Crop production support method and crop production support system

Abstract

To provide a solution that enables prediction of end times of phenological events for a crop including multiple varieties using generally available data and easily usable computational resources, with improved prediction accuracy and flexibility to handle each variety.SOLUTION: When predicting phenological event end times for a crop including multiple varieties on the basis of a relationship between a cumulative value of a first meteorological quantity after the phenological event start times and a target cumulative value using the phenological event start times of the crop including multiple varieties, meteorological data after the phenological event start times, and the target cumulative value, an end time prediction unit quantitatively adjusts the cumulative value of the first meteorological quantity using cumulative adjustment condition information for the first meteorological quantity that changes with a second meteorological quantity.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a crop production support method and a crop production support system. [Background technology]

[0002] A system is known that adjusts the balance between the optimal picking date for a group of tea fields and the operation of a tea factory to help tea factories formulate picking plans during the tea season that take into account the optimum picking time and the factory's processing capacity, etc. This system predicts the picking date based on past data and the actual growth state of the tea, and adjusts the picking date so that it does not exceed the factory's processing capacity, thereby enabling efficient tea harvesting and processing (Patent Document 1 listed below). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-143587 Summary of the Invention [Problem to be solved by the invention]

[0004] For agricultural landowning corporations and commercial farmers, the key issue is when and how much crop will be ready for harvest. This affects the amount of work they need to plan and the revenue they can generate from sales. There are known models that divide a single crop's harvest date into multiple growth stages and predict it using accumulated temperature. However, some agricultural management entities produce multiple crops and varieties, making it difficult to devote significant resources to predicting the harvest date of a single crop or variety, and preparing multiple models for each crop or variety is cumbersome. Furthermore, while research and demonstrations by universities and public research institutes have reported improved prediction accuracy by taking into account advanced phenology and agronomy, the introduction of uncommon explanatory variables, difficult-to-obtain sensing data, and inference using expensive computational resources are economically unreasonable. In other words, when predicting a certain target variable, even if information on specific parameters is collected and added to the explanatory variables, unless this dramatically improves the model's predictive accuracy, the cost of collecting the data for those parameters is significant. Therefore, it is economically rational not to incorporate special parameters that are not significant enough to justify the cost into predictions, and there is a need for a practical, usable, and effective agricultural production support system that is in line with the actual business situation of farmers.

[0005] Therefore, at least one aspect of the problem to be solved by the present disclosure is to provide a solution that predicts the timing of the end of growth events in crops, including multiple varieties, using generally available data and easily available computational resources, while improving prediction accuracy and providing the flexibility to respond to each variety. Note that, if a divisional application based on the present disclosure is filed, problems that are obvious to a person skilled in the art and can be read from the embodiments and their explanations that are characteristic of the present disclosure, as described in the specification, drawings, etc. of the present disclosure, may also be problems to be solved by the divided invention. [Means for solving the problem]

[0006] The agricultural crop production support method of the present disclosure is a method using an agricultural crop production support system including at least one computer device having an input interface operable by a user and an output interface that executes output recognizable by the user, and a storage device, and includes an input step in which a user uses the input interface to input a crop including multiple varieties to be produced and a start time of a growth event for the crop including the multiple varieties; a weather data acquisition step in which a data acquisition unit acquires weather data including at least a first weather value and a second weather value from the start time of the growth event onward; and a target integrated value acquisition step in which an end time prediction unit acquires a target integrated value for the first weather value included in the weather data for the crop. The method includes an obtaining step, an end time prediction step in which the end time prediction unit predicts the end time of the growth event for the crop including multiple varieties based on the relationship between the integrated value of the first weather value after the start time of the growth event and the target integrated value using the crop including multiple varieties and the start time of the growth event for the crop including multiple varieties input in the input step, weather data after the start time of the growth event, and the target integrated value, and an output step in which the output interface outputs the predicted end time of the growth event, and in the end time prediction step, the end time prediction unit quantitatively adjusts the integrated value of the first weather value using crop integration adjustment condition information stored in the storage device that changes the integrated value of the first weather value according to the second weather value.

[0007] In addition, the agricultural crop production support method disclosed herein is a method for supporting agricultural crop production using an agricultural crop production support system including at least one computer device having an input interface that can be operated by a user and an output interface that produces output that can be recognized by a user, and a storage device, and includes an input step in which a user uses the input interface to input the crops that include multiple varieties to be produced and the end time of a growth event for the crops that include the multiple varieties; a weather data acquisition step in which a data acquisition unit acquires weather data that includes at least a first weather value and a second weather value before the end time of the growth event; a target accumulated value acquisition step in which a start time prediction unit acquires a target accumulated value of the first weather value included in the weather data for the crop; a start time prediction step in which the start time prediction unit predicts the start time of the growth event for the crop based on the relationship between the accumulated value of the first weather value before the end time of the growth event and the target accumulated value using the end time of the growth event input in the input step, the weather data before the end time of the growth event, and the target accumulated value; and an output step in which the output interface outputs the start time of the growth event.

[0008] The agricultural crop production support system of the present disclosure also includes at least one computer device having an input interface that can be operated by a user and an output interface that produces output that can be recognized by a user, and a storage device, and is equipped with a data acquisition unit that acquires weather data input from the input interface, including the crop including multiple varieties to be produced, the start time of a growth event for the crop including multiple varieties, and at least a first weather value and a second weather value from the start time of the growth event, and an end time prediction unit that predicts the end time of the growth event for the crop including multiple varieties based on the relationship between the integrated value of the first weather value from the start time of the growth event and the target integrated value using a target integrated value of the first weather value included in the weather data for the crop, the start time of the growth event for the crop including multiple varieties, the weather data from the start time of the growth event, and the target integrated value, and the end time prediction unit quantitatively adjusts the integrated value of the first weather value using integration adjustment condition information stored in the storage device that changes the integrated value of the first weather value according to the second weather value.

[0009] In addition, to achieve the above-mentioned object, the agricultural crop production support system includes at least one computer device having an input interface that can be operated by a user and an output interface that produces output that can be recognized by a user, and a storage device, and is equipped with a data acquisition unit that acquires weather data including at least a first weather value and a second weather value before the end of the growth event, which are input from the input interface, and a target accumulated value of the first weather value included in the weather data for the crop, the end of the growth event for the crop including multiple varieties, the weather data before the end of the growth event, and the target accumulated value, to predict the end of the growth event for the crop including multiple varieties based on the relationship between the accumulated value of the first weather value before the end of the growth event and the target accumulated value. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide a solution that predicts the timing of the end of growth events for multiple crops using commonly available data and easily available computational resources, while improving prediction accuracy and providing the flexibility to adapt to each variety. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of a network configuration of an agricultural crop production support system. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of a computer device. [Figure 3] FIG. 1 is a functional block diagram showing an overall view of an agricultural crop production support system. [Figure 4] FIG. 10 is a diagram for explaining a method for estimating a necessary integrated value. [Figure 5] FIG. 10 is a diagram for explaining a method for predicting the end time of a growth event. [Figure 6] FIG. 10 is a diagram showing the flow of processing up to outputting the predicted end time of a growth event, which is part of the agricultural crop production support method. [Figure 7] FIG. 10 is a diagram showing an example of setting product category data. [Figure 8] FIG. 10 is a diagram illustrating an example of a case where integration adjustment of weather values ​​is performed. [Figure 9] FIG. 10 is a diagram showing integration adjustment condition data. [Figure 10] FIG. 1 is a diagram showing the flow of processing up to output of predicted yield, which is part of the agricultural crop production support method. [Figure 11] FIG. 1 is a diagram showing a processing flow of a crop production support method according to the present disclosure. [Figure 12] 10 shows an example of a screen when information on predicted growth events with predicted yields is displayed on a display. [Figure 13] FIG. 1 is a functional block diagram showing an overall view of an agricultural crop production support system. [Figure 14] FIG. 1 is a diagram showing a processing flow of a crop production support method according to the present disclosure. [Figure 15]FIG. 1 is a diagram showing a processing flow of a crop production support method according to the present disclosure. [Figure 16] 10A and 10B are diagrams illustrating a specific example of how to schedule an interim event and an example of adjusting an integrated value. [Figure 17] 10 is a screen display example of an output work plan. [Figure 18] FIG. 1 is a workflow diagram for formulating a work plan. DETAILED DESCRIPTION OF THE INVENTION

