Systems and methods for modeling disease severity

Abstract

Example embodiments provide systems and methods for simulating disease outbreaks based on a finite number of input parameters. In one embodiment, the disease severity level is calculated based on the relationship between the duration of leaf wetting and the average temperature during the wetting period. The resulting model can be a physical, deterministic model that takes hourly weather data as input and outputs the most significant severity events of disease infection during a specified time period.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. nonprovisional patent application No. 16 / 720,713, filed December 19, 2019, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] This application relates to computational techniques for predicting, detecting, and modeling diseases in plants and animals. Background Technology

[0004] In agriculture, the goal is to maximize the production or yield of a given agricultural product. One factor limiting production or yield is disease, which can include, for example, microorganisms, insects, bacteria, fungi, viruses, pathogens, parasites, and genetic disorders. Disease outbreaks can reduce or even completely destroy harvests.

[0005] In many cases, agricultural diseases can be treated or controlled—for example, by spraying crops with appropriate treatments such as fungicides, insecticides, or pesticides. However, these treatments are expensive and time-consuming to apply. Therefore, they should only be applied where and when they are most effective. To effectively treat diseases, it is essential to understand (and, if possible, predict) the risk of disease outbreaks at a given time and place.

[0006] Numerous techniques exist for simulating disease outbreaks. Typically, these simulations attempt to model the precise relationship between a specific type of disease and a specific host. Therefore, different simulations are needed to model a variety of diseases that could affect a single type of crop; the number of simulations increases proportionally if multiple crop types must be considered. Furthermore, each individual simulation tends to be computationally intensive because a large number of variables and parameters must be considered to model the precise relationship between disease and host. Consequently, existing simulation methods require significant time to assess the risk to a given agricultural product, reducing the available time to respond to predicted outbreaks.

[0007] Furthermore, a great deal of research and work is required to determine the relationship between a given disease and a specific host. This makes it difficult to extend the simulation to new disease types. Summary of the Invention

[0008] According to a first embodiment, a method for predicting, detecting, and modeling diseases in plants and animals is provided. A computing device can receive input including weather data for a given day. The computing device can perform simulations using a disease severity model at predetermined time intervals for the given day, the simulation using air temperature and leaf wet duration as input. Leaf wet duration is derived from the canopy moisture budget and is defined as a continuous period of time during which the leaf canopy is wet. Air temperature can be averaged over the leaf wet duration at predetermined time intervals during this period. The computing device can calculate a disease severity level based on the leaf wet duration and the average air temperature. The computing device can generate an output including a disease severity level for a given day.

[0009] The second embodiment includes the method of the first embodiment, and further includes determining one or more of precipitation, dew, or leaf surface evaporation, wherein precipitation and dew are added to the canopy water balance and leaf surface evaporation is subtracted from it.

[0010] The third embodiment includes the method of the second embodiment, wherein the weather data describes precipitation, relative humidity, wind speed and incident solar radiation, dew increases when the relative humidity exceeds a predetermined threshold, and leaf surface evaporation is a function of relative humidity, wind speed and incident solar radiation.

[0011] The fourth embodiment includes the method of any of the foregoing embodiments, and further determines that the disease severity level increases when the average temperature falls within a predetermined range during the duration of leaf wetting.

[0012] The fifth embodiment includes the method of any of the foregoing embodiments, and further calculates the disease severity level by determining whether the leaf wetting duration exceeds the minimum wetting hours, determining the minimum wetting hours of infection, and determining the disease severity level based on the leaf wetting duration exceeding the minimum wetting hours under favorable temperature.

[0013] The sixth embodiment includes the method of any of the foregoing embodiments, and further defines the disease severity level based on the length of the leaf wetting duration.

[0014] The seventh embodiment includes the method of any of the foregoing embodiments, and further calculates a disease severity level based on the number and severity of disease events occurring in a given day. If a single disease event occurs in a given day, the disease severity level is set to the severity of the single disease event. If multiple disease events with different severities occur in a given day, the disease severity level is set to the severity of the most severe event.

[0015] According to the eighth embodiment, a non-transitory computer-readable medium stores instructions for performing the techniques described above with reference to any one or all of the first to seventh embodiments.

[0016] According to a ninth embodiment, an apparatus is provided for performing the techniques described above with reference to any one or all of the first to seventh embodiments. Attached Figure Description

[0017] Figure 1 An environment suitable for use with exemplary embodiments is described.

[0018] Figure 2 An exemplary data structure suitable for use with an exemplary disease severity model is described.

[0019] Figure 3 It is an exemplary input / output specification that describes the inputs to the optimization logic and the corresponding outputs generated by the optimization logic.

[0020] Figure 4 An exemplary relationship between temperature and minimum leaf moisture value according to one embodiment is depicted.

[0021] Figure 5 It is a description of an exemplary embodiment. Figure 1 A block diagram of the various components of the device in the environment.

[0022] Figure 6A This is a flowchart illustrating key operations according to an example embodiment.

[0023] Figure 6B This is a graph of the severity level of an example disease calculated according to one embodiment.

[0024] Figure 7 An exemplary computing system suitable for use with exemplary embodiments is depicted.

[0025] Figure 8 An exemplary network environment suitable for use with exemplary embodiments is depicted. Detailed Implementation

[0026] An exemplary embodiment provides a technique for simulating disease outbreaks using a relatively simple formula based on a finite number of input parameters. Specifically, disease severity is calculated based on the relationship between the duration of leaf wetting and the average temperature during the wetting period. The model operates on input received in predefined increments (e.g., one hour) and calculates the disease severity level for each increment. The resulting model is a physical, deterministic model that accepts weather data as input in predefined increments and outputs the most significant severity event of disease infection during a specified (e.g., one-day) time period. It can be applied to many different plant and animal species with only small variations based on a few coefficients determined from genetic information, without requiring extensive experiments or significant model modifications.

[0027] The disease severity model first uses the canopy water budget to calculate the duration of leaf wetness. Precipitation and dew contribute to the budget, while leaf surface evaporation is subtracted from it. Dew formation is assumed to occur when relative humidity exceeds a predetermined threshold. Leaf surface evaporation is a function of relative humidity, wind speed, and incident solar radiation. Low humidity, high wind speed, and high solar radiation accelerate leaf evaporation.

