JP7773242B2 - Parameter calculation method, agricultural support information output method, parameter calculation program, and agricultural support information output program - Google Patents

Description

The present invention relates to a parameter calculation method, an agricultural support information output method, a parameter calculation program, and an agricultural support information output program.

Traditionally, in catalogs and other publications containing information on agricultural crops, the characteristics and properties of each variety are typically expressed using words, images, average values, etc. Therefore, when selecting varieties to cultivate, farmers refer to the characteristics and properties of each variety expressed using words, images, average values, etc.

In recent years, a technology has become known that, when a user specifies a cultivation area, cultivation period, and item, displays a list of varieties of the specified item that can be cultivated in the specified cultivation area and period (see, for example, Patent Document 1, etc.).

In addition, recently, a technology has become known for predicting crop growth and yield by generating a model (prediction formula) for more accurately predicting crop growth and yield, and then inputting environmental data (actual measured values and predicted values) into the generated model (see, for example, Patent Document 2, etc.).

Japanese Patent Application Laid-Open No. 2002-203002 Japanese Patent Application Laid-Open No. 2019-030253

Cultivation of strawberries, tomatoes, and other agricultural crops is actively pursued, with many varieties being developed, registered, and cultivated every year. Because growth and yield characteristics vary depending on the variety, even for the same crop, there is a risk that accurate predictions of growth and yield will not be possible unless parameters such as coefficients and thresholds used in the model are adjusted for each variety.

The present invention aims to provide a parameter calculation method and program capable of calculating parameters used in a model for estimating growth or yield for each variety, as well as an agricultural support information output method and program capable of outputting predicted results regarding growth or yield for each variety.

In one aspect, the parameter calculation method is carried out by a computer, and includes the steps of: acquiring environmental data for a first cultivation period during which a predetermined variety of agricultural crop is grown; acquiring cultivation data obtained by cultivating the predetermined variety of agricultural crop during the first cultivation period; and calculating, based on the daily environmental data, values of parameters that are coefficients of an equation for estimating the daily growth of each organ of the predetermined variety of agricultural crop and threshold values corresponding to values obtained from the equation , the values of the parameters being constant regardless of the growth stage of the crop; and in the calculating step, the values of the parameters used in the model for estimating the daily growth of each organ of the predetermined variety of agricultural crop are calculated using at least a portion of the acquired environmental data and cultivation data for the first cultivation period.

In one aspect, the agricultural support information output method is an agricultural support information output method in which a computer executes the following processing: calculates the parameters using the parameter calculation method of the present invention, acquires information related to the cultivation environment of a predetermined variety of agricultural crop; creates an equation for estimating the daily growth of each organ of the predetermined variety of agricultural crop based on the parameters and the acquired information related to the cultivation environment; performs a prediction regarding the daily growth of each organ of the predetermined variety of agricultural crop using the equation; and outputs the prediction results obtained by the prediction.

The parameter calculation method and program of the present invention have the effect of being able to calculate parameters used in a model that estimates growth or yield for each variety. Furthermore, the agricultural support information output method and program of the present invention have the effect of being able to output prediction results regarding growth or yield for each variety.

FIG. 1 is a diagram illustrating a configuration of an agricultural system according to an embodiment. 2A is a diagram showing the hardware configuration of the user terminal of FIG. 1, and FIG. 2B is a diagram showing the hardware configuration of the server of FIG. FIG. 2 is a functional block diagram of a server. 10 is a flowchart showing a process of the server. FIG. 1 is a diagram showing an overview of a growth model. FIG. 10 is a diagram conceptually illustrating changes in crop size in a simulation. This shows the relationship between growth stage and size of leaves and stems as relative values. FIG. 10 is a diagram illustrating an example of weather data used in a simulation. FIG. 10 is a diagram illustrating an example of parameters used in a simulation. 10(a) and 10(b) are diagrams conceptually showing the leaf growth process and the fruit growth process in this simulation. FIG. 10 is a diagram (part 1) showing a screen displaying a simulation result. FIG. 10 is a diagram (part 2) showing a screen displaying simulation results. 13(a) and 13(b) are diagrams (part 3) showing a screen displaying the simulation results. FIG. 4 is a diagram showing a screen displaying a simulation result. 10 is a flowchart showing the processing of a parameter calculation unit. 16 is a flowchart showing details of the process of step S104 in FIG. 15 . FIG. 4 is a diagram illustrating an example of environmental data used by a parameter calculation unit for parameter calculation. Figure 18(a) is a diagram showing an example of cultivation condition data included in the cultivation information, Figure 18(b) is a diagram showing an example of growth survey data included in the cultivation information, and Figure 18(c) is a diagram showing an example of dissection survey data included in the cultivation information. FIG. 19( a ) is a diagram showing an example of flowering survey data included in the cultivation information, and FIG. 19( b ) is a diagram showing an example of harvest survey data included in the cultivation information. FIG. 4 is a diagram illustrating parameters calculated by a parameter calculation unit. 10 is a flowchart illustrating an example of processing by an update unit.

One embodiment of an agricultural system will be described in detail below with reference to Figures 1 to 21. Figure 1 shows a schematic diagram of the configuration of an agricultural system 100 according to one embodiment. The agricultural system 100 of this embodiment is a system that provides information to farmers and other such individuals (hereinafter referred to as "producers") to assist them in selecting cultivars and managing cultivation when cultivating fruit vegetables such as tomatoes, strawberries, cucumbers, and peppers. In this embodiment, the case of a producer cultivating strawberries will be described.

As shown in Figure 1, the agricultural system 100 comprises a server 10 as an agricultural support device and a user terminal 70. The user terminal 70 is a terminal used by producers, such as a personal computer (PC), tablet terminal, or smartphone. The server 10 and user terminal 70 are connected to a network 80 such as the Internet, enabling the exchange of information between the devices.

The user terminal 70 transmits the information entered by the producer to the server 10. Figure 2(a) shows the hardware configuration of the user terminal 70. As shown in Figure 2(a), the user terminal 70 is equipped with a CPU (Central Processing Unit) 190, ROM (Read Only Memory) 192, RAM (Random Access Memory) 194, a storage unit (here, an SSD (Solid State Drive) or HDD (Hard Disk Drive)) 196, a network interface 197, a display unit 193, an input unit 195, and a portable storage medium drive 199 capable of reading the portable storage medium 191. These components of the user terminal 70 are connected to a bus 198. The display unit 193 includes an LCD display or the like, and the input unit 195 includes a keyboard, mouse, touch panel, etc.

The server 10 is a device that acquires information from the user terminal 70, generates information to assist in the selection of strawberry varieties and cultivation management based on the acquired information, and outputs a screen displaying the information to the user terminal 70 used by the producer.

Figure 2(b) shows the hardware configuration of the server 10. As shown in Figure 2(b), the server 10 includes a computer-like component such as a CPU 90, ROM 92, RAM 94, a storage unit (here, an SSD or HDD) 96, a network interface 97, and a portable storage medium drive 99. These components of the server 10 are connected to a bus 98. In the server 10, the CPU 90 executes programs (including the cultivation assistance program) stored in the ROM 92 or HDD 96, or programs (including the cultivation assistance program) read from the portable storage medium 91 by the portable storage medium drive 99, thereby realizing the functions of the components shown in Figure 4. The functions of the components shown in Figure 4 may also be realized by integrated circuits such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

Figure 3 shows a functional block diagram of the server 10. In the server 10, the CPU 90 executes a program to function as a selection receiving unit 41, an environmental data acquisition unit 42 as an acquisition unit, a cultivation information acquisition unit 43, a simulation unit 44, an output unit 45, a parameter calculation unit 47, and an update unit 46, as shown in Figure 3. Note that Figure 3 also illustrates a parameter DB 50 stored in the memory unit 96 of the server 10, etc.

The selection receiving unit 41 receives information on the combination of variety (e.g., Tochiotome, Koiminori, etc.) and cultivation location (e.g., Tsukuba, Morioka, Kurume, etc.) selected by the producer. The producer may select one or more combinations of variety and location. The selection receiving unit 41 transmits the received information to the simulation unit 44.

