Information processing device, information processing method, and program

Abstract

The present technology pertains to an information processing device, an information processing method, and a program that make it possible to provide a vegetation strategy suitable for the weather at a specific location. A plant species is evaluated based on the weather context of the particular site, and a vegetation strategy for the particular site is generated based on the evaluated value of the plant species. The present technology can be applied, for example, to an environment construction support system that supports the construction of an environment.

Description

Technical Field

[0001] The present technology relates to an information processing device, an information processing method, and a program, and particularly to an information processing device, an information processing method, and a program that enable provision of a vegetation strategy suitable for weather in a specific location. Background Art

[0002] A parenting support system is proposed to support parenting of a living being by suggesting a menu to a user according to the weather or by informing the user of the weather (for example, refer to PTL 1).

[0003] [Citation List]

[0004] [Patent Document]

[0005] [PTL 1]

[0006] JP 2012-083986A Summary of the Invention

[0007] [Technical Issues]

[0008] In recent years, symbiotic farming (or Symbiotic Agriculture (registered trademark)), which involves thinning and harvesting mixed plantings under no-tillage, no-fertilization, and no pesticide or herbicide use, and the introduction of only seeds and seedlings, has attracted attention in order to achieve species diversity beyond natural conditions through vegetation cultivation while producing useful plants in an ecologically optimized state. Symbiotic Agriculture (registered trademark) allows the introduction of a variety of plant species to create enhanced ecosystems with increased biodiversity and ecosystem functions.

[0009] In the environment of a specific location where symbiotic agriculture (registered trademark) is practiced, some plant species grow easily while other plant species grow with difficulty. It is desirable to introduce plant species that grow easily in a specific location.

[0010] The environment at a particular site is influenced by a variety of factors, including the site's weather, the species present at the particular site (such as plant species introduced at the particular site), and the site's irrigation and shading practices.

[0011] Therefore, when attempting to practice Symbiotic Agriculture (registered trademark) for growing a variety of plant species, it would be convenient and useful, especially for beginners, to provide relevant information related to the construction of an environment for a specific location (such as vegetation strategies suitable for the weather of the specific location where Symbiotic Agriculture (registered trademark) is to be practiced, and methods for constructing an environment suitable for the growth of plant species observed at the specific location and / or plant species planned to be introduced to the specific location).

[0012] The present technology is designed in consideration of situations such as those described above, and its purpose is to enable provision of information related to the construction of an environment at a specific location, particularly including vegetation strategies suitable for the weather at the specific location.

[0013] [Solution to the problem]

[0014] An information processing device or program according to the present technology is an information processing device comprising: an evaluation unit configured to evaluate plant species based on weather conditions at a specific location; and a generation unit configured to generate a vegetation strategy for a specific location based on the evaluation value of the plant species, or a program that enables a computer to be used as such an information processing device.

[0015] An information processing method according to the present technology is an information processing method including evaluating plant species based on a weather situation at a specific location, and generating a vegetation strategy for the specific location based on the evaluation values of the plant species.

[0016] An information processing method according to the present technology is an information processing method including evaluating plant species based on a weather scenario for a specific location, and generating a vegetation strategy for the specific location based on the evaluated values of the plant species.

[0017] The information processing device may be an independent device or an internal block constituting a single device.

[0018] The program can be provided by being transmitted via a transmission medium or recorded on a recording medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a diagram showing a configuration example of an embodiment of an information processing system to which the present technology is applied.

[0020] Figure 2 is a diagram showing a hardware configuration example of the terminal 11 .

[0021] Figure 3 is a diagram showing a hardware configuration example of the server 12 .

[0022] Figure 4 is a block diagram showing a functional configuration example of the server 12 .

[0023] Figure 5 is a flowchart for explaining an example of the processing of the server 12 .

[0024] Figure 6 : is a flowchart for explaining an example of a process in which the weather scenario calculation unit 42 calculates a weather scenario.

[0025] Figure 7is a diagram showing an example of multiple weather scenarios calculated by ensemble prediction.

[0026] Figure 8 is a diagram showing an example of plant species information stored in the plant DB of the DB 13 .

[0027] Figure 9 is a diagram for explaining an example of processing by which the acquisition unit 41 acquires plant species information.

[0028] Figure 10 is a block diagram illustrating a first configuration example of the evaluation unit 43 and the generation unit 44 .

[0029] Figure 11 ] is a diagram showing an example in which the attribute information conversion unit 51 converts attribute information into corresponding parameters.

[0030] Figure 12 1 is a flowchart for explaining an example of a process of conversion of attribute information performed by the attribute information conversion unit 51 .

[0031] Figure 13 is a diagram for explaining an example of a simplified weather scenario.

[0032] Figure 14 is a diagram for explaining an example of calculation of the evaluation value of a plant species based on a simplified weather scenario by the evaluation value calculation unit 53 .

[0033] Figure 15 : is a flowchart for explaining an example of a process of calculating the evaluation value of a plant species by the evaluation value calculation unit 53 .

[0034] Figure 16 1 is a flowchart for explaining an example of a process of generating, by the generating unit 44 , a presentation UI presenting recommended species.

[0035] Figure 17 is a diagram showing a first display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0036] Figure 18 4 is a diagram showing a second display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0037] Figure 19 is a diagram showing a third display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0038] Figure 20 4 is a diagram showing a fourth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0039] Figure 21is a block diagram illustrating a second configuration example of the evaluation unit 43 and the generation unit 44 .

[0040] Figure 22 is a diagram for explaining an example of calculation of the evaluation value of a plant species based on a simplified weather scenario by the evaluation value calculation unit 132 .

[0041] Figure 23 1 is a flowchart for explaining an example of a process of calculating the evaluation value of a plant species by the evaluation value calculation unit 132 .

[0042] Figure 24 is a diagram showing a fifth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0043] Figure 25 4 is a block diagram illustrating a third configuration example of the evaluation unit 43 and the generation unit 44 .

[0044] Figure 26 16 is a diagram for explaining an overview of searching for an appropriate context by the context search unit 161 .

[0045] Figure 27 is a flowchart for explaining an example of a process of searching for an appropriate context by the context search unit 161 .

[0046] Figure 28 16 is a flowchart for explaining an example of a process of generating environment construction advice by the advice generating unit 162 .

[0047] Figure 29 is a diagram showing a sixth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0048] Figure 30 4 is a block diagram illustrating a fourth configuration example of the evaluation unit 43 and the generation unit 44 .

[0049] Figure 31 1 is a diagram for explaining an example of a process of evaluating, by the recommended species reselecting unit 211 , the interaction information of the candidates for the recommended species and selecting the final recommended species based on the result of the evaluation.

[0050] Figure 32 is a diagram for explaining an example of a process of ranking final recommended species based on interaction information.

[0051] Figure 33 is a block diagram showing a second functional configuration example of the server 12 .

[0052] Figure 34 : is a diagram showing a display example of a presentation UI serving as an input I / F for inputting vegetation survey results.

[0053] Figure 35 4 is a diagram showing the presentation UI 410 in a state where individual coverage ratios are input until the total value of individual coverage ratios input to the individual coverage ratio display unit 414 matches the maximum value of a bar in the status display unit 416 .

[0054] Figure 36 4 is a diagram showing the presentation UI 410 in a state where individual coverage ratios are input until the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 exceeds the maximum value of the bar in the status display unit 416 .

[0055] Figure 37 1 is a diagram showing a first display example of a presentation UI presenting functional diversity.

[0056] Figure 38 2 is a diagram showing a second display example of a presentation UI presenting functional diversity.

[0057] Figure 39 It is a diagram used to explain the CSR triangle.

[0058] Figure 40 is a diagram showing another display example of the presentation UI 430 .

[0059] Figure 41 4 is a diagram showing still another display example of the presentation UI 430 .

[0060] Figure 42 3 is a diagram showing a third display example of a presentation UI presenting functional diversity.

[0061] Figure 43 2 is a diagram showing a fourth display example of a presentation UI presenting functional diversity.

[0062] Figure 44 1 is a diagram showing a display example of a presentation UI that presents a time series of vegetation survey results. DETAILED DESCRIPTION

[0063] <Embodiment of information processing system to which the present technology is applied>

[0064] Figure 1 is a diagram showing a configuration example of an embodiment of an information processing system to which the present technology is applied.

[0065] The information processing system 10 constitutes an environment construction support system that supports construction of an environment at a specific location by evaluating plant species based on weather conditions at the specific location and generating relevant information related to construction of the environment at the specific location based on the evaluation values of the plant species.

[0066] Examples of relevant information related to constructing an environment at a particular site include vegetation strategies appropriate for the weather at the particular site and suggestions on how to construct an environment suitable for the growth of plant species observed and / or planned for introduction at the particular site.

[0067] The information processing system 10 includes one or more terminals 11-i, one or more servers 12, and a database (database) 13. The terminals 11-i, the servers 12, and the database 13 can communicate with each other via a network 14 including a mobile communication network such as a wired LAN (Local Area Network), a wired LAN, the Internet, or 5G.

[0068] Figure 1 In the embodiment, four terminals 11-1, 11-2, 11-3, and 11-4 are provided as terminals 11-i. However, other numbers of terminals 11-i may be used, such as one, three, or five or more. Hereinafter, unless otherwise specified, terminals 11-1, 11-2, 11-3, and 11-4 will be described as terminals 11.

[0069] In addition, although Figure 1 In the embodiment, one server 12 is provided as the server 12, but a plurality of servers 12 may be provided. When a plurality of servers 12 are provided, the plurality of servers 12 may be configured to execute the processing described below in a distributed manner. Responsible terminals 11 may be assigned to the plurality of servers 12, and each server 12 may be configured to execute processing only for the responsible terminal 11.

[0070] Furthermore, in the information processing system 10, the terminal 11 can be caused to execute part or all of the processing to be executed by the server 12. When the terminal 11 is caused to execute all of the processing to be executed by the server 12, the information processing system 10 can be constructed without providing the server 12.

[0071] For example, the terminal 11 is composed of a PC (Personal Computer) and is operated by a user. Otherwise, the terminal 11 may be composed of a mobile terminal (device) such as a smartphone or smart glasses.

[0072] The user can operate the terminal 11 and input the location information (e.g., latitude and longitude) of a specific location where the user wants to grow plants, such as a specific location where the user wants to practice symbiotic agriculture (registered trademark). In addition, the user can input plant species (information thereof) observed at the specific location or plant species (information thereof) planned to be introduced at the specific location. In addition, the user can input time period information of a weather situation (described later) and other necessary information.

[0073] For example, the user may operate the terminal 11 at a specific place and input the position information of the terminal 11 as the position information of the specific place.

[0074] For example, the user can input plant species observed at a specific location or plant species planned to be introduced at a specific location (planned species) by operating the terminal 11. The terminal 11 generates a plant species list describing the plant species (species names) input by the user.

[0075] For example, at a specific location, a user can capture an image of a plant species growing at that specific location by operating the terminal 11. The terminal 11 performs image processing such as image recognition on the captured image using a deep-learned learning model, and generates a plant species list describing the plant species (their species names) appearing in the image.

[0076] In this manner, one or both of the plant species observed at a particular location and the plant species planned for introduction at a particular location are described in a plant species list.

[0077] For example, the user may input time period information of the weather situation by operating the terminal 11 .

[0078] Weather scenarios consist of, for example, a time series of weather parameters, which are the predicted values of one or more (one or more types of) weather data from the current moment to a predetermined moment in the future. Weather data refers to data representing various elements of atmospheric conditions and phenomena, such as temperature, atmospheric pressure, wind direction, wind speed, humidity, visibility, sunshine, cloud cover, cloud formation, and precipitation.

[0079] The time period from the current moment to a predetermined future moment during which the weather parameters constituting a weather scenario are calculated is also referred to as a scenario period. The period information of the weather scenario includes the date and time of the current moment and the scenario period (its information). In addition, the period information of the weather scenario includes the time step of the time series used to calculate the weather parameters constituting the weather scenario, or, in other words, the period from the sample point (date and time) at which the weather parameter is calculated to the sample point at which the next weather parameter is calculated in chronological order.

[0080] The terminal 11 transmits position information of a specific place, a plant species list describing plant species, time period information of weather conditions, and other necessary information to the server 12 (via the network 14 ).

[0081] The terminal 11 receives, for example, an image transmitted from the server 12 (via the network 14) as a presentation UI (user interface) for presenting relevant information related to the construction of an environment at a specific location, weather conditions, etc. The terminal 11 presents the relevant information, weather conditions, etc. to the user by displaying the presentation UI (or by outputting audio).

[0082] The server 12 receives position information of a specific place, a list of plant species, time period information of weather scenarios, and the like transmitted from the terminal 11 (via the network 14 ).

[0083] The server 12 calculates, when necessary, the weather situation at the specific location indicated by the location information within the situation period starting from the current time indicated by the period information, using the information stored in the DB 13. The server 12 evaluates the plant species on the plant species list and the plant species stored in the DB 13 based on the weather situation at the specific location, and generates relevant information related to the construction of the environment at the specific location based on the evaluation values of the plant species.

[0084] The server 12 generates a presentation UI that presents weather conditions, related information, and the like at a specific location, and transmits the presentation UI to the terminal 11 (via the network 14 ).

[0085] The DB 13 stores information used in the processing of the server 12. For example, the DB 13 includes a weather DB, a plant DB, and an interaction DB.

[0086] The weather DB stores various types of weather data. For example, a weather DB provided by Google Earth Engine, a DB of AMeDAS weather data from the Japan Meteorological Agency, or a DB of agricultural meteorological grid data from the National Agriculture and Food Research Organization (NARO) can be used as the weather DB.

[0087] The plant DB stores various types of information about plants, such as plant species information that associates the species name of the plant species with its attribute information (information about the attributes of the plant species, such as growth conditions and traits). As the plant DB, for example, the open source USDA (United States Department of Agriculture) plant database or the TRY plant trait database can be used.

[0088] It is noted that an API (Application Programming Interface) for acquiring data is not currently available for the USDA Plant Database and the TRY Plant Trait Database. Therefore, data from the USDA Plant Database and the TRY Plant Trait Database requires that such data be requested via email, etc. Once the API becomes available, data can be acquired directly from the USDA Plant Database and the TRY Plant Trait Database using the API.

[0089] The interaction DB stores information on biological interactions (interaction information). As the interaction DB, for example, GloBI (Global Biotic Interactions) can be used.

[0090] The DB 13 (information stored therein) can be revised (updated) by the server 12 .

[0091] <Hardware Configuration Example of Terminal 11 and Server 12>

[0092] Figure 2 is a diagram showing a hardware configuration example of the terminal 11 .

[0093] The terminal 11 includes a communication unit 21, a calculation unit 22, an input / output unit 23, a storage device 24, a positioning unit 25, and a sensor unit 26. The communication unit 21 to the sensor unit 26 are connected to each other via a bus and can exchange information with each other.

[0094] The communication unit 21 functions as a transmission unit that transmits information via the network 14 and a reception unit that receives information.

[0095] The calculation unit 22 includes a processor such as a CPU (Central Processing Unit) or a DSP (Digital Signal Processor) and performs various processes by executing a program recorded in the storage device 24 .

[0096] The input / output unit 23 includes a keyboard, a touch panel, a microphone, etc., and accepts user operations and various types of other inputs. In addition, the input / output unit 23 includes a speaker or a display (display unit) and presents information to the user by outputting sound, displaying images, etc.

[0097] The storage device 24 is composed of a semiconductor memory such as RAM (Random Access Memory) or a nonvolatile memory, an SSD (Solid State Drive), an HDD (Hard Disk Drive), etc. The storage device 24 records (stores) programs to be executed by the computing unit 22, data necessary for processing by the computing unit 22, and the like.

[0098] For example, the program to be executed by the computing unit 22 may be installed from a removable recording medium such as a DVD (Digital Versatile Disc) or a memory card to the computer serving as the terminal 11. Alternatively, for example, the program may be downloaded to the computer serving as the terminal 11 via the network 14 or the like and installed in the storage device 24.

[0099] The positioning unit 25 constitutes, for example, a GPS (Global Positioning System), measures the position of the terminal 11 (performs positioning thereof), and outputs position information indicating the position in the form of latitude and longitude (and altitude as necessary).

[0100] The sensor unit 26 includes various types of sensors such as a camera, a distance sensor, a temperature sensor, and a humidity sensor, and performs various types of sensing, such as capturing an image, detecting a distance, detecting a temperature (air temperature), and detecting humidity. The sensor unit 26 outputs an image, distance, temperature, humidity, etc. as a result of the sensing.

[0101] Figure 3 is a diagram showing a hardware configuration example of the server 12 .

[0102] The server 12 includes a communication unit 31, a calculation unit 32, an input / output unit 33, and a storage device 34. Figure 2 The communication unit 21 to the storage device 24 are configured in a similar manner, so their description will not be repeated. Note that components with higher performance than the communication unit 21 to the storage device 24 in terms of capacity, processing speed, etc. can be adopted as the communication unit 31 to the storage device 34.

[0103] <First Function Configuration Example of Server 12>

[0104] Figure 4 is a block diagram showing a first functional configuration example of the server 12 .

[0105] The functional configuration of server 12 is achieved through Figure 3 The computing unit 32 shown in FIG. 1 executes a program to realize the functions.

[0106] exist Figure 4 In the embodiment, the server 12 includes an acquisition unit 41 , a weather scenario calculation unit 42 , an evaluation unit 43 and a generation unit 44 .

[0107] The acquisition unit 41 receives information to acquire location information of a specific place, time period information of a weather situation, a plant species list, etc. transmitted from the terminal 11. In addition, the acquisition unit 41 downloads information to acquire weather data, plant species information, etc. stored in the DB 13.

[0108] The acquisition unit 41 supplies the acquired information to the block that requires it. For example, the acquisition unit 41 supplies location information of a specific location, weather scenario time period information, and weather data to the weather scenario calculation unit 42. In addition, the acquisition unit 41 supplies plant species information and a plant species list to the evaluation unit 43.

[0109] The weather scenario calculation unit 42 calculates a weather scenario for a specific place whose location is indicated by the location information from the acquisition unit 41, using the weather data from the acquisition unit 41. The weather scenario calculation unit 42 calculates one or more weather scenarios composed of a time series of one or more (e.g., several or about ten) weather parameters, such as daily average temperature [degrees Celsius] (daily average temperature), daily maximum temperature [degrees Celsius], daily minimum temperature [degrees Celsius], precipitation [mm], sunshine duration [hours / day], daily average relative humidity [%], daily average wind speed [m / s], and snow depth [cm], for the scenario period indicated by the period information from the acquisition unit 41 and for each time step similarly indicated by the period information.

