JP2022078987A - Agriculture assisting program, agriculture assisting method, and agriculture assisting device - Google Patents

Abstract

To provide an agriculture assisting program, an agriculture assisting method and an agriculture assisting device, which output appropriately information for assisting in breed selection and cultivation management.SOLUTION: Provided is an agriculture system in which a server and a plurality of user terminals are connected to a network such as the Internet, in which the server comprises a selection acceptance unit 41, an environment data acquisition unit 42, a cultivation information acquisition unit 43, a simulation unit 44, an output unit 45, and an update unit 46. The simulation unit 44 reads a parameter that indicates the feature per breed of farm crops from a parameter database 50, and acquires environment data corresponding to a cultivation point selected by a producer from the environment data acquisition unit 42. The simulation unit 44 creates a growth model per breed on the basis of the acquired environment data and the read parameter, and executes a simulation using the created growth model. The output unit 45 outputs simulation results as information for assisting in breed selection and cultivation management.SELECTED DRAWING: Figure 3

Description

The present invention relates to an agricultural support program, an agricultural support method and an agricultural support device.

Conventionally, in catalogs and the like containing information on agricultural products, it is common to express the characteristics and characteristics of each variety with words, images, average values, and the like. Therefore, when selecting varieties to be cultivated, agricultural workers refer to the characteristics and characteristics of each variety expressed in words, images, average values, and the like.

Further, in recent years, when a user specifies a cultivation area, a cultivation time, and an item, a technique for displaying a list of varieties that can be cultivated in the specified cultivation area and time among the specified items is known (for example,). See Patent Document 1 etc.).

Japanese Unexamined Patent Publication No. 2002-203002

However, since the catalog only lists the general characteristics and characteristics of the varieties, it is not possible to accurately grasp whether they are suitable for the place where they are cultivated or the environmental conditions. For this reason, when actually cultivated, there is a risk that the crops will not grow as expected. Further, even if the varieties are listed as in Patent Document 1, it is difficult to determine which cultivar should actually be cultivated.

An object of the present invention is to provide an agricultural support program, an agricultural support method, and an agricultural support device capable of outputting appropriate information for supporting cultivar selection or cultivation management.

In one embodiment, the agricultural support program reads parameters indicating the characteristics of each variety of the crop from the storage unit, acquires information on the cultivation environment of the crop, the parameters read from the storage unit, and the acquired cultivation environment. Based on the information about the above, a growth model for each variety is created, a prediction for the growth for each variety is executed from the growth model for each variety, and the predicted result is used to support selection or cultivation management of the variety. It is a program that outputs information and causes a computer to execute processing.

The agricultural support program, agricultural support method, and agricultural support device of the present invention have the effect of being able to output appropriate information to support cultivar selection or cultivation management.

It is a figure which shows the structure of the agricultural system which concerns on one 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. 1. It is a functional block diagram of a server. It is a flowchart which shows the process of a server. It is a figure which shows the outline of the growth model. It is a figure which conceptually shows the change of the crop size in the simulation. For leaves and stems, the relationship between the growth stage and size is shown as a relative value. It is a figure which shows an example of the meteorological data used for a simulation. It is a figure which shows an example of a parameter 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. It is a figure (the 1) which shows the screen which displays the simulation result. It is a figure (2) which shows the screen which displays the simulation result. 13 (a) and 13 (b) are views (No. 3) showing a screen for displaying a simulation result. It is a figure (4) which shows the screen which displays the simulation result. It is a flowchart which shows an example of the processing of the update part.

Hereinafter, one embodiment of the agricultural system will be described in detail with reference to FIGS. 1 to 15. FIG. 1 schematically shows the configuration of the agricultural system 100 according to the embodiment. In the agricultural system 100 of the present embodiment, when a farmer or the like (hereinafter referred to as "producer") cultivates fruits and vegetables such as tomato, strawberry, cucumber, and paprika, the producer selects a cultivar and manages the cultivation. It is a system for providing information to support. In this embodiment, the case of a producer who cultivates strawberries will be described.

As shown in FIG. 1, the agricultural system 100 includes a server 10 as an agricultural support device and a user terminal 70. The user terminal 70 is a terminal such as a PC (Personal Computer), a tablet-type terminal, or a smartphone used by the producer. The server 10 and the user terminal 70 are connected to a network 80 such as the Internet, and information can be exchanged between the devices.

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

The server 10 acquires information from the user terminal 70, generates information to support strawberry variety selection and cultivation management based on the acquired information, and uses a screen for displaying the information by the producer. It is a device that outputs to the user terminal 70.

FIG. 2B shows the hardware configuration of the server 10. As shown in FIG. 2B, the server 10 includes a CPU 90 as a computer, a ROM 92, a RAM 94, a storage unit (here, SSD or HDD) 96, a network interface 97, a portable storage medium drive 99, and the like. There is. Each component of the server 10 is connected to the bus 98. In the server 10, the CPU 90 executes a program (including a cultivation assistance program) stored in the ROM 92 or the HDD 96, or a program (including a cultivation assistance program) read from the portable storage medium 91 by the portable storage medium drive 99. By doing so, the functions of each part shown in FIG. 4 are realized. The functions of each part in FIG. 4 may be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

FIG. 3 shows a functional block diagram of the server 10. In the server 10, when the CPU 90 executes the program, as shown in FIG. 3, the selection reception unit 41, the environment data acquisition unit 42 as an acquisition unit, the cultivation information acquisition unit 43, the simulation unit 44, the output unit 45, It functions as an update unit 46. Note that FIG. 3 also shows the parameter DB 50 as a storage unit stored in the storage unit 96 or the like of the server 10.

The selection reception unit 41 receives information on the combination of the variety (for example, Tochiotome, Koimi Nori, etc.) selected by the producer and the cultivation point (for example, Tsukuba, Morioka, Kurume, etc.). The producer may select one combination of the variety and the point, or may select a plurality of combinations. The selection reception unit 41 transmits the received information to the simulation unit 44.