The environmental data acquisition unit 42 acquires environmental data for the cultivation location based on the information about the cultivation location accepted by the selection acceptance unit 41. The environmental data acquisition unit 42 acquires data corresponding to the selected cultivation location from past weather data and future weather data (forecast data) managed in the server 10 or in a device other than the server 10. The weather data includes data such as outdoor solar radiation and air temperature, solar radiation and air temperature inside the greenhouse, humidity, _CO2 concentration, soil temperature, and soil moisture. The environmental data acquisition unit 42 transmits the acquired data to the simulation unit 44.

The cultivation information acquisition unit 43 acquires cultivation information entered by the producer (e.g., planting date, cultivation density, soil/solution cultivation, number of leaves at planting, amount of fertilizer applied, concentration of nutrient solution, etc.). The cultivation information acquisition unit 43 transmits the acquired information to the simulation unit 44.

The simulation unit 44 reads parameters corresponding to the variety accepted by the selection acceptance unit 41 from the parameter DB 50 and creates a growth model using the read parameters and data transmitted from the environmental data acquisition unit 42 and the cultivation information acquisition unit 43. Here, the parameters stored in the parameter DB 50 are, for example, parameters such as those shown in FIG. 9, and are assumed to be determined for each variety. The simulation unit 44 then uses the generated growth model to perform a simulation of the growth of the crop when the variety selected by the producer is cultivated at the cultivation location and with the cultivation method selected by the producer. The simulation results obtained by the simulation unit 44 include leaf area, flowering date per inflorescence, yield per inflorescence, fruit dry matter distribution rate, photosynthesis rate, growth amount (stem and leaf), growth amount (fruit), and nutrient absorption amount (fertilizer amount). The simulation unit 44 transmits the simulation results to the output unit 45.

The output unit 45 generates a screen displaying the simulation results received from the simulation unit 44 and transmits them to the user terminal 70 used by the producer. At this time, the output unit 45 compiles the simulation results for each cultivation condition (combination of variety, cultivation location, and cultivation information) and generates a screen displaying the simulation results for different cultivation conditions in a manner that allows them to be compared.

The parameter calculation unit 47 calculates parameters for each variety using the environmental data at the cultivation site acquired by the environmental data acquisition unit 42 and the cultivation information acquired by the cultivation information acquisition unit 43. The calculation method for each parameter is predetermined, and the parameter calculation unit 47 selects the environmental data and cultivation information necessary for the calculation from the acquired environmental data and cultivation information. Furthermore, if some of the environmental data or cultivation information necessary for the calculation is missing, the parameter calculation unit 47 identifies environmental data and cultivation information that can replace the missing data from the acquired environmental data and cultivation information and uses this data in the calculation. Note that the parameters calculated by the parameter calculation unit 47 include growth model coefficients and thresholds used in the growth model.

The update unit 46 acquires information on the varieties actually cultivated and the cultivation results (e.g., leaf area, flowering date per inflorescence, yield per inflorescence, etc.) from the producer. The update unit 46 then updates the parameters stored in the parameter DB 50 so that the actual cultivation results and the simulation results are closer. The update unit 46 may also determine whether to update the parameters using the actual cultivation results based on a predetermined reliability for the producer who input the actual cultivation results.

(Regarding the processing of the server 10)
Next, the processing of the server 10 will be described in detail.

Figure 4 shows the processing of the server 10 in a flowchart. In the processing of Figure 4, first, in step S10, the selection receiving unit 41 waits until the producer selects a combination of variety and cultivation location on the user terminal 70. Here, for example, it is assumed that the producer has selected the combinations of "Tochiotome Tsukuba," "Tochiotome Morioka," "Koiminori Tsukuba," "Oi C Berry Tsukuba," and "Sachinoka Kurume." It is assumed that the parameters of the selected varieties have been calculated in advance by the parameter calculation unit 47 and stored in the parameter DB 50. The method of calculating the parameters will be described later.

If the judgment in step S10 is positive, in the next step S12, the selection reception unit 41 acquires the selected combination of variety and cultivation location and transmits it to the environmental data acquisition unit 42 and the simulation unit 44.

Next, in step S14, the environmental data acquisition unit 42 acquires weather data (past data and forecast data) for the cultivation location based on the cultivation location selected by the producer and accepted by the selection acceptance unit 41.

Next, in step S16, the cultivation information acquisition unit 43 waits until the producer inputs cultivation information (e.g., planting time, planting density, soil cultivation/nutrient cultivation, number of leaves at planting). Once the producer inputs the cultivation information, the process proceeds to step S18, where the cultivation information acquisition unit 43 acquires the input cultivation information and transmits it to the simulation unit 44.

Next, in step S20, the simulation unit 44 reads out the parameters of each variety selected by the producer that are stored in the parameter DB 50, and creates a growth model corresponding to the combination of the selected variety and cultivation location based on the parameters, the weather data acquired by the environmental data acquisition unit 42, and the cultivation information acquired by the cultivation information acquisition unit 43.

The simulation unit 44 then runs a simulation using the created growth model and obtains simulation results that show how each variety will grow at each cultivation location. Details of the growth model created by the simulation unit 44 and the simulation results will be described later. The simulation unit 44 transmits the simulation results to the output unit 45.

Next, in step S22, the output unit 45 generates a screen displaying the simulation results and transmits it to the user terminal 70. The screen displaying the simulation results is, for example, a screen such as that shown in Figures 11 to 13.

(About the simulation)
The simulation performed by the simulation unit 44 will be described in detail below.

(1) Basic Concept of Simulation In this embodiment, a growth model capable of explaining important differences between varieties is used. Specifically, as shown in Fig. 5, a growth model capable of explaining photosynthetic characteristics, distribution of photosynthetic products, leaf development and elongation, flower development and enlargement, nutrient absorption, morphological characteristics (leaf shape, light reception posture), etc. is used. Note that although this embodiment describes a simulation targeting strawberry, the model is designed to clearly indicate the meaning of parameters so that it can be applied to other crops.

(a) Growth Pattern In the simulation, weather data (daily average temperature and daily outdoor global solar radiation) is provided to the growth model on a daily basis, and the growth status of each crop organ (roots, leaves, crown, fruit) is calculated. In reality, crops are always growing, and crop size (size of roots, leaves, crown, number of fruits, etc.) is always changing, but in the simulation, it is assumed that the crop size is fixed at midnight, and that the temperature and solar radiation are received in that state from midnight to midnight. To reduce errors due to this assumption, the calculation interval of the simulation (the calculation interval in this simulation is one day) can be shortened, for example.

In this simulation, when calculating the Nth day, the crop size for that day is determined at midnight on the Nth day. This means that the temperature, solar radiation conditions (relative light interception rate: a value calculated from the leaf area index), and distribution rate (the proportion of photosynthetic products generated that day distributed to each organ: leaves, crown, inflorescence, and roots) for the crop are fixed from midnight to midnight on the Nth day. In this simulation, the amount of photosynthesis is calculated based on the temperature, solar radiation, and _CO2 concentration for that day (day N), and the crop size changes based on this value through crop growth calculations. Figure 6 conceptually illustrates the change in crop size in this simulation. Note that for the first day of the calculation, the crop size at midnight (day 0 crop size) cannot be calculated, so the producer or other person sets the value.

(b) Relationship between photosynthetic product amount and size In this simulation, photosynthetic products are called "sources" and organs that produce the sources are called "source organs." Source organs are mainly leaves. On the other hand, organs that store and consume the sources are called "sink organs." Sink organs are roots, crowns, leaves, and fruits, but the sink organ with the highest consumption is the fruit.

In this simulation, crop growth is assumed to be primarily determined by temperature. For example, the growth process of a single leaf is described as follows using accumulated temperature. The accumulated temperature when growth stops is set to 1.0, and the accumulated temperature when growth begins is set to 0, and the leaf growth stage is shown as a relative value of the accumulated temperature. This is defined as the [Leaf Growth Index].

Here, the relative leaf size corresponding to the [Leaf Growth Index] is defined as the [Leaf Size Index] (Figure 7). The [Leaf Size Index] is set to 1.0 for the size of a fully grown leaf, and 0 for the size before growth begins.

In this simulation, the growth of leaves, fruits, etc. is approximated by a sigmoid curve. The relationship between [Leaf Growth Index] and [Leaf Size Index] is expressed by equation (1) (Figure 7). x is [Leaf Growth Index] and S(x) is [Leaf Size Index].