[0110] Approximately ten items (ten types) of weather data are available as AMeDAS weather data from the Japan Meteorological Agency or agricultural weather grid data from the National Agriculture and Food Research Organization. A similar number of weather parameters as those in the AMeDAS weather data from the Japan Meteorological Agency can also be used as weather parameters constituting a weather scenario. Note that the server 12 can select the weather parameters (their types) constituting a weather scenario based on the user's operation on the terminal 11.

[0111] The weather scenario calculation unit 42 supplies one or more weather scenarios of a specific location to the evaluation unit 43 and the generation unit 44 .

[0112] Based on the weather scenario of the specific place from the weather scenario calculation unit 42, the evaluation unit 43 evaluates the plant species whose species names are included in the plant species information from the acquisition unit 41 (the plant species stored in the plant DB) or the plant species described (whose species names) in the plant species list from the acquisition unit 41. Furthermore, the evaluation unit 43 supplies the evaluation value to the generation unit 44 as the evaluation result of the plant species.

[0113] The evaluation unit 43 calculates an evaluation value that evaluates the degree of matching between the weather according to the weather scenario or the environment formed by the weather and the growth of the plant species. A high evaluation value of the plant species indicates that the weather according to the weather scenario or the environment formed by the weather and the growth of the plant species match.

[0114] Therefore, for the weather parameters constituting the weather scenario, the greater the number (type) of weather parameters, the more accurate the evaluation value tends to be.

[0115] Furthermore, weather scenarios are predictions of future weather and involve chaotic properties, where small errors in the initial values used to calculate weather scenarios, as described below, grow exponentially over time. Therefore, it is difficult to say that weather scenarios calculated using certain initial values always meet the target.

[0116] For example, when practicing symbiotic agriculture (registered trademark), which involves introducing a large number of plant species (e.g., hundreds of species) at a specific location, significant deviations from the weather scenario (such as a significant difference between precipitation as a weather parameter and actual precipitation) may (possibly) lead to poor growth of even plant species with high evaluation values, and crop yields may (possibly) decrease significantly, meaning that the accuracy of the evaluation values is low.

[0117] With this in mind, multiple weather scenarios can be used to evaluate plant species. Using multiple weather scenarios compensates for the chaotic nature of weather scenarios and improves the accuracy of the evaluation values (increasing the likelihood that the accuracy of the evaluation values will improve). In the following, unless otherwise stated, it is assumed that multiple weather scenarios are to be used.

[0118] The generation unit 44 generates a presentation UI for presenting various types of information to the user. For example, based on the evaluation values of the plant species from the evaluation unit 43, the generation unit 44 generates relevant information related to the construction of an environment at a specific location, such as vegetation strategies suitable for the weather at the specific location and recommendations on how to construct an environment suitable for the growth of plant species observed at the specific location and / or planned to be introduced at the specific location. Furthermore, the generation unit 44 generates a presentation UI for presenting relevant information, the weather scenario from the weather scenario calculation unit 42, and the like, and transmits the presentation UI to the terminal 11.

[0119] Figure 5 Is used to explain Figure 4 A flowchart of an example of processing of the server 12 is shown in FIG.

[0120] In step S11, the acquisition unit 41 acquires the position information of the specific place, the time period information of the weather situation, and the plant species list from the terminal 11 as needed, the weather data, the plant species information, and the like stored in the DB 13. After the acquisition unit 41 supplies the position information of the specific place, the time period information of the weather situation, and the weather data to the weather situation calculation unit 42 and supplies the plant species information and the plant species list to the evaluation unit 43, the process proceeds from step S11 to step S12.

[0121] In step S12, the weather scenario calculation unit 42 calculates one or more weather scenarios for the specific location using the location information of the specific location, the time period information of the weather scenario, and the weather data from the acquisition unit 41. The weather scenario calculation unit 42 supplies the weather scenario for the specific location to the evaluation unit 43 and the generation unit 44, and the process proceeds from step S12 to step S13.

[0122] In step S13, based on the weather scenario of the specific place from the weather scenario calculation unit 42, the evaluation unit 43 evaluates the plant species whose species names are included in the plant species information from the acquisition unit 41 (the plant species stored in the plant DB) or the plant species described in the plant species list from the acquisition unit 41. The evaluation unit 43 supplies the calculated evaluation value of the evaluation plant species to the generation unit 44 and the process proceeds from step S13 to step S14.

[0123] In step S14, based on the evaluation value of the plant species from the evaluation unit 43, the generation unit 44 generates relevant information related to the construction of an environment at a specific location, such as a vegetation strategy suitable for the weather at the specific location and suggestions on how to construct an environment suitable for the growth of plant species observed at the specific location and / or planned to be introduced at the specific location, and the processing proceeds to step S15.

[0124] In step S15 , the generation unit 44 generates a presentation UI for presenting the relevant information, the weather scenario from the weather scenario calculation unit 42 , and the like, and transmits the presentation UI to the terminal 11 to end the processing.

[0125] Note that the generation unit 44 may transmit relevant information, weather context, and the like necessary for generating a presentation UI to the terminal 11 , and the terminal 11 may generate a presentation UI for presenting the relevant information, weather context, and the like.

[0126] As described above, the server 12 evaluates plant species based on the weather conditions at a specific location and generates relevant information related to the construction of an environment at the specific location based on the evaluation values of the plant species. Therefore, it is possible to provide users with relevant information or, more specifically, vegetation strategies suitable for the weather at the specific location and suggestions on how to construct an environment suitable for the growth of plant species observed at the specific location and / or planned to be introduced at the specific location.

[0127] In addition, by referring to relevant information, users can actively and efficiently introduce plant species according to vegetation strategies at specific locations such as agricultural land and green spaces and construct environments based on recommendations.

[0128] Figure 6 4 is a flowchart for explaining an example of a process of calculating a weather scenario by the weather scenario calculation unit 42 .

[0129] The weather scenario calculation unit 42 calculates one or more N weather scenarios for a specific location through, for example, ensemble prediction (forecast).

[0130] Ensemble forecasting is a method of making multiple slightly different numerical forecasts and applying statistical processing to the forecast results to allow for probabilistic forecasts that take into account uncertainties.

[0131] In step S21 , the weather scenario calculation unit 42 calculates base values serving as a basis for initial values of the weather simulator through data assimilation using weather data (actually observed values) and predicted (estimated) values of the weather simulator for the weather data.

[0132] A weather simulator is a simulator that simulates weather and, for example, can implement the Weather Research and Forecasting (WRF) model as a numerical prediction model. The WRF model is a numerical prediction model developed by a joint project (WRF project) between the National Center for Atmospheric Research (NCAR) and the National Centers for Environmental Prediction (NCEP) to perform simulations of geospatial data designed to serve weather forecasting needs and academic research.

[0133] After calculating the base value of the weather simulator, the process proceeds from step S21 to step S22, where the weather scenario calculation unit 42 reduces the weather data. Subsequently, the process proceeds to step S23.

[0134] As for open-source weather data worldwide, Google Maps Engine's weather data is available, and for Japan, AMeDAS weather data from the Japan Meteorological Agency and agrometeorological grid data from the National Agriculture and Food Research Organization are available. Google Maps Engine's weather data is based on 20 square kilometers, while agrometeorological grid data is based on 1 square kilometer.

[0135] On the other hand, vegetation maps published by the Biodiversity Center of the Nature Conservation Agency of the Ministry of the Environment of Japan define the smallest community unit as a 100-square-meter unit. Therefore, when dealing with plant species, weather data with a spatial resolution of 20 square kilometers or 1 square kilometer is too low.

[0136] Taking this into account, the weather scenario calculation unit 42 reduces the weather data. In the reduction, the spatial resolution of the weather data is increased in order to fill the gap between the output resolution of the numerical forecast model (climate model) and the resolution required for each application field as reasonably as possible. Examples of reduction methods include methods involving the use of regression models and spatial interpolation methods. Reduction uses (known) weather data to estimate weather data at a location (point) where weather data is unknown through calculation.

[0137] Downscaling the weather data enables the server 12 to use the weather data in a scalable manner even if the spatial resolution of the existing weather data is low.

[0138] In step S23 , the weather scenario calculation unit 42 calculates one or more N weather scenarios using the reduced weather data and ends the process.

[0139] In other words, in step S23-1, the weather scenario calculation unit 42 sets the initial value of the weather simulator (numerical forecast model) using the base value, and the process proceeds to step S23-2. For example, the weather scenario calculation unit 42 sets the base value as the initial value as it is, or sets a value obtained by adding a disturbance to the base value as the initial value.

[0140] In step S23-2, the weather scenario calculation unit 42 calculates one weather scenario of the specific place with respect to the initial value set in the immediately preceding step S23-1 by performing simulation through a weather simulator at the granularity after weather data reduction, and the process proceeds to step S23-3.

[0141] In step S23 - 3 , the weather scenario calculation unit 42 stores a weather scenario at the specific location obtained as a result of the simulation in the immediately preceding step S23 - 2 .

[0142] Then, the process returns from step S23 - 3 to step S23 - 1 , and the process of steps S23 - 1 to S23 - 3 is repeated one or more times N (the number of times is preset). Accordingly, N weather scenarios are calculated.

[0143] The weather scenario calculation unit 42 may adopt only one weather scenario (control operation) calculated using the unmodified base value as the initial value as the final calculation result of the weather scenario. Alternatively, the weather scenario calculation unit 42 may adopt one weather scenario (ensemble average) obtained by averaging N weather scenarios (ensemble members) calculated using N initial values as the final calculation result of the weather scenario. Furthermore, the weather scenario calculation unit 42 may adopt all N weather scenarios calculated using N initial values as the final calculation result of the weather scenario.

[0144] In the weather scenario calculation unit 42, the calculation of the weather scenario (weather parameter) is performed for each (every other) time step represented by the period information, with respect to the scenario period represented by the period information. Considering the amount of calculation, it is desirable that the scenario period does not exceed approximately six months in the future, and the time step is a unit of approximately one week or longer.

[0145] Figure 7 is a diagram showing an example of multiple weather scenarios calculated by ensemble prediction.

[0146] Figure 7 Shown are (time series changes in) the air temperature (850 hPa air temperature) as one weather parameter for each of a plurality of weather scenarios and the frequency distribution of the air temperature.

[0147] Figure 7It was confirmed that the temperature, which is a weather parameter for each weather scenario, varies in various time series according to different initial values.

[0148] <Plant species information>

[0149] Figure 8 is a diagram showing an example of plant species information stored in the plant DB of the DB 13 .

[0150] Figure 8 An example of plant species information of the USDA plant database is shown.

[0151] In the plant species information, the species name of the plant species is associated with the attribute information of the plant species (information on the attributes of the plant species, such as growth conditions and traits).

[0152] exist Figure 8 In the , scientific and common names are used as species names. Attribute information includes plant species’ anaerobic tolerance, drought tolerance, water use efficiency, minimum temperature (Fahrenheit), maximum precipitation, minimum precipitation, shade tolerance, and minimum frost-free period.

[0153] Note that, Figure 8 The names of the corresponding pieces of attribute information are translations (translated into Japanese) of the attribute information stored in the USDA plant database.

[0154] Figure 9 is a diagram for explaining an example of processing by which the acquisition unit 41 acquires plant species information.

[0155] In step S31 , the acquisition unit 41 downloads plant species information in, for example, a CSV format from the plant DB, and the process proceeds to step S32 .

[0156] In step S32 , the acquisition unit 41 converts the plant species information in the CSV format into a format that can be handled by a general DB language such as SQL, and the process proceeds to step S33 .

[0157] In step S33 , the acquisition unit 41 stores the plant species information of the plant DB whose format has been converted in step S32 in the internal DB, and the processing ends.

[0158] <First Configuration Example of the Evaluation Unit 43 and the Generation Unit 44>

[0159] Figure 10 is a block diagram illustrating a first configuration example of the evaluation unit 43 and the generation unit 44 .

[0160] In the first configuration example of the evaluation unit 43 and the generation unit 44, a vegetation strategy is generated for introducing a new plant species to a specific location where few plant species have been introduced. Therefore, the first configuration example of the evaluation unit 43 and the generation unit 44 can be used, for example, in a use case of establishing a new farm at a specific location to practice symbiotic agriculture (registered trademark).

[0161] exist Figure 10 In the example, the evaluation unit 43 includes an attribute information conversion unit 51, a dimension reduction unit 52, and an evaluation value calculation unit 53. The generation unit 44 includes a recommended species selection unit 61 and a presentation UI generation unit 62.

[0162] The attribute information conversion unit 51 is supplied with the plant species information in the internal DB from the acquisition unit 41. The attribute information conversion unit 51 converts the attribute information in the plant species information of each plant species in the internal DB into corresponding parameters that correspond to (match) the weather parameters of the weather scenario, and supplies the corresponding parameters to the dimension reduction unit 52. For each plant species in the internal DB, the attribute information conversion unit 51 supplies the dimension reduction unit 52 with a corresponding parameter group, which is a set of corresponding parameters corresponding to each weather parameter constituting the weather scenario.

[0163] The dimension reduction unit 52 is supplied with the weather scenario from the weather scenario calculation unit 42 in addition to the corresponding parameter group for each plant species in the internal DB from the attribute information conversion unit 51 .

[0164] The dimension reduction unit 52 performs dimension reduction on each weather parameter of the weather scenario and the corresponding parameter group of each plant species in the internal DB.

[0165] In other words, the dimensionality reduction unit 52 performs dimensionality reduction to reduce the number of weather parameters constituting the weather scenario by performing principal component analysis, convolution in a convolutional neural network, etc. The dimensionality reduction unit 52 performs similar dimensionality reduction with respect to the corresponding parameter group of each plant species in the internal DB.

[0166] The weather parameters after dimensionality reduction are also referred to as simplified meteorological parameters, and the weather scenarios composed of simplified weather parameters are also referred to as simplified weather scenarios. In a similar manner, the corresponding parameters after dimensionality reduction are also referred to as simplified corresponding parameters.

[0167] Performing dimensionality reduction on the weather parameters of the weather scenario and the corresponding parameter group of each plant species in the internal DB separately makes it possible to reduce the amount of computation required for subsequent processing.

[0168] Note that when the number of weather parameters constituting a weather scenario is small or when there are sufficient resources for calculation in the server 12 , there is no need to perform dimensionality reduction on the weather parameters of the weather scenario and the corresponding parameter group for each plant species in the internal DB.

[0169] The dimension reduction unit 52 supplies the simplified weather scenario to the evaluation value calculation unit 53 and the presentation UI generation unit 62 , and supplies the simplified corresponding parameter group of each plant species in the internal DB to the evaluation value calculation unit 53 .

[0170] The evaluation value calculation unit 53 evaluates each plant species in the internal DB based on the (simplified) weather scenario using the simplified weather scenario and the simplified corresponding parameter group for each plant species in the internal DB from the dimensionality reduction unit 52. The evaluation value calculation unit 53 supplies the evaluation value, which is the evaluation result of each plant species in the internal DB obtained by the evaluation, to the recommended species selection unit 61.

[0171] Based on the evaluation value of each plant species in the internal DB from the evaluation value calculation unit 53, the recommended species selection unit 61 selects highly evaluated plant species such as plant species whose evaluation values rank in the top M (>1) as recommended species, which are recommended for introduction to a specific location, and supplies the selected recommended species to the presentation UI generation unit 62.

[0172] In addition to being supplied with the M recommended species from the recommended species selection unit 61 , the presentation UI generation unit 62 is also supplied with the simplified weather scenario from the dimension reduction unit 52 and the weather scenario from the weather scenario calculation unit 42 .

[0173] The presentation UI generation unit 62 generates a presentation UI for presenting recommended species, (simplified) weather situations, and the like, and transmits the presentation UI to the terminal 11 .

[0174] Figure 11 ] is a diagram showing an example in which the attribute information conversion unit 51 converts attribute information into corresponding parameters.

[0175] For example, the attribute information conversion unit 51 converts the lowest level air temperature (Fahrenheit) as the attribute information into a corresponding parameter corresponding to the daily lowest air temperature (Celsius) by converting the temperature in Fahrenheit into the temperature in Celsius.

[0176] For example, using a learning model that has learned the relationship between the lowest level air temperature (Fahrenheit) as attribute information and the daily average temperature and daily maximum temperature as weather parameters, the attribute information conversion unit 51 converts the lowest level air temperature (Fahrenheit) as attribute information into corresponding parameters corresponding to the daily average temperature and daily maximum temperature as weather parameters.

[0177] For example, using a learning model that has learned the relationship between the maximum precipitation and minimum precipitation as attribute information and the precipitation amount, the attribute information conversion unit 51 converts the maximum precipitation and minimum precipitation as attribute information into corresponding parameters corresponding to the precipitation amount as a weather parameter.

[0178] For example, using a learning model that has learned the relationship between the minimum number of frost-free days as attribute information and the snow depth as a weather parameter, the attribute information conversion unit 51 converts the minimum number of frost-free days as attribute information into a corresponding parameter corresponding to the snow depth as a weather parameter.

[0179] The attribute information conversion unit 51 converts the attribute information into corresponding parameters corresponding to each weather parameter constituting the weather context in a similar manner.

[0180] Figure 12 1 is a flowchart for explaining an example of a process of conversion of attribute information performed by the attribute information conversion unit 51 .

[0181] In step S41 , the attribute information conversion unit 51 selects one plant species that has not been selected as a plant species worthy of attention from the plant species in the internal DB as a plant species worthy of attention, and the process proceeds to step S42 .

[0182] In step S42 , the attribute information conversion unit 51 selects one weather parameter that has not been selected as a noteworthy weather parameter from among the weather parameters of the weather context as a noteworthy weather parameter, and the process proceeds to step S43 .

[0183] In step S43 , the attribute information conversion unit 51 acquires attribute information necessary for conversion into corresponding parameters corresponding to the noteworthy weather parameters among the pieces of attribute information of the noteworthy plant species from the internal DB, and the process proceeds to step S44 .

[0184] In step S44 , the attribute information conversion unit 51 calculates a corresponding parameter corresponding to the noteworthy weather parameter using the necessary attribute information acquired in step S43 , and the process proceeds to step S45 .

[0185] In step S45 , the attribute information conversion unit 51 determines whether all weather parameters of the weather scenario have been selected as noteworthy weather parameters.

[0186] When it is determined in step S45 that all weather parameters of the weather scenario have not been selected as noteworthy weather parameters, the process returns to step S42 and similar processes are repeated thereafter.