The environmental data acquisition unit 42 acquires environmental data at the cultivation point based on the information of the cultivation point received by the selection reception unit 41. The environmental data acquisition unit 42 is data corresponding to a cultivation point selected from past meteorological data and future meteorological data (predicted data) managed in the server 10 or in a device other than the server 10. To get. Meteorological data includes data such as outdoor solar radiation and air temperature, solar radiation and air temperature in greenhouses, humidity, CO ₂ concentration, soil temperature, and soil moisture. The environment data acquisition unit 42 transmits the acquired data to the simulation unit 44.

The cultivation information acquisition unit 43 acquires cultivation information (for example, planting date, cultivation density, soil cultivation / solution cultivation, number of leaves at the time of planting, fertilizer application amount, culture solution concentration, etc.) input by the producer. The cultivation information acquisition unit 43 transmits the acquired information to the simulation unit 44.

The simulation unit 44 reads the parameters corresponding to the varieties accepted by the selection reception unit 41 from the parameter DB 50, and uses the read parameters and the data transmitted from the environmental data acquisition unit 42 and the cultivation information acquisition unit 43. , Create a growth model. Here, the parameters stored in the parameter DB 50 are, for example, the parameters as shown in FIG. 9, and are defined for each product type. Then, using the created growth model, the simulation unit 44 executes a simulation regarding the growth of the crop when the varieties selected by the producer are cultivated at the cultivation point and cultivation method selected by the producer. As a result of the simulation of the simulation unit 44, the leaf area, the flowering date by flower cluster, the yield by flower cluster, the fruit dry matter distribution rate, the photosynthesis amount, the growth amount (stems and leaves), the growth amount (fruit), and the nutrient absorption amount (fertilizer application amount). ) Etc. are obtained. The simulation unit 44 transmits the simulation result to the output unit 45.

The output unit 45 generates a screen for displaying the simulation result received from the simulation unit 44, and transmits the screen to the user terminal 70 used by the producer. At this time, the output unit 45 summarizes the simulation results for each cultivation condition (combination of varieties, cultivation points, and cultivation information), and generates a screen for displaying the simulation results of different cultivation conditions in a comparable state.

The renewal unit 46 acquires information on the varieties actually cultivated and cultivation results (for example, leaf area, flowering date for each flower cluster, yield for each flower cluster, etc.) from the producer. Then, the updating unit 46 updates the parameter stored in the parameter DB 50 so that the actual cultivation result and the simulation result come close to each other. In addition, the updating unit 46 determines whether or not to update the parameter using the actual cultivation result based on the reliability predetermined for the producer who input the actual cultivation result. May be.

(About the processing of server 10)
Next, the details of the processing of the server 10 will be described.

FIG. 4 shows the processing of the server 10 in a flowchart. In the process of FIG. 4, first, in step S10, the selection reception unit 41 waits until the producer selects a combination of the variety and the cultivation point on the user terminal 70. Here, for example, each combination of "Tochiotome / Tsukuba", "Tochiotome / Morioka", "Koimi Nori / Tsukuba", "Oi C Berry / Tsukuba", and "Sachinoka / Kurume" is selected by the producer. And.

If the determination in step S10 is affirmed, in the next step S12, the selection reception unit 41 acquires the combination of the selected variety and the cultivation point 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 meteorological data (past data and forecast data) at the cultivation point based on the cultivation point selected by the producer, which was accepted by the selection reception unit 41. ..

Next, in step S16, the cultivation information acquisition unit 43 waits until the cultivation information (for example, planting time, planting density, soil cultivation / nutrient cultivation, number of leaves at the time of planting) is input by the producer. When the producer inputs the cultivation information, the process proceeds to step S18, and 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 stored in the parameter DB 50, and the weather data and cultivation information acquisition unit acquired by the parameter and the environmental data acquisition unit 42. Based on the cultivation information acquired by 43, a growth model corresponding to the combination of the selected variety and the cultivation point is created.

Then, the simulation unit 44 executes a simulation using the created growth model, and obtains a simulation result showing how each variety grows at each cultivation point. Details of the growth model and simulation results created by the simulation unit 44 will be described later. The simulation unit 44 transmits the simulation result to the output unit 45.

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

(About simulation)
Hereinafter, the simulation executed by the simulation unit 44 will be described in detail.

(1) Basic concept of simulation In this embodiment, a growth model that can explain important differences between varieties is used. Specifically, as shown in FIG. 5, properties related to photosynthesis, distribution of photosynthesis products, leaf development and elongation, flower development and hypertrophy, nutrient absorption, morphological characteristics (leaf shape, light receiving posture), etc. will be explained. Use a growth model that can be used. In this embodiment, a simulation for strawberries will be described, but it is designed so that the meaning of the parameters is clarified so that it can be diverted to other crops.

(A) Growth pattern In the simulation, meteorological data (daily average temperature and daily outdoor total solar radiation) are given to the growth model on a daily basis, and each organ (root, leaf, crown, fruit) of the crop is used. Calculate the growth situation. Here, the crop is actually always in a growing state, and the crop size (root, leaf, crown size, number of fruits, etc.) is constantly changing, but in the simulation, the crop size is fixed at 0 o'clock and its It is assumed that the temperature and sunlight will be received from 0 to 24:00 in the state. If it is desired to reduce the error due to this assumption, the calculation interval of the simulation (the calculation interval of this simulation is one day) may be shortened.

In this simulation, when calculating the Nth day, the crop size for that day is determined at 0 o'clock on the Nth day. That is, in this simulation, the state for the crop to receive the temperature and the amount of solar radiation from 0 to 24 o'clock on the Nth day (relative light receiving rate: the value obtained from the leaf area index), and the distribution rate (the photosynthetic product produced on the day is the leaf). , The ratio of distribution to the crown, flower cluster, and root organs) is fixed. Then, in this simulation, at 24:00 on the Nth day, the amount of photosynthesis is calculated from the temperature, the amount of solar radiation, and the CO ₂ concentration on the day (Nth day), and the crop size changes from the values by the calculation of the growth of the crop. do. FIG. 6 conceptually shows the change in crop size in this simulation. As for the first day of calculation, the crop size at 0 o'clock (the crop size on the 0th day) cannot be calculated, so the producer or the like sets the value.