[0012] The agricultural crop production support method and agricultural crop production support system according to the present disclosure will be described with reference to the drawings.

[0013] 2 shows the hardware configuration of a typical computer device. Computer device 100 can execute predetermined processes through the cooperation of software and hardware resources by having processor 114 execute application programs based on application data stored in storage device 115. Storage device 115 may store operating system data necessary for computer device 100 to function as a general-purpose computer, and the operating system functions using this operating system data.

[0014] Such a computing device 100 may be any type of electronic device, such as, for example, a server computer, a desktop computer, a laptop computer, a portable or mobile device, a camera, a mobile phone, a smartphone, a tablet computer, a television, a wearable device (such as display glasses or goggles, a head-mounted display (HMD), a head-mounted spatial computing device, a watch, a headset, an armband, etc.), a virtual reality (VR) and / or augmented reality (AR) enabled device, a personal digital assistant, etc.

[0015] Input interface 112 and output interface 113 may be hardware devices that allow a user to input and output information to and from computer device 100 and / or other computer devices. Specific input devices that may constitute input user interface 112 may include a keyboard, a mouse, one or more touch panel sensors, a microphone, etc. Similarly, output devices that may constitute output user interface 113 may include a display, a monitor, a printer, a virtual space projection display, an augmented space projection display, a speaker, etc.

[0016] The communication interface 111 can communicate with other electronic devices using various types of electrical communication lines, including wired and wireless connections, such as wide area network connections via fiber optic networks or digital telephone lines, local wireless connections, short-range wireless communications, and satellite-based location systems.

[0017] Processor 114 may be one or more of any type of computer processing element, such as in the form of an integrated circuit or controller that performs processor operations, such as with a central processing unit (CPU). For example, processor 114 may be one or more single-core processors. Alternatively, processor 114 may be one or more multi-core processors having multiple independent processing units. Processor 114 may also include register memory for temporarily storing instructions being executed and associated data, as well as cache memory for temporarily storing recently used instructions and data. When multiple processors are used for processing, the same processor need not perform all of the processing.

[0018] The system may employ a cluster configuration in which multiple computing devices 100 are grouped and connected via a network. In this case, the same computing system may be installed in multiple locations. The specific locations and connection methods of these computing devices are not important, and they may even be located outside the country in which the user resides. Such a group of computing devices may be treated as a single cloud computing resource distributed across various data centers.

[0019] 1 is a diagram illustrating an example of a network configuration of an agricultural crop production support system according to the present disclosure. The agricultural crop production support system 1 may include a first computer device 100A, a second computer device 100B, and a first management server 100S1, which is also a single computer device, all connected to a network NW. A plurality of other computer devices and management servers may also be connected. An external sensor device 30, an external system, and an external database, which are used to acquire weather data (discussed below), may also be connected to the network NW.

[0020] FIG. 3 is a functional block diagram illustrating an overall view of the agricultural crop production support system according to the present disclosure. In this diagram, the agricultural crop production support system 1 may have a function of using a server device that provides an event-driven, serverless computing environment to execute a container image containing user code, and launching the container image in response to a specific event. The server device may have a containerized execution environment for executing user-provided application code in a cloud computing environment. The server device may employ an event-driven architecture and execute code based on triggers such as data changes, HTTP requests, and timer events. The container image includes application dependencies and an execution environment, enabling deployment. The server device provides a runtime environment that supports the execution of user code. The server device also has multiple layers for security and access control to enhance data protection.

[0021] In the system 1, the data acquisition unit 116 acquires various data acquired from the input interface 112, external sensor devices, external systems, and external databases, performs predetermined preprocessing, stores the data in the storage device 115S, or uses the data to execute applications.

[0022] The storage device 115S may be a non-relational database, such as an object storage. The object storage may be storage that uses an externally provided object storage service. The system provides object-based data storage functionality and automatically replicates data across multiple geographic regions to ensure data redundancy and durability. Furthermore, it has strong access control and encryption functions to ensure data security.

[0023] The system 1 can execute the functions of a target integrated value calculation unit 118, an end time prediction unit 117, and a yield prediction unit 119, which will be described later, using an application code provided by the user and data stored in the storage device 115S. These units output the predicted end time of a growth event for a crop, or the predicted end time of a growth event with a predicted yield. These outputs can be perceived by the user through the five senses via the output interface 113.

[0024] The user of System 1 is intended to be an operator who understands the specifications of System 1, operates System 1, and can also provide consulting services regarding crop production, but it may also be a farmer who is familiar with operating System 1.

[0025] Next, terms related to this disclosure will be explained. In this disclosure, "crop" refers to edible and non-edible plants grown for commercial or personal harvesting purposes, and is divided into different types based on their botanical classification and uses. For example, grapes, cucumbers, and eggplants are each recognized as distinct crop species. In other words, crops are differentiated at the plant species level. Crops may also be further divided into types, including, but not limited to, the following: Fruit trees: Plants that produce fruit such as grapes, apples, and oranges. Vegetable: A plant whose edible parts are leaves, stems, roots, or fruit, such as cucumbers, eggplants, tomatoes, and cabbage. Cereals: Plants grown primarily for their grain, such as wheat, corn, rice, and soybeans.

[0026] In this disclosure, a "variety" refers to a group of plants that can be distinguished from other groups of plants by all or part of characteristics related to important traits, as defined in the Plant Variety Seed Act, and that can be propagated while retaining all of those characteristics. Furthermore, multiple varieties may exist for the same crop. For example, there is Ruby Roman (registered trademark) for the genus Vicia. In other words, as an example, for the crop "grape," "Ruby Roman (registered trademark)" corresponds to a variety.

[0027] In this disclosure, the "start time of a growth event" of a crop refers to the sowing date, planting date, flowering date, etc. Depending on the crop, the names of the base events vary, so these will be collectively referred to as the start time of the growth event. Furthermore, the "end time of a growth event" refers to the date by which the desired final product is obtained, such as the harvest date. Similarly, the names of the events that result in fruition vary depending on the crop, so these will be collectively referred to as the end time of the growth event.