[0028] In each increment of the disease severity model simulation, the canopy water balance is adjusted for the addition of precipitation and dew, and the subtraction of leaf surface evaporation. If, in a given increment, the canopy water balance is greater than 0 (e.g., measured in millimeters), the leaves are assumed to be wet, and an increment is added to the wet duration. For each consecutive increment with a balance greater than 0, the duration is incremented by one. The leaf wet duration ends at the increment where the canopy water balance equals 0.

[0029] The disease severity model then calculates the average temperature during the wet period of the leaves. A disease severity event is assumed to occur when the wet duration exceeds a predetermined number of hours and the average temperature for the wet period is within a favorable range. The favorable temperature range is defined by an upper and a lower threshold. If the temperature is below or above these thresholds, infection cannot develop regardless of whether the canopy is wet. If the temperature is favorable, the intensity of the disease severity event is defined by the length of the wet duration.

[0030] Next, the disease severity model calculates the degree of infection (severity level) based on the duration of leaf wetting and the average temperature during the wetting period. The relationship between the duration of leaf wetting and the favorable temperature determines the minimum number of wetting hours required for infection. In one embodiment, this relationship is given as shown in Equation 1 below:

[0031]

[0032] Where temp specifies the average air temperature during the wet period, wet hours are the minimum number of canopy wet hours required for infection, and a, b, and c are coefficients determined by the plant or animal species (or a subset of plants or animals, such as specific organs or systems) and the disease in question. Coefficients can be determined from laboratory or field data, or from phylogenetic tree analysis. Species sharing common traits or genetic similarities may be associated with similar a, b, and c coefficients. Coefficient a represents half the minimum number of wet hours required for infection at the optimal temperature. Coefficient b is the optimal temperature for infection. Coefficient c is a scalar that adjusts the minimum number of wet hours required for infection relative to a given temperature (e.g., increasing the c value decreases the minimum number of wet hours required at a given temperature).

[0033] This relationship can also be used to determine the level of infection or severity as the number of wet hours increases under favorable temperatures. The number of wet hours after the onset of infection can be normalized to a scale ranging from 0 to 100%. A severity level of 0% indicates no infection, while 100% indicates a severe infection, with predefined levels of infection falling between these limits.

[0034] If only a single disease severity event occurs in a day, the daily severity level is set to the value of that event. However, it is possible for multiple severity events to occur in a day. In this case, the daily level is set to the event with the highest severity. The daily severity level (ranging from 0 to 100%) is delivered as model output.

[0035] The following description of the embodiments provides non-limiting representative examples of reference figures to particularly illustrate the features and teachings of different aspects of the invention. The described embodiments should be considered as being achievable alone or in combination with other embodiments as described herein. The description of the embodiments is intended to facilitate an understanding of the invention to the extent that other implementations not specifically covered but within the knowledge of those skilled in the art upon reading the description of the embodiments will be understood to be consistent with the application of the invention.

[0036] It should be noted that although exemplary embodiments have been described in conjunction with specific examples (the number of hours that the leaves of certain plants must be kept moist for disease events to occur), the invention is not limited to these examples.

[0037] Figure 1 A diagnostic, predictive, and processing environment 100 according to an example embodiment is shown.

[0038] Environment 100 includes one or more plant species 102 that may be susceptible to one or more diseases. Although exemplary embodiments are shown with plants, the principles described herein can be applied to different types of disease hosts, including animals, organs (e.g., heart, liver, lungs), and systems (e.g., respiratory system, nervous system, etc.). In some embodiments, relatively simple equations such as those described in Equation 1 can be used to model the effects of a given disease on a given organism, and these disease severity can then be combined to determine the effects of different diseases on organisms and systems composed of multiple organisms.

[0039] As used herein, the term "disease" refers to any vector that tends to cause structural or functional disturbances in an organism affected by the disease. This may include bacterial infections, viral infections, fungal infections, outbreaks associated with insects or other organisms, or any other condition that impairs normal function and is not directly related to physical injury. In some embodiments, a particular disease may involve fungal infections and outbreaks associated with insects. The term "insect" should also be understood to include spiders, nematodes, and mollusks.

[0040] As used herein, the terms “disease severity” and “disease severity level” can, for example, when used in conjunction with a disease severity model, refer to any of the following: (1) the probability, in percentage (e.g., disease risk), of a specific disease occurring on a specific crop / plant under given weather or environmental conditions; (2) the percentage of leaf (or leaf blade) area infected / affected by a specific disease; or (3) the vertical extent of a specific disease infection on a plant. For a specific disease, the disease severity level can be 0%, or it can be greater than zero (e.g., 10% severity of wheat Fusarium wilt, 20% of maize Fusarium wilt, etc.). For the vertical extent of disease severity, the severity may depend on which parts of the plant are infected (e.g., Case 1: infection of the top and bottom layers of the plant results in a high disease severity level; Case 2: infection of only the bottom layer of the plant results in a low disease severity level). In a preferred embodiment, “disease severity” and “disease severity level” refer to the probability, in percentage (e.g., disease risk), of a specific disease occurring on a specific crop / plant under given weather or environmental conditions. In another preferred embodiment, "disease severity" and "disease severity level" refer to the percentage of leaf (or leaf leaf) area infected / affected by a specific disease. In another preferred embodiment, "disease severity" and "disease severity level" refer to the vertical extent of infection of a specific disease on a plant.

[0041] Certain processes related to plant 102 can be monitored or regulated by agricultural system 104. Agricultural system 104 may be a computer or server located at the agricultural facility and may be responsible for monitoring and / or controlling irrigation procedures, fertilizer application, etc.

[0042] One such task could be to treat plant 102 to prevent, mitigate, or control disease outbreaks. For this purpose, agricultural system 104 could interface with treatment system 106, which could control the application of one or more disease treatment agents. For example, a given disease could be treated by spraying plant 102 with a topical treatment agent, and treatment system 106 could control a spraying device to apply a given amount of treatment agent to plant 102.

[0043] The treatment system 106 can select the type of treatment agent, treatment dose, and other treatment parameters to be applied based on a disease severity level associated with the treatment type, as determined by the disease severity system 108. The disease severity system 108 can be a standalone computing device, such as a server or a remotely operated computing system, or it can be co-located with or integrated with the agricultural system 104 and / or the treatment system 106.