The difference in the S(x) values between midnight and midnight on the Nth day (see Figure 7) is defined as the Leaf Increase Index, an index of the amount of leaf growth increase per day. Similarly, the relative growth of the inflorescence is defined as the Fruits Increase Index. Note that the values of the sigmoid curve used in this simulation are S(x) = 0.007 at x = 0 and S(x) = 0.993 at x = 1, so we will use S(x) = 0 below x = 0 and S(x) = 1 above x = 1.

(c) Overview of the Simulation Model The simulation is performed based on meteorological data and parameters (constants). The meteorological data used in the simulation includes the average daily temperature (°C), the amount of outdoor solar radiation (MJ/ ^m2 ), and the _CO2 concentration (ppm), as shown in Figure 8. An example of parameters set by users such as producers is shown in Figure 9. This simulation is performed on a daily basis, with values calculated for each leaf and each inflorescence, as well as values calculated for the entire crop. Growth is calculated for the state of each organ of the crop (leaves, crown, fruit, roots, and inflorescence).

(2) Simulation Method: (a) Simulation Overview. This simulation is broadly divided into the following processes: photosynthesis calculation, crop growth calculation, fruit yield calculation, and nutrient absorption calculation. These calculations are performed on a daily basis. The variables used to calculate photosynthesis (relative light reception, distribution rate) are values at midnight on the calculation day (the day before the calculation day). Relative light reception is a variable used to calculate the amount of light received by the crop for photosynthesis from the amount of solar radiation. The distribution rate is the proportion of the source synthesized by the crop on that day distributed to each organ (root, leaf, crown, fruit). The photosynthesis rate at 24:00 on the calculation day is calculated using the distribution rate, these values, and the temperature and solar radiation on that day. The crop growth rate at 24:00 on the calculation day is then calculated based on the photosynthesis rate. The crop growth rate calculation is divided into (a) distribution rate calculation, (b) leaf area calculation, (c) fruit number calculation, (d) fruit dry weight calculation, and (e) distribution rate calculation.

In this specification, the subscript N attached to a variable name indicates the result at 24:00 on the Nth day. Variables calculated for each leaf will have the subscript M (leaf rank), and variables calculated for each inflorescence will have the subscript F (inflorescence rank). Variables and constants used in formulas will be indicated with [ ]. In this simulation, the value at 0:00 on the first day of calculation will be considered the "initial value."

(b) Crop Growth Process In this simulation, crop growth is determined by the relationship between the previous leaf and the order of the previous inflorescence (the order in which leaves and inflorescences develop; the earlier they develop, the lower the order), and the accumulated temperature. Figures 10(a) and 10(b) are conceptual diagrams illustrating the leaf and inflorescence growth process in this simulation (the values listed below are fictitious values assuming a fictitious variety and are not actual values). As shown in Figure 10(a), the growth of the next leaf begins when the accumulated temperature reaches 150°C, starting from the start of growth of the previous leaf. Leaf growth ends when the accumulated temperature reaches 450°C, starting from the start of flower bud differentiation of the previous inflorescence. Meanwhile, as shown in Figure 10(b), the flower bud differentiation of the next inflorescence begins when the accumulated temperature reaches 600°C, starting from this flower bud differentiation. Starting from this flower bud differentiation, the flowering period begins when the accumulated temperature reaches 600°C, and the fruit that makes up the inflorescence begins to enlarge. Furthermore, starting from flowering, the growth of the inflorescence ends when the accumulated temperature reaches 600° C. As shown in FIG. 10( b), the period from the start of flower bud differentiation to an accumulated temperature of 150° C. is the flower number determination period, and the number of fruits is determined by the conditions during this period.

(c) Calculation of photosynthesis amount Light use efficiency (LUE) (gDW/MJ) is a value that indicates the dry matter production of the entire crop per unit of received light. It is given as a function of temperature or _CO2 concentration.

The amount of solar radiation inside the greenhouse [global solar radiation inside the greenhouse] (MJ/m ² ) is calculated by multiplying the [global solar radiation outside] (MJ/m ² ) by the [solar transmittance] (constant) of the greenhouse (equation (2)).
[Indoor global solar radiation] _N = [Solar transmittance] × [Outdoor global solar radiation] _N … (2)

The amount of light received (MJ/m ² ) used by crops for photosynthesis is calculated using the following formula (3): The relative light receiving rate is calculated from the leaf area on the previous day.
[Amount of received light] _N = [Relative amount of received light] _(N-1) × [Total amount of solar radiation in the greenhouse] _N … (3)

Furthermore, the amount of photosynthesis of the entire crop per unit area, [dry matter production] (gDW/m ² ), is calculated using the following formula (4).
[Dry matter production] _N = [Amount of light received] _N · [LUE] _N ... (4)

The amount of photosynthesis per plant, [Photosynthesis Amount] (gDW/plant), is calculated using the number of plants per unit area (constant), [Plant Density] (plant/m ² ), using the following formula (5).
[Photosynthesis rate] _N = [Dry matter production rate] _N / [Plant density] ... (5)

In addition, in the leaf area calculations that will be performed later, we will use [SLA] (m ² /gDW), which is the specific leaf area, which is the leaf area per dry weight of the leaf. [SLA] _N is expressed as a function of temperature.

(3) Crop growth calculations (a) Allocation calculation Allocation calculations determine the amount of photosynthesis produced distributed to each organ. Allocation is calculated as a value for the entire crop, and is basically determined from the amount of photosynthesis and the distribution rate. However, a correction is made by setting [inflorescence potential growth] (gDW/plant) as the maximum amount of source that can be received by an inflorescence (maximum amount of allocation). [Inflorescence potential growth] can be calculated using the following formula (6). Note that the [inflorescence growth coefficient] (gDW/(plant·°C)) in formula (6) is a coefficient that indicates the maximum amount of source that one inflorescence can receive per 1°C of temperature, and the [Total Fruits Increase Index] is the sum of the relative growth amounts of all inflorescences.
[Flower cluster potential growth] _N
= [Flower cluster growth coefficient] × [Temperature] _N × [Total Fruits Increase Index] _N-1 … (6)

Allocation is calculated in three stages. For this reason, the variables for the first two stages are labeled (1) and (2) to distinguish them from the final results. The unit for photosynthesis and allocation is (gDW/plant). [Leaf allocation (1)], [Crown allocation (1)], [Inflorescence allocation (1)], [Root allocation (1)] (gDW/plant) are calculated using the following formulas (7A) to (7D), assuming that the amount of photosynthesis from 0:00 to 24:00 on the day is allocated to each organ according to the allocation rate from the previous day (0:00 on the day).
[Leaf distribution amount (1)] _N = [Leaf distribution rate] _N-1・[Photosynthesis amount] _N … (7A)
[Crown distribution amount (1)] _N = [Crown distribution rate] _N-1 × [Photosynthetic amount] _N … (7B)
[Inflorescence distribution amount (1)] _N = [Inflorescence distribution rate] _N-1・[Photosynthesis amount] _N … (7C)
[Root distribution amount (1)] _N = [Root distribution rate] _N-1・[Photosynthesis amount] _N … (7D)

In addition, [Leaf distribution amount (2)], [Crown distribution amount (2)], and [Root distribution amount (2)] (gDW/plant) are corrected as follows so that the inflorescence distribution amount does not exceed the inflorescence potential growth amount.
[Leaf distribution amount (2)] _N = [Leaf distribution amount (1)] _N … (7A)'
[Crown distribution amount (2)] _N = [Crown distribution amount (1)] _N ... (7B)'
[Root distribution amount (2)] _N = [Root distribution amount (1)] _N … (7D)'

Regarding [Flower cluster allocation amount (2)], if [Flower cluster allocation amount (1)] _N < [Flower cluster potential growth amount] _N ,
[Inflorescence distribution amount (2) _N = [Inflorescence distribution amount (1)] _N … (7C1)'
In other cases,
[Flower cluster distribution amount (2)] _N = [Flower cluster potential growth amount] _N ... (7C2)'
Let's say.