[0028] "Weather data" in this disclosure may include weather values ​​per unit period (including minimum temperature, maximum temperature, average temperature, rainfall (precipitation), solar radiation, sunshine hours, humidity, wind speed, air pressure, etc.). The unit period may be divided into years, months, weeks, days, hours, etc., and is generally days. For example, when the unit period is days, the temperatures are the daily average temperature, daily minimum temperature, and daily maximum temperature.

[0029] Weather data may include multiple types of weather values, or different types of weather values. Specific examples of use will be described later. For example, if the daily mean temperature is the first weather value, the daily maximum temperature can be the second weather value, and the daily precipitation can be the third weather value.

[0030] Furthermore, weather data can be acquired from the sensor device 30, external systems, and external databases, including actual measured values, forecast values, and average values ​​for each region, with a resolution of 1 km square for each latitude and longitude of the field where crops are actually produced. The actual measured values ​​are values ​​actually measured in the current year, the average values ​​are values ​​statistically processed from past measured values, and the forecast values ​​may be values ​​derived from past and recent actual values ​​using a predetermined statistical process or algorithm. Note that, in terms of forecast accuracy, the forecast values ​​may have a limited future date range. This allows the address, latitude, longitude, and other location of the field to be registered, making it possible to acquire weather data according to the location.

[0031] This section explains how an accumulation model using these meteorological values ​​can be used to predict the end of a growth event. For example, it is known that the harvest date of a crop depends heavily on the cumulative effect of temperature. This model and concept are based on the assumption that plants grow and ultimately bear fruit when exposed to a certain amount of temperature. Conventional accumulated temperature models use this relationship to predict the harvest date. This will be explained using Figure 4.

[0032] Figure 4 is a diagram illustrating a method for estimating the required cumulative temperature from historical data on the start and end of a growing event for a specific variety of a specific crop. In this diagram, the horizontal axis represents the date, the first vertical axis on the left represents the average daily temperature, and the second vertical axis on the right represents the cumulative temperature. Assume that the historical data records a planting date of April 1, the start of the growing event, and a harvest date of May 19, the end of the growing event. In this case, the cumulative temperature required for harvest can be estimated by acquiring meteorological data including the average daily temperature from the planting date onward and accumulating it from April 1 to May 19. For example, in this example, the estimated required cumulative temperature is 1000°C. This cumulative temperature of 1000°C can be used as the target cumulative temperature for the next growing of the same variety of the same crop.

[0033] Figure 5 illustrates a method for predicting the end of a growing event for a specific crop, starting from the start of the event. As with Figure 4, this diagram shows the date on the horizontal axis, the average daily temperature on the first vertical axis on the left, and the accumulated temperature on the second vertical axis on the right. The planting date, which is the planned start of the growing event, is set to April 1, and a target accumulated temperature of 1000°C is used, calculated from actual data for the same crop. Then, the forecast or average daily temperature values ​​from the start of the growing event onward are obtained from an external database or system, and these values ​​are accumulated to calculate the end date of the growing event when the target accumulated temperature is reached, such as May 21. This allows for the prediction of the harvest time. Furthermore, by updating the forecast or average values ​​with actual values ​​or the latest forecast values, a more accurate end date of the growing event can be predicted and updated to that date.

[0034] In addition to this, there are methods that set the effective accumulated temperature of the daily average temperature within a numerical range and accumulate only the daily average temperatures that fall within that range, or that set a standard temperature and accumulate only the value obtained by subtracting the standard temperature from the daily average temperature, but it is basically used as a meteorological value to accumulate the daily average temperature.

[0035] However, conventional temperature accumulation models have several problems. 1. Failure to consider environmental factors other than average temperature Plant growth and flowering are influenced not only by average temperature, but also by various environmental factors such as sunshine hours, humidity, and soil moisture. However, because the cumulative temperature model focuses only on average temperature, it cannot reflect the influence of other factors. This limits the accuracy of its predictions. However, introducing less common environmental factors also poses problems in terms of data acquisition costs. 2. The need for parameter settings for each crop and variety In the accumulated temperature model, target accumulated values ​​must be set for each crop and variety. However, these parameters are often determined empirically, requiring experience and know-how. At a minimum, past performance data, farmer knowledge, and domain knowledge are required. 3. The problem of spatial resolution of meteorological data The accumulated temperature model generally uses temperature data measured at meteorological stations. However, if the location of the observation station is too far from the cultivation site, differences in the microclimate will cause a discrepancy with the temperature in the actual cultivation environment. For this reason, if the observation station is located far from the cultivation site, the prediction accuracy may decrease. 4. Not considering differences in timing The target accumulated value varies depending on the characteristics of each variety. The accumulated temperature model uses a uniform target accumulated value without taking into account differences in the early and late maturity of each variety. Even for the same crop, the early and late maturity of each variety varies, and the target accumulated value also differs. On the other hand, it is not realistic to set an optimal target accumulated value for all varieties. 5. Failure to consider the impact of extreme weather In recent years, due to global warming, extreme weather events that exceed certain indicators, such as extremely high or low temperatures or heavy rain, have become more frequent. Specifically, these events include days with a daily maximum temperature of 35°C or higher (extremely hot days) and heavy rain with an hourly precipitation of 50mm or more. These events have a significant impact on plant growth. Conventional accumulated temperature models only take into account the average temperature and are unable to reflect the impact of extreme weather events. However, because the average daily temperature is an average value, it is a meteorological value that is suitable for accumulation, and it is not desirable to accumulate the maximum or minimum temperature.

[0036] Solving even just some of the above issues can improve the accuracy of forecasting the end of crop growth events. For example, regarding part of the problem of spatial resolution of meteorological data, the discrepancy between meteorological values ​​can be improved by using meteorological data that corresponds to the location of the cultivation field, as mentioned above.

[0037] A workflow for solving these problems is shown in Fig. 6. Fig. 6 is a diagram showing the flow of processing up to the output of the predicted growth event end time, which is part of the agricultural crop production support method according to the present disclosure.

[0038] The prediction of the growth event end time is performed by the end time prediction unit 117 in Fig. 3. In Fig. 6, first, the user uses the input interface 112 to input the variety of the crop to be produced and the start time of the growth event. Specific items to be input include, for example, the name of the crop, the variety name, and the (planned) planting date. In practice, the production area may also be input for the purpose of yield prediction, which will be described later. Furthermore, since multiple varieties of one crop are usually produced, multiple variety names and (planned) planting dates are input.

[0039] To predict the end time of a growth event for a crop using a meteorological integration model, a target integrated value is required. Here, if the target integrated value is known based on the farmer's know-how, there is no need to calculate the target integrated value using the system, and the user may obtain the target integrated value by directly inputting it. However, if only performance data exists and the target integrated value has not been determined, the target integrated value calculation unit 118 may calculate the target integrated value using performance data for multiple varieties.