[0044] The disease severity system 108 can determine the severity level of a disease outbreak based on weather information provided to it by the weather server 110. The weather server 110 may be a third-party server that provides weather data to subscribers, or it may be operated by the same entity that operates the disease severity system 108, the agricultural system 104, and / or the processing system 106.

[0045] Weather information provided from weather server 110 to disease severity system 108 can be represented as input to disease severity system 108. Figure 2 An example of a data structure representing input 200 is depicted. While the exemplary input 200 includes specific variables in a particular order, those skilled in the art will recognize that more, fewer, or different variables may be used, depending on the application. If no value is specified for a variable, a default value (e.g., a predefined minimum, average, etc.) may be used. The value of a variable may be qualitative, quantitative, or expressed in both ways.

[0046] Values ​​can be specified as minimum or maximum values, a range of acceptable values, etc. Values ​​can be associated with weights or priorities, indicating how important a particular performance characteristic is relative to other performance characteristics. Weights or priorities may be zero, indicating that the performance characteristic is unimportant or should not be prioritized.

[0047] Input 200 may include weather data 202 describing actual or predicted weather within a specific time interval. Therefore, weather data 202 may include an identifier 204 specifying or describing the time interval in question. For example, time interval ID 204 may give a time and / or date range for the time interval, or a timestamp indicating the start or end time of the time interval. Time interval ID 204 may optionally include the length of the time interval, or this information may optionally be stored elsewhere or known prior to it.

[0048] Weather data 202 may include various measurements, parameters, or forecasts of weather during a time interval. This may include, for example, measurements of temperature 206, precipitation 208, relative humidity 210, wind speed 212 and / or wind direction, and solar radiation 214 levels. These measurements, parameters, or forecasts may be single values ​​(e.g., averages over the time interval) or multiple values ​​over the entire interval, which may be averaged by the disease severity system 108 prior to use. Measurements, parameters, or forecasts may be values ​​for a specified element, and / or may include values ​​for other elements from which values ​​for the specified element can be derived.

[0049] like Figure 3 As shown in the input / output specification, input 200 can be provided from the weather server 110 to the disease severity model 300 running on the disease severity system 108.

[0050] The disease severity model 300 can further utilize various parameters 302. These parameters 302 may include, for example, one or more relationships 304, such as the relationship given by Equation 1 above (which correlates the average temperature over a time interval with the minimum humidity required for a disease event to occur).

[0051] One or more of relations 304 can utilize constant 306, which allows a given relation to be used for different types of plant / animal species and different types of diseases. By replacing one set of constants with another set of constants 306, different diseases can be simulated in a given organism, the same disease can be simulated in different organisms, or different diseases can be simulated in different organisms. Constants 306 can be derived from laboratory or field data, simulations, or based on the similarity and / or differences in phylogenetic trees of two species.

[0052] For example, Equation 1 can be used to assess the minimum number of wet hours required for gray leaf spots to form on a lawn. By setting the values ​​of the constants to a = 1.5, b = 82, and c = 10, the following results are obtained: Figure 4The curve shown represents the number of hours the canopy must remain moist for the disease to have an effect at various temperatures. The minimum number of moist hours occurs at a minimum point 404 on curve 402. In this case, the minimum is 3 hours, which occurs at approximately 82°F. This relationship can also be used to derive the minimum number of moist hours at other temperatures; for example, the graph shows a minimum of approximately 3.25 hours at 78°F or 3.6 hours at 88°F. As discussed in more detail below, disease formation can be confined to a certain temperature band, which can define the range within which disease development may occur.

[0053] return Figure 3 Relationship 304 may also include other relationships. For example, a relationship may be specified between precipitation (derived from weather data 202) and dew with canopy humidity. To determine the level of dew, parameter 302 may include a humidity threshold 308, which defines the minimum relative humidity at which dew occurs; any amount of relative humidity exceeding this threshold may contribute to dew formation.

[0054] Relationship 304 may also include the relationship between relative humidity, wind speed, and incident solar radiation (as derived from weather data 202) and leaf surface evaporation. All other things being equal, low humidity, high wind speed, and high solar radiation all accelerate leaf evaporation.

[0055] Another relation, 304, describes how precipitation, dew, and evaporation adjust canopy moisture levels (referred to as the canopy water balance). In this relation, precipitation and dew are added to the canopy water balance, while evaporation is subtracted from it.

[0056] Parameter 302 may include a temperature range 310, which represents a temperature range favorable for disease development. In some embodiments, a disease event is considered to have occurred when the canopy moisture balance exceeds zero for an extended period exceeding a predefined minimum wet hour count 312 (or some other interval), and the average temperature during the expected disease event is within the temperature range 310. The temperature range may include a minimum and a maximum value (representing temperatures below or above which a given disease tends to be suppressed).

[0057] Parameter 302 may include other values, such as threshold levels for different disease severity, a normalization factor that allows scaling of severity levels (e.g., based on percentages), etc.

[0058] Based on input 200 and parameter 302, the disease severity model 300 can determine whether a disease event occurs within a given time interval, the length of a severity event, and the level of severity. If multiple severity events occur within a given day or some other time period, the severity level for that day can also be generated. The severity level for that day can be set to the severity level of the most severe event of the day, or it can be set to the average severity level of the day, among other possibilities. Once this information is determined, it can be stored in the output data structure 314, which can be displayed on the interface of display 316 and / or sent to agricultural system 104 for use by processing system 106.

[0059] like Figure 5 As shown, the disease severity model 300 can be implemented in disease severity logic 522, which is stored in memory 514 on the disease severity system 108. Parameter 302 can also be stored in memory 514 of the disease severity system 108. Figure 5 The block diagram depicts these and other components of environment 100 in more detail.

[0060] To obtain input weather data, the disease severity system 108 can interface with the weather server 110 via corresponding network interfaces 512 and 528. The weather server 110 can store the weather data 202 in a storage device 530, which can be a non-transitory computer-readable storage medium (e.g., HDD, SDD, RAM, ROM, etc.). The weather data 202 can be provided to the network interface 528 for transmission via the network 510 to the network interface 512 of the disease severity system 108.