(B) Relationship between the amount and size of photosynthetic products In this simulation, the photosynthetic products are called "sources" and the organs that produce the sources are called "source organs". The source organs are mainly leaves. On the other hand, the organ that stores and consumes the source is called a "sink organ". The sink organs are roots, crowns, leaves and fruits, but the most consumed sink organs are fruits.

In this simulation, the growth of crops is mainly determined by the temperature. For example, the growth process of a single leaf is described as follows using the integrated temperature. The cumulative temperature at the start of growth is 1.0, the cumulative temperature at the start of growth is 0, and the leaf growth stage is shown as a relative value of the cumulative temperature. This is defined as [Leaf Growth Index].

Here, the relative leaf size (size) corresponding to the [Leaf Growth Index] is defined as the [Leaf Size Index] (Fig. 7). In the [Leaf Size Index], the size of the fully grown state is 1.0, and the size before the start of growth is 0.

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

The difference in the value of S (x) between the time point of 0 o'clock and the time point of 24:00 on the Nth day (see FIG. 7) is defined as [Leaf Increase Index] as an index value of the amount of increase in leaf growth per day. Similarly, for the flower cluster, the relative growth amount of the flower cluster [Fruits Increase Index] is defined. The value of the sigmoid curve for this simulation is S (x) = 0.007 when x = 0 and S (x) = 0.993 when x = 1, so S (x) when x = 0 or less. When = 0, x = 1 or more, change to S (x) = 1 and use.

(C) Outline of simulation model The simulation is executed based on meteorological data and parameters (constants). As shown in FIG. 8, the meteorological data used for the simulation are the daily average temperature (° C), the day-outdoor total solar radiation (MJ / m ² ), the CO ₂ concentration (ppm), and the like. FIG. 9 shows an example of parameters set by a user such as a producer. In this simulation, the calculation is performed on a daily basis, and there are values calculated for each leaf, for each flower cluster, and for the entire crop. Growth is calculated for each organ (leaf, crown, fruit, root, inflorescence) of the crop.

(2) Simulation method (a) Outline of simulation This simulation is roughly divided into the processes of "photosynthesis amount calculation", "crop growth calculation", "fruit yield calculation" and "nutrient absorption amount". .. These calculations are performed on a daily basis. The variables (relative light receiving amount, distribution rate) used in the photosynthesis amount calculation are the values at midnight on the calculation day (the day before the calculation day). The relative light receiving amount is a variable for obtaining the light receiving amount used by the crop for photosynthesis from the amount of solar radiation. The distribution rate is the ratio at which the source synthesized by the crop on that day is distributed to each organ (root, leaf, crown, fruit). The amount of photosynthesis at 24:00 on the calculation day is calculated from the distribution rate, these values, the temperature of the day, and the amount of solar radiation. Then, the degree of growth of the crop at 24:00 on the calculation day is calculated based on the amount of photosynthesis. The calculation of crop growth is divided into (a) distribution amount calculation, (b) leaf area calculation, (c) fruit set number calculation, (d) fruit dry matter weight calculation, and (e) distribution rate calculation.

In the present specification, the subscript N attached to the variable name indicates the result at 24:00 on the Nth day. Further, the variable calculated for each leaf is given the subscript M (order of leaves), and the variable calculated for each flower cluster is given the subscript F (order of flower clusters). Variables and constants used in the formula shall be indicated by adding []. In this simulation, the value at 0:00 on the first day of calculation is used as the "initial value".

(B) Crop growth process In this simulation, the growth of crops is determined by the relationship between the previous leaves and the order of the previous flower clusters (the order of occurrence of leaves and flower clusters. The earlier the development, the smaller the order), and the integrated temperature. FIGS. 10 (a) and 10 (b) are diagrams conceptually showing the growth process of leaves and flower clusters in this simulation (the numerical values described below are fictitious values assuming fictitious varieties. Not the actual value). As shown in FIG. 10A, the growth of the next leaf of the leaf starts after the integrated temperature reaches 150 ° C., starting from the start of the growth of the previous leaf. Starting from the start of this growth, the leaf growth ends when the integrated temperature reaches 450 ° C. On the other hand, as shown in FIG. 10B, in the flower cluster, the flower bud differentiation of the next flower cluster starts after the integrated temperature reaches 600 ° C., starting from the flower bud differentiation of the previous flower cluster. Starting from this flower bud differentiation, the flowering period is reached when the integrated temperature reaches 600 ° C., and the enlargement of the fruits constituting the inflorescence begins. Further, the growth of the flower cluster ends when the integrated temperature reaches 600 ° C. starting from the time of flowering. Further, as shown in FIG. 10B, the period from the start of flower bud differentiation to the integrated temperature of 150 ° C. is the flower number determination period, and the number of fruit set is determined under the condition of this period.

(C) Calculation of photosynthesis amount The light utilization efficiency LUE (gDW / MJ) is a value indicating the dry matter production amount of the entire crop per light receiving amount. Given as a function of temperature or CO ₂ concentration.

In addition, the amount of solar radiation in the house [total solar radiation in the house] (MJ / m ² ) is calculated by multiplying the [outdoor total solar radiation] (MJ / m ² ) by the [solar transmission] (constant) of the house. Find (Equation (2)).
[Insolation in the house] _N = [Insolation transmittance] ・ [Outdoor insolation] _N … (2)

The [light receiving amount] (MJ / m ² ) used by crops for photosynthesis is calculated by the following equation (3). As the relative light receiving rate, a value obtained from the leaf area on the previous day is used.
[Light receiving amount] _N = [Relative light receiving amount] _(N-1)・ [Total solar radiation in the house] _N … (3)

Furthermore, the [dry matter production amount] (gDW / m ² ), which is the amount of photosynthesis of the entire crop per unit area, is calculated by the following equation (4).
[Dry matter production] _N = [Received light] _N・ [LUE] _N … (4)

In addition, the [photosynthesis amount] (gDW / strain), which is the amount of photosynthesis per strain, uses the [Plant Density] (stock / m ² ), which is the number of crops (constant) per unit area, and the following equation (5). ).
[Photosynthesis amount] _N = [Dry matter production amount] _N / [Plant Density]… (5)

In the leaf area calculation performed later, [SLA] (m ² / gDW), which is the specific leaf area, which is the leaf area per dry matter weight of the leaves, is used. [SLA] _N is expressed as a function of temperature.