[0040] For example, using the method shown in Figure 4, the estimated required integrated value can be calculated using the start and end times of a growth event for variety A produced in the past, as well as weather data during that time. The estimated required integrated value is also calculated in a similar manner for variety B. A statistical value, such as the median of the estimated required integrated values ​​for variety A and variety B, may then be calculated as the target integrated value. The same applies when there are even more varieties.

[0041] In this way, by calculating the target integrated value using at least the performance data, it is possible to improve to some extent the point of determining the target integrated value to be set for each product type. The target integrated value may be determined by a person or may be calculated by the target integrated value calculation unit 118.

[0042] In addition, the target integrated value may be determined according to the earliness of the variety. In the present disclosure, the storage device 115S stores characteristics based on the length of the cultivation period from flowering to harvest for each variety as variety category data related to earliness. An example of the earliness of varieties is shown below. Very early: The earliest ripening and harvestable variety. Early: A variety that matures and can be harvested earlier than standard varieties, although slower than very early varieties. Nakawase: A variety with a maturity period between early and mid-season. Medium-ripening: A variety with a standard ripening period. Mid-late ripening: A variety with a ripening period between mid-season and late-season varieties. Late: A variety that matures late and can be harvested. Such maturity can also be obtained from information on seeds for sale published by various seed manufacturers.

[0043] Figure 7 shows an example of setting variety category data related to varieties, variety categories, and category target cumulative values. Given the wide variety of varieties for a given crop, it is impractical to determine detailed target cumulative values ​​for every variety. Therefore, by grouping varieties with similar maturity characteristics into variety categories and standardizing them to a certain extent, this work can be reduced. These variety names, variety categories, and corresponding category target cumulative values ​​can be manually entered and stored in advance. While these correspondences are data that are determined and established through human mental activity, by storing them in association with variety names, it is possible to automatically calculate the corresponding variety category and its category target cumulative value when a variety name is entered. For example, in the example shown in this figure, if varieties A and B have similar maturity characteristics, these varieties can be stored in a single variety category group. This allows the same category target cumulative value to be used whether variety A or variety B is called.

[0044] For example, in FIG. 6, suppose varieties A and E are entered as crops to be produced. Furthermore, suppose that actual data exists for varieties A and D. For variety A, since it is the same variety, it is possible to accurately predict the end time of the growth event. However, even if variety E has never been cultivated and has no actual data, if it has a similar earliness and belongs to the same variety category group in the variety category data confirmed and registered in advance, it is possible to acquire the variety category data (step S104: variety category data acquisition step, described later) and accurately predict the end time of the growth event using the same category target integrated value. In this case, for example, since variety E belongs to the same variety category group as variety D as shown in FIG. 7, it is possible to apply the same category target integrated value.

[0045] In this way, by preparing target integrated values ​​according to variety categories in advance, the problem of having to set target integrated values ​​for each crop and variety can be solved to some extent.

[0046] Next, we will explain how to integrate the weather values ​​(forecast values, average values) contained in the weather data toward the determined target integrated value and adjust the integrated value when predicting the end of a predicted growth event. Basically, the weather values ​​corresponding to the location of the field can be integrated as is, as shown in Figure 5. However, depending on the variety, it may be better to adjust the integration conditions to take into account the effects of extreme weather or the unique growth characteristics of that variety.

[0047] FIG. 8 is a diagram showing an example of weather value integration adjustment. This diagram shows an example in which weather values ​​to be integrated are adjusted based on integration adjustment conditions set for a specific crop and a specific variety. First, in this diagram, the horizontal axis represents the date, the first vertical axis on the left represents the temperature, and the second vertical axis on the right represents the integrated temperature for the daily mean temperature, daily maximum temperature, adjusted daily mean temperature, and adjusted integrated daily mean temperature. The weather value to be integrated (first weather value) is the daily mean temperature, and the weather value (second weather value) that triggers the adjustment of the integration conditions for the first weather value is the daily maximum temperature.

[0048] In this diagram, the integration adjustment condition is set as follows: if the maximum temperature (second meteorological value) is above a certain degree Celsius, that day is considered to satisfy the integration adjustment condition. The daily mean temperature for that day is multiplied by a specified coefficient, output as the adjusted daily mean temperature, and then integrated to calculate the adjusted integrated daily mean temperature. For days that do not satisfy the integration adjustment condition, the integration is performed as is without any adjustment. While the above example is merely an example, the technical reason for this integration adjustment is to reflect empirical knowledge that, for a given crop and variety category, extreme heat, such as the one mentioned above, has a greater impact on crop growth (e.g., better growth) than the integrated daily mean temperature. In other words, rather than relying solely on the mechanically integrated daily mean temperature to predict the end of a growth event, the integration adjustment condition is set so that the farmers who benefit from this system can utilize their accumulated know-how. By using the integration adjustment condition data set for each crop and variety category, it is possible to solve to some extent both the problems of taking into account the characteristics of the crop and variety and of responding to extreme weather. Note that, as in this example, the daily mean temperature and the daily maximum temperature are both index values ​​that represent temperature, but in the integration adjustment conditions, they may be treated as different meteorological values.

[0049] FIG. 9 shows such integrated adjustment condition data. The integrated adjustment conditions may be stored in the storage device 115S in a state where the relationship with a specific crop and variety category can be referenced. Multiple conditions can be written for one crop and variety category. Therefore, multiple adjustments may be made if the conditions are met. When accumulating weather values, weather values ​​taking all adjustments into account may be integrated.

[0050] In this way, it is possible to predict the end times of growth events according to the crop and variety. Next, a method for displaying the predicted end times of growth events together with the predicted yield will be described.

[0051] FIG. 10 is a diagram showing the flow of processing up to the output of the predicted yield, which is part of the agricultural crop production support method according to the present disclosure. The calculation of the predicted yield is performed by the yield prediction unit 119 in FIG. 3. The yield indicates the harvest amount when the crop is, for example, a vegetable, and can be expressed in terms of weight, such as (kg) or (t). The production area is the area where actual planting has been carried out, such as the planting area, and is expressed in square meters (m 2 It can be expressed in units of area such as ares (a), hectares (ha), and tan (tan). One tan is equivalent to approximately 10 ares.

[0052] Yield per tan originally refers to the amount of harvest obtained from one tan of farmland, but in this disclosure it may be defined as the yield per unit production area and can be used using any of the above area units. For example, yield per tan may be expressed in units such as kg / a or t / ha. These yields are basically calculated in advance from actual data and stored in a storage device.

[0053] In addition, producers may be ranked according to their level of skill in producing agricultural crops, and the yield may be adjusted accordingly.

[0054] For a given crop and variety category, the predicted yield can be calculated using the input production area and previously acquired yield data. By combining this predicted yield with the predicted growth event end time, predicted growth event yield time information with predicted yield can be output.

[0055] The flow of the agricultural crop production support method using the agricultural crop production support system of the present disclosure is shown in the form of a flowchart in FIG.

[0056] First, in step S101, the user inputs the crops containing multiple varieties to be produced and the start times of the growth events of the crops containing multiple varieties using the input interface 112 (input step). This step clarifies the crops and varieties to be predicted in the system 1, and also clarifies the base point for predicting the end times, such as the harvest date.

[0057] Next, in step S102, the data acquisition unit 116 acquires weather data including at least the first weather value and the second weather value after the start of the growth event (weather data acquisition step).