[0061] Disease severity system 108 can receive weather data 202 via network interface 512 and can combine it with parameters 302 stored in memory 514 (see Figure 3 The system uses weather data. Memory 514 can be a non-transitory computer-readable storage medium (e.g., HDD, SDD, RAM, ROM, etc.). Memory 514 can further store logic 516 configured to use weather data 202 and parameter 302 to determine when a disease event occurs, the severity level of the event, and / or the daily severity level. Logic 516 can correspond to... Figure 6A The logic blocks depicted herein (e.g., retrieval logic 518 can perform the actions described in conjunction with blocks 606-608; analog logic 520 can correspond to blocks 610-616; and disease severity logic 522 can correspond to blocks 618-624). Output generation logic 524 can generate an output suitable for use or display by an appropriate output device (see [link to relevant documentation]). Figure 3For example, the output may include daily severity level, number of disease events, severity level and / or length of each severe event, affected organisms, effective disease, etc.

[0062] The output can be sent to agricultural system 104 and received at network interface 508. Agricultural system 104 can store the output as data 507 in memory 505, which can be a non-transitory computer-readable storage medium (e.g., HDD, SDD, RAM, ROM, etc.). Based on the results obtained from the disease severity system, agricultural system can determine whether to take corrective action. For example, if the disease severity level for a given day exceeds a threshold level, agricultural system can interface with processing system 106 via corresponding interfaces 502, 504. If processing system 106 receives an instruction to apply treatment to the disease, processing system can operate control logic 506, which can control the treatment application device that causes the treatment to be applied. Alternatively or additionally, agricultural system 104 can send an alarm or notification to one or more registered users, who can then control the application of treatment.

[0063] These actions combined Figure 6A The flowchart shown is described in more detail. Figure 6A The frame can be implemented as logic 600 or instructions stored on a non-transitory computer-readable medium for execution at, for example, a disease severity system 108, an agricultural system 104, a processing system 106, and / or a weather server 110.

[0064] although Figure 6A Examples of executing specific boxes on specific devices are provided, but it should be understood that the invention is not limited to that specific configuration. Some boxes may be combined into other boxes or omitted, some devices may be combined together, and some individual boxes may be distributed among different devices. In some embodiments, a box depicted as executing on one device may instead be executed on different devices, depending on the specific application (e.g., for efficiency, reduced latency, improved power usage, better processing power, memory limitations, etc.).

[0065] Processing can begin at box 602. At box 604, the weather server can collect weather data. Weather data can be collected from sensors, user input, or other technologies. Weather data can be collected from other sources and then aggregated at the weather server, or it can be received from multiple weather servers at the disease severity system (boxes 606-608).

[0066] At box 606, the disease severity system may interface with a weather server (e.g., via a direct connection, via a network, etc.). Using the connection established in box 606, the disease severity system may obtain weather data for a predefined interval at box 608. This interval may be a current interval (e.g., the data value for the current hour), a past interval (e.g., as specified in a request from the disease severity system), and / or a future interval (allowing the weather server to provide weather forecasts, and thus allowing the disease severity system to predict the outbreak and severity level of future disease events).

[0067] At box 610, the disease severity system can adjust the canopy water balance. The canopy water balance can be initially set to zero, and for... Figure 6A Each iteration of the process shown can be set to a value for income and expenditure that exists in a previously predefined interval.

[0068] The canopy water balance can be adjusted by adding precipitation and dew and subtracting leaf surface evaporation. Precipitation can be determined directly from the weather data provided in box 608. When the relative humidity (determined from the weather data) exceeds a predetermined humidity threshold (see...), the canopy water balance is adjusted accordingly. Figure 3 When dew formation occurs, leaf surface evaporation can be based on the relationship between relative humidity, wind speed, and incident solar radiation (each determined based on weather data). Figure 3 The relationship is defined in 304 to determine the relationship.

[0069] At box 612, the disease severity system can determine the leaf wet duration based on the canopy water balance. If the canopy water balance exceeds 0 mm within a given hour (or other predefined time interval), the plant leaves are assumed to be wet, and one hour (or other corresponding predefined time interval) is added to the wet duration. For each consecutive hour with a balance greater than 0 mm, the duration can be increased by one hour. The leaf wet duration ends when the canopy water balance decreases to 0 mm.

[0070] At box 614, the system determines the average temperature over a predefined interval. This information can be obtained directly from or derived from the weather data received at box 608. In some embodiments, the average temperature over the predefined interval is calculated as an operating average over the wet duration (e.g., for temperatures A, B, and C, corresponding to hours 1, 2, and 3 respectively, the average temperature at hour 2 can be calculated as the average of A and B, and the average temperature at hour 3 can be calculated as the average of A, B, and C). In some embodiments, if the temperature is within a favorable range (see...) Figure 3Outside of element 310, it is assumed that a disease event is impossible. If the temperature at box 614 is unfavorable for the occurrence of a disease event, the treatment can skip boxes 616-624 and proceed directly to box 626. If a disease event is underway while the temperature is rising or falling outside the favorable range, the ongoing disease event can be considered terminated. If no disease event is underway and the temperature is outside the favorable range, no new disease event will begin regardless of how long the canopy remains moist.

[0071] At box 616, the disease severity system can determine whether the wetting duration determined in box 612 exceeds the minimum wetting threshold for the occurrence of a disease severity event. The minimum wetting threshold can be based on a predefined relationship between temperature and minimum leaf wetting (see Equation 1 and Equation 2). Figure 3 Element 304) and constants specific to the organism and disease under consideration (see element 304) Figure 3 The combination of elements 306 is used to define it.

[0072] If the determination at box 616 is "No" (i.e., the leaf wetting duration determined at box 612 does not exceed the minimum wetting threshold for a disease severity event), it is assumed that no disease severity event occurred during that interval (and / or an ongoing disease severity event may have ended). However, it should be noted that although the wetting duration may not exceed the minimum wetting threshold for the current time interval, the wetting duration may continue to build over subsequent time intervals, and therefore future time intervals may build over the wetting duration from the current interval to exceed the minimum wetting threshold. Processing can then proceed to box 626, and the system can determine whether any further intervals still need to be considered.

[0073] On the other hand, if the determination at box 616 is "yes" (i.e., the duration of leaf wetting exceeds the minimum wetting threshold and the temperature was previously determined to be favorable for the disease), then the disease event is considered to have started (or the ongoing disease event continues).