(3) Crop growth calculation (a) Distribution amount calculation In the distribution amount calculation, the distribution amount of the produced photosynthetic amount to each organ is obtained. The distribution amount is calculated as the value of the whole crop and is basically obtained by the photosynthesis amount and the distribution rate, but the maximum value of the source that the flower cluster can receive (the maximum distribution amount) is [flower cluster potential growth amount] (gDW / strain). To make corrections. [Flower cluster potential growth amount] can be obtained by the following equation (6). The [flower cluster growth coefficient] (gDW / ° C.) in equation (6) is a coefficient indicating the maximum value of the source that one flower cluster can receive for a temperature of 1 ° C., and is a [Total Fruits Increase Index]. Is the total value of the relative growth of all flower clusters.
[Hanabo potential growth amount] _N
= [Flower cluster growth coefficient] ・ [Temperature] _N・ [Total Fruits Increase Index] _N-1 … (6)

The amount of distribution is calculated in three steps. Therefore, (1) and (2) are added to the variables in the first two stages, and they are described separately from the final result. The unit of photosynthesis amount and distribution amount is (gDW / strain). [Leaf distribution (1)], [Crown distribution (1)], [Flower distribution (1)], [Root distribution (1)] (gDW / strain) are distributed the day before (0:00 on the day). According to the rate, the amount of photosynthesis from 0 to 24:00 on the day is assigned to each organ, and it is calculated by the following formulas (7A) to (7D).
[Leaf distribution amount (1)] _N = [Leaf distribution rate] _N-1 · [Photosynthesis amount] _N … (7A)
[Crown distribution amount (1)] _N = [Crown distribution rate] _N-1 · [Photosynthesis amount] _N … (7B)
[Flower cluster distribution amount (1)] _N = [Flower cluster 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 (2)], [crown distribution (2)], and [root distribution (2)] (gDW / strain) are as follows so that the flower cluster distribution does not exceed the flower cluster potential growth. Correct as follows.
[Leaf distribution (2)] _N = [Leaf distribution (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 distribution amount (2)], if [Flower cluster distribution amount (1)] _N <[Flower cluster potential growth amount] _N , then
[Flower cluster distribution amount (2) _N = [Flower cluster distribution amount (1)] _N … (7C1)'
And in other cases,
[Flower cluster distribution amount (2)] _N = [Flower cluster potential growth amount] _N … (7C2)'
And.

Here, the [surplus dry matter amount] (gDW / strain), which is the amount of photosynthesis that was not distributed to the flower cluster because it exceeded the flower cluster potential growth amount, is expressed by the following equation (8).
[Amount of excess dry matter] _N = ([Leaf distribution (1)] _N + [Crown distribution (1)] _N + [Flower distribution (1)] _N + [Root distribution (1)] _N )-( [Leaf distribution (2)] _N + [Crown distribution (2)] _N + [Flower distribution (2)] _N + [Root distribution (2)] _N )… (8)

The excess dry matter amount is redistributed to other than the flower cluster, and the respective distribution amounts are calculated by the following formulas (9A) to (9D).
[Leaf distribution amount] _N = [Leaf distribution amount (2)] _N + [Leaf surplus distribution rate] ・ [Surplus dry matter amount] _N … (9A)
[Crown distribution amount] _N
= [Crown distribution amount (2)] _N + [Crown surplus distribution rate] / [Surplus dry matter amount] _N … (9B)
[Flower cluster distribution amount] _N = [Flower cluster distribution amount (2)] _N … (9C)
[Root distribution amount] _N = [Root distribution amount (2)] _N + [Root surplus distribution rate] / [Supplementary dry matter amount] _N … (9D)

(B) Leaf area calculation In the leaf area calculation, the area and the dry matter weight (DW) of the leaves and crowns are calculated for each leaf. First, the [Leaf Growth Index] (dimensionless) showing the leaf growth stage as a relative value of the integrated temperature is obtained by the following equation (10A). In addition, M is the order of leaves.
[Leaf Growth Index] _{M, N}
= {[Integrated temperature] _N- (M-1) · 150} / 450 ... (10A)

The [Leaf Size Index] (dimensionless), which is a leaf size index, is calculated by the following equation (10B). The initial value of [Leaf Size Index] is zero.

Further, the [Leaf Increase Index] (dimensionless), which is a leaf growth index, is obtained by the following equation (11) from the change in the leaf size index from 24:00 on the previous day (0:00 on the day) to 24:00 on the day.
[Leaf Increase Index] _{M, N}
= [Leaf Size Index] _{M, N} － [Leaf Size Index] _{M, N-1} … (11)

Further, the total of [Leaf Increase Index] of all leaves is set as [Total Leaf Increase Index], and [Total Leaf Increase Index] is obtained from the following equation (12). In addition, NL means the number of leaves.