[0187] When it is determined in step S45 that all weather parameters of the weather scenario have been selected as noteworthy weather parameters, the process proceeds to step S46 .

[0188] In step S46 , the attribute information conversion unit 51 determines whether all plant species in the internal DB have been selected as plant species worthy of attention.

[0189] When it is determined in step S46 that not all of the plant species in the internal DB are selected as plant species worthy of attention, the process returns to step S41 and similar processing is repeated thereafter.

[0190] When it is determined in step S46 that all plant species in the internal DB have been selected as plant species worthy of attention, the process ends.

[0191] Figure 13 is a diagram for explaining an example of a simplified weather scenario.

[0192] exist Figure 13 In the embodiment of the present invention, dimensionality reduction is performed, wherein a weather scenario consisting of a predetermined number of three or more weather parameters is simplified to a simplified weather scenario consisting of two simplified weather parameters α and β.

[0193] Figure 13 A in FIG shows the frequency distribution of two simplified weather parameters α and β at a certain time point t within the scenario period, which constitute a plurality of simplified weather scenarios.

[0194] Figure 13 B in FIG. 1 shows the distribution of simplified weather parameters α and β at a certain time point t within the scenario period in a parameter space with two simplified weather parameters α and β constituting multiple simplified weather scenarios as axes.

[0195] In parameter space, the points (α, β) corresponding to simplified weather parameters α and β with given values will also be called parameter points. Figure 13 In FIG. 8B , parameter points (α, β) having higher frequencies of simplified weather parameters α and β are shown more densely.

[0196] The distribution of the parameter points of the simplified weather parameters that make up the simplified weather scenario in the parameter space is also referred to as the parameter distribution of the simplified weather scenario. The parameter distribution of the simplified weather scenario exists at each time point separated by the time step of the scenario period. In addition, the number of parameter points that make up the parameter distribution at each time point of the simplified weather scenario is the same as the number of weather scenarios. Therefore, when there is only one weather scenario, the parameter distribution for the simplified weather scenario is one parameter point.

[0197] In dimensionality reduction unit 52, the corresponding parameter set for each plant species also undergoes dimensionality reduction similar to that of the weather scenario and is converted into a simplified corresponding parameter set corresponding to the simplified weather parameters that make up the simplified weather scenario. In parameter space, points corresponding to the simplified corresponding parameter set for a plant species are also referred to as parameter points for that plant species.

[0198] Figure 14 is a diagram for explaining an example of calculation of the evaluation value of a plant species based on a simplified weather scenario by the evaluation value calculation unit 53 .

[0199] The evaluation value calculation unit 53 calculates the center of gravity of the parameter distribution of the simplified weather scenario at time point t in the parameter space (in the scenario period). For example, assuming that each parameter point constituting the parameter distribution has the same mass, the center of mass of the parameter point can be calculated as the center of gravity of the parameter distribution.

[0200] The evaluation value calculation unit 53 calculates a barycentric distance which is a distance between the parameter point of the plant species and the barycentric distance of the parameter distribution when the parameter point of the plant species is mapped into the parameter space.

[0201] For each plant species, the evaluation value calculation unit 53 calculates the centroid distance between the parameter point of the plant species and the centroid of the parameter distribution at each time point of the simplified weather scenario, and calculates the sum of the centroid distances of the corresponding time points in the scenario period as the evaluation value of the plant species.

[0202] In this case, the smaller the evaluation value of the plant species is, the better the evaluation (evaluation value) is.

[0203] Figure 15 : is a flowchart for explaining an example of a process of calculating the evaluation value of a plant species by the evaluation value calculation unit 53 .

[0204] In step S51 , the evaluation value calculation unit 53 calculates the center of gravity of the parameter distribution at each time point of the scenario period of the simplified weather scenario, and the process proceeds to step S52 .

[0205] In step S52 , the evaluation value calculation unit 53 initializes a variable t for counting the time points of the situation period to 0, and the process proceeds to step S53 .

[0206] In step S53 , the evaluation value calculation unit 53 selects one plant species that has not been selected as a plant species worthy of attention from the plant species in the internal DB as a plant species worthy of attention, and the process proceeds to step S54 .

[0207] In step S54, the evaluation value calculation unit 53 calculates the centroid distance between the parameter point of the plant species worthy of attention and the centroid of the parameter distribution at time point t in the simplified weather scenario when the parameter point of the plant species worthy of attention is mapped into the parameter space, and the processing proceeds to step S55.

[0208] In step S55 , the evaluation value calculation unit 53 determines whether the variable t is equal to the time point at which the situation period ends.

[0209] When it is determined in step S55 that the variable t is not equal to the time point at which the situation period ends (or in other words, the variable t is the time point before the situation period ends), the process proceeds to step S56.

[0210] In step S56 , the evaluation value calculation unit 53 increments the variable t by 1 and the process returns to step S54 .

[0211] On the other hand, when it is determined in step S55 that the variable t is equal to the time point at which the situation period ends, the process proceeds to step S57 .

[0212] In step S57 , the evaluation value calculation unit 53 calculates the sum of the center-of-gravity distances at the corresponding time points of the situation period calculated in step S54 with respect to the noteworthy plant species as the evaluation value of the noteworthy plant species, and the process proceeds to step S58 .

[0213] In step S58 , the evaluation value calculation unit 53 determines whether all plant species in the internal DB have been selected as plant species worthy of attention.

[0214] When it is determined in step S58 that not all of the plant species in the internal DB are selected as plant species worthy of attention, the process returns to step S52 and similar processing is repeated thereafter.

[0215] When it is determined in step S58 that all plant species in the internal DB have been selected as plant species worthy of attention, the process ends.

[0216] Figure 16 Is used to explain the Figure 10 The generation unit 44 shown in FIG. 4 is a flowchart of an example of a process in which the presentation UI that presents recommended species is generated.

[0217] In step S71 , the recommended species selection unit 61 of the generation unit 44 uses the evaluation values of the corresponding plant species in the internal DB from the evaluation value calculation unit 53 to sort the plant species in the internal DB in order of the evaluation values, and the process proceeds to step S72 .

[0218] In step S72 , the recommended species selection unit 61 selects the plant species whose evaluation values rank top M as recommended species recommended for introduction to a specific location, and supplies the selected recommended species to the presentation UI generation unit 62 , and the process proceeds to step S73 .

[0219] In step S73 , the presentation UI generation unit 62 generates a presentation UI for presenting the M recommended species from the recommended species selection unit 61 and the like, and transmits the presentation UI to the terminal 11 to end the processing.

[0220] <Present UI>

[0221] Figure 17 is a diagram showing a first display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0222] In the server 12, for example, when a request for an initial screen is issued from the terminal 11 to the server 12, the generating unit 44 generates Figure 17 The presentation UI 70 shown in FIG is used as an initial screen and the presentation UI 70 is sent to the terminal 11 for display.

[0223] A map 71 and buttons 72, 73, and 74 are displayed on the presentation UI 70. On the presentation UI 70, the map 71 is arranged at an upper portion and buttons 72 to 74 are arranged at a lower portion of the map 71 in this order.

[0224] For example, the map 71 may be displayed using Google Maps.

[0225] The button 72 is operated (tapped) when inputting a specific location. For example, by tapping a point on the map 71 and then tapping the button 72, the user can input the tapped point on the map 71 as a specific location.

[0226] The button 73 is operated when a scenario period and a time step constituting period information are input.

[0227] When a request is issued to calculate the weather scenario of the specific place input through the operation button 72 for each time step input through the operation button 73 in the same manner with respect to the scenario period input through the operation button 73, the button 74 is operated. Through the operation button 74, position information indicating the position of the specific place input through the operation button 72 and period information including the scenario period and the time step input through the operation button 73 are transmitted from the terminal 11 to the server 12 together with the request to calculate the weather scenario.

[0228] In the server 12, in response to the request from the terminal 11, the weather scenario calculation unit 42 calculates the weather scenario (its weather parameters) for the specific place whose position is represented by the position information for each time step represented by the period information in a similar manner relative to the scenario period represented by the period information. In addition, the generation unit 44 generates Figure 18 The presentation UI 80 presenting the parameter distribution of the weather scenario (the simplified weather scenario subjected to dimensionality reduction) shown in FIG is presented and transmitted to the terminal 11 .

[0229] Figure 18 4 is a diagram showing a second display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0230] In the server 12, in response to Figure 17 The generation unit 44 generates the button 74 of the presentation UI 70 shown in FIG. Figure 18 The presentation UI 80 shown in FIG is presented and sent to the terminal 11 for display.

[0231] A parameter distribution image 81 and buttons 82, 83, 84, and 85 are displayed on the presentation UI 80. On the presentation UI 80, the parameter distribution image 81 is arranged at the upper portion, and buttons 82 to 85 are arranged below the parameter distribution image 81 in this order.

[0232] The parameter distribution image 81 is a graphical representation of the parameter distribution of the (simplified) weather scenario for the particular location calculated in response to operation of the button 74 on the presentation UI 70 .

[0233] In this embodiment, the weather scenario for the five-month scenario period from July 1 to December 1, 2022 is calculated, and a message reading "The weather scenario from July 1, 2022 to December 1, 2022 has been calculated" is displayed below the parameter distribution image 81.

[0234] As parameter distribution image 81, a time series of the parameter distribution of the weather scenario within the scenario period is displayed in a parameter space with simplified weather parameters α, β, and γ as axes. Since it is difficult to display a parameter space with four or more axes as parameter distribution image 81, two or three axes are used as the axes of the simplified weather parameters in the parameter space to be displayed as parameter distribution image 81. The simplified weather parameters that become the axes of the parameter space to be displayed as parameter distribution image 81 can be randomly selected by server 12 or selected based on the user's operation on terminal 11.

[0235] Furthermore, in the parameter distribution image 81, when the parameter distributions at all time points separated by time steps are displayed as a time series within the scenario period of the parameter distribution of the weather scenario, and the display of the parameter distribution becomes complicated and difficult to see, the parameter distribution may be thinned in the time direction to display only the parameter distribution for a portion of the time points. Figure 18 , the parameter distribution is shown for the following four time points (Time (Day)): July 1, August 1, October 1, and December 1, 2022.

[0236] As described above, by displaying the time series of the parameter distribution of the weather scenario within the scenario period as parameter distribution image 81, the user can easily see the overall trend (forecast) of the weather at a specific location and can easily follow the overall trend of the weather at a specific location. In addition, for example, a novice agricultural user can understand the relationship between the overall trend of the weather at a specific location and the growth of vegetation by continuously observing the growth of plant species at a specific location while referring to parameter distribution image 81.

[0237] The button 82 is operated when the details of the weather situation are displayed.

[0238] When it is displayed that the recommended species is a (suitable) plant species recommended for introduction at a specific location where weather is expected to follow the weather scenario, the button 83 is operated.

[0239] When evaluation of suitability of growing an observed species (a plant species observed at a specific location) or a planned species (a plant species planned to be introduced at a specific location) at a specific location where weather is expected to follow a weather scenario is displayed, the button 84 is operated.

[0240] Operation button 85 is used to display an environment construction suggestion as a suggestion about an environment construction method that involves changing the environment formed by the weather into an environment suitable for the growth of the observed species or the planned species (constructing an appropriate environment) when an observed species observed at a specific location or a planned species planned to be introduced at a specific location is expected to grow at a specific location following the weather scenario.

[0241] In the server 12 , in response to the operation of the buttons 82 to 85 , the generation unit 44 generates a presentation UI according to the operated button.

[0242] Figure 19 is a diagram showing a third display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0243] In the server 12, in response to Figure 18 The generation unit 44 generates the operation of the button 82 of the presentation UI 80 shown in FIG. Figure 19The presentation UI 90 shown in FIG is presented and sent to the terminal 11 for display.

[0244] On the presentation UI 90, a weather parameter image 91 and a button 92 are displayed, and at the same time, Figure 18 The buttons 83 to 85 are displayed in a similar manner to the presentation UI 80 shown in FIG. On the presentation UI 90, a weather parameter image 91 is arranged at the upper portion, and a button 92 and buttons 83 to 85 are arranged below the weather parameter image 91 in this order.

[0245] The weather parameter image 91 is an image representing one weather parameter of the weather situation of a specific place calculated in response to the operation of the button 74 on the presentation UI 70. Figure 19 , the weather parameter image 91 is an image of a time series of the highest temperature among the weather parameters representing the weather situation. The weather parameter (its type) displayed as the weather parameter image 91 can be switched by flicking the weather parameter image 91 upward or downward or left or right, for example.

[0246] In this embodiment, weather scenarios have been calculated for a scenario period of five months, from July 1 to December 1, 2022. Therefore, weather parameters for the scenario period, or, in this case, a time series of maximum temperatures from July 1 to December 1, 2022, are displayed as a weather parameter image 91, and a message that reads “Displaying changes in maximum temperatures from July 1, 2022 to December 1, 2022” is displayed at the bottom of the weather parameter image 91.

[0247] Note that, in Figure 19 In the weather parameter image 91 shown in , the horizontal axis represents time and the vertical axis represents air temperature (maximum temperature).

[0248] Based on the weather parameter image 91, the user can check the specific values (predictions) of future changes in weather parameters such as maximum temperature. For example, a user with agricultural practical experience can use the weather parameter image 91 to plan future vegetation strategies, etc.

[0249] When the display on the terminal 11 is restored from presenting the UI 90 to Figure 18 When the UI 80 is presented as shown in , the button 92 is operated.

[0250] Figure 20 4 is a diagram showing a fourth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0251] In the server 12, in response to Figure 18 The operation of the button 83 of the presentation UI 80 shown in FIG. 1 , Figure 10 The generating unit 44 shown in FIG generates Figure 20 The presentation UI 110 shown in FIG is presented and sent to the terminal 11 for display.

[0252] Specifically, in Figure 10 In the generation unit 44 shown in FIG, based on the evaluation value of each plant species in the internal DB calculated by the evaluation value calculation unit 53, the recommended species selection unit 61 selects M plant species whose evaluation values rank in the top M as recommended species and supplies the selected recommended species to the presentation UI generation unit 62. The presentation UI generation unit 62 generates a presentation UI 110 for presenting the M recommended species from the recommended species selection unit 61 and transmits the presentation UI 110 to the terminal 11 for display.

[0253] A planting plan portfolio 111 and buttons 112 and 113 are displayed on the presentation UI 110. On the presentation UI 110, the planting plan portfolio 111 is arranged at an upper portion, and buttons 112 and 113 are arranged below the planting plan portfolio 111 in this order.

[0254] The planting plan combination 111 is a vegetation strategy for planting multiple plant species and, in this case, is a list of M recommended species ranked in order of evaluation value. The list of M recommended species ranked in order of evaluation value is a list of M recommended species ranked in order of evaluation value and is a ranking of plant species suitable for the environment formed by the weather of a specific location.

[0255] In the planting plan combination 111, the species names of the M recommended species are arranged in the order of evaluation values in the "Ranking" column. In addition, in the planting plan combination 111, the recommended planting rate per unit area (e.g., per square meter) (hereinafter referred to as "planting rate") and the number of plants to be planted in the field (hereinafter referred to as "number of plants to be planted") recommended when the recommended species are introduced into the field at a specific location are arranged in the "Initial Planting" column.

[0256] For example, in Figure 20 In the planting plan combination 111 shown in , the plant species ranked first in terms of evaluation value is white radish. In addition, the planting rate of white radish is 33%, and the number of plants to be planted is 30.

[0257] In the presentation UI generation unit 62, a value corresponding to the evaluation value is calculated as the planting rate of the recommended species based on the evaluation value of the recommended species. The number of plants of the recommended species to be planted is calculated based on the planting rate of the recommended species, the area covered when the recommended species grows (its estimated value), and the field area of the specific location. For example, the field area of the specific location can be estimated by the server 12 based on the terrain of the specific location, etc., or input by the user through the operation terminal 11.

[0258] The user can determine the plant species or quantity of plant species to be introduced at a specific location by referring to the planting plan combination 111. In addition, the user can purchase seeds and seedlings of the plant species to be introduced at a specific location from a seed store or the like, and introduce the plant species into a field at the specific location to begin cultivation.

[0259] When a plant species to be introduced at a specific location is selected from the recommended species (their species names) arranged in the planting plan combination 111, button 112 is operated. Operating button 112 makes the recommended species arranged in the planting plan combination 111 selectable. When the user selects a recommended species arranged in the planting plan combination 111, the selected recommended species is selected as the plant species to be introduced at the specific location and the selected recommended species is stored in a file in the terminal 11. By opening the file, the user can check the recommended species selected as the plant species to be introduced at the specific location.

[0260] When restoring the display on the terminal 11 from the presentation UI 110 to the presentation UI used when the button 83 was operated, the button 113 is operated.

[0261] <Second Configuration Example of the Evaluation Unit 43 and the Generation Unit 44>

[0262] Figure 21 is a block diagram illustrating a second configuration example of the evaluation unit 43 and the generation unit 44 .

[0263] Note that in this figure, Figure 10 Corresponding parts to those in are denoted by the same reference numerals, and descriptions of such parts will not be repeated hereinafter when appropriate.

[0264] In the second configuration example of the evaluation unit 43 and the generation unit 44, for example, a vegetation strategy is generated for a specific location where vegetation already exists, including the introduction of new plant species to the specific location if necessary. Therefore, the second configuration example of the evaluation unit 43 and the generation unit 44 can be used, for example, in a use case of developing a vegetation strategy for an existing farm or land where natural vegetation has been neglected.

[0265] Note that in the second configuration example of the evaluation unit 43 and the generation unit 44, a vegetation strategy for introducing a new plant species to a specific location where no plant species have been introduced can also be generated. Therefore, the second configuration example of the evaluation unit 43 and the generation unit 44 can also be used in the use case of establishing a new farm in a manner similar to the first configuration example.

[0266] exist Figure 21 , the evaluation unit 43 includes an attribute information conversion unit 51, a dimension reduction unit 52, an extraction unit 131, and an evaluation value calculation unit 132. The generation unit 44 includes a presentation UI generation unit 141.

[0267] Therefore, in Figure 21 In the evaluation unit 43, the evaluation unit 43 shares the Figure 10 The same feature as the case shown in , which is that the evaluation unit 43 includes an attribute information conversion unit 51 and a dimension reduction unit 52. However, the evaluation unit 43 is the same as Figure 10 The case shown in is different in that the evaluation unit 43 is newly provided with an extraction unit 131 and includes an evaluation value calculation unit 132 instead of the evaluation value calculation unit 53 .