[0074] At box 618, the disease severity system calculates the disease severity level for disease events within the interval in question. The intensity of a disease severity event is typically defined by the length of the wet duration. It may also be influenced by the average temperature during the wet period. For example, the disease severity level can be calculated at each time point within the wet duration based on the running average temperature at a given time point. Relationship ( Figure 3Element 304 can define a specific relationship between the duration of wetting, temperature, and disease severity level. This relationship can vary from organism to organism and from disease to disease. Severity levels can be normalized (e.g., on a percentage-based scale). For example, a severity level of 0% can indicate no infection, while a severity level of 100% indicates a severe infection. In some embodiments, disease severity can be categorized into bands or ranges (e.g., 0-10% indicates no infection, 10%-30% indicates a minor infection, 30%-60% indicates an intermediate infection, and 60%-100% indicates a severe infection).

[0075] In some embodiments, it may be helpful to determine the disease severity level for a given day, rather than considering only the disease severity level for each predefined interval. Therefore, the disease severity level for that day can be calculated in box 620. This particular embodiment sets the disease severity level for a given day to the level for the most severe event of that day (if multiple severe events occur during that day), or to the level for that single severe event if only one severe event occurs during that day. For a given day, the severity level can be initialized to 0% so that if no disease event occurs, the disease severity level for that day remains at a minimum.

[0076] If other techniques for determining the daily severity level are to be used (e.g., calculating the average severity across multiple severity events), then boxes 620-624 can be modified accordingly.

[0077] If the severity level of the disease is classified as a band or range, then the band or range can be used instead of the specific value at boxes 620-624.

[0078] At box 620, the system determines whether the severity level calculated at box 618 exceeds the previous most severe event for that day. If no disease event has occurred on the day under consideration, then it is assumed that the current most severe event for that day has a default severity level of 0%. If at least one disease event has occurred, then the current most severe event level is regardless of the level of the event with the highest disease severity for a given day.

[0079] If the current disease event level does not exceed the level of the previous most severe event for that day ("No" at box 620), then processing can proceed to box 624 and the disease severity system can maintain the existing daily severity level. If the current disease event level does exceed the level of the previous most severe event for that day ("Yes" at box 620), then processing can proceed to box 622, and the disease severity system can set the severity level of the current disease event to the daily severity level.

[0080] Processing can then proceed to box 626, where the system can determine whether more intervals still need to be evaluated (e.g., whether there are more intervals to calculate for a given day). If yes, processing can return to box 606 and obtain more weather data for the new intervals. If not, processing can proceed to box 628 for further action by the agricultural system and / or the processing system.

[0081] In some embodiments, the agricultural system and / or processing system may be provided with information about the level of disease severity, even while further processing is in progress. For example, after box 626, processing may return to box 606, allowing further weather data to be obtained, and parallel processing may proceed to box 628.

[0082] At box 628, the disease severity system can send the daily severity level, as defined in boxes 620-624, to the agricultural system. If the disease severity system serves multiple agricultural systems, then individual sending can be made to each of the agricultural systems it serves.

[0083] At box 630, the agricultural system can determine whether the daily severity level received from the disease severity system exceeds a threshold severity. In some embodiments, the disease severity system can classify disease events into different severity levels, and the agricultural system can determine whether a severity level exceeds a level threshold. The threshold severity (or level threshold) can be set differently at different agricultural systems based on many different factors (e.g., user risk tolerance, treatment costs, number of plants or area to be treated, treatment availability, etc.).

[0084] If the determination at box 630 is "No" (i.e., the disease severity level does not exceed the daily threshold, and therefore treatment is not guaranteed), then treatment can proceed to box 634 and terminate.

[0085] If the determination at box 630 is "yes" (i.e., the disease severity level does indeed exceed the daily threshold, and therefore treatment is indeed guaranteed), then treatment can proceed to box 632. Here, the agricultural system can send an instruction to the treatment system, which can trigger the treatment system's control logic to apply the treatment. Treatment can then proceed to box 634 and terminate.

[0086] In some embodiments (e.g., other than or alternatively to box 634), the calculated disease severity level may be sent to one or more registered users along with the recommended treatment action.

[0087] In a preferred embodiment of the invention, the method further includes the step of outputting signals, such as control signals or switching signals, for operating, controlling, and / or monitoring agricultural machinery, depending on the output including a disease severity level for a given day, particularly if the output exceeds a predefined threshold for the disease severity level. More preferably, where the output including the disease severity level relates to a specific disease that can be controlled by chemical or biological agents (such as chemical or biological fungicides, insecticides, acaricides, nematicides, or molluscicides), a control or switching signal is output for operating the agricultural machinery in a manner targeted at that specific disease; for example, a control signal is output for operating the agricultural machinery to apply or spray, or to prepare for applying or spraying, a chemical or biological agent targeting that specific disease.

[0088] Agricultural machinery may include one or more treatment mechanisms to treat plants in a field. Treatment mechanisms may include chemical, biological, mechanical, electrical treatment mechanisms, or combinations thereof, to treat diseases or insects. Agricultural machinery may also include detection and control systems. Detection systems may be configured to detect field conditions as the agricultural machinery moves through the field. Control systems may be configured to control one or more treatment mechanisms based on the detected field conditions.

[0089] In one embodiment, the treatment mechanism is a chemical treatment mechanism. Agricultural machinery in such embodiments includes a sprayer with one or more nozzles to release chemical agents into the field.

[0090] In another embodiment, the treatment mechanism is a biological treatment mechanism. Agricultural machinery in such embodiments includes a sprayer with one or more nozzles for releasing biological agents into the field.

[0091] In one embodiment, the detection system includes one or more detection components to detect field conditions as agricultural machinery passes through the field. The detection components may be, or may include, the equipment claimed in this invention, or may be connected to or operatively coupled to the equipment claimed in this invention.

[0092] Figure 6B It shows Figure 6A This is an illustrative example of the output of the steps, which can be sent to one or more registered users. Figure 6B The graphs correspond to the severity levels of gray leaf spot (Cercozoma var. gravidarum, CERCZM) and common rust (Cercozoma rust, PUCCSO) on maize, as determined at a location in North Carolina between May 2, 2020 and June 20, 2020.