Then, using these values, the dry weight of leaves [Leaf DW] (gDW / strain) is calculated as follows. The initial value of [Leaf DW] is set by the producer etc. only for the first leaf (here, the leaf newly developed after planting is the first leaf) (for example, 10.0), and the others are zero. And.
[Leaf DW] _{M, N} = [Leaf DW] _{M, N-1} + [Leaf ΔDW] _{M, N} … (13A)
[Leaf ΔDW] _{M and N} are expressed by the following equation (13B).
[Leaf ΔDW] _{M, N}
= [Leaf Increase Index] M _{, N}・ [Leaf Increase Index] _{M, N} / [Total Leaf Increase Index] _N
… (13B)

In addition, the crown considers that the crown increases at a certain rate at the same time as the increase of one leaf, and the dry weight [Stem DW] (gDW / strain) is calculated as follows in the same manner as [Leaf DW]. The initial value of [Stem DW] is set by the producer etc. only in the first section (here, the section newly generated after planting is referred to as the first section) (for example, 5.0), and zero in other cases. do.
[Stem DW] _{M, N} = [Stem DW] _{M, N-1} + [Stem ΔDW] _{M, N} … (14A)
[Stem ΔDW] _{M and N} are expressed by the following equation (14B).
[Stem ΔDW] _{M, N}
= [Crown Distribution] _{M, N}・ [Leaf Increase Index] _{M, N} / [Total Leaf Increase Index] _N
... (14B)

The leaf area [Leaf Area] (m ² / strain) is calculated by the following equation (15) using the specific leaf area [SLA] (m ² / gDW). The [Leaf Area] is set by the producer or the like only for the first leaf, and 0 for the other leaves.
[Leaf Area] _{M, N} = [Leaf Area] _{M, N-1} + [SLA] _N・ [Leaf Distribution] _{M, N}・ [Leaf Increase Index] _{M, N} / [Total Leaf Increase Index] _N … ( 15)

Assuming that the total area of leaves [Leaf Area] on the crop is [Total Leaf Area (1)] (m ² / strain), [Total Leaf Area (1)] is given by the following equation (16). ).

Furthermore, the leaf parameters used for the photosynthesis amount calculation are calculated.

First, for the leaf area index [LAI] (dimensionless), which is the area of leaves per unit land area, the following equation (stock / m ² ), which is the number of crops per land area, is used. Obtained from 17).
[LAI] _N = [Total Leaf Area] _N・ [Plant Density]… (17)

The [relative light receiving amount] (dimensionless), which represents the ratio of the amount of solar radiation received by the crop to the amount of solar radiation per land area, is the extinction coefficient [K] (dimensionless) and the leaf area index of the previous day. Is obtained from the following equation (18). The extinction coefficient is a coefficient indicating the ease of reaching the inside of the canopy.
[Relative light reception amount] _N = 1-exp (－ [K] ・ [LAI] _N )… (18)

(C) Calculation of the number of fruit set The number of fruit set is the amount of photosynthesis (period dry matter production amount) in a specific period (flower number determination period) starting from the day after the flower bud differentiation of the crop starts, and the period dry matter production amount and fruit set number. It is calculated for each flower cluster from the relational expression of. Flower bud differentiation occurs in the order of flower cluster order, and after the one less flower cluster order (previous order flower cluster) is differentiated, the next flower cluster order is not differentiated unless a certain amount of temperature is accumulated.

The [Differentiation start index] indicating that differentiation has started is determined by [Differentiation condition determination A] (° C) and [Differentiation condition determination B] described below. Here, the [integrated temperature during flower bud differentiation period] (° C.) is the integrated temperature on the day when the [differentiation start index] first becomes 1, and is set to −1.0 before that.

In this simulation, based on the above value of [Integrated temperature during flower bud differentiation period], did the integrated temperature from the day after the start of flower bud differentiation in the previous order flower cluster exceeded a certain value (for example, 600 ° C) as follows? Find [Differentiation Condition Judgment A], which is an index to judge whether or not. As for the value of 600 ° C., since four leaves are usually generated between the flower clusters in the cultivar, the integrated temperature required for one leaf to be generated is set to 150 ° C. F in the formula indicates the flower cluster order.
[Differentiation start index] When _{F-1, N} = 1,
[Differentiation condition judgment A] _{F, N}
= [Integrated temperature] _N -([Integrated temperature during flower bud differentiation period] _{F-1, N} +600)… (19A)
In other cases
[Differentiation condition judgment A] _{F, N} = -1.0 ... (19B)
Ask.

Further, [differentiation condition determination B] (gDW / strain), which is an index for determining whether the amount of photosynthesis is in a state where flower bud differentiation is possible, is obtained from the following equation (20).
[Differentiation condition judgment B] _N = (N-6 to N day average value of [photosynthesis amount]) ... (20)

In addition, once the [differentiation start index] indicating that flower bud differentiation has started becomes 1, it is set to 1 thereafter.
That is, if [Differentiation start index] _{F, N-1} = 1, then
[Differentiation start index] _{F, N} = 1 ... (21A)
If 0.0 ≤ [differentiation condition judgment A] _{F, N} and 1.0 <[differentiation condition judgment B] _N ,
[Differentiation start index] _{F, N} = 1 ... (21B)
In other cases
[Differentiation start index] _{F, N} = 0 ... (21C)
And.

Further, the [flower bud differentiation stage integrated temperature] in the above equation (19A) is the integrated temperature on the day when the value on the previous day of the [differentiation start index] is 0 and the value on the day is 1. The initial value of [Integrated temperature for flower bud differentiation period] is -1.0, and once [Integrated temperature for flower bud differentiation period] is set, the same value is used. The reason why the initial value is set to a negative value is to classify the [integrated temperature at the flower bud differentiation stage] after the start of the flower bud differentiation stage as a positive value and the value before that as a negative value. This classification based on positive and negative values is also performed for the integrated temperature during the flowering period.
That is, if [Differentiation Start Index] _{F, N-1} = 0 and [Differentiation Start Index] _{F, N} = 1.
[Integrated temperature during flower bud differentiation period] _{F, N} = [Integrated temperature] _N ... (22A)
In other cases
[Integrated temperature during flower bud differentiation period] _{F, N} = [Integrated temperature during flower bud differentiation period] _{F, N-1} ... (22B)
Is.