[0268] The extraction unit 131 is supplied with the plant species list from the acquisition unit 41. Figure 1 As described above, plant species lists describe observed species, which are plant species observed at a specific location. Furthermore, plant species lists, when necessary, describe planned species, which are plant species planned for introduction at a specific location. Note that plant species lists can only describe planned species. Hereinafter, plant species (observed species and planned species) described in plant species lists will also be referred to as listed plant species.

[0269] In addition to being supplied with the plant species list, the extraction unit 131 is also supplied with the corresponding parameter group for each plant species in the internal DB from the attribute information conversion unit 51 .

[0270] The extraction unit 131 extracts the corresponding parameter group of each listed plant species described in the plant species list from the corresponding parameter groups of the corresponding plant species in the internal DB, and supplies the extracted corresponding parameter group to the dimension reduction unit 52 .

[0271] exist Figure 21 In the dimensionality reduction unit 52, dimensionality reduction is performed on each weather parameter of the weather scenario and the corresponding parameter group of each listed plant species, and a simplified weather scenario and a simplified corresponding parameter group of each listed plant species are generated. The simplified weather scenario is supplied to the evaluation value calculation unit 132 and the presentation UI generation unit 141, and the simplified corresponding parameter group of each listed plant species is supplied to the evaluation value calculation unit 132.

[0272] The evaluation value calculation unit 132 evaluates each listed plant species based on the (simplified) weather scenario using the simplified weather scenario from the dimensionality reduction unit 52 and the simplified corresponding parameter group for each listed plant species. The evaluation value calculation unit 132 supplies the evaluation value obtained as the evaluation result of each listed plant species to the presentation UI generation unit 141.

[0273] In addition to being supplied with the evaluation value of each listed plant species from the evaluation value calculation unit 132 , the presentation UI generation unit 141 is also supplied with the simplified weather scenario from the dimension reduction unit 52 and the weather scenario from the weather scenario calculation unit 42 .

[0274] The presentation UI generation unit 141 generates a presentation UI for presenting listed plant species, (simplified) weather scenarios, and the like sorted in order of evaluation values, and transmits the presentation UI to the terminal 11 .

[0275] Figure 22 is a diagram for explaining an example of calculation of the evaluation value of a plant species based on a simplified weather scenario by the evaluation value calculation unit 132 .

[0276] The evaluation value calculation unit 132 performs Voronoi tessellation of the parameter space using the parameter points of each listed plant species as generation points when mapping the parameter points of each listed plant species to the parameter space, and identifies the Voronoi cells of each listed plant species.

[0277] The evaluation value calculation unit 132 calculates the volume of the overlapping area where the Voronoi cells of the listed plant species overlap with the parameter distribution of the simplified weather scenario at time point t for the listed plant species. In this case, as the parameter distribution of the simplified weather scenario, for example, a minimum ellipsoid containing the parameter points constituting the parameter distribution can be used. Note that when the parameter space is a two-dimensional plane, the evaluation value calculation unit 132 calculates the area of the overlapping area where the Voronoi cells of the listed plant species overlap with the parameter distribution of the simplified weather scenario at time point t. Hereinafter, the volume of the overlapping area shall include this area.

[0278] The evaluation value calculation unit 53 calculates the volume of the overlapping area between the Voronoi cells of the listed plant species and the parameter distribution of the simplified weather scenario at each time point for the listed plant species, and calculates the sum of the volumes of the overlapping areas at the corresponding time points as the evaluation value of the listed plant species.

[0279] In this case, the larger the evaluation value of the listed plant species is, the better the evaluation (evaluation value) is.

[0280] Note that, when calculating the evaluation value, the number of parameter points in the parameter distribution of the simplified weather scenario included in the Voronoi cell of the listed plant species at the time point t may be used instead of the volume of the overlapping area.

[0281] Furthermore, in the evaluation value calculation unit 132, the centroid distance may be used to Figure 10 Even in the evaluation value calculation unit 53, the evaluation value can be calculated in a similar manner to the evaluation value calculation unit 132 using the volume of the overlapping area (or the number of parameter points included in the Voronoi cell).

[0282] Figure 23 1 is a flowchart for explaining an example of a process of calculating the evaluation value of a plant species by the evaluation value calculation unit 132 .

[0283] In step S81 , the evaluation value calculation unit 132 acquires the parameter distribution at each time point of the situation period of the simplified weather situation, and the process proceeds to step S82 .

[0284] In step S82 , the evaluation value calculation unit 132 performs Voronoi tessellation on the parameter space using the parameter point of each listed plant species as a generating point when mapping the parameter point of the listed plant species to the parameter space, and identifies the Voronoi cell of each listed plant species.

[0285] Subsequently, the process proceeds from step S82 to step S83 , in which the evaluation value calculation unit 132 resets the variable t for counting the time points of the situation period to 0, and the process proceeds to step S84 .

[0286] In step S84 , the evaluation value calculation unit 132 selects one listed plant species that has not been selected as a noteworthy plant species from among the listed plant species as a noteworthy plant species, and the process proceeds to step S85 .

[0287] In step S85 , the evaluation value calculation unit 132 calculates the volume of the overlapping area between the Voronoi cell of the notable plant species and the parameter distribution of the simplified weather scenario at the time point t, and the process proceeds to step S86 .

[0288] In step 86 , the evaluation value calculation unit 132 determines whether the variable t is equal to the time point at which the situation period ends.

[0289] When it is determined in step S86 that the variable t is not equal to the time point at which the situation period ends, the process proceeds to step S87 .

[0290] In step S87 , the evaluation value calculation unit 132 increments the variable t by 1 and the process returns to step S85 .

[0291] On the other hand, when it is determined in step S86 that the variable t is equal to the time point at which the situation period ends, the process proceeds to step S88 .

[0292] In step S88 , the evaluation value calculation unit 132 calculates the sum of the volumes of the overlapping areas at the corresponding time points of the scenario periods calculated in step S85 with respect to the noteworthy plant species as the evaluation value of the noteworthy plant species, and the process proceeds to step S89 .

[0293] In step S89 , the evaluation value calculation unit 132 determines whether all listed plant species have been selected as plant species worthy of attention.

[0294] When it is determined in step S89 that not all of the listed plant species have been selected as plant species worthy of attention, the process returns to step S83 and similar processing is repeated thereafter.

[0295] When it is determined in step S89 that all listed plant species have been selected as plant species worthy of attention, the process ends.

[0296] <Present UI>

[0297] Figure 24 is a diagram showing a fifth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0298] In the server 12, in response to Figure 18 The operation of the button 84 of the presentation UI 80 shown in FIG. Figure 21 The generating unit 44 shown in FIG generates Figure 24 The presentation UI 150 shown in FIG. 1 is presented and sent to the terminal 11 for display.

[0299] Specifically, in Figure 21 In the generation unit 44 shown in , the presentation UI generation unit 141 generates a presentation UI 150 presenting the listed plant species sorted in order of the evaluation value based on the evaluation value of each listed plant species in the plant species list calculated by the evaluation value calculation unit 132, and sends the presentation UI 150 to the terminal 11 for display.

[0300] A planting plan combination 151 and buttons 152 and 153 are displayed on the presentation UI 150. On the presentation UI 150, the planting plan combination 151 is arranged at an upper portion, and buttons 152 and 153 are arranged below the planting plan combination 151 in this order.

[0301] The planting plan combination 151 is a vegetation strategy for planting the listed plant species described in the plant species list. In this case, the planting plan combination 151 is a list of listed plant species arranged in order of evaluation value. The list of listed plant species arranged in order of evaluation value is a list of listed plant species ranked in order of evaluation value and is a ranking of listed plant species suitable for the environment formed by the weather of a specific location.

[0302] In the planting plan combination 151, the species names of the listed plant species are arranged in the "Ranking" column by sorting in the order of evaluation values. In addition, in the planting plan combination 151, the recommended planting rate recommended when the listed plant species are introduced into a field at a specific location is arranged in the "Evaluation of Observed Species" column.

[0303] For example, in Figure 24 In the planting plan combination 151 shown in , the plant species ranked first in terms of evaluation value is carrot. In addition, the planting rate of carrot is 33%.

[0304] The planting plan combination 151 can be used as a planting plan for the same period of next year (fiscal year), for example.

[0305] For example, a user who is an experienced farmer can empirically acquire knowledge of the relationship between weather transitions and the extent of crop growth and use this knowledge to create a planting plan. Creating a presentation UI 150 on server 12, on which a planting plan combination 151 is displayed, can be described as externalizing the process of creating a planting plan by an experienced user. Based on planting plan combination 151, even a novice user can create a planting plan as if they had gone through the same process as an experienced user.

[0306] In this case, the evaluation value of a listed plant species indicates how well the weather-induced environment, depending on the weather scenario, matches the growth of the listed plant species. Therefore, if the observed species, which is a listed plant species, is a plant species that is part of natural vegetation, the evaluation value of the observed species may be very high because the observed species thrives without human intervention.

[0307] Furthermore, if the evaluation value of the listed plant species is poor (low), the growth potential of the planned species in the environment formed by the weather according to the weather scenario is low. On the other hand, if the evaluation value of the listed plant species is good, the growth potential of the planned species in the environment formed by the weather according to the weather scenario is high. Therefore, the user can determine whether to introduce the planned species to a specific location based on the evaluation value (ranking) of the planned species.

[0308] Note that the planting plan combination 151 can also be used in conjunction with Figure 20 The planting plan assembly 111 shown in FIG. 1 shows the number of plants to be planted in a similar manner.

[0309] Furthermore, the planting plan combination 151 may display listed plant species whose evaluation values rank high, rather than displaying all listed plant species in order of evaluation values.

[0310] When a listed plant species to be planted at a specific location is selected from the listed plant species (their species names) arranged in the planting plan combination 151, the button 152 is operated. The operation button 152 creates a selectable state for the listed plant species arranged in the planting plan combination 151. When the user selects the listed plant species arranged in the planting plan combination 151, the selected listed plant species is selected as the plant species to be planted at the specific location and stored in a file in the terminal 11. By opening the file, the user can check the listed plant species selected as the plant species to be planted at the specific location.

[0311] When restoring the display on the terminal 11 from the presentation UI 150 to the presentation UI used when the button 84 was operated, the button 153 is operated.

[0312] <Third Configuration Example of the Evaluation Unit 43 and the Generation Unit 44>

[0313] Figure 25 4 is a block diagram illustrating a third configuration example of the evaluation unit 43 and the generation unit 44 .

[0314] Note that in this figure, Figure 10 or Figure 21 Corresponding parts to those in are denoted by the same reference numerals, and descriptions of such parts will not be repeated hereinafter when appropriate.

[0315] In the third configuration example of the evaluation unit 43 and the generation unit 44, an environment construction proposal is generated regarding how to construct an environment suitable for the growth of an observed species that has been introduced and observed at a specific location, or a planned species that is planned to be introduced at a specific location. Therefore, the third configuration example of the evaluation unit 43 and the generation unit 44 can be used, for example, in a use case where the environment formed by the weather at a specific location is changed to an environment that enhances the growth potential of the introduced species or the planned species.

[0316] exist Figure 25 , the evaluation unit 43 includes an attribute information conversion unit 51, a dimension reduction unit 52, an extraction unit 131, and an evaluation value calculation unit 132. The generation unit 44 includes a context search unit 161, a suggestion generation unit 162, and a presentation UI generation unit 163.

[0317] Therefore, in Figure 25In the example, the evaluation unit 43 is used with Figure 21 The configuration is similar to the situation.

[0318] The situation search unit 161 is supplied with the evaluation value of each listed plant species, or in other words, the observed species and the planned species described in the plant species list, from the evaluation value calculation unit 132. Note that the user can input a plant species that has been introduced at a specific location but has not been observed on the ground because it has not yet sprouted, etc., as a planned species to be described in the plant species list.

[0319] The scenario search unit 161 searches for a weather scenario that provides a better evaluation value for the listed plant species than the evaluation value from the evaluation value calculation unit 132 as a suitable scenario for the growth of the listed plant species, and supplies the weather scenario to the suggestion generation unit 162. For example, the search for a suitable scenario can be performed by a random search. Alternatively, for example, the search for a suitable scenario can be performed by a heuristic method such as a genetic algorithm.

[0320] In addition to being supplied with the appropriate context from the context search unit 161, the suggestion generation unit 162 is also supplied with the weather context of the specific location from the weather context calculation unit 42. Based on the appropriate context and the weather context of the specific location, the suggestion generation unit 162 generates an environment construction suggestion on how to make (change) the environment formed by the weather according to the weather context of the specific location closer to the environment formed by the weather according to the appropriate context, and supplies the environment construction suggestion to the presentation UI generation unit 163.

[0321] The appropriate scenario is a weather scenario that increases the evaluation value of the listed plant species, and in an environment formed by the weather according to the appropriate scenario, the growth potential of the listed plant species increases. Therefore, the environmental construction proposal can be described as a proposal regarding an environment for the listed plant species to grow, and more specifically, a proposal regarding a method for constructing an environment suitable for the growth of the listed plant species.

[0322] In actual agriculture, an environment with controlled sunlight levels can be constructed by providing cheesecloth for plant species that are less tolerant to sunlight, or an environment with high soil moisture can be constructed by increasing the frequency of watering for plant species that prefer high humidity. The environment construction suggestion is a suggestion that encourages the user to construct such an environment construction suggestion (to change to such an environment).

[0323] In addition to being supplied with the environment construction advice from the advice generating unit 162 , the presentation UI generating unit 163 is also supplied with the simplified weather scenario from the dimension reduction unit 52 and the weather scenario from the weather scenario calculating unit 42 .

[0324] The presentation UI generation unit 163 generates a presentation UI for presenting environment construction advice, (simplified) weather situations, and the like and transmits the presentation UI to the terminal 11 .

[0325] Figure 26 16 is a diagram for explaining an overview of searching for an appropriate context by the context search unit 161 .

[0326] For example, when searching for an appropriate context using random search, the context search unit 161 sets an initial value for the weather simulator, runs a weather simulation, and calculates one or more weather contexts (hereinafter also referred to as "search contexts") to be used for searching for an appropriate context in a manner similar to that of the weather context calculation unit 42. As the number of one or more weather contexts to be calculated as the search context, for example, the same number as the number of weather contexts at a specific location can be employed.

[0327] Furthermore, the context search unit 161 performs dimensionality reduction of the search context in a similar manner to the dimensionality reduction unit 52 , and calculates evaluation values of the listed plant species based on the search context after dimensionality reduction in a similar manner to the evaluation value calculation unit 132 .

[0328] The scenario search unit 161 repeats the above-described process a predetermined number of times by changing the initial value of the weather simulator.

[0329] Then, the context search unit 161 selects a search context from the search contexts as an appropriate context, in which the evaluation value of the listed plant species based on the search context (after dimensionality reduction) is better than the evaluation value of the listed plant species based on the (simplified) weather context of the specific location, such as the search context with the highest evaluation value of the listed plant species.

[0330] In the above search for appropriate situations, e.g. Figure 26 As shown in , parameter distributions of (simplified) weather scenarios are moved in parameter space, and weather scenarios having parameter distributions that provide higher evaluation values than the evaluation values of listed plant species based on weather scenarios at specific locations are searched for as appropriate scenarios.

[0331] In this case, since the evaluation value of the plant species based on the weather scenario is calculated for each plant species, the evaluation value of the plant species based on a given first weather scenario is better (higher) than the evaluation value of the plant species based on a different second weather scenario, which means that the evaluation value of the plant species based on the first weather scenario is generally better than the evaluation value of the plant species based on the second weather scenario.

[0332] Generally better means, for example, that all evaluation values of the plant species for which evaluation values are calculated are better, that the evaluation values of a predetermined number of plant species among all plant species are better, that the predetermined number is equal to or greater than a threshold, or that the sum of the evaluation values of all plant species is better.

[0333] Figure 27 is a flowchart for explaining an example of a process of searching for an appropriate context by the context search unit 161 .

[0334] In step S111 , the context search unit 161 initializes a variable c for counting the number of searches for a context to 1 and the process proceeds to step S112 .

[0335] In step S112, the context search unit 161 searches for Figure 6 In a similar manner to step S23 in the above, one or more weather scenarios to be used for searching for appropriate scenarios as search scenarios are calculated and the process proceeds to step S113. In this case, the calculation of the search scenario in step S112 is performed by randomly changing the initial value (base value) of the weather simulator from the initial value in the previously executed step S112.

[0336] In step S113, based on the search context (after dimensionality reduction), the context search unit 161 evaluates each listed plant species or, in other words, calculates the evaluation value of each listed plant species in a manner similar to the evaluation value calculation unit 132, and the processing proceeds to step S114.

[0337] In step S114 , the context search unit 161 determines whether the evaluation value (of the listed plant species) based on the search context (after dimensionality reduction) is better than the evaluation value (of the listed plant species) based on the (simplified) weather context of the specific location.

[0338] In step S114 , when it is determined that the evaluation value based on the search context is superior to the evaluation value based on the weather context of the specific place, the process proceeds to step S115 .

[0339] In step S115 , the context search unit 161 stores the search context as a candidate context to be a candidate for an appropriate context, and the process proceeds to step S116 .

[0340] On the other hand, in step S114 , when it is determined that the evaluation value based on the search context is not superior to the evaluation value based on the weather context of the specific place, the process skips step S115 and proceeds to step S116 .

[0341] In step S116 , the context search unit 116 determines whether the variable c is equal to a predetermined number of times C determined in advance.

[0342] When it is determined in step S116 that the variable c is not equal to the predetermined number of times C, the process proceeds to step S117.

[0343] In step S117 , the context search unit 116 increments the variable c by 1 and the process returns to step S112 .

[0344] On the other hand, when it is determined in step S116 that the variable c is equal to the predetermined number of times C or, in other words, when the search context has been calculated the predetermined number of times C, the process proceeds to step S118 .

[0345] In step S118 , the scenario search unit 116 selects an appropriate scenario from the candidate scenarios by, for example, selecting the candidate scenario with the best evaluation value as the appropriate scenario, and the process ends.

[0346] Figure 28 16 is a flowchart for explaining an example of a process of generating environment construction advice by the advice generating unit 162 .

[0347] In step S131 , the advice generating unit 162 selects one weather parameter that has not been selected as a noteworthy weather parameter from among the weather parameters of the weather scenario of the specific place as a noteworthy weather parameter and the process proceeds to step S132 .