[0058] Next, in step S103, the end time prediction unit 117 acquires a target integrated value of the first weather value for the crop contained in the weather data (target integrated value acquisition step). Note that the acquisition of the target integrated value in this step may be a value calculated by the target integrated value calculation unit 118, which is a function executed by an application of the system 1, or may be a value input by the user using the input interface 112.

[0059] Next, in step S104, the end time prediction unit 117 uses the start time of the growth event for the crop containing multiple varieties and the crop containing multiple varieties input in the input step, the weather data from the start time of the growth event onwards, and the target accumulated value to predict the end time of the growth event for the crop containing multiple varieties based on the relationship between the accumulated value of the first weather value from the start time of the growth event onwards and the target accumulated value (end time prediction step).

[0060] Next, in step S105, the yield prediction unit 119 predicts the predicted end time of the growth event with the predicted yield for the crop including multiple varieties using the predicted end time of the growth event, the production area, and the yield per unit production area (yield prediction step).

[0061] Next, in step S106, the output interface 113 outputs the predicted end time of the growth event (output step). An example of output of the predicted end time with the predicted yield is shown below.

[0062] FIG. 12 shows an example of a screen displaying the predicted end time of a growth event with predicted yield output from the output interface 113. As shown in this figure, the harvest date and predicted yield for each variety are displayed for each week. This allows each farmer to consider the amount of work required for harvesting and the timing of when that work is required. Furthermore, since harvesting is related to sales, future business profits can also be taken into consideration. Understanding peak yields is important in agricultural management, and the agricultural crop production support system disclosed herein makes it possible to accurately predict harvest dates, including yields.

[0063] As described above, by using the agricultural crop production support system and agricultural crop production support method disclosed herein, it is possible to predict the timing of the end of growth events for multiple crops using generally available data and easily available computational resources, while providing a solution that improves prediction accuracy and is flexible enough to accommodate each variety.

[0064] Next, a system and method that are further developed from the agricultural crop production support system and agricultural crop production support method of the present disclosure will be described. Fig. 13 is a functional block diagram showing the overall image of the agricultural crop production support system, similar to Fig. 3, but with the addition of a new backcasting function and an intermediate event scheduling function.

[0065] (Outline of the backcast function) For farmers, producing more valuable crops means accurately capturing market demand and shipping crops accordingly. For example, individual contracts with specific restaurants or retailers allow them to trade at higher unit prices than mass retailers. However, even if they know when and how much a customer needs, a forecasting function that predicts the harvest date from the sowing date may not be sufficient. In other words, for such contracts, they must be able to start producing crops according to the desired shipping timing. However, crops generally have seasonality, and throughout history, crop-first production has been practiced in accordance with natural climate cycles. In contrast, backcasting, which determines the harvest date in advance and then calculates the start date for production operations, is a production method that can be described as demand-first. The present disclosure can contribute to increasing the value of such crops.

[0066] (Overview of the interim event schedule function) Furthermore, whether agricultural planning is based on forecasting or backcasting, how to schedule intermediate work events such as top dressing becomes increasingly important. For example, as mentioned above, the growth stages from sowing to harvest are influenced by meteorological values ​​such as temperature. However, as has been repeatedly shown, recent climate change can sometimes force changes to the originally planned schedule, making it necessary to set milestones in the plan and operate it flexibly. Furthermore, since overlapping intermediate work in multiple fields necessitates adjusting the assignment of workers and resources such as agricultural machinery, predicting when intermediate work will occur in advance is important for agricultural planning. Therefore, the ability to set intermediate events is important in both forecasting and backcasting.

[0067] The following describes an agricultural crop production support system having a backcasting function and an intermediate event scheduling function. In Fig. 13, explanations of parts that overlap with Fig. 3 are omitted for the sake of brevity. In Fig. 13, the start time prediction unit 120, intermediate event time prediction unit 121, and integrated value adjustment processing unit 122 are functions realized by application programs. Furthermore, intermediate event data and intermediate target integrated values ​​related to intermediate event work may be stored in advance in the storage device 115S.

[0068] Fig. 14 is a flowchart showing the flow of the agricultural production support method using the agricultural production support system related to the backcasting function. Using this figure and Fig. 13, the basic processing flow related to the backcasting function will be explained, followed by the basic processing flow of the intermediate event scheduling function, and then a detailed example of combining the backcasting function and the intermediate event scheduling function will be described later.

[0069] In step S201 of Figure 14, the user uses the input interface 112 to input the crops containing multiple varieties to be produced and the end time of the growth event for the crops containing multiple varieties. Specifically, as described above, the timing of the planned (desired) harvest time is input. Note that, because harvesting is a time-consuming task as will be described later, the start time of harvesting or the end time of harvesting can be appropriately set.

[0070] Next, in step S202, the data acquisition unit 116 acquires weather data including at least a first weather value and a second weather value before the end of the growth event. Here, the weather values ​​that can be used in backcasting are basically predicted values ​​or past actual values ​​(average values).

[0071] Next, in step S203, the start time prediction unit 120 acquires a target integrated value of the first weather value included in the weather data for the crop. This target integrated value is common for the same crop and variety, and may be the same as that used by the end time prediction unit 117.

[0072] Next, in step S204, the start time prediction unit 120 uses the growth event end time input in the input step, the weather data before the growth event end time, and the target integrated value to predict the start time of the growth event for the crop based on the relationship between the integrated value of the first weather value before the growth event end time and the target integrated value. While forecasting adds weather values ​​such as average temperature toward the target integrated value, backcasting starts from the target integrated value and subtracts predicted values ​​or normal values ​​such as average temperature. In this way, it is possible to predict when growth should start so that it will end at the desired time.

[0073] However, the difference between backcasting and forecasting is that while forecasting can improve prediction accuracy by updating forecast values ​​or average values ​​with actual values ​​for the current year, backcasting does not allow for such adjustments to be made when harvesting on a desired date. This is because updating by replacing weather values ​​with actual values ​​for the current year results in an earlier or later end date, which does not result in the desired harvest date. Even so, if you are forced to adjust your plan and want to understand the extent of any discrepancy, or if you want to predict the harvest date as accurately as possible, you can update by replacing weather values ​​with actual values ​​for the current year and adjust the end date.

[0074] Next, in step S205, the output interface 113 outputs the growth event start time.

[0075] As with forecasting, in backcasting, the start time prediction unit 120 can quantitatively adjust the accumulated value of the first weather value before the end of the growth event using crop accumulation adjustment condition information stored in the storage device 115S, which changes the accumulated value of the first weather value according to the second weather value, and it goes without saying that all other accumulation adjustments described above are possible.

[0076] Next, the interim event scheduling function will be described. FIG. 15 is a flowchart showing the flow of the agricultural production support method using the agricultural production support system relating to the interim event scheduling function. FIG. 15 shows both the flow of processing the schedule of interim events in forecasting and the flow of processing the schedule of interim events in backcasting. FIG. 16 is a diagram showing a specific example of how to schedule interim events in backcasting and an example of adjusting integrated values. Below, the processing of the schedule of interim events in backcasting and the adjustment of integrated values ​​will be described using FIG. 15 and FIG. 16, but processing can also be done in forecasting using a similar concept.