Furthermore, in this simulation, the period in which the integrated temperature after the start of flower bud differentiation is in the range of 0 to 150 ° C. is set as the flower number determination period, and it is an index indicating whether the flower number determination period is [Flower number determination period Index (1). )] (Dimensionless) is calculated as follows. The period for determining the number of flowers is defined as a period during which one leaf develops, and the integrated temperature for the period for determining the number of flowers is set to 150 ° C., assuming that the integrated temperature per leaf is 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}
= ([Integrated temperature] _N- [Integrated temperature during flower bud differentiation period] _{F, N} ) / 150 ... (23B)

Then, the [flower number determination period Index] (dimensionless) is obtained as follows so that it becomes 1 when the integrated 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)

By the way, [decision period photosynthesis amount] (gDW / strain), which is an integrated value of the photosynthesis amount in the flower number determination period, is expressed by the following equation (25).
[Determining period photosynthesis amount] _{F, N} = [Determining period photosynthesis amount] _{F, N-1} + [Flower number determination period Index] _{F, N} · [Photosynthesis amount] _{F, N} ... (25)

The [number of fruit set (1)] (pieces / strain) is the [determined period photosynthesis amount] and [number of fruit set per determined period dry matter production amount] (m ² · pieces / gDW ·) in the above formula (25). Strain) and [Plant Density] (strain / m ² ) can be used to obtain the following equation (26).
[Number of fruits set (1)] _{F, N} = [Amount of photosynthesis during the determination period] _{M, N}・ [Number of fruits set per dry matter production during the determination period] ・ [Plant Density]… (26)

The [number of fruit set] (pieces / strain) is corrected so as to be in the range of 1.0 to 10.0 as follows.
That is, if [number of fruits set (1)] _{F, N} <1.0,
[Number of fruits set] _{F, N} = 1.0 ... (27A)
If 1.0 ≤ [number of fruits set (1)] _{F, N} ≤ 10.0,
[Number of fruits set] _{F, N =} [Number of fruits set (1)] _{F, N} … (27B)
In other cases
[Number of fruits set] _{F, N} = 10.0 ... (27C)
And.

The flowering period is defined as the time when the integrated temperature from the flower bud differentiation period reaches 600 ° C., and the integrated temperature at that time is defined as [flowering period integrated temperature] (° C). The value of 600 ° C. is set on the assumption that four leaves bloom after the flower buds are differentiated and an integrated temperature of 150 ° C. is required for each leaf. The flowering period integrated temperature is set to be effective only after the flower bud differentiation period integrated temperature is determined, and the initial value is set to -1.0. Moreover, once the flowering period integrated temperature is set, it shall be the same value.
That is, if [Fahrenheit differentiation temperature integrated temperature] _{F, N} <0,
[Fahrenheit integrated temperature] _{F, N} = -1.0 ... (28A)
In other cases
[Fahrenheit integrated temperature] _{F, N} = [Fahrenheit differentiation temperature integrated temperature] _{F, N} +600 ... (28B)
Will be.

(D) Calculation of dry weight of flower clusters The [Fruits Growth Index] (dimensionless), which is the relative growth stage of flower clusters, is calculated as follows. If the integrated flowering temperature is invalid (less than 0), the Fruits Growth Index value shall be -1.0. The initial value of the Fruits Growth Index is also set to -1.0.
That is, if [Fahrenheit integrated temperature] _{F, N} <0,
[Fruits Growth Index] _{F, N} = -1.0 ... (29A)
In other cases
[Fruits Growth Index] _{F, N}
= ([Integrated temperature] _N- [Integrated flowering temperature] _{F, N} ) / 600 ... (29B)
And.

Further, the [Fruits Size Index] (dimensionless) is calculated by the following equation (30). The initial value of the Fruits Size Index is zero.

In addition, from the magnitude of the relative change in the inflorescence from 24:00 on the previous day (0:00 on the day) to 24:00 on the day, the [Fruits Increase Index (1)] (dimensionless) is used as the relative growth increase in the inflorescence. Obtained from (31).
[Fruits Increase Index (1)] _{F, N}
= [Fruits Size Index] _{F, N} － [Fruits Size Index] _{F, N-1} … (31)

However, since it is premised that the number of fruit set is 10, the flower cluster is corrected as in the following equation (32) to obtain [Fruits Increase Index] (dimensionless).
[Fruits Increase Index] _{F, N}
= ([Fruits Increase Index (1)] _{F, N} · [Number of fruits set] _{F, N} ) / 10… (32)

In addition, the total of [Fruits Increase Index] of all flower clusters is set to [Total Fruits Increase Index] (dimensionless) (see the following equation (33)). In addition, NF means the number of flower clusters.

In this simulation, these values are used to determine the weight of Hanafusa dry matter [Fruits DW] (gDW / strain). Basically, [Fruits DW] does not increase after [Fruits Growth Index] becomes larger than 1. The initial value of [Fruits DW] is 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] _N・ [Fruits Increase Index] _{F, N} / [Total Fruits Increase Index] _N
… (34B)

(E) Calculation of distribution rate The [distribution rate] (dimensionless) of a crop is calculated as the value of the entire crop based on the amount of crop growth. The distribution rate is obtained from [flower cluster sink intensity] (dimensionless), which is the ease of receiving the source in the flower cluster, and [total sink intensity], which is the sum of the sink intensities of all organs.

[Total Fruits Increase Index], [Total Leaf Increase Index], and [Total Stem Increase Index] are weighted from experimental data such as during dismantling surveys. [Fruit distribution adjustment coefficient], [Leaf distribution adjustment coefficient], and [Crown distribution adjustment coefficient] were set to [α], [β], and [γ]. From these, the flower cluster sink strength, leaf sink strength, crown sink strength, and total sink strength are calculated from the following equations.
[Hanafusa 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)

[Flower cluster distribution rate], [Root distribution rate], [Leaf distribution rate], and [Crown distribution rate] (dimensionless) are obtained from the following equations (36A) to (36D).
[Flower cluster distribution rate] _N = [Flower cluster sink strength] _N / [Sink strength total] _N … (36A)
[Root distribution rate] _N = 0.05 ... (36B)
[Leaf distribution rate] _N = [Leaf sink strength] _N / [Total sink strength] _N … (36C)
[Crown distribution rate] _N = [Crown sink strength] _N / [Total sink strength] _N … (36D)

(4) Calculation of fruit yield In this simulation, it is assumed that the target crops are not harvested in one day in the flower cluster, but are gradually harvested. The [Harvest Start Index] (dimensionless) indicating that harvesting is possible is set to 1 the day after the day after the [Fruits Increase Index] becomes 1.0 or more for the first time, and is set to zero before that. The day when the [Harvest Start Index] becomes 1 for the first time is set as the harvest start date. The [Harvest Start Index] is expressed as follows, and the initial value is zero.
That is, if [Harvest 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)
Will be.