[0093] The methods described above can be implemented as instructions or part of a computing architecture on a computer-readable medium. Figure 7 An embodiment of an exemplary computing architecture 700 suitable for implementing the various embodiments described above is shown. In one embodiment, the computing architecture 700 may include or be implemented as part of an electronic device (such as a computer 701). The embodiments are not limited to this context.

[0094] As used herein, the terms "system" and "component" are intended to refer to computer-related entities, or hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 700. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, an application running on a server and the server itself can both be components. One or more components may reside within an executing process and / or thread, and components may reside on a single computer and / or be distributed across two or more computers. Furthermore, components can be communicatively coupled to each other via various types of communication media to coordinate operation. Coordination can involve one-way or two-way exchange of information. For example, components can transmit information in the form of signals transmitted via a communication medium. Information can be implemented as signals assigned to various signal lines. In such an assignment, each message is a signal. However, other embodiments may alternatively employ data messages. Such data messages can be sent across various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0095] The computing architecture 700 includes various common computing elements, such as one or more processors, multi-core processors, coprocessors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, and so on. However, embodiments are not limited to implementations of the computing architecture 700.

[0096] like Figure 7 As shown, the computing architecture 700 includes a processing unit 702, a system memory 704, and a system bus 706. The processing unit 702 can be any commercially available processor, including but not limited to... and processor; Application, embedded, and security processors; and and Processors; IBM and Cell processor; Core(2) and Processor; and similar processors. Dual microprocessors, multi-core processors, and other multiprocessor architectures can also be used as processing units 702.

[0097] System bus 706 provides interfaces for system components, including but not limited to system memory 704 to processing unit 702. System bus 706 can be any of several types of bus architectures, which can be further interconnected to memory buses (with or without a memory controller), peripheral buses, and local buses using any of a variety of commercially available bus architectures. Interface adapters can be connected to system bus 706 via slot architectures. Example slot architectures can include, but are not limited to, Accelerated Graphics Port (AGP), plug-in bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, PCMCIA, etc.

[0098] The computing architecture 700 may include or implement various articles of manufacture. Articles of manufacture may include computer-readable storage media that store logic. Examples of computer-readable storage media may include any tangible medium capable of storing electronic data, including volatile or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writable or rewritable memory, etc. Examples of logic may include executable computer program instructions implemented using any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, object-oriented code, visual code, etc. Embodiments may also be implemented, at least in part, as instructions contained in or on a non-transitory computer-readable medium that can be read and executed by one or more processors to perform the operations described herein.

[0099] System memory 704 may include various types of computer-readable storage media in the form of one or more high-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, austenite memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic cards or optical cards, device arrays such as redundant array of independent disks (RAID) drives, solid-state storage devices (e.g., USB storage), solid-state drives (SSDs), and any other type of storage media suitable for storing information. Figure 7In the illustrated embodiment, system memory 704 may include non-volatile memory 708 and / or volatile memory 710. The Basic Input / Output System (BIOS) may be stored in non-volatile memory 708.

[0100] The computing architecture 700 may include various types of computer-readable storage media in the form of one or more low-speed memory units, including an internal (or external) hard disk drive (HDD) 712, a floppy disk drive (FDD) 714 for reading from or writing to a removable disk 716, and a primary optical disk drive 718 for reading from or writing to a removable optical disk 720 (e.g., a CD-ROM or DVD). The HDD 712, FDD 714, and optical disk 720 may be connected to the system bus 706 via an HDD interface 722, an FDD interface 724, and an optical disk drive interface 726, respectively. The HDD interface 722 for external drive implementations may include at least one or both of Universal Serial Bus (USB) and IEEE 694 interface technologies.

[0101] The drive and associated computer-readable medium provide volatile and / or non-volatile storage for data, data structures, computer-executable instructions, etc. For example, numerous program modules may be stored in the drive and memory units 708, 712, including an operating system 728, one or more application programs 730, other program modules 732, and program data 734. In one embodiment, one or more application programs 730, other program modules 732, and program data 734 may include various applications and / or components, such as those of a messaging system 500.

[0102] Users can input commands and information into computer 701 using one or more wired / wireless input devices (e.g., keyboard 736) and pointing devices (such as mouse 738). Other input devices may include microphones, infrared (IR) remote controls, radio frequency (RF) remote controls, gamepads, styluses, card readers, dongles, fingerprint readers, gloves, graphics tablets, joysticks, keyboards, retinal readers, touchscreens (e.g., capacitive, resistive, etc.), trackballs, trackpads, sensors, light pens, etc. These and other input devices are typically connected to processing unit 702 via input device interface 740, which is coupled to system bus 706, but can be connected via other interfaces (such as parallel ports, IEEE 694 serial ports, game ports, USB ports, IR interfaces, etc.).

[0103] The monitor 742 or other type of display device is also connected to the system bus 706 via an interface (such as an audio adapter). The monitor 742 can be internal or external to the computer 701. In addition to the monitor 742, the computer typically includes other peripheral output devices, such as speakers, printers, etc.

[0104] Computer 701 can operate in a networked environment using logical connections via wired and / or wireless communications to one or more remote computers (such as remote computer 744). Remote computer 744 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer-to-peer device, or other public network node, and generally includes many or all of the elements described relative to computer 701; however, for simplicity, only memory / storage device 746 is illustrated. The depicted logical connections include wired / wireless connectivity to a local area network (LAN) 748 and / or a larger network (e.g., a wide area network (WAN) 750). Such LAN and WAN network environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to global communication networks, such as the Internet.

[0105] When used in a LAN network environment, computer 701 connects to LAN 748 via a wired and / or wireless communication network interface or adapter 752. Adapter 752 can facilitate wired and / or wireless communication to LAN 748, and LAN 748 may also include a wireless access point disposed thereon for communicating with the wireless functionality of adapter 752.

[0106] When used in a WAN network environment, computer 701 may include modem 754, or a communication server connected to WAN 750, or other means for establishing communication (such as via the Internet) on WAN 750. Modem 754, which may be internal or external and wired and / or wireless, is connected to system bus 706 via input device interface 740. In a networked environment, program modules depicted relative to computer 701 or parts thereof may be stored in remote memory / storage device 746. It will be understood that the network connections shown are exemplary, and other means for establishing communication links between computers may be used.