[0348] In step S132, the suggestion generation unit 162 calculates the average value of the noteworthy weather parameters of the weather scenario at the specific location, and the process proceeds to step S133. When calculating the average value of the noteworthy weather parameters of the weather scenario at the specific location, for example, the average value is calculated for one or more weather scenarios as the weather scenario at the specific location, and furthermore, the average value is calculated for the time direction (of the scenario period).

[0349] In step S133, the suggestion generation unit 162 calculates the average value of the noteworthy weather parameters for the appropriate scenarios, and the process proceeds to step S134. When calculating the average value of the noteworthy weather parameters for the weather scenarios, the average value is calculated for one or more weather scenarios that are appropriate scenarios, and furthermore, the average value is calculated with respect to the time direction in a manner similar to step S132.

[0350] In step S134, the suggestion generation unit 162 generates environment construction suggestions based on the difference between the weather situation of the specific place and the average value of the noteworthy weather parameters of each of the appropriate situations (hereinafter also referred to as the average difference), and the processing proceeds to step S135.

[0351] Based on the average difference, the suggestion generation unit 162 generates environment construction suggestions on how to make (change) the environment formed by the noteworthy weather parameters of the weather scenario of the specific place closer to the environment formed by the noteworthy weather parameters of the appropriate scenario.

[0352] For example, when the weather parameter of interest is precipitation, if the average value of precipitation for the appropriate scenarios is 100 mm, which is greater than the average value of precipitation for the weather scenarios for the specific location, which is 60 mm, then the average difference in precipitation between the weather scenarios for the specific location and each of the appropriate scenarios is +40 mm. The suggestion generation unit 162 generates a suggestion as an environment building suggestion, such as a suggestion to increase the frequency of watering, that encourages the user to take action to bring the environment for the specific location closer to an environment where the average difference in precipitation is 40 mm higher than the environment for the specific location.

[0353] For example, when the weather parameter of interest is the duration of sunshine, if the average value of the sunshine duration of the appropriate scenario is 9 hours / day, which is less than the average value of the sunshine duration of the weather scenario of the specific location, 12 hours / day, then the average difference in sunshine duration between the weather scenario of the specific location and each of the appropriate scenarios is -3 hours / day. The suggestion generation unit 162 generates a suggestion serving as a guide for constructing an environment (an action guide) or a suggestion regarding a specific action as an environment construction suggestion, which encourages the user to take an action to bring the environment of the specific location closer to an environment in which the sunshine duration is shorter than the average difference in sunshine duration of 3 hours / day for the specific location. Examples of suggestions serving as a guide for constructing an environment include "controlling the sunshine duration through structures, etc.", and examples of suggestions regarding specific actions include "randomly providing cheesecloth corresponding to x% of the field area."

[0354] In step S135 , the advice generating unit 162 determines whether all weather parameters of the weather scenario of the specific location have been selected as noteworthy weather parameters.

[0355] When it is determined in step S135 that all weather parameters of the weather scenario of the specific place have not been selected as noteworthy weather parameters, the process returns to step S131 and similar processes are repeated thereafter.

[0356] When it is determined in step S135 that all weather parameters of the weather scenario of the specific location have been selected as weather parameters worthy of attention, the process ends.

[0357] <Present UI>

[0358] Figure 29 is a diagram showing a sixth display example of the presentation UI generated by the generation unit 44 and displayed on the terminal 11 .

[0359] In the server 12, in response to Figure 18 The operation of the button 85 of the presentation UI 80 shown in FIG. Figure 25 The generating unit 44 shown in FIG generates Figure 29 The presentation UI 180 shown in FIG. 1 is presented and sent to the terminal 11 for display.

[0360] Specifically, in Figure 25 In the generation unit 44 shown in FIG, the presentation UI generation unit 163 generates a presentation UI 180 that presents the environment construction suggestion generated by the suggestion generation unit 162 and transmits the presentation UI 180 to the terminal 11 for display.

[0361] An environment construction combination 181, a message 182, and a button 183 are displayed on the presentation UI 180. On the presentation UI 180, the environment construction combination 181 is arranged at the upper portion, and the message 182 and the button 183 are arranged below the environment construction combination 181 in this order.

[0362] The environment configuration set 181 is a set that lists, as part of the environment configuration suggestion, a set of guidelines for an environment configuration suitable for growing the listed plant species. Each guideline for the environment configuration includes an element of the environment to be changed, an action (task) (operation) to be taken with respect to the element, and, if necessary, action parameters for the action.

[0363] In the environment construction set 181, the elements of the environment to be changed are arranged in the "Ranking" column, and the actions to be taken and the necessary action parameters for the elements of the environment to be changed are arranged in the "Environment Change Point" column.

[0364] exist Figure 29 In the environment construction combination 181 shown in , for example, as a first guideline for environment construction, the environmental element that is the object of change is soil moisture, the action to be taken regarding soil moisture is an action to improve soil moisture, and the degree of improvement of soil moisture as an action parameter is 23%.

[0365] Message 182 is the specific suggestion that encourages the user to take action as the remainder of the environment construction suggestion. As message 182, the suggestion of the specific action (task) (operation) that should be taken according to the guideline selected among the guidelines of the environment construction in the environment construction combination 181 is displayed.

[0366] exist Figure 29In the example, the element of the environment selected as the target for change in the environment construction set 181 is the soil moisture guideline (the first guideline of the environment construction). Furthermore, as message 182, suggestions for specific actions to be taken based on the soil moisture guideline are displayed, such as "Manage soil moisture," "Pay attention to moisture control to minimize drying," "Increase watering with a watering can by 25%," and "Increase mist control value by 36%." In this case, "Increase watering with a watering can by 25%" means increasing the frequency of watering with the watering can by 25% from the current level, while "Increase mist control value by 36%" means increasing the amount of mist sprayed by 36%.

[0367] According to the environment construction combination 181 and the message 182, the user's actions can be guided (encouraged) to construct an environment suitable for growing the listed plant species.

[0368] By following the guidelines for the environment build in the environment build combination 181 and taking specific actions indicated in the suggestions as messages 182, the user can (is expected to) make the environment of the particular location closer to the environment formed by the weather according to the appropriate context or, in other words, an environment suitable for the growth of the listed plant species. Thus, at the particular location, the listed plant species can (is expected to) grow in a manner similar to how they would grow in an environment formed by the weather according to the appropriate context.

[0369] When restoring the display on the terminal 11 from the presentation UI 180 to the presentation UI used when the button 85 was operated, the button 183 is operated.

[0370] According to the presentation UI 180 , the user may receive environment construction suggestions for constructing a suitable growing environment with respect to listed plant species or, in other words, plant species that have been introduced or are planned to be introduced at a specific location.

[0371] For a single crop field dealing with a single plant species, the user only needs to create an environment suitable for that single plant species. Therefore, the user can relatively easily identify which environmental manipulations should be performed to create such an environment.

[0372] On the other hand, in fields where symbiotic farming (a registered trademark) involving the introduction of multiple plant species is practiced, the effects of a given environmental manipulation on the plant species vary depending on the plant species. Therefore, it is difficult for users to specify which environmental manipulations should be performed to create a suitable environment for all plant species in the field.

[0373] According to the third configuration example of the evaluation unit 43 and the generation unit 44, since appropriate situations suitable for a plurality of plant species are searched for and environment construction suggestions are generated on how to make the environment closer to (change to) an environment formed by weather according to the appropriate situation or, in other words, an environment suitable for a plurality of plant species, an environment suitable for a plurality of plant species can be constructed by executing the environment according to the environment construction suggestions.

[0374] <Fourth Configuration Example of the Evaluation Unit 43 and the Generation Unit 44>

[0375] Figure 30 4 is a block diagram illustrating a fourth configuration example of the evaluation unit 43 and the generation unit 44 .

[0376] Note that in this figure, Figure 10 Corresponding parts in the figure are denoted by the same reference numerals, and descriptions of such parts will not be repeated hereinafter when appropriate.

[0377] exist Figure 30 In the example, the evaluation unit 43 includes an attribute information conversion unit 51, a dimension reduction unit 52, and an evaluation value calculation unit 53. The generation unit 44 includes a recommended species selection unit 61, a presentation UI generation unit 62, and a recommended species reselection unit 211.

[0378] Therefore, in Figure 30 In the example, the evaluation unit 43 is used with Figure 10 The generating unit 44 is configured in a similar manner to the case of Figure 10 The case shown in shares the same features in that the generation unit 44 includes a recommended species selection unit 61 and a presentation UI generation unit 62. However, the generation unit 44 is different from Figure 10 The case shown in is different in that a recommended species reselection unit 211 is newly provided.

[0379] Species form interaction networks (ecosystem networks), where each species is a node and biotic interactions are links (edges). Within these interaction networks, plant species interact with other species (including other plant species) in a variety of ways, including mutualistic biotic interactions (hereinafter referred to as interactions). In ecosystems, the diversity of interactions maintains and promotes biodiversity, particularly species diversity.

[0380] Increased biodiversity is also expected to improve various ecosystem services. Taking this into account, recommended species can also be selected from the perspective of increasing biodiversity.

[0381] The recommended species reselection unit 211 selects recommended species based on the interaction, thereby selecting recommended species from the perspective of increasing biodiversity.

[0382] The recommended species reselecting unit 211 is supplied with the plant species whose evaluation values rank top M from the recommended species selecting unit 61 as M recommended species.

[0383] The recommended species reselection unit 211 acquires interaction information from the interaction DB of the DB 13 and (re)evaluates the M recommended species from the recommended species selection unit 61 based on the interaction information.

[0384] In other words, the recommended species reselection unit 211 evaluates the M recommended species from the recommended species selection unit 61 as candidates for recommended species based on the interaction information.

[0385] Furthermore, based on the evaluation results of the candidates of the recommended species based on the interaction information, the recommended species reselection unit 211 (re)selects the final recommended species from the candidates of the recommended species and supplies the selected final recommended species to the presentation UI generation unit 62 .

[0386] Figure 31 1 is a diagram for explaining an example of a process of evaluating, by the recommended species reselecting unit 211 , the interaction information of the candidates for the recommended species and selecting the final recommended species based on the result of the evaluation.

[0387] In step S211, the recommended species reselection unit 211 generates a candidate list that describes plant species as candidates for recommended species. The recommended species reselection unit 211 generates multiple combinations of one or more plant species as search targets for interactions from the plant species described in the candidate list. The plant species that constitute the combination will also be referred to as primary species.

[0388] exist Figure 31 In the example, plant species A, B, C, ... are described in a candidate list, and all possible combinations of one or more plant species are generated from the plant species A, B, C, ... described in the candidate list. Specifically, all N combinations of plant species are generated, including combination #1, ... consisting of only plant species A, combination #n, ... consisting of plant species A to C.

[0389] In step S212 , using the interaction information in the interaction DB, the recommended species reselection unit 211 constructs an interaction network with respect to each of the N combinations of plant species.

[0390] In an interaction network regarding a combination of plant species, plant species as primary species and secondary species as other species that interact with the primary species in a prey relationship, a predatory relationship, a symbiotic relationship, or a parasitic relationship constitute nodes, and the interactions between the primary species and the secondary species constitute links.

[0391] In the interaction information in the interaction DB, for example, two species, or, in other words, one species and another species interacting with the one species, are associated with the interaction occurring between the two species.

[0392] exist Figure 31 The interaction DB stores interaction information indicating that plant species A is in a parasitic relationship with another species a, plant species A is in a symbiotic relationship with another species b, plant species A is in a growth inhibition relationship with another species c, and the like.

[0393] The recommended species reselection unit 211 sequentially selects all N combinations of plant species as interesting combinations worthy of attention. Furthermore, using the interaction information, the recommended species reselection unit 211 searches for interactions between the plant species that are the main species constituting the interesting combination and other species that are secondary species interacting with the main species.

[0394] Furthermore, the recommended species reselection unit 211 constructs an interaction network composed of nodes representing primary species and secondary species and links representing interactions between species (interactions between primary species and secondary species).

[0395] For example, in Figure 31 In

[15] , an interaction network is constructed in which, regarding combination #1 consisting of only plant species A, the node of the main species A (plant species) and each node of the secondary species a to c that interact with the main species A are connected by links representing the interactions. In this case, Figure 31 In the figure, nodes of major species are represented by black circles, while nodes of minor species are represented by white circles. The same applies to the subsequent figures.

[0396] For example, in Figure 31 In [ 1 ], an interaction network is constructed in which, regarding a combination #n consisting of plant species A to C, a node of (a plant species that is) the main species A and each node of the secondary species a to c that interact with the main species A are connected by links. In addition, in [ 1 ], Figure 31, an interaction network is constructed in which the node of (the plant species that is) the main species B, each node of the secondary species c to f that interact with the main species B, and the node of C are connected by links.

[0397] Note that in combination #n consisting of plant species A to C, (plant species as) main species B and C interact with each other and also constitute secondary species as other species interacting with the main species.

[0398] In step S213, the recommended species reselection unit 211 evaluates the interaction network. Specifically, the recommended species reselection unit 211 sets a calculation formula (evaluation function) for evaluating the interaction network and calculates the interaction network evaluation score for each of the N combinations of plant species based on this calculation formula. For example, the calculation formula for the evaluation score can be set based on user operation of the terminal 11.

[0399] exist Figure 31 , a calculation formula is set to calculate the diversity of other species interacting with the main species in the interaction network, or, in other words, the number of secondary species (the number of white circles) as the evaluation score, and the evaluation score is calculated based on this calculation formula.

[0400] In step S214, the recommended species reselection unit 211 detects the best evaluation score from the evaluation scores of the interaction networks corresponding to all N combinations of plant species. The recommended species reselection unit 211 selects the combination that produces the best evaluation score from all N combinations of plant species with respect to the interaction network as an appropriate combination, which is the combination most suitable as a recommended species.

[0401] exist Figure 31 Among them, the interaction network regarding the combination #n of plant species A to C has the best evaluation score, and the combination #n of plant species A to C is selected as the appropriate combination.

[0402] The recommended species reselection unit 211 selects plant species constituting an appropriate combination or, in other words, candidates for recommended species as final recommended species, and supplies the final recommended species to the presentation UI generation unit 62 .

[0403] In the description given above, calculation of the evaluation score of the interaction network corresponds to evaluation based on interaction information of candidate recommended species of plant species constituting the combination corresponding to the interaction network. Furthermore, selection of the combination corresponding to the interaction network that yields the best evaluation score as the appropriate combination corresponds to selection of the final recommended species based on the evaluation result of the evaluation based on interaction information of the candidate recommended species.

[0404] In the presentation UI generating unit 62, when the presentation UI 110 displaying the list of the final recommended species as described above is generated as Figure 20 When the planting plans shown in FIG1 are combined 111, in addition to the evaluation values of the recommended species, the final recommended species can also be ranked based on the interaction information.

[0405] Figure 32 A diagram illustrating an example of a process for ranking final recommended species based on interaction information.

[0406] Figure 32 In the above example, the appropriate combination is a combination of (the final recommended species of) the main species A to C. Furthermore, an interaction network is constructed in which, with respect to the appropriate combination, the node of the main species A and each of the nodes of the secondary species a to c that interact with the main species A are connected by links. Furthermore, an interaction network is constructed in which the node of the main species B, each of the nodes of the secondary species c to f that interact with the main species B, and the node of C are connected by links.

[0407] exist Figure 32 In

[15] , ranking was performed according to degree centrality in the interaction network and the main species A to C that constituted the appropriate combination were ranked.

[0408] The degree centrality of major species A to C, or, in other words, the number of connected links, are three, five, and one, respectively, and major species B ranks first, major species A ranks second, and major species C ranks third in descending order of the number of links.

[0409] Note that in this case, as a ranking method for ranking the final recommended species as the main species based on the interaction information, a method is adopted in which the higher the degree centrality in the interaction network, the higher the ranking. As a ranking method, a method may be adopted in which the higher the centrality based on another centrality measure in the interaction network, the higher the ranking.

[0410] In addition to degree centrality, measures of centrality in interaction networks also include betweenness centrality and closeness centrality. Degree centrality is represented by the degree of a node or, in other words, by the number of links that are (directly) connected to the node. Closeness centrality is represented by the average of the corresponding distances from a node to other nodes. The distance between two nodes is represented by the number of links that would be passed through if the shortest path connecting the two nodes were taken. Betweenness centrality is represented by the percentage of shortest paths connecting two nodes other than the node of interest that pass through the node of interest.

[0411] Which method is adopted as the ranking method can be set according to, for example, the user's operation on the terminal 11 .

[0412] As described above, when the recommended species are selected based on the interaction in the recommended species reselection unit 211, plant species that have a high probability of survival not only in the environment formed by the weather according to the weather scenario at a specific location but also in terms of symbiosis and other interactions are selected as recommended species. Figure 20 The recommended species displayed as the planting plan combination 111 on the presentation UI 110 shown in are plant species whose seeds and seedlings have higher growth potential.

[0413] <Second Function Configuration Example of Server 12>

[0414] Figure 33 is a block diagram showing a second functional configuration example of the server 12 .

[0415] Note that in this figure, Figure 4 Corresponding parts to those in are denoted by the same reference numerals, and descriptions of such parts will not be repeated hereinafter when appropriate.

[0416] exist Figure 33 In the embodiment, the server 12 includes an acquisition unit 41 to a generation unit 44 and a correction unit 311 .

[0417] therefore, Figure 33 Server 12 in Figure 4 The case shown in shares the same features in that the server 12 includes an acquisition unit 41 to a generation unit 44, but Figure 4 The case shown in is different in that a correction unit 311 is newly provided.

[0418] The correction unit 311 is supplied with the observation data from the acquisition unit 41 and is also supplied with the data to be corrected from the generation unit 44. Figure 24 The presenting UI 150 shown in FIG. 1 shows the planting plan combination 151 to be displayed by Figure 20 The presentation UI 110 shown in FIG. 1 displays a planting plan combination 111 .

[0419] In this case, hereinafter, a description will be given concerning the planting plan combination 111 among the planting plan combinations 111 and 151. The description given regarding the planting plan combination 111 can be applied to the planting plan combination 151 in a similar manner.

[0420] exist Figure 33 In the planting plan, the user plants the recommended species in the planting plan combination 111 at a specific location, observes the growth conditions or yield of each recommended species, and inputs the growth conditions or yield into the terminal 11.

[0421] The terminal 11 will recommend the growing conditions or yield of the species and, if necessary, the information about the growth conditions or yield of the species generated by the sensor unit 26 ( Figure 2 ) The detected (observed) information on the environment of a specific location after the recommended species is planted (such as air temperature and humidity) is sent to the server 12 as observation data actually observed at the specific location.

[0422] In the server 12 , the acquisition unit 41 acquires the observation data by receiving the observation data from the terminal 11 and supplies the observation data to the correction unit 311 .