Here, the total yield of each inflorescence of the fruit is [Fruits DW] (gDW / strain) on the harvest start date. Basically, [Fruits DW] does not increase after the [Fruits Growth Index] becomes larger than 1, so it is the same as long as it is after the harvest start date. It is assumed that one fruit is harvested over a period of 60 ° C., which is the dry weight of the fruit to be harvested per 1 ° C. [harvested fruit temperature DW] (gDW / strain) as follows.・ ℃) is calculated. The initial value of [harvest fruit temperature DW] is set to -1.0, and once a valid value is set, it is set to the same value.
That is, if [harvest fruit temperature DW] _{F, N-1} ≧ 0.0,
[Harvest fruit temperature DW] _{F, N} = [Harvest fruit temperature DW] _{F, N-1} … (38A)
If [Harvest 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}・ 60)… (38B)
In other cases
[Harvest fruit temperature DW] _{F, N} = -1.0 ... (38C)
To calculate.

Then, the weight of the dry fruit that can be harvested before the harvesting work on the calculation day is set to [Fruits un Harvest] (gDW / strain), and [Fruits un Harvest] is calculated as follows. In addition, [Fruits un Harvest] is given [Fruits DW] on the day before the harvest start date, and after that, the harvested amount decreases. The initial value of [Fruits un Harvest] is -1.0, and the value before and after the harvest period is 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 [Harvest Start Index] _{F, N-1} = 0 and [Harvest 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)
Will be.

If there are fruits that can be harvested after the start of the harvest period, the weight of the harvested fruit dry matter [Fruits HARVEST] (gDW / strain) is calculated as follows.
That is, when [Harvest Start Index] _{F, N} = 1 and [Fruits unHarvest] _{F, N} > 0.0,
[Fruits HARVEST] _{F, N}
= Min ([Harvest fruit temperature DW] _F・ [Temperature] _N , [Fruits unHarvest] _{F, N} )… (40A)
In other cases
[Fruits HARVEST] _{F, N} = 0.0… (40B)

Further, assuming that the total [Fruits HARVEST] of all flower clusters is [Fruit harvest dry weight] (gDW / strain), [Fruit harvest dry weight] can be expressed by the following equation (41).

By multiplying this [fruit harvesting dry matter weight] by the [fruit bioweight] (g / gDW), the [fruit harvesting bioweight] (g / strain) can be obtained as in the following equation (42).
[Fruit harvesting bioweight] _N = [Fruit harvesting dry matter weight] _N・ [Fruit bioweight]… (42)

Then, by multiplying the [fruit harvesting biological weight] by the [number of field strains] (strains), the [field fruit harvesting biological weight] (kg), which is the yield of the entire field, can be obtained as in the following equation (43).
[Biological weight of field fruit harvest] _N
= 0.001 ・ [Fruit harvesting biological weight] _N・ [Number of field strains]… (43)

The simulation unit 44 creates a growth model as described above using environmental data and parameters, and uses the growth model to execute a simulation on growth for each combination of varieties, cultivation points, and cultivation information selected by the producer. do. Then, the simulation result (value obtained by the simulation) of each combination is transmitted to the output unit 45. The simulation unit 44 can also simulate fruits and vegetables other than strawberries, such as tomatoes, strawberries, cucumbers, and paprika, by using parameters according to items and varieties.

(5) Calculation of nutrient absorption amount The nutrient absorption amount is obtained by multiplying the amount of increase in dry matter of leaves, crowns, fruits, and roots by the nutrient content rate. Here, N (nitrogen) is described as an example, but other elements can be obtained by the same calculation.
[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 and N} are leaf nutrient absorption amounts, [Leaf ΔDW] _{M and N} are leaf dry matter increases, and [Leaf N%] _{M and N} are leaves. Nutrient content. The same applies to the other formulas (44B) to (44D).

Here, the nutrient content ([Leaf N%] _{M, N,} etc.) is expressed by, for example, the following formula as a function of the growth index of the organ. Note that 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, each nutrient absorption amount of leaves, crowns, flower clusters, and roots is expressed by the following formula.

Further, the nutrient absorption amount [Total ΔN] _N per strain is expressed by the following formula (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 matter weight per strain. This can be multiplied by the planting density to obtain the "nutrient absorption amount per area". Further, by dividing the "nutrient absorption amount" by the [fertilizer utilization efficiency], the "fertilizer application amount" can be obtained.

(About the processing of the output unit 45)
When the output unit 45 receives the simulation result transmitted from the simulation unit 44, the output unit 45 generates a screen for displaying the simulation result. For example, the output unit 45 generates a screen for displaying information as shown in FIG. 11 as a simulation result. In this case, since the screen of FIG. 11 is displayed on the display unit 193 of the user terminal 70, the producer can determine which variety is cultivated at which cultivation point and how it grows. It is possible to confirm whether a certain yield can be obtained. In addition, since the simulation results when multiple items are cultivated under different cultivation conditions are displayed side by side, the producer can compare what kind of cultivation results can be obtained when different varieties and cultivation conditions are obtained. can.

Further, the output unit 45 generates, for example, a screen as shown in FIG. 12 showing the shipment amount by flower cluster when the cultivar selected by the producer is cultivated at the selected cultivation point, and outputs the screen to the user terminal 70. You can also do it. By referring to the screen of FIG. 12, the producer can confirm the transition of the yield in the top flower cluster, the second flower cluster, the third flower cluster, and the like. Although the yield of each flower cluster is displayed in a separate graph in FIG. 12, each graph may be displayed together in one graph. In this case, a bar graph showing the yield of each flower cluster may be displayed in a color-coded manner in an easy-to-understand manner. Further, the output unit 45 may display the transition of the total yield in a graph as shown in FIG. 13 (a), or may display the transition of LAI in a graph as shown in FIG. 13 (b). good. In addition, other information on growth (for example, leaf area, flowering date by flower cluster, photosynthesis amount, growth amount (crown, leaves, fruits, fruit set burden), nutrient absorption amount (fertilization amount), etc.) can be displayed numerically. , May be displayed as a graph.