Here, the amount of photosynthesis that was not distributed to the inflorescence because it exceeded the inflorescence potential growth amount, [excess dry matter] (gDW/plant), is expressed by the following formula (8).
[Excess dry matter] _N = ([Leaf allocation (1)] _N + [Crown allocation (1)] _N + [Inflorescence allocation (1)] _N + [Root allocation (1)] _N ) - ([Leaf allocation (2)] _N + [Crown allocation (2)] _N + [Inflorescence allocation (2)] _N + [Root allocation (2)] _N ) ... (8)

The excess dry matter is redistributed to areas other than the inflorescence, and the respective distribution amounts are calculated using the following formulas (9A) to (9D).
[Leaf distribution amount] _N = [Leaf distribution amount (2)] _N + [Leaf surplus distribution rate] × [Excess dry matter amount] _N … (9A)
[Crown distribution amount] _N
= [crown distribution amount (2)] _N + [crown surplus distribution rate] · [surplus dry matter amount] _N ... (9B)
[Inflorescence distribution amount] _N = [Inflorescence distribution amount (2)] _N … (9C)
[Root distribution amount] _N = [Root distribution amount (2)] _N + [Root surplus distribution rate] · [Excess dry matter amount] _N ... (9D)

(b) Leaf Area Calculation The leaf area calculation involves calculating the area of each leaf and the dry weight (DW) of the leaf and crown. First, the Leaf Growth Index (dimensionless), which indicates the leaf growth stage as a relative value of the accumulated temperature, is calculated using the following equation (10A). M is the leaf rank.
[Leaf Growth Index] _M,N
= {[Accumulated temperature] _N - (M - 1) 150} / 450 ... (10 A)

The leaf size index, [Leaf Size Index] (dimensionless), is calculated using the following formula (10B). Note that the initial value of [Leaf Size Index] is set to zero.

In addition, the leaf growth index [Leaf Increase Index] (dimensionless) is calculated from the change in leaf size index from midnight of the previous day (midnight of the current day) to midnight of the current day using the following equation (11).
[Leaf Increase Index] _M,N
=[Leaf Size Index] _M,N -[Leaf Size Index] _M,N-1 ...(11)

The sum of the [Leaf Increase Index] of all leaves is defined as the [Total Leaf Increase Index], which is calculated using the following formula (12). NL stands for the number of leaves.

These values are then used to calculate the leaf dry weight, [Leaf DW] (gDW/plant), as follows: The initial value of [Leaf DW] is set by the producer (e.g., 10.0) only for the first leaf (here, the first leaf is defined as the leaf that newly unfolds after planting), and all other values are set to zero.
[Leaf DW] _M,N = [Leaf DW] _M,N-1 + [Leaf ΔDW] _M,N … (13A)
[Leaf ΔDW] _M,N is expressed by the following formula (13B).
[Leaf ΔDW] _M,N
= [Leaf Allocation Amount] _M,N * [Leaf Increase Index] _M,N / [Total Leaf Increase Index] _N
…(13B)

In addition, the crown is assumed to increase by a fixed percentage with each additional leaf, and the dry weight [Stem DW] (gDW/plant) is calculated in the same way as [Leaf DW], as follows: The initial value of [Stem DW] is set by the producer (e.g., 5.0) only for the first node (here, the node newly generated after planting is defined as the first node), and all other nodes are set to zero.
[Stem DW] _M,N = [Stem DW] _M,N-1 + [Stem ΔDW] _M,N … (14A)
[Stem ΔDW] _M,N is expressed by the following formula (14B).
[Stem ΔDW] _M,N
= [Crown Distribution Amount] _M,N * [Leaf Increase Index] _M,N / [Total Leaf Increase Index] _N
…(14B)

Leaf area ( ^m² /plant) is calculated using specific leaf area (SLA) ( ^m² /gDW) using the following formula (15): Leaf area is set by the producer only for the first leaf, and other leaves are set to 0.
[Leaf Area] _M,N = [Leaf Area] _M,N-1 + [SLA] _N・[Leaf distribution amount] _M,N・[Leaf Increase Index] _M,N ／[Total Leaf Increase Index] _N … (15)

Furthermore, if the total area of leaves attached to a crop [Leaf Area] is [Total Leaf Area(1)] (m ² /plant), [Total Leaf Area(1)] can be calculated using the following formula (16).

Furthermore, leaf parameters used to calculate photosynthesis are calculated.

First, the leaf area index [LAI] (dimensionless), which is the leaf area per unit land area, is calculated from the following equation (17) using the plant density (plants/m ² ), which is the number of crop plants per land area.
[LAI] _N = [Total Leaf Area] _N・[Plant Density] …(17)

The relative amount of received light (dimensionless), which represents the ratio of the amount of solar radiation received by the crop to the amount of solar radiation per unit area of land, is calculated using the light absorption coefficient [K] (dimensionless) and the leaf area index of the previous day using the following equation (18). The light absorption coefficient is a coefficient that indicates the ease with which light reaches the interior of the canopy.
[Relative light receiving amount] _N = 1 - exp(-[K]·[LAI] _N ) ... (18)

(c) Calculation of fruit number The number of fruits is calculated for each inflorescence from the amount of photosynthesis (period dry matter production) during a specific period (flower number determination period) starting the day after flower bud differentiation of the crop begins, and the relational equation between period dry matter production and fruit number. Flower bud differentiation occurs in the order of inflorescence rank, and after the differentiation of the next inflorescence rank (previous inflorescence), a certain amount of temperature must accumulate before the next inflorescence rank will differentiate.

The [Differentiation Start Index], which indicates the start of differentiation, is determined by [Differentiation Condition Judgment A] (°C) and [Differentiation Condition Judgment B], which are explained below. Here, the [Flower Bud Differentiation Stage Accumulated Temperature] (°C) is the accumulated temperature on the day when the [Differentiation Start Index] first becomes 1, and before that it is -1.0.

In this simulation, based on the value of the [Flower Bud Differentiation Period Accumulated Temperature], [Differentiation Condition Judgment A] is calculated as follows, which is an index for determining whether the accumulated temperature from the day after flower bud differentiation of the pre-order inflorescence begins exceeds a certain value (for example, 600°C). Note that the value of 600°C is set at 150°C, the accumulated temperature required for one leaf to emerge, since in this variety, four leaves normally emerge between inflorescences. F in the formula indicates the inflorescence order.
[Differentiation Start Index] _F-1,N = 1
[Differentiation condition judgment A] _F,N
= [accumulated temperature] _N - ([accumulated temperature at flower bud differentiation stage] _F-1,N + 600) ... (19A)
In other cases,
[Differentiation condition judgment A] _F,N = -1.0...(19B)
He demands.

In addition, [Differentiation Condition Judgment B] (gDW/strain), which is an index for judging whether the amount of photosynthesis is in a state where flower bud differentiation is possible, is calculated using the following formula (20).
[Differentiation condition determination B] _N = (N-6 to N-day average value of [photosynthetic amount]) ... (20)

In addition, once the [Differentiation Start Index], which indicates the start of flower bud differentiation, becomes 1, it will remain 1 thereafter.
That is, if [differentiation start index] _F,N-1 = 1,
[Differentiation start index] _F,N = 1...(21A)
If 0.0≦[Differentiation Condition Judgment A] _F,N and 1.0<[Differentiation Condition Judgment B] _N , then
[Differentiation start index] _F,N = 1...(21B)
In other cases,
[Differentiation start index] _F,N = 0...(21C)
Let's say.

Furthermore, the [Flower bud differentiation stage accumulated temperature] in the above formula (19A) is the accumulated temperature on the day when the value of the [Differentiation start index] the previous day was 0 and the value of the day was 1. The initial value of the [Flower bud differentiation stage accumulated temperature] is -1.0, and once the [Flower bud differentiation stage accumulated temperature] is set, it remains the same value. The reason for setting the initial value to a negative value is to classify the [Flower bud differentiation stage accumulated temperature] after the start of the flower bud differentiation stage as a positive value and those before that as negative values. This classification by positive and negative values is also performed on the flowering stage accumulated temperature.
That is, if [differentiation start index] _F,N-1 = 0 and [differentiation start index] _F,N = 1,
[Accumulated temperature during flower bud differentiation] _F,N = [Accumulated temperature] _N ... (22A)
In other cases,
[Cumulative temperature during flower bud differentiation stage] _F,N = [cumulative temperature during flower bud differentiation stage] _F,N-1 ...(22B)
is.