[0423] Based on the observation data from the acquisition unit 41 , the correction unit 311 corrects the attribute information of the plant species information of the recommended species in the planting plan combination 111 among the plant species in the plant DB stored in the DB 13 .

[0424] In plant databases that include open-source plant species information, such as USDA Plants, attribute information is not always correct. Furthermore, even if plant species information of plant species similar to native species of Japan is stored in plant databases such as USDA Plants, the characteristics of the plant species may differ from those of the native species.

[0425] When the attribute information in the plant DB is incorrect, the growth conditions or yield of the recommended species are declining. When the growth conditions or yield of the recommended species are declining, the correction unit 311 uses the weather data of the specific location stored in the weather DB or the information about the environment of the specific location included in the observation data to correct the attribute information of the recommended species whose growth conditions or yield are declining in the plant DB.

[0426] For example, the correction unit 311 corrects the growth conditions in the attribute information of the recommended species that cannot grow so as not to include the environment of a specific location.

[0427] The server 12 can perform processing using the plant species information of the plant DB whose attribute information has been corrected as described above. Accordingly, a presentation UI 110 ( Figure 20 ).

[0428] Note that the evaluation unit 43 and the generation unit 44 may be configured to include any two, three, or all of the first to fourth configuration examples.

[0429] <Presentation UI that presents diverse functions>

[0430] The paper shows that the higher the biodiversity of plants above the surface of an ecosystem, the greater its biomass and the more stable its biomass over time. Using this knowledge, one goal of Symbiotic Agriculture (registered trademark) is to increase the biodiversity of plants above ground. To increase biodiversity, using findings from community ecology, it is recommended to increase "functional diversity," a term used in community ecology. According to community ecology, increasing functional diversity is considered important for increasing surface biomass and robustness.

[0431] For example, when performing an operation (work) on an ecosystem to increase its functional diversity, it is necessary for the user to understand the functional diversity of the ecosystem in order to examine how the functional diversity of the ecosystem has changed due to the operation. Therefore, it is desirable to express and present the functional diversity to the user in some form.

[0432] As a method of expressing functional diversity, although methods of calculating various indicators have been proposed, there is still a demand for new expression methods. Hereinafter, a presentation UI that presents functional diversity according to a new expression method will be described.

[0433] For example, a user can periodically or irregularly survey vegetation in an ecosystem such as a farm and use the results of the vegetation survey (vegetation survey results) to generate a presentation UI that presents functional diversity. The user can input the vegetation survey results through the operation terminal 11 and transmit the vegetation survey results to the server 12. Server 12 can use the vegetation survey results from terminal 11 to generate a presentation UI for presenting functional diversity and transmit the presentation UI to terminal 11 for display.

[0434] When the user inputs the vegetation survey result to the terminal 11 , the server 12 may generate a presentation UI serving as an input I / F for inputting the vegetation survey result and transmit the presentation UI to the terminal 11 for display.

[0435] Figure 34 : is a diagram showing a display example of a presentation UI serving as an input I / F for inputting vegetation survey results.

[0436] exist Figure 34 , the presentation UI 410 is configured such that a capture button 411, an overall coverage display unit 412, a sort button 413, an individual coverage display unit 414, an add button 415, and a status display unit 416 are arranged in order from the top (as a GUI).

[0437] The presentation UI 410 may be generated for each ecosystem, which is a survey unit for the user to conduct a vegetation survey. As the survey unit, for example, the user may specify an arbitrary range such as a farm, a field, or a ridge.

[0438] Capture button 411 is operated when performing photography using terminal 11. The image captured by operating capture button 411 is stored in association with presentation UI 410, which includes capture button 411, in terminal 11. Therefore, when a user operates capture button 411 included in presentation UI 410 generated for an ecosystem in a given survey unit U and captures the ecosystem in survey unit U, the image of the ecosystem in survey unit U obtained through the capture is stored in association with presentation UI 410 generated for the ecosystem in survey unit U. By performing a predetermined operation on presentation UI 410 generated for the ecosystem in survey unit U, terminal 11 can display the image associated with presentation UI 410. Accordingly, the user can view the image of the ecosystem in survey unit U from presentation UI 410 generated for the ecosystem in survey unit U.

[0439] In this case, the survey unit of the object whose vegetation survey result is input to the presentation UI 410 is also referred to as the object survey unit. In the vegetation survey, for example, the plant species (their species names) growing in the ecosystem in the object survey unit, the coverage rate of each plant species covering the ecosystem in the object survey unit, and the total coverage rate as the coverage rate of all plants growing in the ecosystem in the object survey unit covering the ecosystem are observed (surveyed). In the vegetation survey, the plant species (their species names) harvested in the ecosystem in the object survey unit, the yield (weight) of each plant species harvested, and other necessary information can be observed.

[0440] The total coverage ratio display unit 412 is operated when the total coverage ratio of the ecosystem is input in the target survey unit. By operating the total coverage ratio display unit 412, the total coverage ratio display unit 412 displays the input total coverage ratio.

[0441] The overall coverage display unit 412 is a horizontal rectangle having a slide bar 412A and a menu button 412B.

[0442] Slide bar 412A can be slid horizontally from the left end of the rectangle serving as the overall coverage ratio display unit 412. Slide bar 412A can be moved (slid) to input the coverage ratio in 10% increments (e.g., 0 to 9%, 10 to 19%, etc.) with a width of 10%. The user can input the overall coverage ratio by operating slide bar 412A. To re-enter (correct) the overall coverage ratio, operate menu button 412B.

[0443] Note that slider 412A can be configured to allow continuous input of coverage from 0 to 100%, rather than in increments of 10%. However, the coverage that a user can obtain by observing (viewing) the ecosystem in the target survey unit is subject to some error. As described above, gradually entering a certain range of values as the overall coverage takes into account the fact that the input overall coverage may contain some error. The same applies to the individual coverages described later.

[0444] The sort button 413 is operated when sorting the individual coverage ratio display units 414 arranged below the sort button 413. The individual coverage ratio display units 414 can be sorted in descending or ascending order of coverage ratio, species name of plant species, etc. Figure 34 , the individual coverage ratio display unit 414 sorts the data in descending order of coverage ratio.

[0445] The individual coverage ratio display unit 414 is operated when an individual coverage ratio, which is the coverage ratio of a given plant species covering the ecosystem as the target survey unit, is input. The individual coverage ratio display unit 414 displays the individual coverage ratio input by operating the individual coverage ratio display unit 414. Alternatively, the individual coverage ratio display unit 414 is operated when the species name (carrot, daikon radish, buckwheat, etc.) of the plant species for which the individual coverage ratio display unit 414 displays the individual coverage ratio is input, and the individual coverage ratio display unit 414 displays the species name. The individual coverage ratio display unit 414 is configured in a manner similar to the overall coverage ratio display unit 412, and the input of the individual coverage ratio can be performed in a manner similar to the input of the overall coverage ratio display unit in the overall coverage ratio display unit 412.

[0446] Add button 415 is operated to add individual coverage display cells 414 to presentation UI 410. When a user newly observes a plant species in the ecosystem of the target survey unit for which individual coverage has not yet been entered, the user can operate add button 415. In this case, in response to the operation of add button 415, a new individual coverage display cell 414 is additionally arranged in presentation UI 410. By operating the new individual coverage display cell 414, the user can enter the species name of the newly observed plant species and the individual coverage of the plant species. When the number of individual coverage display cells 414 arranged on presentation UI 410 becomes too large to be arranged, a scroll bar is displayed on presentation UI 410 to scroll through and display the individual coverage display cells 414.

[0447] The status display unit 416 displays the input (recording) status of the individual coverage ratio in the form of a bar (bar graph) starting from the left end and extending in the right direction.

[0448] Theoretically, if the individual coverage of all plant species growing in the ecosystem of the target survey unit is input, the total value of the individual coverage will equal the overall coverage. The status display unit 416 displays the total value of the individual coverage displayed in the corresponding individual coverage display unit 414 in the form of a bar, where the overall coverage displayed in the overall coverage display unit 412 is the maximum value of the bar. Note that the status display unit 416 can also display the total value of the overall coverage and the individual coverage as a numerical value, or display the numerical value obtained by subtracting the total value of the individual coverage from the overall coverage as the remaining value of the individual coverage that can be input.

[0449] In the vegetation survey of the ecosystem of the object survey unit, the user first observes the overall coverage and inputs the overall coverage into the presentation UI 410. Subsequently, the user observes the individual coverage of each plant species in turn and inputs the individual coverage into the presentation UI 410. In the presentation UI 410, each time the user inputs the individual coverage of the plant species, the bar of the status display unit 416 changes (becomes longer) according to the total value of the individual coverage input so far. The user can easily understand the progress of the observation of the individual coverage by checking the bar in the status display unit 416. For example, the user can understand how much individual coverage has been observed, how much work still needs to be done, etc.

[0450] Furthermore, by viewing the bar in status display unit 416, the user can determine whether to stop observing the individual plant species coverage. For example, when the bar in status display unit 416 reaches or near the left end, the user can stop observing the individual plant species coverage. Consequently, observation of the individual plant species coverage in the ecosystem of the target survey unit can be completed in a short period of time.

[0451] Specifically, each time a user discovers a new plant species in the ecosystem of the object survey unit for which individual coverage has not yet been observed, the user observes the individual coverage of the new plant species. Although some new plant species are easy to spot at first glance, other plant species are difficult to spot without careful observation, for example, because they are hidden behind other plant species. Since it is impossible to know in advance all the plant species that inhabit the ecosystem of the object survey unit, if the goal is to discover all the difficult-to-find plant species in addition to the easy-to-find species, then conducting a vegetation survey will take an unrealistically long time. With this in mind, it is expected that the observation of individual coverage will be stopped after a certain number of observations have been made. By viewing the bars in the status display unit 416, the user can understand the progress of the observation of individual coverage and determine whether to stop observing individual coverage. Therefore, the observation of individual coverage can be completed in a short period of time.

[0452] exist Figure 34 In the presentation UI 410 in FIG. 4 , the bar in the status display unit 416 shows that the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 is 69%, and the maximum value of the bar (the right end of the bar) is 80 to 89% (for example, its maximum value is 89%) as the overall coverage ratio. In this case, since the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 has not reached the maximum value of the bar in the status display unit 416, the user can determine that the individual coverage ratios should continue to be observed.

[0453] Note that the individual coverage ratios input to the individual coverage ratio display unit 414 are values with a width of 10% interpreted in association with the overall coverage ratio display unit 412. As the total value of the individual coverage ratios input to the individual coverage ratio display unit 414, for example, the total value of the minimum values of the individual coverage ratios with a width of 10% (in Figure 34 The total is 30% + 20% + 10%) and 9%.

[0454] Figure 35 4 is a diagram showing the presentation UI 410 in a state where individual coverages are input until the total value of the individual coverages input to the individual coverage display unit 414 matches the maximum value of the bar in the status display unit 416 .

[0455] exist Figure 35 , individual coverages are input until the total value of the individual coverages input to the individual coverage display unit 414 matches the maximum value (89%) of the bar in the status display unit 416. As a result, the bar in the status display unit 416 extends to (reaches) the right end of the bar indicating the maximum value.

[0456] In this case, the user can decide to stop observing individual coverages.

[0457] Note that when individual coverages have been input until the total value of the individual coverages input to the individual coverage display unit 414 matches the maximum value of the bar in the status display unit 416, in order to make it easier to understand that the maximum value of the bar has been reached, the bar in the status display unit 416 may be displayed in the same manner as the maximum value. Figure 34 When the total value of the individual coverage ratios in the status display unit 416 does not reach the maximum value of the bar in the status display unit 416, a different display state is displayed. For example, Figure 35 The bars in the status display unit 416 can be displayed in the same way as Figure 34 Specifically, for example, when Figure 34 When the bar in the status display unit 416 is displayed in green, Figure 35 The bar of the status display unit 416 may be displayed in yellow.

[0458] Figure 36 4 is a diagram showing the presentation UI 410 in a state where individual coverage ratios are input until the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 exceeds the maximum value of the bar in the status display unit 416 .

[0459] exist Figure 36 , individual coverage ratios are input until the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 exceeds the maximum value (89%) of the bar of the status display unit 416. Since the bar in the status display unit 416 cannot extend beyond the right end indicating the maximum value of the bar, the bar in the status display unit 416 is in a state of having extended to the right end indicating the maximum value of the bar.

[0460] It is difficult to distinguish the state in this case from the state in which the individual coverage ratios are input until the total value of the individual coverage ratios input to the individual coverage ratio display unit 414 matches the maximum value of the bar in the status display unit 416. In view of this, when the total value of the individual coverage ratios exceeds the maximum value of the bar in the status display unit 416, the bar in the status display unit 416 may be displayed in the same manner as the total value of the individual coverage ratios. Figure 35 When the total value of the individual coverage ratio reaches the maximum value of the bar of the status display unit 416, a different display state is displayed. For example, Figure 36 The bars in the status display unit 416 can be displayed in the same way as Figure 35 Specifically, for example, when Figure 35 When the bar in the status display unit 416 is displayed in yellow, Figure 36 The bar of the status display unit 416 may be displayed in red.

[0461] In addition, Figure 35 In the case where the total value of the individual coverage in matches the maximum value of the bar in the status display unit 416, and in Figure 36 In a case where the total value of the individual coverage exceeds the maximum value of the bar in the status display unit 416, the bar in the status display unit 416 can be displayed in a display state that attracts the user's attention (such as by flashing) so as to inform the user that the observation of the individual coverage can be stopped.

[0462] The vegetation survey result input to the presentation UI 410 is transmitted from the terminal 11 to the server 12. In the terminal 11, the vegetation survey result can be transmitted to the server 12 by associating the survey date on which the vegetation survey has been performed with the vegetation survey result.

[0463] As described above, in a vegetation survey, the plant species growing in the ecosystem of the target survey unit, the individual plant species coverage, the overall coverage of the ecosystem of the target survey unit, the yield of the harvested plant species, etc. are observed. In a vegetation survey, the plant species observed in the ecosystem of the target survey unit are also referred to as observed plant species. The vegetation survey results sent from the terminal 11 to the server 12 can include the observed plant species (their species names), individual plant species coverage, overall coverage, and yield, as needed.

[0464] When the user performs an operation to cause the presentation UI 410 to present functional diversity, the server 12 generates a presentation UI for presenting functional diversity using the vegetation survey results from the terminal 11 and transmits the presentation UI to the terminal 11 for display.

[0465] Hereinafter, while explaining display examples of presentation UIs presenting functional diversity, the user can select which presentation UI is to be displayed by operating the terminal 11 or the like.

[0466] Figure 37 1 is a diagram showing a first display example of a presentation UI presenting functional diversity.

[0467] Server 12 categorizes the observed plant species from terminal 11 on a predetermined survey date into family-level vegetation survey results and performs statistical processing. For example, server 12 counts the number of observed plant species per family and generates a pie chart showing the number of observed plant species per family (a pie chart of the number of species per family). Server 12 generates a presentation UI 420 displaying the pie chart of the number of species per family and transmits presentation UI 420 to terminal 11 for display. The user can specify the survey date for the vegetation survey results to be used in generating presentation UI 420 by operating terminal 11.

[0468] In the species number pie chart of each family displayed by the presentation UI 410 , the number of sectors is equal to the total number of families of observed plant species, and the central angle of a sector is proportional to the species number of observed plant species belonging to the family represented by the sector.

[0469] A pie chart showing the number of species per family within the ecosystem of the survey unit represents (an aspect of) the functional diversity of the ecosystem. Specifically, the number of observed plant species belonging to each family can be considered a type of indicator of the genetic diversity of the ecosystem of the survey unit. Furthermore, the genetic diversity of the ecosystem of the survey unit represents a simplified representation of the functional diversity of the ecosystem. Therefore, a pie chart showing the number of observed plant species per family within the ecosystem of the survey unit can be considered to represent (the degree of build-up of) the functional diversity of the ecosystem.

[0470] The more uniform the number of observed plant species belonging to each family and the greater the number of families, the greater the functional diversity. Therefore, by displaying a pie chart of the number of species per family, UI 420 allows the user to understand the degree of functional diversity of the ecosystem of the target survey unit based on the uniformity and number of sectors (central angles) within the pie chart of the number of species per family. For example, the user can confirm that the number of observed plant species in the ecosystem of the target survey unit is not biased towards any particular family or that there are a large number of families, thereby understanding that the ecosystem of the target survey unit has high functional diversity.

[0471] Note that when functional diversity is high, it is inferred that an enhanced ecosystem is being constructed. Therefore, a pie chart showing the number of species per family expressing functional diversity can be used as an indicator of the degree of accumulation of an enhanced ecosystem.

[0472] Since the presentation UI 420 that presents functional diversity is generated using observed plant species, the user can easily generate the presentation UI 420 by observing at least the plant species growing in the ecosystem of the target survey unit during the vegetation survey. Therefore, according to the presentation UI 420, the user can easily understand the functional diversity without spending too much time and money.

[0473] Figure 38 2 is a diagram showing a second display example of a presentation UI presenting functional diversity.

[0474] Server 12 generates a CSR triangle from terminal 11. This triangle plots (represents) the CSR values of the observed plant species as the vegetation survey results for a predetermined survey date. Server 12 generates a presentation UI 430 for displaying the CSR triangle and transmits presentation UI 430 to terminal 11 for display. The user can specify the survey date for which the vegetation survey results for presentation UI 430 are to be generated by operating terminal 11.

[0475] The CSR triangle is a triangle used in plant ecology to classify plants based on their hypotheses about survival strategies. The vertices C, S, and R of the CSR triangle represent survival strategies. C represents a competitor strategy, S represents a stress-tolerant strategy, and R represents a weed strategy.

[0476] The server 12 generates a CSR triangle in which the observed plant species are plotted at points whose coordinates are C values, S values, and R values, which represent the degree of C, S, and R (indicators) of the observed plant species. The C value, S value, and R value are also collectively referred to as CSR values. The server 12 can calculate a representative value of the CSR values of all observed plant species, such as a median, mean, or other statistical value, and plot the value on the CSR triangle. Figure 38In the CSR triangle shown in (and in CSR triangles in figures to be described later), the smaller point represents the CSR value of the observed plant species and the larger point P1 represents the median as a representative value of the CSR values of all observed plant species.

[0477] Figure 39 It is a diagram used to explain the CSR triangle.

[0478] In the CSR triangle, the CS axis from vertex C to vertex S represents the S value, and the SR axis from vertex S to vertex R represents the R value. The RC axis from vertex R to vertex C represents the C value.