[0107] Computer 701 is operable to communicate with wired and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operable in wireless communication (e.g., IEEE 802.13 air modulation technology). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wireless technologies, among others. Therefore, communication can be a predefined structure like a conventional network or simply ad hoc network communication between at least two devices. Wi-Fi networks use radio technologies known as IEEE 802.13x (a, b, g, n, etc.) to provide secure, reliable, and fast wireless connectivity. Wi-Fi networks can be used to connect computers to each other, the Internet, and wired networks (which use IEEE 802.3 related media and functions).

[0108] Figure 8 This is a block diagram depicting an exemplary communication architecture 800 suitable for implementing the various embodiments described above. Communication architecture 800 includes various common communication elements, such as transmitters, receivers, transceivers, radios, network interfaces, baseband processors, antennas, amplifiers, filters, power supplies, etc. However, embodiments are not limited to implementations of communication architecture 800.

[0109] like Figure 8 As shown, the communication architecture 800 includes one or more clients 802 and servers 804. Clients 802 and servers 804 are operatively connected to one or more corresponding client data stores 806 and server data stores 808, which can be used to store information local to the respective client 802 and server 804, such as cookies and / or associated context information.

[0110] Client 802 and server 804 can use communication framework 810 to exchange information with each other. Communication framework 810 can implement any well-known communication technology and protocol. Communication framework 810 can be implemented as a packet-switched network (e.g., a public network such as the Internet, a private network such as an enterprise intranet, etc.), a circuit-switched network (e.g., a public switched telephone network), or a combination of packet-switched and circuit-switched networks (with suitable gateways and converters).

[0111] The communication framework 810 can implement various network interfaces arranged to receive, communicate to, and connect to communication networks. Network interfaces can be considered as dedicated forms of input / output interfaces. Network interfaces can employ connectivity protocols, including but not limited to direct connection, Ethernet (e.g., thick, thin, twisted pair 10 / 100 / 1000BaseT, etc.), token ring, wireless network interfaces, cellular network interfaces, IEEE 802.8ax network interfaces, IEEE 802.16 network interfaces, IEEE 802.20 network interfaces, etc. Furthermore, multiple network interfaces can be used to interface with various communication network types. For example, multiple network interfaces can be used to allow communication over broadcast, multicast, and unicast networks. If processing requirements indicate a greater amount of speed and capacity, a distributed network controller architecture can be similarly used for pooling, load balancing, and other ways to increase the communication bandwidth required by the client 802 and server 804. The communication network can be any and a combination of wired and / or wireless networks, including but not limited to direct interconnection, secure custom connections, private networks (e.g., corporate intranets), public networks (e.g., the Internet), personal area networks (PANs), local area networks (LANs), metropolitan area networks (MANs), operational tasks as nodes on the Internet (OMNIs), wide area networks (WANs), wireless networks, cellular networks, and other communication networks.

[0112] The components and features of the device described above can be implemented using any combination of discrete circuits, application-specific integrated circuits (ASICs), logic gates, and / or single-chip architectures. Furthermore, the features of the device can be implemented using microcontrollers, programmable logic arrays, and / or microprocessors, or any combination thereof where suitably appropriate. It should be noted that hardware, firmware, and / or software elements may be collectively or individually referred to herein as “logic” or “circuit.”

[0113] It will be understood that the exemplary apparatus shown in the block diagrams described above may represent a functionally descriptive example of many potential implementations. Therefore, the division, omission, or inclusion of block functions depicted in the figures does not necessarily mean that hardware components, circuits, software, and / or elements used to implement these functions will be divided, omitted, or included in the embodiments.

[0114] At least one computer-readable storage medium may include instructions that, when executed, cause a system to perform any computer-implemented method described herein.

[0115] Some embodiments may be described using the expressions "an embodiment" or "embodiment" together with their derivatives. These terms mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment. The appearance of the phrase "in an embodiment" in various places in the specification does not necessarily refer to the same embodiment. Moreover, unless otherwise stated, the features described above are considered to be used in any combination. Thus, any feature discussed individually may be used in combination with each other unless it is noted that these features are incompatible with each other.

[0116] By referring generally to the notation and nomenclature used herein, the detailed description herein can be presented according to program procedures executed on a computer or computer network. Those skilled in the art use these procedures and representations to most effectively convey the substance of their work to others skilled in the art.

[0117] The process here is, and is generally conceived, as a self-consistent sequence of operations leading to the desired result. These operations are operations that require physical manipulation of physical quantities. Typically, though not strictly necessary, these quantities take the form of electrical, magnetic, or optical signals that can be stored, transmitted, combined, compared, and otherwise manipulated. Sometimes, primarily for common reasons, it proves convenient to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc. However, it should be noted that all these and similar terms will be associated with the appropriate physical quantities and are merely convenient labels applied to those quantities.

[0118] Furthermore, the operations performed are typically referred to using terms such as addition or comparison, which are generally associated with mental operations performed by a human operator. In most cases, such capabilities of a human operator are unnecessary or undesirable in any of the operations described herein that form part of one or more embodiments. Instead, the operations are machine operations. Useful machines for performing the operations of the various embodiments include general-purpose digital computers or similar devices.

[0119] Some embodiments may be described using the expressions “coupled” and “connected” along with their derivatives. These terms are not necessarily intended to be synonyms for each other. For example, some embodiments may be described using the terms “connected” and / or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term “coupled” may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0120] Various embodiments also relate to apparatus or systems for performing these operations. The apparatus may be specifically constructed for the claimed purpose or may include a general-purpose computer, such as one selectively activated or reconfigured by a computer program stored in a computer. The processes presented herein do not inherently relate to a particular computer or other apparatus. Various general-purpose machines may be used with programs written in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the claimed method steps. The desired structures for various such machines will be apparent from the given description.