Furthermore, in this simulation, the period after the start of flower bud differentiation in which the accumulated temperature is in the range of 0 to 150°C is defined as the flower number determination period, and the [flower number determination period Index(1)] (dimensionless), which serves as an index indicating whether or not the period is in the flower number determination period, is calculated as follows: The flower number determination period is defined as the period in which one leaf unfolds, and assuming that the accumulated temperature per leaf is 150°C, the accumulated temperature during the flower number determination period is set to 150°C.
That is, if [differentiation start index] _F,N = 0,
[Flower number determination period Index (1)] _F,N = -1.0...(23A)
In other cases,
[Flower number determination period Index(1)] _F,N
= ([accumulated temperature] _N - [accumulated temperature at flower bud differentiation stage] _F,N ) / 150 ... (23B)

The [Flower Number Determination Period Index] (dimensionless) is calculated as follows so that it is 1 when the accumulated temperature from the start of flower bud differentiation is in the range of 0 to 150°C.
If 0.0≦[Flower number determination period Index(1)] _F,N ≦1.0,
[Flower number determination period Index] _F,N = 1...(24A)
In other cases,
[Flower number determination period Index] _F,N = 0...(24B)

The integrated value of the amount of photosynthesis during the flower number determination period, [determination period photosynthesis amount] (gDW/plant), is expressed by the following formula (25).
[Photosynthesis amount during determination period] _F,N = [Photosynthesis amount during determination period] _F,N-1 + [Flower number determination period Index] _F,N・[Photosynthesis amount] _F,N … (25)

Then, the number of fruits (1) (pieces/plant) can be calculated using the [Photosynthetic amount during the determination period], [Number of fruits per dry matter production during the determination period] ( ^m2 ·pieces/gDW·plant), and [Plant Density] (plant/ ^m2 ) from the above formula (25) as shown in the following formula (26).
[Number of fruits (1)] _F,N = [Photosynthetic amount in the critical period] _M,N × [Number of fruits per dry matter production in the critical period] × [Plant Density] ... (26)

The number of fruits (pieces/plant) is corrected to fall within the range of 1.0 to 10.0 as follows:
That is, if [fruit number (1)] _F,N < 1.0,
[Number of fruit set] _F,N = 1.0…(27A)
If 1.0≦[Number of fruits (1)] _F,N ≦10.0,
[Number of fruit set] _F,N= [Number of fruit set (1)] _F,N … (27B)
In other cases,
[Number of fruit set] _F,N = 10.0…(27C)
Let's say.

The flowering stage is defined as the point at which the accumulated temperature from the flower bud differentiation stage reaches 600°C, and the accumulated temperature at that point is defined as the [flowering stage accumulated temperature] (°C). The value of 600°C is set on the assumption that four leaves bloom after flower bud differentiation, and that an accumulated temperature of 150°C is required per leaf. The flowering stage accumulated temperature is set to be valid only after the flower bud differentiation stage accumulated temperature has been determined, and its initial value is set to -1.0. Once the flowering stage accumulated temperature is set, it remains the same value.
That is, if [flower bud differentiation stage accumulated temperature] _F,N < 0,
[Flowering stage accumulated temperature] _F,N = -1.0 ... (28A)
In other cases,
[Accumulated temperature at flowering stage] _F,N = [Accumulated temperature at flower bud differentiation stage] _F,N + 600 ... (28B)
This becomes:

(d) Calculation of inflorescence dry weight The Fruits Growth Index (dimensionless), which is the relative growth stage of the inflorescence, is calculated as follows. If the flowering stage accumulated temperature is invalid (less than 0), the value of the Fruits Growth Index is set to -1.0. The initial value of the Fruits Growth Index is also set to -1.0.
That is, if [Flowering stage accumulated temperature] _F,N < 0,
[Fruits Growth Index] _F,N =-1.0…(29A)
In other cases,
[Fruits Growth Index] _F,N
= ([accumulated temperature] _N - [accumulated temperature at flowering stage] _F,N ) / 600 ... (29B)
Let's say.

Fruits Size Index (dimensionless) is calculated using the following equation (30). The initial value of Fruits Size Index is set to zero.

In addition, the relative growth increase of the inflorescence is calculated from the magnitude of the relative change in the inflorescence from midnight the previous day (midnight on the current day) to midnight on the current day, and the [Fruits Increase Index(1)] (dimensionless) is calculated using the following equation (31).
[Fruits Increase Index(1)] _F,N
=[Fruits Size Index] _F,N -[Fruits Size Index] _F,N-1 …(31)

However, since the inflorescence is based on the assumption that the number of fruits is 10, the index is corrected as shown in the following equation (32) to obtain the [Fruits Increase Index] (dimensionless).
[Fruits Increase Index] _F,N
=([Fruits Increase Index(1)] _F,N・[Number of fruit set] _F,N )/10…(32)

The sum of the [Fruits Increase Index] of all inflorescences is defined as the [Total Fruits Increase Index] (dimensionless) (see the following equation (33)). NF stands for the number of inflorescences.

In this simulation, these values are used to calculate the inflorescence dry weight, [Fruits DW] (gDW/plant). Basically, [Fruits DW] does not increase once [Fruits Growth Index] exceeds 1. The initial value of [Fruits DW] is set to zero.
[Fruits DW] _F,N = [Fruits DW] _F,N-1 + [Fruits ΔDW] _F,N … (34A)
[Fruits ΔDW] _F,N is expressed by the following equation (34B).
[Fruits ΔDW] _F,N
＝[Flower cluster distribution amount] _N・[Fruits Increase Index] _F,N ／[Total Fruits Increase Index] _N
…(34B)

(e) Calculation of the distribution ratio The distribution ratio (dimensionless) of a crop is calculated as a value for the whole crop based on the amount of crop growth. The distribution ratio is calculated from the inflorescence sink strength (dimensionless), which is the ease with which the inflorescence receives the source, and the total sink strength, which is the sum of the sink strengths of all organs.

The [Total Fruits Increase Index], [Total Leaf Increase Index], and [Total Stem Increase Index] are weighted based on experimental data from dissection surveys, etc. The [Fruit Distribution Adjustment Coefficient], [Leaf Distribution Adjustment Coefficient], and [Crown Distribution Adjustment Coefficient] are designated as [α], [β], and [γ]. From these, the inflorescence sink strength, leaf sink strength, crown sink strength, and total sink strength are calculated using the following formula.
[Flower cluster sink strength] _N = [Total Fruits Increase Index] _N · [α] ... (35A)
[Leaf sink strength] _N = [Total Leaf Increase Index] _N · [β] ... (35B)
[Crown sink strength] _N = [Total Stem Increase Index] _N · [γ] ... (35C)
[Total sink strength] _N = [Flower cluster sink strength] _N + [Leaf sink strength] _N + [Crown sink strength] _N + [Root sink strength] ... (35D)

The [inflorescence distribution rate], [root distribution rate], [leaf distribution rate], and [crown distribution rate] (dimensionless) are calculated from the following equations (36A) to (36D).
[Flower cluster distribution rate] _N = [Flower cluster sink strength] _N / [Total sink strength] _N ... (36A)
[Root distribution ratio] _N = 0.05...(36B)
[Leaf distribution ratio] _N = [Leaf sink strength] _N / [Total sink strength] _N ... (36C)
[Crown distribution ratio] _N = [Crown sink strength] _N / [Total sink strength] _N ... (36D)

(4) Fruit Yield Calculation This simulation assumes that the target crops are not harvested in one day within the inflorescence, but are harvested gradually. The [Harvest Start Index] (dimensionless), which indicates that harvesting is possible, is set to 1 from the day after the [Fruits Increase Index] first reaches 1.0 or higher, and is set to zero before that. The first day the [Harvest Start Index] reaches 1 is considered the harvest start date. The [Harvest Start Index] is expressed as follows, and its initial value is zero.
That is, if [Harvesting Start Index] _F,N-1 = 1,
[Harvest start index] _F,N = 1...(37A)
[Fruits Source Index] If _F,N-1 ≧ 1.0,
[Harvest start index] _F,N = 1 ... (37B)
In other cases,
[Harvest start index] _F,N = 0…(37C)
This becomes:

Here, the total harvest volume per fruit inflorescence is [Fruits DW] (gDW/plant) on the start date of harvest. Basically, [Fruits DW] does not increase once the [Fruits Growth Index] exceeds 1, so it remains the same from the start date of harvest. Each fruit is harvested over a period of time until the accumulated temperature reaches 60°C. The dry weight of fruit harvested per 1°C of air temperature, [Harvested Fruit Temperature DW] (gDW/plant·°C), is calculated as follows. The initial value of [Harvested Fruit Temperature DW] is -1.0, and once a valid value is set, it remains the same.
That is, if [Harvested fruit temperature DW] _F,N-1 ≧ 0.0,
[Harvested fruit temperature DW] _F,N = [Harvested fruit temperature DW] _F,N-1 ... (38A)
If [Harvesting Start Index] _F,N = 1 and [Number of Fruits] _F,N-1 > 0.0,
[Harvested Fruit Temperature DW] _F,N = [Fruits DW] _F,N-1 / ([Number of Fruits] _F,N-1 x 60) ... (38B)
In other cases,
[Harvested fruit temperature DW] _F,N = -1.0 ... (38C)
Calculate as follows.