[0077] The process flow shown in Fig. 15 shows the process flow for scheduling intermediate events, but may be inserted at any timing between steps S101 and S104 in Fig. 11 or between steps S201 and S204 in Fig. 14, or processed in parallel. In Fig. 15, setting of a first intermediate event begins. The intermediate events and intermediate target integrated values ​​corresponding to each variety of each crop may be stored in advance in the storage device 115S, as shown in Fig. 13.

[0078] Intermediate event data is data on intermediate work to be carried out between the start and end of a growth event, and is related to accumulated weather values. For example, in the growth stage from germination to seedling production, each farmer can set appropriate data such as transplanting, planting, top dressing and hilling from the perspective of nutritional management, staking, attracting, bud removal, topping, flower removal, fruit removal, pollination, spraying of pesticides, insecticides, fungicides, etc. as pest control measures, and harvest preparation, start of harvest, end of harvest, etc.

[0079] The intermediate target integrated value is a target integrated value related to the weather integrated value that indicates the timing at which the intermediate event should be performed. These intermediate target integrated values ​​may be stored in association with the above-mentioned intermediate event data, crops, and varieties. The intermediate event and the intermediate target integrated value may be stored in association with each crop or variety.

[0080] In step S301, when this processing flow is performed, the target cumulative value for the crop variety and its scheduled start or end time have already been entered, so this is the starting point. For example, in the case of backcasting as shown in Figure 16, the (scheduled) end time is used as the starting point, and a target cumulative value of 170°C corresponding to that variety is set as shown in this figure.

[0081] Next, in step S302, the intermediate event data acquisition unit 123 acquires first intermediate event data related to at least a first intermediate event related to agricultural work that should be performed between the start time and end time of the growth event, and at least a first integrated intermediate target value related to the integrated value of a first weather value that indicates the time when the first intermediate event should be performed. Specifically, as shown in Fig. 16, task X, task Y, and task Z that should be performed between the start time and end time of the growth event are read out together with their respective integrated intermediate target values.

[0082] Next, in step S303, weather data is acquired. For example, as shown in Fig. 16, if the (planned) end date is March 4, 2024, weather values ​​in an appropriate range up to and including that date are acquired.

[0083] Next, in step S304, the intermediate event timing prediction unit 121 predicts the occurrence time of the first intermediate event based on the relationship between the integrated value before the growth event end time and the first intermediate target integrated value, using at least the first intermediate target integrated value, the growth event end time, and weather data after the growth event end time. Specifically, in FIG. 16, if task Z, whose intermediate target integrated value is closest to the growth event end time, is the first intermediate event, the average temperature value (average value) for each day is subtracted from the target integrated value of 170°C at the end time, and the time when the intermediate target integrated value of task Z will be 150°C is identified as March 2, 2024. Thereafter, a method of determining the time when a specific intermediate target integrated value will be achieved by simply repeating the subtraction of weather values ​​in accordance with this method is referred to as pattern 1.

[0084] On the other hand, simply repeating subtractions can easily cause discrepancies between the progress of the mechanically calculated weather integrated value calculation process and the work and growth of plants when there are a large number of intermediate tasks. Therefore, the integrated value can be adjusted when a specific intermediate target integrated value is reached. That is, when the weather integrated value exceeds the intermediate target integrated value (when the weather integrated value and the intermediate target integrated value differ because they exceed the intermediate target integrated value in the case of addition or fall below the intermediate target integrated value in the case of subtraction), the weather integrated value can be adjusted to match the intermediate target integrated value. For example, for task Z, which is an intermediate event, when the intermediate target integrated value reaches 150, the integrated value adjustment processing unit 122 in FIG. 13 can adjust the integrated value, which would normally be recorded as 145, to match the intermediate target integrated value of 150 and resave it. This method of adjusting the integrated value is referred to as Pattern 2.

[0085] In Figure 16, it can be seen that even when the same weather values ​​are used, differences arise in how the interim event schedule is created between Pattern 1 and Pattern 2. For example, in Pattern 1, the timing for performing Task Y is predicted to be February 28, 2024, but in Pattern 2, it is predicted to be February 27, 2024. In each pattern, the integrated value is adjusted to match the interim target integrated value. Either method can be adopted, but in some cases, adjusting the integrated value for each intermediate task using Pattern 2 reduces the discrepancy between the actual task and the integrated value, improving prediction accuracy.

[0086] A plurality of these intermediate event schedules can be set, such as task Z, task Y, and task X. Next, the output of a work plan that can be made by formulating an intermediate event schedule will be described.

[0087] FIG. 17 is an example of a screen display of an output work plan, and FIG. 18 is a workflow diagram for formulating a work plan. FIG. 17 shows specific planned work dates for multiple fields. Multiple examples of planned work dates may be displayed, and for example, both the above-mentioned Pattern 1 (no adjustment) and Pattern 2 (adjusted) schedules of the interim event schedule can be displayed. Note that specific schedule examples are omitted. Such a work plan can be created for each agricultural management entity that has multiple fields, taking into account multiple crops and multiple varieties. Note that the number of fields may be one.

[0088] To enable the output shown in FIG. 17 , a work plan is formulated in FIG. 18 using the occurrence times of intermediate events. The work plan can be formulated by any one of the end time prediction unit 117, start time prediction unit 120, and intermediate event time prediction unit 121 shown in FIG. 13 , and output along with the prediction. In FIG. 18 , the variety to be grown and the start or end time of the growth event are input, and the start or end time of the growth event is predicted, and the amount of work is estimated using work plan data or work plan-related data stored in the storage device 115S. For example, if there is an intermediate event such as top dressing, the work plan data may store the standard work time for each intermediate event, such as work time / production area, required to complete the top dressing work. This allows the total amount of work to be estimated using the production area as well.

[0089] Additionally, a work time limit / period, such as an upper limit on work time per day, may be stored, allowing a work schedule to be created that allows the total amount of work to be completed.

[0090] Using the timing of the above-mentioned intermediate events, it is possible to output a work plan that indicates when the intermediate event work should be carried out. The work plan can be output according to multiple scenarios. This allows the farm manager to roughly predict what periods will be busy, whether in the case of forecasting or backcasting. This can be used to optimize the allocation of personnel and materials.

