival times. The peak pressure estimated arrival legend 1904 provides the user with an explanation of the color coding.

[0093] For example, the peak pressure estimate arrival legend 1904 includes an arrangement of colored circles 1908 showing how the colors displayed change based on the number of weeks until the expected peak pest pressure is reached. Those skilled in the art will understand that the peak pressure estimate arrival legend 1904 is just one example, and that many different appropriate scales and legends can be used to provide users with an explanation of the color coding.

[0094] In some embodiments, data on past peak hazardous biopressures (i.e., hazardous biopressure peaks that have already occurred) can be displayed to the user. For example, Figures 20A and 20B show alternative embodiments of the GDD overview screen 2002, which may be displayed on a computing device such as a client system 114 (shown in Figures 1 and 2). The GDD overview screen 2002 includes a peak pressure estimated arrival time legend 2004 and a past peak pressure legend 2006. Furthermore, as shown in Figures 20A and 20B, geographical regions 2008 are color-coded (or otherwise displayed) based on their associated peak pressure estimated arrival times and their past peak pressure times. The peak pressure estimated arrival time legend 2004 and the past peak pressure legend 2006 provide the user with an explanation of the color coding. This allows the user to quickly and easily understand past and future hazardous biopressure patterns.

[0095] In the examples shown in Figures 20A and 20B, geographical region 2008 is displayed in association with a numerical value (e.g., weeks or days) indicating the time until the estimated peak pressure or the elapsed time since the last peak pressure. In these examples, positive numbers (e.g., 1, 2, 3, or 4) indicate the number of weeks until the peak pressure is expected in geographical region 2008, while negative numbers (e.g., -1, -2, or -3) indicate how long ago the peak pressure occurred in geographical region 2008.

[0096] At least one of the technical problems this system addresses includes: i) the inability to accurately monitor harmful biopressure; ii) the inability to accurately predict future and peak harmful biopressure; and iii) the inability to communicate harmful biopressure information to users in a comprehensive and direct manner.

[0097] The technical effects provided by the embodiments described herein include at least i) monitoring harmful biopressure in real time, ii) accurately predicting future harmful biopressure, including peak harmful biopressure, using machine learning, iii) controlling other systems or equipment based on predicted future harmful biopressure and peak harmful biopressure, and iv) generating a comprehensive display showing harmful biopressure information.

[0098] Furthermore, the technical effects of the systems and processes described herein are achieved by performing at least one of the following steps: i) receiving historical pest pressure data for a geographic location, which includes current and past pest pressure data for that geographic location; ii) receiving meteorological data for a geographic location, which includes current and historical meteorological conditions for that geographic location; iii) applying a machine learning algorithm to the historical pest pressure data and meteorological data to generate predicted future pest pressure data for that geographic location; iv) from the predicted future pest pressure data, relevant predictions for each of a plurality of geographic regions within the geographic location. a) determining the harmful organism pressure value, the associated predicted peak harmful organism pressure, and the associated estimated time to reach the peak pressure; a) causing a computing device to display multiple geographic regions, with each geographic region displayed in a color corresponding to the estimated time to reach the peak pressure associated with that region; vi) selecting a specific geographic region from among the multiple geographic regions, and in response to user input on the computing device, causing the user computing device to display the specific geographic region associated with a graph showing the predicted pressure values ​​for that geographic region over time and when the predicted peak harmful organism pressure for that geographic region is expected to occur.

[0099] The processors or processing elements of the embodiments described herein may employ artificial intelligence and / or be trained using supervised or unsupervised machine learning, the machine learning program may employ a convolutional neural network, a deep learning neural network, or a neural network which is a composite learning module or program that learns in two or more fields or subject areas. Machine learning may include identifying and recognizing patterns in existing data to facilitate prediction of subsequent data. A model may be built on example inputs to make effective and reliable predictions for new inputs.