The dry weight of harvestable fruit before harvesting on the calculation date is defined as [Fruits unHarvest] (gDW/plant), and [Fruits unHarvest] is calculated as follows: [Fruits unHarvest] is calculated by taking the [Fruits DW] of the previous day on the day harvesting begins, and then decreasing by the amount harvested thereafter. The initial value of [Fruits unHarvest] is -1.0, and the values before and after the harvest period are also -1.0.
That is, if [Fruits unHarvest] _F,N-1 ≧0.0, then
[Fruits unHarvest] _F,N
=[Fruits unHarvest] _F,N-1 -[Fruits HARVEST] _F,N-1 …(39A)
If [Harvesting Start Index] _F,N-1 = 0 and [Harvesting Start Index] _F,N = 1,
[Fruits unHarvest] _F,N = [Fruits DW] _F,N-1 …(39B)
In other cases,
[Fruits unHarvest] _F,N =-1.0...(39C)
This becomes:

After the start of the harvest period, if there are fruits available for harvest, the dry weight of the harvested fruits [Fruits HARVEST] (gDW/plant) is calculated as follows.
That is, if [Harvest Start Index] _F,N = 1 and [Fruits unHarvest] _F,N > 0.0,
[Fruits HARVEST] _F,N
= min([Fruits unHarvest] _{F ,N )} ... (40A)
In other cases,
[Fruits HARVEST] _F,N =0.0…(40B)

Furthermore, if the total [Fruits HARVEST] of all inflorescences is [Fruits Harvested Dry Weight] (gDW/plant), [Fruits Harvested Dry Weight] can be expressed by the following equation (41).

By dividing this [fruit harvest dry weight] by the [fruit dry matter ratio] (g/gDW), the [fruit harvest live weight] (g/plant) can be calculated using the following equation (42).
[Fruit harvest live weight] _N = [Fruit harvest dry weight] _N / [Fruit dry matter rate] ... (42)

Then, by multiplying the [live weight of fruit harvested] by the [number of plants in the field] (plants), the [live weight of fruit harvested in the field] (kg), which is the yield of the entire field, can be calculated as shown in the following equation (43).
[Field-harvested live weight of fruit] _N
= 0.001 · [fruit harvested live weight] _N · [number of plants in the field] ... (43)

The simulation unit 44 creates a growth model as described above using environmental data and parameters, and uses the growth model to perform a growth simulation for each combination of variety, cultivation location, and cultivation information selected by the producer. The simulation results for each combination (values obtained by the simulation) are then sent to the output unit 45. Note that the simulation unit 44 can also simulate fruit and vegetable crops other than strawberries, such as tomatoes, strawberries, cucumbers, and peppers, by using parameters according to the item and variety.

(5) Calculation of nutrient absorption The amount of nutrient absorption is calculated by multiplying the increase in dry matter of leaves, crowns, fruits, and roots by the nutrient content. Here, N (nitrogen) is used as an example, but similar calculations can be used for other elements.
[Leaf ΔN] _M,N = [Leaf ΔDW] _M,N × [Leaf N%] _M,N … (44A)
[Stem ΔN] _M,N = [Stem ΔDW] _M,N × [Stem N%] _M,N … (44B)
[Fruits ΔN] _F,N = [Fruits DW] _F,N × [Fruits N%] _F,N … (44C)
[Root ΔN] _M,N = [Root DW] _M,N × [Root N%] _M,N … (44D)

In the above formula (44A), [Leaf ΔN] _M,N is the amount of nutrient absorbed by the leaf, [Leaf ΔDW] _M,N is the increase in dry matter in the leaf, and [Leaf N%] _M,N is the nutrient content in the leaf. The same applies to the other formulas (44B) to (44D).

Here, the nutrient content ( _{M, N,} etc. [Leaf N%]) is expressed as a function of the growth index of the organ, for example, by the following formula: where β and γ are constants.
[Leaf N%] _M,N = β _L × ln ([Leaf Growth Index] _M,N ) + γ _L … (45A)
[Stem N%] _M,N = β _S × ln ([Stem Growth Index] _M,N ) + γ _S … (45B)
[Fruits N%] _F,N = β _F × ln ([Fruits Growth Index] _F,N ) + γ _F … (45C)
[Root N%] _M,N = γ _R … (45D)

In addition, the amount of nutrients absorbed by leaves, crown, inflorescence, and roots can be expressed by the following formula.

Furthermore, the nutrient absorption amount per plant [Total ΔN] _N is expressed by the following equation (47).
[Total ΔN] _N
＝[Total Leaf ΔN] _N ＋[Total Stem ΔN] _N ＋[Total Fruits ΔN] _N ＋[Total Root ΔN] _N
…(47)

[Total ΔN] _N is the "nutrient absorption amount" estimated from the increase in dry weight per plant. This can be multiplied by the planting density to obtain the "nutrient absorption amount per area." Furthermore, the "nutrient absorption amount" can be calculated by dividing it by the [fertilizer use efficiency] to obtain the "fertilizer amount."

(Regarding processing by the output unit 45)
When the output unit 45 receives the simulation results transmitted from the simulation unit 44, it generates a screen displaying the simulation results. For example, the output unit 45 generates a screen displaying information such as that shown in FIG. 11 as the simulation results. In this case, the screen shown in FIG. 11 is displayed on the display unit 193 of the user terminal 70, allowing the producer to confirm which variety, at which cultivation location, and how it will grow, as well as the yield that can be obtained. In addition, because the simulation results for cultivating multiple items under different cultivation conditions are displayed side by side, the producer can compare the cultivation results that can be obtained when the variety and cultivation conditions are changed.

The output unit 45 can also generate a screen such as that shown in FIG. 12, which shows the shipping volume by inflorescence when the variety selected by the producer is cultivated at the selected cultivation site, and output the screen to the user terminal 70. By referring to the screen shown in FIG. 12, the producer can check the trends in the yield for the terminal inflorescence, the second inflorescence, the third inflorescence, etc. While FIG. 12 shows the yield for each inflorescence in separate graphs, these graphs may be displayed together in a single graph. In this case, the bar graphs showing the yield for each inflorescence may be displayed in different colors for easier understanding. Furthermore, the output unit 45 may also display a graph showing the trends in total yield, as shown in FIG. 13(a), or a graph showing the trends in LAI, as shown in FIG. 13(b). Other growth-related information (e.g., leaf area, flowering date by inflorescence, photosynthesis rate, growth rate (crown, leaf, fruit, fruit load), nutrient absorption rate (fertilizer amount), etc.) may also be displayed numerically or graphically.

The output unit 45 may also display the simulation results of the changes in dry weight of leaves, crowns, and each inflorescence when varieties A, B, and C are cultivated at a certain cultivation location, as shown in Figure 14, for example.