[0091] This concludes the description of the embodiments of the present disclosure, but the following additional embodiments included in the present disclosure will be mentioned for completeness. <Additional Notes> [1] 1. A method for supporting agricultural production using a crop production support system including at least one computer device having an input interface operable by a user and an output interface that produces output recognizable by the user, and a storage device, an input step in which the user inputs, using the input interface, a crop including a plurality of varieties to be produced and a start time of a growth event of the crop including the plurality of varieties; a weather data acquisition step in which a data acquisition unit acquires weather data including at least a first weather value and a second weather value after the start time of the growth event; a target integrated value acquisition step in which an end time prediction unit acquires a target integrated value of a first weather value included in the weather data for the crop; an end time prediction step in which the end time prediction unit predicts an end time of the growth event for the crop including multiple varieties based on a relationship between the integrated value of the first weather value after the start time of the growth event and the target integrated value, using the start time of the growth event for the crop including multiple varieties and the crop including multiple varieties input in the input step, weather data after the start time of the growth event, and the target integrated value; an output step in which the output interface outputs the predicted end time of the growth event; A crop production support method in which, in the end time prediction step, the end time prediction unit quantitatively adjusts the accumulated value of the first weather value using crop accumulation adjustment condition information stored in the storage device that changes the accumulated value of the first weather value according to the second weather value. [2] 1. A method for supporting agricultural production using a crop production support system including at least one computer device having an input interface operable by a user and an output interface that produces output recognizable by the user, and a storage device, an input step in which the user inputs, using the input interface, the crops to be produced, which include multiple varieties, and the end times of the growth events of the crops, which include the multiple varieties; a weather data acquisition step in which a data acquisition unit acquires weather data including at least a first weather value and a second weather value before the end of the growth event; a target integrated value acquisition step in which the start time prediction unit acquires a target integrated value of a first weather value included in the weather data for the crop; a start time prediction step in which the start time prediction unit predicts a start time of the growth event for the crop based on a relationship between an integrated value of the first weather value before the end time of the growth event and the target integrated value, using the end time of the growth event input in the input step, the weather data before the end time of the growth event, and the target integrated value; The agricultural crop production support method includes an output step in which the output interface outputs a start time of a growth event. [3] The agricultural crop production support method described in [2], in the start time prediction step, the start time prediction unit quantitatively adjusts the accumulated value of the first weather value before the end of the growth event using crop accumulation adjustment condition information stored in the storage device that changes the accumulated value of the first weather value according to the second weather value. [4] Furthermore, variety category data, which is quantitative data indicating meteorological value characteristics related to the earliness and maturity of each variety of the crops including the plurality of varieties, is stored in the storage device; The method further includes a product category data acquisition step in which the data acquisition unit acquires the product category data, The agricultural crop production support method according to [1] or [2], wherein the target integrated value for the variety is determined in accordance with the variety category data. [5] The storage device further includes historical data on the start and end times of growth events for multiple varieties of at least one crop that have been produced in the past, The agricultural crop production support method described in [1] or [2] further includes a target accumulated value calculation step in which a target accumulated value calculation unit uses the performance data and the first weather value from the start of the growth event included in the performance data to calculate a target accumulated value of the first weather value for the crop including the multiple varieties input in the input step. [6] In the input step, the user further inputs, using the input interface, a production area of ​​the crop including the plurality of varieties to be produced and a yield per unit production area; a yield prediction step in which a yield prediction unit predicts a predicted yield of the crop including the plurality of varieties using the growth event end time, the production area, and the yield per unit production area, The agricultural crop production support method according to [1] or [2], wherein in the output step, the output interface outputs the predicted yield. [7] an intermediate event acquisition step in which an intermediate event data acquisition unit acquires at least first intermediate event data related to the farm work to be performed between the start time of the growth event and the end time of the growth event, and at least a first intermediate target integrated value related to the integrated value of a first weather value that indicates the time when the first intermediate event should be performed; The agricultural crop production support method described in [1] further includes an intermediate event time prediction step in which the intermediate event time prediction unit predicts the occurrence time of the first intermediate event based on the relationship between the integrated value after the start time of the growth event and the first intermediate target integrated value, using at least the first intermediate target integrated value, the start time of the growth event, and weather data after the start time of the growth event. [8] an intermediate event acquisition step in which an intermediate event data acquisition unit acquires first intermediate event data related to the farm work to be performed between the start time of the growth event and the end time of the growth event, and at least a first intermediate target integrated value related to the integrated value of a first weather value that indicates the time when the first intermediate event should be performed; The agricultural crop production support method described in [1] further includes an intermediate event time prediction step in which the intermediate event time prediction unit predicts the occurrence time of the first intermediate event based on the relationship between the integrated value after the end of the growth event and the first intermediate target integrated value, using at least the first intermediate target integrated value, the end of the growth event, and weather data before the end of the growth event. [9] The agricultural production support method described in [6] or [7] further includes an intermediate target integrated value adjustment step in which an integrated value adjustment processing unit adjusts the integrated value that meets at least the requirements of the occurrence time of the first intermediate event so that it matches the first intermediate target integrated value.

[10] The agricultural production support method described in [1] or [3], wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfies a predetermined numerical condition when the second weather value per unit period satisfies a predetermined numerical condition after multiplying it by a predetermined coefficient.

[11] The agricultural production support method described in [1] or [3], wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfied a predetermined numerical condition after multiplying it by a predetermined coefficient when the ratio of the second weather value per unit period to the second weather value per immediately preceding unit period satisfies a predetermined numerical condition.

[12] The agricultural production support method described in [1] or [3], wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfied the predetermined numerical condition after multiplying it by a predetermined coefficient when the total value of the second weather value over multiple unit periods satisfies a predetermined numerical condition.

[13] 1. A crop production support system including at least one computer device having an input interface operable by a user and an output interface for producing output recognizable by the user, and a storage device, A crop including a plurality of varieties to be produced and a start time of a growth event of the crop including the plurality of varieties, which are inputted through the input interface; a data acquisition unit that acquires weather data including at least a first weather value and a second weather value after the start time of the growth event; a target integrated value of a first weather value included in the weather data for the crop; an end time prediction unit that predicts an end time of the growth event of the crop including the plurality of varieties based on a relationship between an integrated value of the first weather value after the start time of the growth event and the target integrated value, using the start time of the growth event of the crop including the plurality of varieties, meteorological data after the start time of the growth event, and the target integrated value; The end time prediction unit quantitatively adjusts the integrated value of the first weather value using integration adjustment condition information stored in the storage device that changes the integrated value of the first weather value according to the second weather value.

[14] 1. A crop production support system including at least one computer device having an input interface operable by a user and an output interface for producing output recognizable by the user, and a storage device, A crop including a plurality of varieties to be produced and an end time of a growth event of the crop including the plurality of varieties, which are inputted through the input interface; a data acquisition unit that acquires weather data including at least a first weather value and a second weather value before the end of the growth event; a target integrated value of a first weather value included in the weather data for the crop; an end time prediction unit that predicts the end time of the growth event of the crop including the plurality of varieties based on the relationship between the integrated value of the first weather value before the end time of the growth event and the target integrated value, using the end time of the growth event of the crop including the plurality of varieties, weather data before the end time of the growth event, and the target integrated value; Crop production support system. [Explanation of symbols]

[0092] 100 Computer Devices 111 Communication Interface 112 Input User Interface 113 Output User Interface 114 processors 115 Storage Devices 116 Data Acquisition Unit 117 End Time Prediction Department 118 Target integrated value calculation unit 119 Yield Forecasting Department 120 Start Time Prediction Department 121 Intermediate Event Timing Prediction Unit 122 Integrated value adjustment processing unit 100A First Computer Device 100B Second Computer Device 100S1 First management server 115S Storage Device 30 Sensor Devices

Claims

1. 1. A method for supporting agricultural production using a crop production support system including at least one computer device having an input interface operable by a user and an output interface that produces output recognizable by the user, and a storage device, an input step in which the user inputs, using the input interface, a crop including a plurality of varieties to be produced and a start time of a growth event of the crop including the plurality of varieties; a weather data acquisition step in which a data acquisition unit acquires weather data including at least a first weather value and a second weather value after the start time of the growth event; a target integrated value acquisition step in which an end time prediction unit acquires a target integrated value of a first weather value included in the weather data for the crop; an end time prediction step in which the end time prediction unit predicts an end time of the growth event for the crop including multiple varieties based on a relationship between the integrated value of the first weather value after the start time of the growth event and the target integrated value, using the start time of the growth event for the crop including multiple varieties and the crop including multiple varieties input in the input step, weather data after the start time of the growth event, and the target integrated value; an output step in which the output interface outputs the predicted end time of the growth event; A crop production support method in which, in the end time prediction step, the end time prediction unit quantitatively adjusts the accumulated value of the first weather value using crop accumulation adjustment condition information stored in the storage device that changes the accumulated value of the first weather value according to the second weather value.