[0479] The CSR value is expressed as %, with higher values indicating a higher degree of adoption of the competitor strategy (C), the stress tolerant strategy (S), and the weed strategy (R). In the figure, 0, 0.2, 0.4, 0.6, 0.8, and 1 represent 0%, 20%, 40%, 60%, 80%, and 100% as CSR values.

[0480] The CSR value can be assumed to be a value that satisfies the equation of C value + S value + R value = 100%.

[0481] For a point on the CSR triangle, the intersection of the straight line LC passing through the point and parallel to the SR axis and the RC axis represents the C value. The intersection of the straight line LS passing through the point on the CSR triangle and parallel to the RC axis and the CS axis represents the S value. The intersection of the straight line LR passing through the point on the CSR triangle and parallel to the CS axis and the SR axis represents the R value. Figure 39 , the CSR values (C value, S value, and R value) of point P11 are (23.2%, 9.802%, 66.998%).

[0482] The CSR triangle displayed on the presentation UI 430 expresses (an aspect of) the functional diversity of the ecosystem of the subject survey unit.

[0483] For example, if appropriate human manipulation (watering, ensuring sunlight, disturbance (harvesting, pruning, etc.), introduction of predetermined plant species (seeding, etc.), etc.) is not performed with respect to the ecosystem of the target survey unit and the functional diversity of the ecosystem is low, it is inferred that the distribution of (the points representing) the CSR values of the observed plant species in the CSR triangle is biased toward a specific position, such as one of the vertices C, S, or R. In addition, it is inferred that the median, which is a representative value of the CSR values of all observed plant species, is located at a position away from the center (center of gravity) of the CSR triangle.

[0484] The user can understand the degree of functional diversity of the ecosystem of the target survey unit based on the degree to which the distribution of CSR values of observed plant species is evenly distributed within the CSR triangle or the proximity of the median of the representative value of the CSR values of all observed plant species to the center of the CSR triangle. For example, by confirming that the distribution of CSR values of observed plant species is evenly distributed within the CSR triangle or the proximity of the median of the representative value of the CSR values of all observed plant species to the center of the CSR triangle, the user can understand that the functional diversity of the ecosystem of the target survey unit is high. For example, Figure 38 The distribution of CSR values for observed plant species is uniform within the CSR triangle, and the median, representing the CSR values of all observed plant species, is close to the center of the CSR triangle. This allows users to understand that the functional diversity of the ecosystem in the target survey unit is high and that management, including manipulation of the ecosystem in the target survey unit, is appropriate.

[0485] Since the presentation UI 430 representing functional diversity is generated using observed plant species in a similar manner to the presentation UI 420, the user can easily generate the presentation UI 430 by observing at least the plant species growing in the ecosystem of the target survey unit during the vegetation survey. Therefore, according to the presentation UI 430, the user can easily understand the functional diversity without spending too much time and money.

[0486] Figure 40 is a diagram showing another display example of the presentation UI 430 .

[0487] exist Figure 40 , the distribution of the CSR values of the observed plant species in the CSR triangle displayed in the presentation UI 430 is biased toward the vertex S. Furthermore, a point P21 representing the median of the representative values of the CSR values of all observed plant species is also located toward the vertex S.

[0488] Therefore, according to Figure 40 Through the presentation UI 430 shown in FIG, the user can understand that the ecosystem of the target survey unit is an environment that is conducive to the growth of plant species with a high stress tolerance strategy (S). In other words, the user can understand that the ecosystem of the target survey unit is an environment with a high stress level due to stressors such as lack of sunlight and lack of water. Accordingly, the user can understand that in order to increase the functional diversity of the ecosystem of the target survey unit, stress-reducing manipulations (interventions) such as mowing the grass to ensure sunlight, removing branches from tall trees, and watering are necessary.

[0489] Figure 41 4 is a diagram showing still another display example of the presentation UI 430 .

[0490] Figure 38 The presentation UI 430 shown in shows a CSR triangle that plots the CSR values of all observed plant species as a result of a vegetation survey on a predetermined survey date. Figure 41 Presentation UI 430 in the diagram shows a CSR triangle plotting the CSR values of observed plant species belonging to a specific family among the observed plant species, as a result of a vegetation survey on a predetermined survey date. Within the CSR triangle plotting the CSR values of observed plant species belonging to a specific family, representative values of the CSR values of observed plant species belonging to the specific family, such as the median, mean, or other statistical value, can be plotted. Point P31 represents the median, which is a representative value of the CSR values of observed plant species belonging to the specific family.

[0491] Figure 41 The CSR triangle displayed on the presentation UI 430 shown in FIG plots the CSR values of observed plant species belonging to the Brassicaceae family and uses the median as a representative value of the CSR value.

[0492] The user can specify a survey date and a specific department to be used for generating the vegetation survey results of the presentation UI 430 by operating the terminal 11 .

[0493] One or more families can be specified as the specific family. When multiple families are specified as the specific family, the representative CSR value can be displayed for each of the multiple families as the specific family, or the representative CSR value of all observed plant species belonging to each of the multiple families can be displayed.

[0494] The median, which is a representative value of the CSR values of observed plant species belonging to a specific family (hereinafter also referred to as a family representative value), can be calculated, for example, as follows.

[0495] Let the C value, S value, and R value, which are medians of the CSR values of the observed plant species belonging to a specific family m, be represented by Cm, Sm, and Rm, respectively. Let the number of observed plant species belonging to family m be represented by n.

[0496] When calculating the median Cm, Sm and Rm of the family representative value of family m, the C value Ci, S value Si and R value Ri (coordinates on the represented CSR triangle) of each observed plant species i (i = 1, 2, ..., n) belonging to the family m are transformed into xy coordinates (xi, yi) according to equation (A1).

[0497] xi=100-(Ci / 2+Ri)

[0498] yi=√3 / 2*Ci

[0499] …(A1)

[0500] Furthermore, the medians xm and ym of the x-coordinate xi and the y-coordinate yi are calculated according to equation (A2).

[0501] xm=median({x1,x2,...,xn})

[0502] ym=median({y1, y2,...,yn})

[0503] …(A2)

[0504] median() represents the median of the values in the brackets.

[0505] The medians xm and ym (represented xy coordinates) are transformed into medians Cm, Sm and Rm (represented coordinates on the CSR triangle) according to equation (A3) as representative values of the science.

[0506] Cm=2 / √3*ym

[0507] Rm=100-(Cm / 2+xm)

[0508] Sm=100-(Cm+Rm)

[0509] …(A3)

[0510] The medians Cm, Sm, and Rm, which are representative values of the family, can be calculated as described above.

[0511] Figure 42 3 is a diagram showing a third display example of a presentation UI presenting functional diversity.

[0512] Figure 42 The presentation UI 450 shown in FIG. Figure 38 Presentation UI 450 has the same feature as presentation UI 430 shown in FIG. 1 , in that presentation UI 450 displays a CSR triangle. However, presentation UI 450 differs from presentation UI 430, which displays the CSR triangle of the CSR values of the observed plant species as a result of the vegetation survey on a predetermined survey date, in that presentation UI 450 displays a CSR triangle that plots the locus of representative CSR values of the observed plant species in the ecosystem of the target survey unit.

[0513] The server 12 calculates an ecosystem representative value for each unit period that divides the predetermined survey period using the observed plant species as a result of the vegetation survey for the predetermined survey period and the coverage rate (individual coverage rate) of each observed plant species from the terminal 11. The ecosystem representative value is a representative value of the CSR values of the observed plant species observed during the unit period in the ecosystem of the target survey unit.

[0514] For example, using the observed plant species and the coverage rate of each observed plant species as a vegetation survey result for a one-year period from January to December as a survey period, the server 12 calculates the ecosystem representative value for each month of the one-year period as a unit period.

[0515] Furthermore, server 12 plots the ecosystem representative value for each month of a year, which is the survey period, and generates a CSR triangle. This triangle is a trajectory of the ecosystem representative value created by connecting the ecosystem representative values for each month with a line segment. Server 12 generates a presentation UI 450 for displaying the CSR triangle and transmits presentation UI 450 to terminal 11 for display. The user can specify the survey period or unit period by operating terminal 11.

[0516] As the ecosystem representative value per unit time period, for example, a statistical value obtained by using the coverage of observed plant species per unit time period and statistically processing the CSR values of the observed plant species can be used. The statistical value serving as the ecosystem representative value per unit time period can be calculated, for example, as follows.

[0517] Let the ecosystem representative values C, S and R per unit time period be represented by Cs, Ss and Rs.

[0518] Furthermore, in this case, for the sake of simplicity, let us assume that the family m to which the observed plant species observed during the survey period belongs is any one of the three families m1, m2, and m3. Let the family representative values C, S, and R of family m#j (j = 1, 2, 3) calculated from the observed plant species observed during a month as a unit period according to equations (A1) to (A3) be represented by Cm#j, Sm#j, and Rm#j.

[0519] Let Adm#j be the cumulative coverage obtained by accumulating the coverage of observed plant species belonging to family m#j among the observed plant species observed every day for a month d (d=1, 2, ..., 12) within a unit period d. Let Ad(=Adm1+Adm2+Adm3) be the overall cumulative value of coverage obtained by accumulating the cumulative coverage Adm#j of each family m#j during the unit period d across all families m1, m2, and m3.

[0520] The C value Cs, S value Ss, and R value Rs, which are representative values of the ecosystem per unit time period d, are calculated according to equation (A4).

[0521] Cs=(Adm1 / Ad)*Cm1+(Adm2 / Ad)*Cm2+(Adm3 / Ad)*Cm3

[0522] Ss=(Adm1 / Ad)*Sm1+(Adm2 / Ad)*Sm2+(Adm3 / Ad)*Sm3

[0523] Rs=(Adm1 / Ad)*Rm1+(Adm2 / Ad)*Rm2+(Adm3 / Ad)*Rm3

[0524] …(A4)

[0525] According to equation (A4), when calculating the ecosystem representative value (Cs, Ss, Rs) of the unit time period d, the coverage rate of the observed plant species (the cumulative coverage rate Adm#j obtained) is used as the weight for the weighted addition of the family representative values (Cm#j, Sm#j, Rm#j).

[0526] The server 12 calculates the CSR value (C value Cs, S value Ss, and R value Rs) as the ecosystem representative value for each month as a unit period for a year as described above as a research period. The server 12 plots (represents) the ecosystem representative value (point) of each month and generates a CSR triangle that depicts the trajectory of the ecosystem representative value created by connecting the ecosystem representative values of each month with line segments in order of months. Figure 42 In the , two digits represent the month as the unit period.

[0527] exist Figure 42 In the example, track LS1 shows an example of the trajectory of the ecosystem representative value when the target survey unit is a site where all above-ground vegetation has been cut down. Track LS2 shows an example of the trajectory of the ecosystem representative value when the target survey unit is a site where 70% of direct sunlight is blocked. Track LS3 shows an example of the trajectory of the ecosystem representative value when the target survey unit is a site where no human manipulation has been applied.

[0528] For example, the ecosystem at a site where all aboveground vegetation has been cut down is inferred to be an environment where plant species with a high degree of weed strategy (R) tend to grow. Locus LS1, which shows the ecosystem representative value for the surveyed site where all aboveground vegetation has been cut down, is located closer to vertex R than the other trajectories LS2 and LS3, confirming that the environment in the ecosystem is as inferred.

[0529] The CSR triangle displayed on the presentation UI 450 expresses (an aspect of) the functional diversity of the ecosystem of the subject survey unit.

[0530] For example, when appropriate human manipulation is not performed on the ecosystem of the target survey unit and the functional diversity of the ecosystem is low, it can be inferred that, in the CSR triangle, the locus of the ecosystem representative value will be located relatively far from the center of the CSR triangle.

[0531] Users can understand the degree of functional diversity of the ecosystem in the target survey unit based on the proximity of the trajectory of the ecosystem representative value to the center of the CSR triangle. For example, by confirming that the trajectory of the ecosystem representative value is close to the center of the CSR triangle, users can understand that the functional diversity of the ecosystem in the target survey unit is high.

[0532] Furthermore, according to the presentation UI 450, the trajectory of the ecosystem representative value enables the user to check not only the transition of the ecosystem representative value within a year as the survey period, but also the vegetation transition of plant species that adopt a survival strategy that allows them to grow in each season or each month in the ecosystem of the target survey unit. The vegetation transition in the ecosystem of the target survey unit can be used as reference information for the manipulation (watering, sun exposure, disturbance, etc.) that should be performed on the ecosystem in each season and each month.

[0533] In addition to the observed plant species used to generate presentation UI 420 and presentation UI 430, presentation UI 450 is also generated using the coverage of the observed plant species. Since the coverage of the observed plant species is input into presentation UI 410 along with the observed plant species, presentation UI 450 can be easily generated to present functional diversity. Therefore, using presentation UI 450, the user can easily understand functional diversity without spending too much time and money.

[0534] Figure 43 2 is a diagram showing a fourth display example of a presentation UI presenting functional diversity.

[0535] Server 12 calculates a predicted service value for predicted service intensity based on the observed plant species in the vegetation survey results for a predetermined survey date from terminal 11. This service intensity indicates the degree to which the ecosystem service is being utilized (the degree of benefit provided by the ecosystem service) within the target survey unit. Server 12 generates a presentation UI 460 displaying the predicted service value and transmits presentation UI 460 to terminal 11 for display. The user can specify the survey date for the vegetation survey results to be used to generate presentation UI 460 by operating terminal 11.

[0536] For example, using interaction information or, in other words, information associated with biological interactions of other species that will have biological interactions with the given species for each given species, the server 12 detects species that have biological interactions with the observed plant species and calculates a value as a predicted service value based on the number of such species.

[0537] A predicted service value is calculated for each type of ecosystem service. Specifically, ecosystem services can be grouped into five types: provisioning services (PRO), regulating services (REG), cultural services (CUL), supporting services (SUP), and retention services (PRE). As a predicted service value, a predicted service value is calculated for each of the five ecosystem services.

[0538] As the predicted service value for the provisioning service, for example, the number of species that can contribute to the provisioning service is calculated in a species group consisting of the observed plant species and species of the plant kingdom and the animal kingdom that have biological interactions with the observed plant species (hereinafter also referred to as the interacting species group). In a similar manner, as the predicted service value for each of the regulating service, cultural service, supporting service, and retention service, the number of species that can contribute to the regulating service, cultural service, supporting service, or retention service is calculated in the interacting species group.

[0539] One date or multiple dates may be specified as the survey date (hereinafter also referred to as generated survey date) to be used for generating the vegetation survey result presenting UI 460. As the multiple dates, individual dates may be specified or a period including multiple consecutive dates may be specified.

[0540] When a date is specified as a generated survey date, server 12 calculates the predicted service value of each of the five ecosystem services based on the observed plant species as the vegetation survey result for the generated survey date. Server 12 generates a radar chart 461 in which points representing the corresponding predicted service values of the five ecosystem services are plotted and a curve graph connecting these points is drawn. Server 12 generates a presentation UI 460 that displays radar chart 461. Note that on radar chart 461, PRO, REG, CUL, SUP, and PRE represent provisioning services, regulating services, cultural services, supporting services, and retention services, respectively. A similar description will apply to radar chart 462, which will be described later.

[0541] When multiple dates are specified as generated survey dates, server 12 calculates the predicted service value for each of the five ecosystem services for each of the multiple generated survey dates. Furthermore, server 12 selects one of the multiple generated survey dates as a reference date for comparison. The user can specify the reference date by operating terminal 11. Server 12 generates a radar chart 461, plotting points representing the corresponding predicted service values of the five ecosystem services relative to the reference date, and draws a graph connecting these points.

[0542] Furthermore, for each of the plurality of generated survey dates, server 12 calculates a ratio (ratio value) of the predicted service value relative to the generated survey date using the predicted service value relative to the reference date as the basis (1) for each type of ecosystem service. Server 12 generates a radar chart 462 in which points representing the ratios of the five ecosystem services relative to the corresponding predicted service value for each of the plurality of generated survey dates are plotted, and a graph connecting these points is drawn. In radar chart 462, the ratios of the corresponding predicted service values of the five ecosystem services relative to the reference date are 1.0.

[0543] Furthermore, the server 12 generates a presentation UI 460 that displays a radar graph 461 and a radar graph 462 .

[0544] Figure 43 The presentation UI 460 shown in FIG. 4 shows a radar chart 461 and a radar chart 462. In addition, in FIG. Figure 43 , the points representing the predicted service values of ecosystem services relative to the reference date shown in radar chart 461 and the points representing the ratios of the predicted service values of ecosystem services relative to the reference date shown in radar chart 462 are connected by dotted lines for clear correspondence.

[0545] Figure 43 Three curves are drawn in the radar chart 462 shown in FIG. 4 , and therefore, three dates corresponding to the three curves are designated as generated survey dates.

[0546] The radar chart 461 displayed on the presentation UI 460 expresses (an aspect of) the functional diversity of the ecosystem of the subject survey unit.

[0547] For example, when appropriate human manipulation is not performed on the ecosystem of the target survey unit and the functional diversity of the ecosystem is low, it can be inferred that the curve graph plotted on the radar chart 461 will exhibit a shape that significantly deviates from a regular pentagon.

[0548] The user can understand the degree of functional diversity of the ecosystem of the target survey unit based on the shape of the curve plotted on the radar chart 461. For example, the user can understand that the functional diversity of the ecosystem of the target survey unit is high by confirming that the curve plotted on the radar chart 461 is relatively close to a regular pentagon.

[0549] Furthermore, based on the service intensity of the ecosystem service on the reference date, the user can understand the degree of change in the service intensity of the ecosystem service on the generated survey dates other than the reference date, according to the radar graph 462 displayed on the presentation UI 460. Therefore, based on the radar graph 462, for example, if the user introduces a new plant species into the ecosystem of the target survey unit on a date after the reference date and specifies this date as one of the generated survey dates, the user can understand the degree of change in the service intensity of the ecosystem service with the introduction of the new plant species as a factor (one of the factors).

[0550] Since presentation UI 460 presenting functional diversity is generated using observed plant species in a similar manner to presentation UI 420 and presentation UI 430, the user can easily generate presentation UI 460 by observing at least the plant species growing in the ecosystem of the target survey unit in the vegetation survey. Therefore, according to presentation UI 460, the user can easily understand functional diversity without spending too much time and money.

[0551] <Presentation UI for presenting time series of vegetation survey results>

[0552] Figure 44 1 is a diagram showing a display example of a presentation UI that presents a time series of vegetation survey results.