[0100] In addition, or instead, a machine learning program may be trained by inputting sample datasets or specific data, such as image data, text data, report data, and / or numerical analysis data. A machine learning program may utilize deep learning algorithms, primarily focused on pattern recognition, and may be trained after processing multiple examples. Machine learning programs may include, individually or in combination, Bayesian program learning (BPL), speech recognition and synthesis, image or object recognition, optical character recognition, and natural language processing. Machine learning programs may also include natural language processing, semantic analysis, automated reasoning, and machine learning.

[0101] In supervised machine learning, a processing element is given example inputs and their associated outputs, and attempts to discover general rules that map inputs to outputs. This allows the processing element to accurately predict the correct output based on the discovered rules when new inputs are provided. In unsupervised machine learning, the processing element may be required to find its own structure within unlabeled example inputs. In one embodiment, machine learning techniques can be used to extract data about computer devices, the users of computer devices, the computer networks hosting computer devices, the services running on computer devices, and / or other data.

[0102] Based on these analyses, the processing element can learn how to identify characteristics and patterns and apply them to the analysis of trap data, weather data, image data, and geospatial data (e.g., using one or more models) to predict future harmful biopressures.

[0103] As used herein, the term “non-transient computer-readable medium” is intended to mean any computer-based device of any entity implemented by any method or technique for storing computer-readable instructions, data structures, program modules and submodules, or other data on any device for short and long periods. Thus, the methods described herein, but not limited to, may be encoded as executable instructions embodied in tangible non-transient computer-readable medium, including 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-transient computer-readable medium” includes all tangible computer-readable medium, including but not limited to non-transient computer storage devices (including, but not limited to, volatile and non-volatile media, and removable and non-removable media such as firmware, physical and virtual storage, CD-ROMs, DVDs, etc.), and any other digital sources, such as networks or the internet, and any digital means not yet developed, excluding transient propagated signals.

[0104] This specification provides the best mode of the disclosure and uses examples that enable a person skilled in the art to construct and use any device or system and to perform the embodiments, including any incorporated methods. The patentable scope of the disclosure is defined by the claims and may include other embodiments that a person skilled in the art can conceive of. Such other embodiments are intended to be within the scope of the claims if they have structural elements that are not different from the literal wording of the claims, or if they include equivalent structural elements that have no substance to differ from the literal wording of the claims.

Claims

1. A computing device for predicting harmful biological pressure, Memory and The memory includes a processor that is communicatively coupled to the memory, and the processor is Historical pest pressure data for a geographical location, which includes current and past pest pressure data for the said geographical location, The weather data for the aforementioned geographic location includes weather data that includes current and historical weather conditions for the aforementioned geographic location. A machine learning algorithm is applied to the aforementioned historical pest pressure data and weather data to generate predicted future pest pressure data for the geographic location. From the predicted future harmful biopressure data, for each of the multiple geographical areas within the geographical location, the relevant predicted harmful biopressure value, the relevant predicted peak harmful biopressure, and the relevant estimated time to reach the peak pressure are determined. The computing device displays the plurality of geographical regions, with each geographical region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region. In response to user input on the computing device, the user computing device is made to display a specific geographic region, in association with a graph that shows the predicted pressure values ​​for that geographic region over time and when the predicted peak harmful biological pressure for that geographic region is expected to occur. A computing device programmed to predict harmful biological pressures.

2. The harmful biological pressure prediction computing device according to claim 1, wherein the processor is further programmed to cause the computing device to display a peak pressure estimate arrival time scale in association with the plurality of geographical regions.

3. The harmful biological pressure computing device according to claim 1, wherein the graph includes a movable slider.

4. The harmful biopressure computing device according to claim 3, wherein the processor is further programmed to cause the computing device to dynamically display temperature information based on the position of the movable slider.

5. The harmful biopressure computing device according to claim 1, wherein the processor is further programmed to cause the computing device to display predicted harmful biopressure levels for a sub-region of the particular geographical area.

6. The harmful biopressure computing device according to claim 1, wherein the processor is further programmed to cause the computing device to display a display indicating how much time has passed since a past peak harmful biopressure occurred in at least one additional geographical area.