[0121] It should be emphasized that the abstract of this disclosure is provided to allow the reader to quickly determine the essence of the technical disclosure. It should be understood that it will not be used to interpret or limit the scope or meaning of the claims. Furthermore, as can be seen in the foregoing detailed description, various features are combined in a single embodiment for the purpose of facilitating the flow of this disclosure. The method of this disclosure should not be construed as reflecting an intention that the claimed embodiments require more features than expressly recited in each claim. Rather, as reflected in the following claims, the subject matter of the invention lies in fewer than all features of a single disclosed embodiment. Therefore, the following claims are hereby incorporated into the detailed description, wherein each claim is independent as a separate embodiment. In the appended claims, the terms “including” and “in which” are used as simple English equivalents to the corresponding terms “comprising” and “wherein”, respectively. Moreover, the terms “first,” “second,” “third,” etc., are used merely as labels and are not intended to impose numerical requirements on their objects.

[0122] The foregoing description includes examples of the disclosed architecture. It is certainly impossible to describe every conceivable combination of components and / or methods, but those skilled in the art will recognize that many further combinations and permutations are possible. Therefore, the novel architecture is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: The computing device receives input including weather data for a given day. Using the computing device, simulations are performed for the given day at predefined intervals using a disease severity model, the simulations adjusting the canopy water balance for the plant at each of the predefined intervals. The computing device is used to determine the duration of leaf wetness based on the canopy water balance, where the duration of leaf wetness represents the time period during which the leaves of the plant are assumed to be wet. The computing device is used to determine the average air temperature based on the duration of leaf wetting; The calculation device is used to calculate the disease severity level based on the leaf wetting duration and the average temperature, wherein the disease severity level is represented by a value within a range associated with one of a plurality of severity categories, and wherein the disease severity level is set as the severity level of the most severe event among a plurality of disease events of different severity occurring on a given day; and The computing device generates an output including the severity level of the disease for the given day.

2. The medium according to claim 1, further storing instructions for determining one or more of precipitation, dew, or leaf surface evaporation, wherein, Rainfall and dew are added to the canopy moisture balance, and leaf surface evaporation is subtracted from it.

3. The medium according to claim 2, wherein: The weather data describes the precipitation, relative humidity, wind speed, and incident solar radiation. When the relative humidity exceeds a predetermined threshold, the amount of dew increases; and The leaf surface evaporation is a function of the relative humidity, the wind speed, and the incident solar radiation.

4. The medium according to claim 1, wherein, The disease severity level is determined by increasing the average temperature when it falls within a predetermined range during the duration of leaf wetting.

5. The medium according to claim 1, wherein, Calculating the disease severity level includes determining the minimum number of wet hours for infection, determining whether the duration of leaf wetness exceeds the minimum number of wet hours, and determining the disease severity level based on the duration of leaf wetness exceeding the minimum number of wet hours at favorable temperatures.

6. The medium according to claim 1, wherein, The severity level of the disease is defined by the duration of the leaf wetting.

7. A method for modeling disease severity, comprising: The computing device receives input including weather data for a given day. The computing device is used to simulate a disease severity model for a given day at predefined intervals, the simulation adjusting the canopy water balance for the plant at each of the predefined intervals. The computing device is used to determine the duration of leaf wetness based on the canopy water balance, where the duration of leaf wetness represents the time period during which the leaves of the plant are assumed to be wet. The computing device is used to determine the average air temperature based on the duration of leaf wetting; The calculation device is used to calculate the disease severity level based on the leaf wetting duration and the average temperature, wherein the disease severity level is represented by a value within a range associated with one of a plurality of severity categories, and wherein the disease severity level is set as the severity level of the most severe event among a plurality of disease events of different severity occurring on a given day; and The computing device generates an output including the severity level of the disease for the given day.

8. The method of claim 7, further storing instructions for determining one or more of precipitation, dew, or leaf surface evaporation, wherein, Rainfall and dew are added to the canopy moisture balance, and leaf surface evaporation is subtracted from it.

9. The method according to claim 8, wherein: The weather data describes the precipitation, relative humidity, wind speed, and incident solar radiation. When the relative humidity exceeds a predetermined threshold, the amount of dew increases; and The leaf surface evaporation is a function of the relative humidity, the wind speed, and the incident solar radiation.

10. The method according to claim 7, wherein, The disease severity level is determined by increasing the average temperature when it falls within a predetermined range during the duration of leaf wetting.

11. The method according to claim 7, wherein, Calculating the disease severity level includes determining the minimum number of wet hours for infection, determining whether the duration of leaf wetness exceeds the minimum number of wet hours, and determining the disease severity level based on the duration of leaf wetness exceeding the minimum number of wet hours at favorable temperatures.

12. The method according to claim 7, wherein, The severity level of the disease is defined by the duration of the leaf wetting.

13. An apparatus for modeling disease severity, comprising: The hardware interface is configured to receive input including weather data for a given day; A non-transitory computer-readable medium storing a disease severity model configured to be simulated for a given day at predefined intervals, the simulation adjusting for canopy water balance for the plant at each of the predefined intervals. The hardware processor is configured as follows: The duration of leaf wetness is determined based on the canopy water balance, whereby the duration of leaf wetness represents the time period during which the leaves of the plant are assumed to be wet. The average air temperature was determined based on the duration of leaf wetness. The disease severity level is calculated based on the duration of leaf wetting and the average temperature, wherein the disease severity level is represented by a numerical value within a range associated with one of a plurality of severity categories, and wherein the disease severity level is set as the severity level of the most severe event among a plurality of disease events of different severity occurring on a given day; and Generate output including the severity level of the disease for the given day.

14. The apparatus according to claim 13, wherein, The computer-readable medium stores one or more of precipitation, dew, or leaf surface evaporation, wherein in the simulation, precipitation and dew are added to the canopy water balance and leaf surface evaporation is subtracted therefrom.

15. The apparatus according to claim 14, wherein: The weather data describes the precipitation, relative humidity, wind speed, and incident solar radiation. When the relative humidity exceeds a predetermined threshold, the amount of dew increases; and The leaf surface evaporation is a function of the relative humidity, the wind speed, and the incident solar radiation.

16. The apparatus according to claim 13, wherein, The disease severity level is determined as an increase when the average temperature falls within a predetermined range during the leaf wetting duration, and the disease severity level is defined by the length of the leaf wetting duration.

17. The apparatus according to claim 13, wherein, Calculating the disease severity level includes determining the minimum number of wet hours for infection, determining whether the duration of leaf wetness exceeds the minimum number of wet hours, and determining the disease severity level based on the duration of leaf wetness exceeding the minimum number of wet hours at favorable temperatures.

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