As described above, the output unit 45 may display simulation results for each combination of variety, cultivation location, and cultivation information in a comparable manner, as shown in FIG. 11, or may display simulation results for cultivating a certain variety at a certain cultivation location, as shown in FIGS. 12 to 13(b). Furthermore, as shown in FIG. 14, simulation results for cultivating different varieties at the same cultivation location may be displayed in a comparable manner. Furthermore, simulation results for cultivating a certain variety at different cultivation locations may be displayed in a comparable manner. In either case, it is possible to appropriately express the characteristics of a variety compared to conventional catalogs that express the characteristics of a variety using words, images, average values, etc. Therefore, by checking the simulation results, producers can appropriately select varieties and cultivation locations. Furthermore, by understanding how crops will grow in the future, producers can appropriately manage cultivation (securing workers, procuring materials, etc.).

(Regarding the processing of the parameter calculation unit 47)
When an instruction to calculate parameters for a variety whose parameters are not stored in the parameter DB 50 is input, the parameter calculation unit 47 calculates the parameters using environmental data when the variety was cultivated and cultivation information obtained from the cultivation results of the variety, and stores the parameters in the parameter DB 50.

Below, the processing of the parameter calculation unit 47 will be explained in detail in accordance with the flowcharts of Figures 15 and 16, with appropriate reference to other drawings.

The parameter calculation unit 47 starts the processing of FIG. 15 upon receiving an input of a parameter calculation instruction specifying the variety for which parameters are to be calculated (referred to as the target variety). In the processing of FIG. 15, first, in step S100, the parameter calculation unit 47 acquires environmental data via the environmental data acquisition unit 42. This environmental data is environmental data obtained when the target variety is cultivated, and includes data such as that shown in FIG. 17. Specifically, the environmental data includes the date, the average daily temperature on that date, the daily accumulated solar radiation, the _CO2 concentration, and the like. When calculating variety parameters from past cultivation results, environmental data from past cultivation is acquired. However, when creating variety parameters in real time to accommodate a new variety, real-time environmental data may be acquired from an external server.

Next, in step S102, the parameter calculation unit 47 acquires cultivation information obtained when the target item was cultivated via the cultivation information acquisition unit 43. The cultivation information includes, for example, cultivation condition data such as that shown in FIG. 18(a), growth survey data such as that shown in FIG. 18(b), and dissection survey data such as that shown in FIG. 18(c). The cultivation information also includes flowering survey data such as that shown in FIG. 19(a) and harvest survey data such as that shown in FIG. 19(b). The cultivation condition data in FIG. 18(a) includes information on cultivation conditions such as crop name, variety name, planting date, cultivation method, and cultivation density. The growth survey data in FIG. 18(b) includes information such as the survey date, plant height, leaf age, leaf length, leaf blade length, petiole length, and leaf width, as a result of a producer's survey of an actually cultivated crop (strawberry). The dissection survey data in Figure 18(c) includes information such as the survey date, number of epiphytic leaves, epiphytic leaf weight, LAI, crown weight, number of fruits, and fruit weight, as a result of a dissection survey conducted by a producer on an actually cultivated crop (strawberry). The flowering survey data in Figure 19(a) includes information on the flowering period of each inflorescence of the actually cultivated crop (strawberry). Furthermore, the harvest survey data in Figure 19(b) includes information on the harvest yield of the actually cultivated crop (strawberry) on each harvest date.

Returning to FIG. 15, in the next step S104, the parameter calculation unit 47 executes a process to calculate the parameters of the target variety. Details of the process of step S104 will be described later.

Then, in the next step S106, the parameter calculation unit 47 associates the calculated parameters with information about the target variety and stores them in the parameter DB 50.

(Regarding step S104)
The processing of step S104 will be described in detail below with reference to the flowchart of FIG. 16. In this embodiment, it is assumed that it is necessary to calculate parameters such as those shown in FIG. 20. As shown in FIG. 20, the parameters include coefficients used in the equations described above (e.g., leaf growth coefficient, crown growth coefficient) and thresholds corresponding to values obtained from the equations (e.g., leaf emergence interval, leaf maturation period, inflorescence maturation period). FIG. 16 illustrates the processing performed by the parameter calculation unit 47 when it calculates parameters related to leaf growth, such as the leaf growth coefficient, leaf emergence interval, and leaf maturation period.

When the processing of Figure 16 starts, first, in step S120, the parameter calculation unit 47 extracts data on the survey date, leaf age, and leaf length from the cultivation information (growth survey data in Figure 18 (b)).

Next, in step S122, the parameter calculation unit 47 determines whether all the information necessary to calculate the parameters is available. In this case, the parameter calculation unit 47 determines whether all the data necessary to calculate the leaf growth coefficient, leaf emergence interval, and leaf maturation period is available. If the determination in step S122 is positive, the parameter calculation unit 47 proceeds to step S124.

When proceeding to step S124, the parameter calculation unit 47 calculates the leaf emergence interval (°C) from the extracted cultivation information and environmental data.

Next, in step S126, the parameter calculation unit 47 calculates the estimated leaf emergence date for each leaf position from the leaf emergence interval (°C).

Next, in step S128, the parameter calculation unit 47 creates a function equation representing the amount of leaf growth from the accumulated temperature from the estimated leaf emergence date and the extracted cultivation information.

Next, in step S130, the parameter calculation unit 47 calculates the coefficient (leaf growth coefficient) of the created function equation.

Next, in step S132, the parameter calculation unit 47 calculates the leaf maturation period from the created function formula. Once the leaf growth coefficient, leaf emergence interval, and leaf maturation period have been calculated as described above, all processing in Figure 16 is terminated, and the process proceeds to step S106 in Figure 15.

On the other hand, if the determination in step S122 of FIG. 16 is negative, the parameter calculation unit 47 proceeds to step S134 and determines whether alternative information for the missing information exists. For example, if leaf length information is not available, the parameter calculation unit 47 determines whether leaf blade length data and petiole length data are available in the growth survey data. If the determination in step S122 is positive, the parameter calculation unit 47 proceeds to step S136.

When the process proceeds to step S136, the parameter calculation unit 47 extracts alternative information (leaf length data and petiole length data) from the cultivation information. The parameter calculation unit 47 then proceeds to step S124 and executes the processes of steps S124 to S132 using the alternative information.

On the other hand, if the determination in step S134 is negative, the parameter calculation unit 47 proceeds to step S138 and outputs an error. After that, the processing in FIG. 16 ends, and the processing in FIG. 15 also ends.

In the above explanation, the parameter calculation unit 47 calculates a parameter related to leaf growth, but other parameters can also be calculated in a similar manner using environmental data and cultivation information in a predetermined manner. Examples of other parameters include parameters used when determining the sink-source balance (e.g., the [fruit distribution adjustment coefficient], [leaf distribution adjustment coefficient], and [crown distribution adjustment coefficient] included in the above-mentioned equations (35A) to (35C)), and parameters that represent variety-specific cultivation management methods. When calculating these parameters, the parameter calculation unit 47 will use alternative cultivation information if the necessary cultivation information is not available.

(Regarding the processing of the update unit 46)
When the producer inputs actual measurements (actual cultivation results) such as changes in yield or LAI, the update unit 46 compares the actual measurements with the corresponding simulation results, and adjusts and updates various parameters so that the simulation results approach the actual measurements. In this way, the parameters are appropriately updated based on the actual measurements, making it possible to obtain appropriate simulation results in the next and subsequent simulations.

Note that if the parameters are updated based on information input from all producers, there is a risk that the parameters may not be updated appropriately. For this reason, the update unit 46 may update the parameters based on the input information only when information is input from a predetermined producer (a producer with a high level of reliability). In this case, the update unit 46 executes processing in accordance with the flowchart shown in FIG. 21.

In the processing of FIG. 21, first, in step S30, the update unit 46 waits until the producer inputs actual cultivation results (actual measurements) via the user terminal 70. Then, when the actual cultivation results are input, the processing proceeds to step S32, where the update unit 46 determines whether the input was from a predetermined producer (a producer with a high level of reliability). If the determination in step S32 is negative, the entire processing of FIG. 21 ends, but if the determination is positive, the processing proceeds to step S34. If the processing proceeds to step S34, the update unit 46 updates the parameters so that the simulation results approach the actual cultivation results, stores the updated parameters in the parameter DB 50, and ends the entire processing of FIG. 21.

When the update unit 46 updates parameters using information input by a producer, the updated parameters may be managed as parameters exclusive to that producer. This allows each producer to customize parameters to suit actual cultivation results. It may also be possible for one producer to call up and use the parameters of another producer.