2. 1. A method for supporting agricultural production using a crop production support system including at least one computer device having an input interface operable by a user and an output interface that produces output recognizable by the user, and a storage device, an input step in which the user inputs, using the input interface, the crops to be produced, which include multiple varieties, and the end times of the growth events of the crops, which include the multiple varieties; a weather data acquisition step in which the data acquisition unit acquires weather data including at least a first weather value and a second weather value before the end of the growth event; a target integrated value acquisition step in which the start time prediction unit acquires a target integrated value of a first weather value included in the weather data for the crop; a start time prediction step in which the start time prediction unit predicts a start time of the growth event for the crop based on a relationship between an integrated value of the first weather value before the growth event end time and the target integrated value, using the growth event end time input in the input step, the weather data before the growth event end time, and the target integrated value; The agricultural crop production support method includes an output step in which the output interface outputs a start time of a growth event.

3. 3. The agricultural crop production support method of claim 2, wherein in the start time prediction step, the start time prediction unit quantitatively adjusts the accumulated value of the first weather value before the end of the growth event using crop accumulation adjustment condition information stored in the storage device that changes the accumulated value of the first weather value according to the second weather value.

4. Furthermore, variety category data, which is quantitative data indicating meteorological value characteristics related to the earliness and maturity of each variety of the crops including the plurality of varieties, is stored in the storage device; The method further includes a product category data acquisition step in which the data acquisition unit acquires the product category data, The agricultural crop production support method according to claim 1 , wherein the target integrated value for the variety is determined in accordance with the variety category data.

5. The storage device further includes historical data on the start and end times of growth events for multiple varieties of at least one crop that have been produced in the past, The agricultural crop production support method of claim 1 or 2, further comprising a target accumulated value calculation step in which a target accumulated value calculation unit calculates a target accumulated value of the first weather value for the crop including the multiple varieties input in the input step using the actual data and the first weather value from the start of the growth event included in the actual data.

6. In the input step, the user further inputs, using the input interface, a production area of ​​the crop including the plurality of varieties to be produced and a yield per unit production area; a yield prediction step in which a yield prediction unit predicts a predicted yield of the crop including the plurality of varieties using the growth event end time, the production area, and the yield per unit production area, The agricultural crop production support method according to claim 1 or 2, wherein in the output step, the output interface outputs the predicted yield.

7. an intermediate event acquisition step in which an intermediate event data acquisition unit acquires at least first intermediate event data related to the farm work to be performed between the start time of the growth event and the end time of the growth event, and at least a first intermediate target integrated value related to the integrated value of a first weather value that indicates the time when the first intermediate event should be performed; 2. The agricultural crop production support method according to claim 1, further comprising an intermediate event time prediction step in which the intermediate event time prediction unit predicts the occurrence time of the first intermediate event from the relationship between the integrated value after the start time of the growth event and the first intermediate target integrated value, using at least the first intermediate target integrated value, the start time of the growth event, and meteorological data after the start time of the growth event.

8. an intermediate event acquisition step in which an intermediate event data acquisition unit acquires first intermediate event data related to the farm work to be performed between the start time of the growth event and the end time of the growth event, and at least a first intermediate target integrated value related to the integrated value of a first weather value indicating the time when the first intermediate event should be performed; 3. The agricultural crop production support method according to claim 2, further comprising an intermediate event time prediction step in which the intermediate event time prediction unit predicts the occurrence time of the first intermediate event from the relationship between the integrated value after the end time of the growth event and the first intermediate target integrated value, using at least the first intermediate target integrated value, the end time of the growth event, and weather data before the end time of the growth event.

9. The agricultural production support method described in claim 7 or 8 further includes an intermediate target integrated value adjustment step in which an integrated value adjustment processing unit adjusts the integrated value that meets at least the requirements of the occurrence time of the first intermediate event so that it matches the first intermediate target integrated value.

10. The agricultural production support method described in claim 1 or 3, wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfies a predetermined numerical condition when the second weather value per unit period satisfies a predetermined numerical condition after multiplying it by a predetermined coefficient.

11. The agricultural production support method described in claim 1 or 3, wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfied a predetermined numerical condition after multiplying it by a predetermined coefficient when the ratio of the second weather value per unit period to the second weather value per immediately preceding unit period satisfies a predetermined numerical condition.

12. The agricultural production support method described in claim 1 or 3, wherein the accumulation adjustment condition information is information for accumulating the first weather value corresponding to the period in which the second weather value satisfied a predetermined numerical condition after multiplying it by a predetermined coefficient when the sum of the second weather values ​​over multiple unit periods satisfies a predetermined numerical condition.

13. 1. A crop production support system including at least one computer device having an input interface operable by a user and an output interface for producing output recognizable by the user, and a storage device, A crop including a plurality of varieties to be produced and a start time of a growth event of the crop including the plurality of varieties, which are inputted through the input interface; a data acquisition unit that acquires weather data including at least a first weather value and a second weather value after the start time of the growth event; a target integrated value of a first weather value included in the weather data for the crop; an end time prediction unit that predicts an end time of the growth event of the crop including the plurality of varieties based on a relationship between an integrated value of the first weather value after the start time of the growth event and the target integrated value, using the start time of the growth event of the crop including the plurality of varieties, meteorological data after the start time of the growth event, and the target integrated value; The end time prediction unit quantitatively adjusts the integrated value of the first weather value using integrated adjustment condition information stored in the storage device that changes the integrated value of the first weather value according to the second weather value.

14. 1. A crop production support system including at least one computer device having an input interface operable by a user and an output interface for producing output recognizable by the user, and a storage device, A crop including a plurality of varieties to be produced and an end time of a growth event of the crop including the plurality of varieties, which are inputted through the input interface; a data acquisition unit that acquires weather data including at least a first weather value and a second weather value before the end of the growth event; a target integrated value of a first weather value included in the weather data for the crop; an end time prediction unit that predicts the end time of the growth event of the crop including the plurality of varieties based on the relationship between the integrated value of the first weather value before the end time of the growth event and the target integrated value, using the end time of the growth event of the crop including the plurality of varieties, weather data before the end time of the growth event, and the target integrated value; Crop production support system.

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