7. The pest pressure computing device according to claim 1, wherein the processor is further programmed to cause the computing device to display the daily growth interval of pests associated with the particular geographic area in response to a user selection.

8. A method for generating and displaying harmful biopressure prediction data, the method being implemented using a harmful biopressure prediction computing device including a memory communicably coupled to a processor, the method is Receiving historical pest pressure data for a geographical location, which includes current and past pest pressure data for said geographical location. Receiving weather data for the aforementioned geographic location, including current and historical weather conditions for the aforementioned geographic location; The process involves applying a machine learning algorithm to the aforementioned historical pest pressure data and weather data to generate predicted future pest pressure data for the geographic location, From the aforementioned predicted future harmful biopressure data, for each of the multiple geographical areas within the aforementioned geographical location, the relevant predicted harmful biopressure value, the relevant predicted peak harmful biopressure, and the relevant estimated time to reach the peak pressure are determined. The computing device displays the plurality of geographical regions, with each geographical region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region. In response to user input on the computing device to select a specific geographical region from the plurality of geographical regions, the user computing device displays the specific geographical region in association with a graph showing the predicted pressure values ​​for that geographical region over time and when the predicted peak harmful biological pressure for that geographical region is expected to occur. A method that includes this.

9. The method according to claim 8, further comprising displaying a peak pressure estimation arrival time scale in association with the plurality of geographical regions on the computing device.

10. The method according to claim 8, wherein the graph includes a movable slider.

11. The method according to claim 10, further comprising dynamically displaying time information on the computing device based on the position of the movable slider.

12. The method of claim 8, further comprising causing the computing device to display predicted harmful biopressure levels for subregions of the specific geographic region.

13. The method according to claim 8, further comprising causing the computing device to display a display indicating how much time has passed since a past peak in harmful biological pressure occurred in at least one additional geographic area.

14. The method according to claim 8, further comprising, in response to a user selection, causing the computing device to display the daily growth interval of a pest associated with the particular geographical area.

15. A computer-readable storage medium in which a computer-executable instruction is materialized, and when executed by a pest control pressure prediction computing device including at least one processor that communicates with memory, the computer-readable instruction is transmitted to the pest control pressure prediction computing device. Historical pest pressure data for a geographical location, which includes current and past pest pressure data for the said geographical location, The system receives weather data for the aforementioned geographic location, including current and historical weather conditions for the aforementioned geographic location. A machine learning algorithm is applied to the aforementioned historical pest pressure data and weather data to generate predicted future pest pressure data for the geographic location. From the predicted future harmful biopressure data, for each of the multiple geographical areas within the geographical location, the relevant predicted harmful biopressure value, the relevant predicted peak harmful biopressure, and the relevant estimated time to reach the peak pressure are determined. The computing device is configured to display the plurality of geographic regions, with each geographic region displayed in a color corresponding to the estimated peak pressure arrival time associated with that region. In response to user input on the computing device to select a specific geographic region from the plurality of geographic regions, the user computing device is caused to display the specific geographic region in association with a graph that shows the predicted pressure values ​​for that geographic region over time and when the predicted peak harmful biological pressure for that geographic region is expected to occur. A computer-readable storage medium.

16. The computer-readable storage medium according to claim 15, wherein the instruction further causes the harmful biological pressure prediction computing device to display a peak pressure estimate arrival time scale associated with the plurality of geographical regions on the computing device.

17. The computer-readable storage medium according to claim 15, wherein the graph includes a movable slider.

18. The computer-readable storage medium according to claim 17, wherein the instruction further causes the harmful organism pressure prediction computing device to dynamically display temperature information on the computing device based on the position of the movable slider.

19. The computer-readable storage medium according to claim 15, wherein the instruction further causes the harmful biopressure prediction computing device to display on the computing device the predicted harmful biopressure levels for sub-regions of the particular geographical area.

20. The computer-readable storage medium according to claim 15, wherein the instruction further causes the hazardous biopressure prediction computing device to display a display indicating how much time has passed since a past peak hazardous biopressure occurred for at least one additional geographical area.

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