The producer may also modify the parameters directly. If the producer modifies the parameters, the update unit 46 may update the parameter DB 50 in accordance with the modifications.

As can be seen from the explanation so far, the simulation unit 44 of this embodiment functions as a readout unit that reads out parameters indicating the characteristics of each crop variety from the memory unit (parameter DB 50). The simulation unit 44 of this embodiment also functions as a prediction unit that creates a growth model for each variety based on the readout parameters and information about the cultivation environment (environmental data), and performs predictions regarding the growth of each variety from the growth model for each variety.

As described above in detail, in this embodiment, the parameter calculation unit 47 acquires environmental data from the cultivation of the crop via the environmental data acquisition unit 42 (S100), and also acquires cultivation information obtained as a result of the cultivation of the crop via the cultivation information acquisition unit 43 (S102). The parameter calculation unit 47 then uses at least a portion of the environmental data and at least a portion of the cultivation information to calculate parameters (coefficients and thresholds) to be used in a model that estimates the growth or yield of each crop variety using a calculation method defined for each parameter (S104). This allows the parameters (coefficients and thresholds) for each variety to be automatically determined by providing the environmental data and cultivation information, making it possible to easily obtain a model for each variety that estimates growth and yield. Furthermore, even when a new variety is developed, the parameters can be easily obtained, allowing for accurate growth and yield estimation for that variety.

In addition, in this embodiment, if the environmental data or cultivation data required for the calculation method defined for each parameter to be calculated is insufficient (S122: No), the parameter calculation unit 47 extracts data that can replace the missing data from the environmental data or cultivation data (S136) and uses this data in the calculation (S124-S132). As a result, even if the environmental data or cultivation data required to calculate the parameter is insufficient, the parameter is calculated using the substitutable data, thereby reducing the possibility of an error occurring due to the parameter not being able to be calculated.

In this embodiment, the parameter calculation unit 47 calculates parameters and stores them in the parameter DB 50. The simulation unit 44 acquires environmental data corresponding to the cultivation location selected by the producer from the environmental data acquisition unit 42. The simulation unit 44 creates a growth model for each variety based on the acquired environmental data and the parameters read from the parameter DB 50, and performs a simulation using the created growth model. The output unit 45 then outputs the simulation results as information to support variety selection and cultivation management. In this way, in this embodiment, the results of a simulation of crop growth using a growth model created for each variety and cultivation location are displayed as information to support variety selection and cultivation management. This makes it possible to appropriately express the characteristics of a variety compared to conventional catalogs that express the characteristics of a variety using words, images, average values, etc. Therefore, producers can appropriately select varieties and cultivation locations, and can also understand how the crops will grow in the future, allowing them to appropriately manage cultivation.

In addition, in this embodiment, the update unit 46 updates the parameters based on the actual cultivation results (yield, etc.) entered by the producer, and updates the parameter DB 50. This allows the parameters to be updated to appropriate values based on the cultivation results. In this case, the update unit 46 can improve the accuracy of the simulation by updating the parameters so that the simulation results approach the cultivation results.

In addition, in this embodiment, if the producer is a predetermined producer (a producer with a predetermined level of reliability or higher) (S32: Yes), the update unit 46 updates the parameters based on the cultivation history entered by the producer (S34). This increases the likelihood that the parameters will be updated appropriately.

In addition, in this embodiment, the output unit 45 performs a simulation for each combination of variety, cultivation location, and cultivation information selected by the producer, and outputs the simulation results in a comparable manner. This allows the producer to compare the simulation results for multiple combinations of variety, cultivation location, and cultivation information, and determine which variety should be cultivated and how.

In the above embodiment, the server 10 is described as having an update unit 46, but this is not limited to this. In other words, the server 10 does not have to have an update unit 46.

In the above embodiment, the server 10 has the functions of the agricultural support device of the present invention, but this is not limited to this. The user terminal 70 may also have the functions of the agricultural support device. In other words, the above processing may be realized by a standalone user terminal 70 operating independently.

In the above embodiment, the parameter calculation unit 47 automatically calculates the parameters stored in the parameter DB 50, but this is not limited to this. For example, each parameter may be calculated manually from environmental data or cultivation information. In this case, the parameter calculation procedure is the same as in Figures 15 and 16.

The above processing functions can be realized by a computer. In this case, a program is provided that describes the processing content of the functions that the processing device should have. By executing the program on a computer, the above processing functions are realized on the computer. The program that describes the processing content can be recorded on a computer-readable storage medium (excluding carrier waves).

When distributing a program, it is sold, for example, in the form of a portable storage medium on which the program is recorded, such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory). The program can also be stored in the storage device of a server computer and transferred from the server computer to other computers via a network.

A computer that executes a program stores, for example, a program recorded on a portable storage medium or a program transferred from a server computer in its own storage device. The computer then reads the program from its own storage device and executes processing in accordance with the program. Note that the computer can also read the program directly from a portable storage medium and execute processing in accordance with that program. The computer can also execute processing in accordance with the received program each time a program is transferred from the server computer.

The above-described embodiment is a preferred example of the present invention. However, it is not limited to this, and various modifications are possible within the scope of the present invention.

10 Server (agricultural support device)
42 Environmental data acquisition unit (acquisition unit)
44 Simulation unit (reading unit, prediction unit)
45 Output unit 47 Parameter calculation unit 50 Parameter DB (storage unit)
100 Agricultural Systems

Claims (7)

acquiring environmental data for a first cultivation period during which a predetermined variety of agricultural crop is cultivated;
acquiring cultivation data obtained by cultivating the predetermined variety of the agricultural crop during the first cultivation period;
calculating the values of parameters that are coefficients of an equation that estimates the daily growth of each organ of the predetermined variety of agricultural product based on daily environmental data and threshold values corresponding to values obtained from the equation , the values of the parameters being constant regardless of the growth stage of the agricultural product;
The computer executes
In the calculating step, values of each parameter of an equation for estimating the daily growth of each organ of the agricultural crop of the predetermined variety are calculated using at least a part of the acquired environmental data and cultivation data for the first cultivation period.
A parameter calculation method comprising: The parameter calculation method described in claim 1, characterized in that the parameters include at least one of a parameter related to the growth rate of each organ of the plant body, a parameter used to determine the sink-source balance, and a parameter representing a variety-specific cultivation management method. 2. The parameter calculation method according to claim 1, wherein in the calculation step, if the environmental data or the cultivation data is insufficient when calculating the parameter, data that can replace the insufficient data is obtained from the environmental data or the cultivation data and used as a substitute. 2. The parameter calculation method according to claim 1, wherein the agricultural crops are fruit vegetables. Calculating the parameters using the parameter calculation method according to any one of claims 1 to 4 ;
Obtaining information about the cultivation environment of a specified variety of crop;
creating a formula for estimating the daily growth of each organ of the predetermined variety of agricultural produce based on the parameters and the acquired information on the cultivation environment, and performing a prediction of the daily growth of each organ of the predetermined variety of agricultural produce from the formula ;
outputting the prediction result obtained by the prediction;
An agricultural support information output method characterized in that processing is executed by a computer. acquiring environmental data for a first cultivation period during which a predetermined variety of agricultural crop is cultivated;
acquiring cultivation data obtained by cultivating the predetermined variety of the agricultural crop during the first cultivation period;
calculating the values of parameters that are coefficients of an equation that estimates the daily growth of each organ of the predetermined variety of agricultural product based on daily environmental data and threshold values corresponding to values obtained from the equation , the values of the parameters being constant regardless of the growth stage of the agricultural product;
on the computer,
In the calculating step, values of each parameter of an equation for estimating the daily growth of each organ of the agricultural crop of the predetermined variety are calculated using at least a part of the acquired environmental data and cultivation data for the first cultivation period.
A parameter calculation program comprising: Calculating the parameters using the parameter calculation program according to claim 6 ,
Obtaining information about the cultivation environment of a specified variety of crop;
creating a formula for estimating the daily growth of each organ of the predetermined variety of agricultural produce based on the parameters and the acquired information on the cultivation environment, and performing a prediction of the daily growth of each organ of the predetermined variety of agricultural produce from the formula ;
outputting the prediction result obtained by the prediction;
An agricultural support information output program that causes a computer to execute processing.