[0553] The server 12 calculates the aggregate value of the vegetation survey results for the predetermined survey period from the terminal 11 for each unit period that divides the predetermined survey period. The server 12 generates a presentation UI 510 for displaying the aggregate value for each unit period in time series and transmits the presentation UI 510 to the terminal 11 for display. The user can specify the survey period and the unit period date by, for example, operating the terminal 11.

[0554] For example, using observed plant species and the coverage of each observed plant species for a one-year period from January to December as a survey period as vegetation survey results, the server 12 calculates an aggregate value of the coverage of each family using each month of the one-year period as a unit period.

[0555] In other words, for each month as a unit period, for each family to which the observed plant species observed during the month belong, the server 12 calculates an aggregate value of the coverage of each family by adding the coverage of the observed plant species belonging to the family observed in the month.

[0556] The server 12 generates a strip chart in which the aggregated values of the coverage rate for each subject for each month as a unit period are arranged in order of the months as the unit period, and generates a presentation UI 510 that displays the strip chart.

[0557] Shown in Figure 44 In the strip chart on the presentation UI 510 shown in , the horizontal axis represents time or, in other words, a time series of months (January, February, . . . , December) as a unit period within the survey period, and the vertical axis represents (aggregate values of) coverage.

[0558] A band in the strip chart represents a time series of aggregated values of the cover of observed plant species belonging to a family. For example, Figure 44 , band 511 is the band of Leguminosae, which represents the time series of aggregated values of the coverage of observed plant species belonging to the Leguminosae family, and band 512 is the band of Cucurbitaceae, which represents the time series of aggregated values of the coverage of observed plant species belonging to the Cucurbitaceae family.

[0559] In the strip chart, the width W of each strip represents the aggregate value of coverage. In the strip chart, since the strips are arranged vertically in order of width and length, for each month, the widest strip, or in other words, the strip with the largest aggregate value of coverage, is located at the top.

[0560] According to the presentation UI 510 displaying the strip graph as described above, the user can understand seasonal changes in the family to which the plant species growing in the ecosystem of the target survey unit belong, the composition of the family, etc., to be used as a reference for vegetation strategy.

[0561] For example, in Figure 44 In the strip diagram shown in , the band 511 of the Leguminosae family is positioned relatively high throughout the entire year, which is the survey period. Therefore, for example, in the ecosystem of the target survey unit, when a stable harvest is required throughout the entire year, the user can determine that it is desirable to adopt a vegetation strategy that prioritizes plant species belonging to the Leguminosae family.

[0562] In addition, for example, Figure 44 In the strip chart shown in , the Cucurbitaceae band 512 rises sharply from June to September and then remains stable at a high level until December. Therefore, for example, a user can formulate a vegetation strategy that harvests plant species belonging to the Cucurbitaceae from October to December when the coverage (aggregate value) in the ecosystem of the target survey unit is high. Furthermore, for example, to harvest plant species belonging to the Cucurbitaceae from October to December, a user can formulate a vegetation strategy that sows or introduces seeds and seedlings of plant species belonging to the Cucurbitaceae from March to April, before June, when the coverage begins to rise sharply.

[0563] In addition to displaying a strip chart for a single survey period, the presentation UI 510 can also display strip charts for multiple survey periods simultaneously by arranging the strip charts side by side. For example, the presentation UI 510 can display a strip chart for the current year and a strip chart for last year, or display strip charts for the past three years (this year, last year, and the year before last) including this year.

[0564] According to the presentation UI 510 , the user may be encouraged to compare the strip chart of this year (year) with the strip chart of last year (year) to consider changes in the environment of the ecosystem of the target survey unit.

[0565] For example, when a predetermined band in a strip chart displayed in the presentation UI 510 rises rapidly in last year's strip chart but does not rise rapidly in this year's strip chart, the user may be given the opportunity to consider how the temperature, precipitation, etc. of the ecosystem of the object survey unit changed between last year and this year as the reason for the change.

[0566] As described above, the user can consider changes in the environment of the ecosystem of the target survey unit and use this consideration to help determine which plant species to introduce into the ecosystem in the future. For example, if the user considers changes in the environment of the ecosystem of the target survey unit and learns that precipitation in the ecosystem has decreased, the user can decide to introduce drought-tolerant plant species.

[0567] Although Figure 44 Although the coverage of the observed plant species is used to generate the strip chart, other vegetation survey results can be used to generate the strip chart. For example, the yield of the plant species harvested in the ecosystem of the target survey unit (harvested plant species) can be used to generate the strip chart. In this case, for example, using the harvested plant species and the yield of each harvested plant species as the vegetation survey results for the one-year period from January to December as the survey period, the server 12 calculates the aggregated value of the yield of each family for each month of the one-year period as the unit period. In addition, the server 12 generates a strip chart with the aggregated value of the yield of each family arranged for each month as the unit period in the order of the month as the unit period.

[0568] Note that the user can specify which part of the vegetation survey result is to be used to generate the strip chart by operating the terminal 11 .

[0569] In this specification, the processing performed by the computer according to the program does not necessarily need to be performed in chronological order according to the order described in the flowchart. In other words, the processing performed by the computer according to the program includes processing performed in parallel or individually (for example, parallel processing or object processing).

[0570] In addition, the program may be processed by one computer (processor), or may be processed in a distributed manner by a plurality of computers. In addition, the program may be transferred to a remote computer and executed there.

[0571] In addition, in this specification, a system refers to a collection of multiple components (devices, modules (parts), etc.), and it does not matter whether all components exist in the same housing. Therefore, multiple devices housed in separate housings but connected to each other via a network, as well as a single device that houses multiple modules in a single housing, are both considered systems.

[0572] Note that the embodiments of the present technology are not limited to the above-described embodiments, and various modifications can be made without departing from the gist of the present technology.

[0573] For example, the present technology may adopt a configuration of cloud computing in which a single function is shared among a plurality of devices via a network and processed cooperatively by the plurality of devices.

[0574] Furthermore, each step explained in the above flowchart may be performed by a plurality of devices in a shared manner in addition to being performed by a single device.

[0575] Furthermore, when a single step includes a plurality of processing steps, the plurality of processing steps included in the single step may be performed by a plurality of devices in a shared manner in addition to being performed by a single device.

[0576] Furthermore, the advantageous effects described in this specification are merely exemplary and non-limiting, and other advantageous effects may occur.

[0577] Note that the present technology may be related to at least Goal 1 "No Poverty", Goal 2 "Zero Hunger", Goal 13 "Climate Action" and Goal 15 "Life on Land" of the SDGs (Sustainable Development Goals) adopted at the 2015 United Nations Summit.

[0578] Symbiotic Agriculture (registered trademark) used in this technology makes it possible to cultivate plants by controlling ecosystems to promote biodiversity and combat climate change caused by natural disasters such as droughts and landslides. Furthermore, by creating a high-density plant mix, greenhouse gas (GHG) emissions can be increased, contributing to the reduction of GHG emissions without the use of fertilizers or pesticides.

[0579] The present technology can also be configured as follows.

[0580] <1>

[0581] An information processing device, comprising:

[0582] an evaluation unit configured to evaluate plant species based on weather conditions at a specific location; and

[0583] A generating unit is configured to generate a vegetation strategy for the specific site based on the assessed value of the plant species.

[0584] <2>

[0585] according to <1> an information processing device, wherein

[0586] The generating unit is configured to generate a presentation UI (User Interface) presenting the vegetation strategy.

[0587] <3>

[0588] according to <2> an information processing device, wherein

[0589] The generation unit is configured to generate a presentation UI that presents the plant species sorted in order of the evaluation values as the vegetation strategies.

[0590] <4>

[0591] according to <2> or <3> an information processing device, wherein

[0592] The generating unit is configured to generate the presentation UI presenting weather parameters constituting a weather context.

[0593] <5>

[0594] according to <4> an information processing device, wherein

[0595] The evaluation unit is configured to evaluate the plant species based on a plurality of weather scenarios, and

[0596] The generating unit is configured to generate a presentation UI presenting a parameter distribution, where the parameter distribution is a distribution of weather parameters constituting a weather scenario in a parameter space having the weather parameters as axes.

[0597] <6>

[0598] according to <2> to <5> Any one of the information processing devices, wherein

[0599] The generation unit is configured to select a plurality of plant species whose evaluation values are ranked high as recommended species to be introduced into the specific site and generate a presentation UI that presents the recommended species as vegetation strategies.

[0600] <7>

[0601] according to <6> an information processing device, wherein

[0602] The generation unit is configured to select a recommended species from among a plurality of plant species whose evaluation values are ranked higher based on the biological interaction.

[0603] <8>

[0604] according to <2> to <7> Any one of the information processing devices, wherein

[0605] The generating unit is configured to generate a suggestion on a construction method for constructing an environment for growing the plant species based on the evaluation value of the plant species.

[0606] <9>

[0607] according to <8> an information processing device, wherein

[0608] The generating unit is configured to generate a presentation UI presenting a suggestion on the construction method.

[0609] <10>

[0610] according to <1> to <9> Any one of the information processing devices, wherein

[0611] The evaluation unit is configured to evaluate the plant species based on a plurality of weather scenarios.

[0612] <11>

[0613] according to <10> an information processing device, wherein

[0614] The evaluation unit is configured to calculate the evaluation value of the plant species using a barycentric distance, which is a distance between:

[0615] the center of gravity of a parameter distribution, the parameter distribution being the distribution of the weather parameters constituting the plurality of weather scenarios in a parameter space with the weather parameters as axes, and

[0616] The corresponding parameters of the plant species corresponding to the weather parameters constituting the weather scenario are parameter points in the parameter space, and the corresponding parameters of the plant species are obtained by converting attribute information about the attributes of the plant species into corresponding parameters.

[0617] <12>

[0618] according to <10> an information processing device, wherein

[0619] The evaluation unit is configured to calculate the evaluation value of the plant species using a volume of an overlapping area where there is overlap between:

[0620] parameter distribution, the parameter distribution being the distribution of weather parameters constituting the plurality of weather scenarios in a parameter space with the weather parameters as axes, and

[0621] A Voronoi cell, whose generating points are parameter points in the parameter space of corresponding parameters of a plant species corresponding to the weather parameters constituting the weather scenario, wherein the corresponding parameters of the plant species are obtained by converting attribute information about the attributes of the plant species into corresponding parameters.

[0622] <13>

[0623] according to <1> to <12> Any one of the information processing devices, wherein

[0624] The evaluation unit is configured to convert attribute information about attributes of a plant species into corresponding parameters corresponding to weather parameters constituting a weather scenario and calculate an evaluation value of the plant species using the weather parameters constituting the weather scenario and the corresponding parameters of the plant species.

[0625] <14>

[0626] according to <13> The information processing device further includes:

[0627] A correction unit is configured to correct the attribute information based on observation data actually observed at the specific point.

[0628] <15>

[0629] according to <1> to <14> The information processing device of any one of the items , further comprising:

[0630] A dimensionality reduction unit is configured to reduce the number of weather parameters constituting the weather scenario.

[0631] <16>

[0632] according to <1> to <15> Any one of the information processing devices, wherein

[0633] The evaluation unit is configured to evaluate one or both of plant species stored in a plant DB (database) or plant species observed at a specific location and plant species planned to be introduced at the specific location.

[0634] <17>

[0635] according to <1> to <16> The information processing device of any one of the items , further comprising:

[0636] A weather scenario calculation unit is configured to calculate a weather scenario.

[0637] <18>

[0638] according to <17> an information processing device, wherein

[0639] The weather scenario calculation unit is configured to reduce weather data used to calculate the weather scenario.

[0640] <19>

[0641] An information processing method, comprising:

[0642] Assessing plant species based on site-specific weather scenarios; and

[0643] A vegetation strategy for the particular site is generated based on the assessed values of the plant species.

[0644] <20>

[0645] A program that causes a computer to:

[0646] an evaluation unit configured to evaluate plant species based on weather conditions at a specific location; and

[0647] A generating unit is configured to generate a vegetation strategy for the specific site based on the assessed values of the plant species.

[0648] [Reference Symbol List]

[0649] 10 Information Processing System

[0650] 11-1 to 11-4 Terminal

[0651] 12 Servers

[0652] 13 DB

[0653] 14 Network

[0654] 21 Communication Unit

[0655] 22 computing units

[0656] 23 Input / Output Units

[0657] 24 Storage Devices

[0658] 25 positioning units

[0659] 26 sensor units

[0660] 31 Communication Unit

[0661] 32 computing units

[0662] 33 Input / Output Units

[0663] 34 Storage Devices

[0664] 41 Get Unit

[0665] 42 Weather Scenario Calculation Unit

[0666] 43 evaluation units

[0667] 44 generation units

[0668] 51 Attribute Information Conversion Unit

[0669] 52 Dimensionality Reduction Unit

[0670] 53 Evaluation value calculation unit

[0671] 61 Recommended Species Selection Units

[0672] 62 Presentation UI Generation Unit

[0673] 70 Presenting the UI

[0674] 71 Map

[0675] Buttons 72 to 74

[0676] 80 Presenting UI

[0677] 81 parameter distribution images

[0678] Buttons 82 to 85

[0679] 90 Presenting UI

[0680] 91 weather parameter images

[0681] 92 buttons

[0682] 110 Presenting UI

[0683] 111 Planting Plan Combination

[0684] 112, 113 buttons

[0685] 131 Extraction Unit

[0686] 132 Evaluation value calculation unit

[0687] 141 Presentation UI Generation Unit

[0688] 150 Presenting UI

[0689] 151 Planting Plan Combination

[0690] 152, 153 buttons

[0691] 161 Situational Search Unit

[0692] 162 Suggestion Generation Unit

[0693] 163 Presentation UI Generation Unit

[0694] 180 Presenting UI

[0695] 181 Environment Construction Combination

[0696] 182 messages

[0697] 183 buttons

[0698] 211 Recommended Species Reselection Unit

[0699] 311 calibration unit

[0700] 410 Presenting UI

[0701] 411 Shoot Button

[0702] 412 Overall coverage display unit

[0703] 412A Slider

[0704] 412B Menu button

[0705] 413 Sort Button

[0706] 414 individual coverage display unit

[0707] 415 Add Button

[0708] 416 Status Display Unit

[0709] 420, 430, 450, 460 presents the UI

[0710] 461, 462 Radar Chart

[0711] 511, 512 belt

Claims

1. An information processing device, comprising: an assessment unit configured to assess plant species based on a weather scenario at a specific location; as well as A generating unit is configured to generate a vegetation strategy for the specific site based on the assessed value of the plant species.

2. The information processing apparatus according to claim 1, wherein The generating unit is configured to generate a presentation UI (User Interface) presenting the vegetation strategy.

3. The information processing apparatus according to claim 2, wherein The generation unit is configured to generate a presentation UI that presents the plant species sorted in order of the evaluation values as the vegetation strategies.

4. The information processing apparatus according to claim 2, wherein The generating unit is configured to generate the presentation UI presenting weather parameters constituting a weather context.

5. The information processing apparatus according to claim 4, wherein The evaluation unit is configured to evaluate the plant species based on a plurality of weather scenarios, and The generating unit is configured to generate a presentation UI presenting a parameter distribution, where the parameter distribution is a distribution of weather parameters constituting a weather scenario in a parameter space having the weather parameters as axes. The information processing apparatus according to claim 2 , wherein The generation unit is configured to select a plurality of plant species whose evaluation values are ranked high as recommended species to be introduced into the specific site and generate a presentation UI that presents the recommended species as vegetation strategies.

7. The information processing apparatus according to claim 6, wherein The generation unit is configured to select a recommended species from among a plurality of plant species whose evaluation values are ranked higher based on the biological interaction. The information processing apparatus according to claim 2 , wherein The generating unit is configured to generate a suggestion on a construction method for constructing an environment for growing the plant species based on the evaluation value of the plant species.

9. The information processing apparatus according to claim 8, wherein The generating unit is configured to generate a presentation UI presenting a suggestion on the construction method.

10. The information processing apparatus according to claim 1, wherein The evaluation unit is configured to evaluate the plant species based on a plurality of weather scenarios. The information processing apparatus according to claim 10 , wherein The evaluation unit is configured to calculate the evaluation value of the plant species using a barycentric distance, which is a distance between: the center of gravity of a parameter distribution, the parameter distribution being the distribution of the weather parameters constituting the plurality of weather scenarios in a parameter space with the weather parameters as axes, and The corresponding parameters of the plant species corresponding to the weather parameters constituting the weather scenario are parameter points in the parameter space, and the corresponding parameters of the plant species are obtained by converting attribute information about the attributes of the plant species into corresponding parameters.

12. The information processing apparatus according to claim 10, wherein The evaluation unit is configured to calculate the evaluation value of the plant species using a volume of an overlapping area where there is overlap between: parameter distribution, the parameter distribution being the distribution of weather parameters constituting the plurality of weather scenarios in a parameter space with the weather parameters as axes, and A Voronoi cell, whose generating points are parameter points in the parameter space of corresponding parameters of a plant species corresponding to the weather parameters constituting the weather scenario, wherein the corresponding parameters of the plant species are obtained by converting attribute information about the attributes of the plant species into corresponding parameters.

13. The information processing apparatus according to claim 1, wherein The evaluation unit is configured to convert attribute information about attributes of a plant species into corresponding parameters corresponding to weather parameters constituting a weather scenario and calculate an evaluation value of the plant species using the weather parameters constituting the weather scenario and the corresponding parameters of the plant species.

14. The information processing apparatus according to claim 13, further comprising A correction unit is configured to correct the attribute information based on observation data actually observed at the specific point.

15. The information processing apparatus according to claim 1, further comprising A dimensionality reduction unit is configured to reduce the number of weather parameters constituting the weather scenario.

16. The information processing apparatus according to claim 1, wherein The evaluation unit is configured to evaluate one or both of plant species stored in a plant DB (database) or plant species observed at a specific location and plant species planned to be introduced at the specific location.

17. The information processing apparatus according to claim 1, further comprising A weather scenario calculation unit is configured to calculate a weather scenario.

18. The information processing apparatus according to claim 17, wherein The weather scenario calculation unit is configured to reduce weather data used to calculate the weather scenario.

19. An information processing method, comprising: Assess plant species based on site-specific weather scenarios; as well as A vegetation strategy for the particular site is generated based on the assessed values of the plant species.

20. A program for causing a computer to function as: an evaluation unit configured to evaluate plant species based on weather conditions at a specific location; and A generating unit is configured to generate a vegetation strategy for the specific site based on the assessed values of the plant species.

Images