Further, as shown in FIG. 14, for example, the output unit 45 displays the simulation result of the transition of the dry matter weight of the leaves, the crown, and each flower cluster when the varieties A, B, and C are cultivated at a certain cultivation point. May be good.

As described above, as shown in FIG. 11, the output unit 45 may display the simulation results for each combination of varieties, cultivation points, and cultivation information in a comparable manner, and FIGS. 12 to 13 (b) show. As shown, the simulation result when a certain variety is cultivated at a certain cultivation point may be displayed. Further, as shown in FIG. 14, simulation results when different varieties are cultivated at the same cultivation point may be displayed in a comparable manner. Further, the simulation results when cultivars at different cultivation points may be displayed in a comparable manner. In any case, it is possible to appropriately express the characteristics of the varieties as compared with the conventional catalog in which the characteristics of the varieties are expressed by words, images, average values, and the like. Therefore, the producer can appropriately select the variety and the cultivation point by confirming the simulation result. In addition, since it is possible to grasp how crops will grow in the future, cultivation management (securing workers, procuring materials, etc.) can be appropriately performed.

(About the processing of the update unit 46)
When the producer inputs measured values (actual cultivation results) such as yield transitions and LAI transitions, the updating unit 46 compares the measured values with the corresponding simulation results, and the simulation results are obtained. Adjust and update various parameters so that they approach the measured values. By doing so, the parameters are appropriately updated based on the measured values, so that appropriate simulation results can be obtained in the next and subsequent simulations.

If the parameters are updated based on the information input from all the producers, the parameters may not be updated properly. Therefore, the update unit 46 may update the parameters based on the input information only when the information is input from the predetermined producer (producer with high reliability). In this case, the update unit 46 executes the process according to the flowchart as shown in FIG.

In the process of FIG. 15, first, in step S30, the update unit 46 waits until the producer inputs the actual cultivation result (actual measurement value) via the user terminal 70. Then, when the actual cultivation result is input, the process proceeds to step S32, and the updating unit 46 determines whether or not the input is from a predetermined producer (producer with high reliability). If the determination in step S32 is denied, the entire process of FIG. 15 is terminated as it is, but if it is affirmed, the process proceeds to step S34. When the process proceeds to step S34, the update unit 46 updates the parameters so that the simulation result approaches the actual cultivation result, stores the parameters in the parameter DB 50, and ends all the processing of FIG.

When the parameter is updated using the information input from a certain producer, the update unit 46 may manage the updated parameter as a parameter dedicated to the producer. This allows each producer to customize the parameters according to the actual cultivation results. It should be noted that one producer may call and use the parameters of another producer.

The producer may directly modify the parameters. When the producer modifies the parameter, the updating unit 46 may update the parameter DB 50 according to the modified content.

As can be seen from the above description, the simulation unit 44 of the present embodiment functions as a reading unit that reads parameters indicating the characteristics of each crop variety from the storage unit (parameter DB 50). Further, the simulation unit 44 of the present embodiment creates a growth model for each variety based on the read parameters and information (environmental data) regarding the cultivation environment, and grows for each variety from the growth model for each variety. Functions as a predictor that executes predictions about.

As described above in detail, according to the present embodiment, the simulation unit 44 reads out the parameters indicating the characteristics of each crop variety from the parameter DB 50, and from the environmental data acquisition unit 42 to the cultivation point selected by the producer. Get the corresponding environment data. Further, the simulation unit 44 creates a growth model for each variety based on the acquired environmental data and the read parameters, and executes the simulation using the created growth model. Then, the output unit 45 outputs the simulation result as information for supporting the selection of varieties and cultivation management. As described above, in this embodiment, the result of simulating the growth of crops using the growth model created for each variety and each cultivation point is displayed as information for supporting variety selection and cultivation management, as in the conventional case. Compared with a catalog that expresses the characteristics of varieties with words, images, average values, etc., it is possible to appropriately express the characteristics of varieties. Therefore, the producer can appropriately select varieties and cultivation points, and can grasp how they will grow in the future, so that cultivation management can be appropriately performed.

Further, in the present embodiment, the updating unit 46 updates the parameter and updates the parameter DB 50 based on the actual cultivation result (yield, etc.) input by the producer. This makes it possible to update the parameters to appropriate values based on the cultivation results. In this case, the updating unit 46 can improve the accuracy of the simulation by updating the parameters so that the simulation result approaches the cultivation result.

Further, in the present embodiment, when the producer is a predetermined producer (producer whose reliability is equal to or higher than a predetermined value) (S32: affirmative), the renewal unit 46 sets the cultivation results input by the producer. Update the parameters based on (S34). This can increase the possibility that the parameters will be updated appropriately.

Further, in the present embodiment, the output unit 45 performs a simulation for each combination of varieties, cultivation points, 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 each combination of a plurality of varieties, cultivation points, and cultivation information, and determine which variety should be cultivated and how.

In the above embodiment, the case where the server 10 includes the update unit 46 has been described, but the present invention is not limited to this. That is, the server 10 does not have to include the update unit 46.

In the above embodiment, the case where the server 10 has the function of the agricultural support device of the present invention has been described, but the present invention is not limited to this, and the user terminal 70 may have the function of the agricultural support device. That is, the above processing may be realized by operating the stand-alone user terminal 70 independently.

The above processing function can be realized by a computer. In that case, a program that describes the processing content of the function that the processing device should have is provided. By executing the program on a computer, the above processing function is realized on the computer. The program describing the processing content can be recorded on a computer-readable storage medium (however, the carrier wave is excluded).

When a program is distributed, it is sold in the form of a portable storage medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. It is also possible to store the program in the storage device of the server computer and transfer the program from the server computer to another computer via the network.

The computer that executes the program stores, for example, the program recorded on the portable storage medium or the program transferred from the server computer in its own storage device. Then, the computer reads the program from its own storage device and executes the processing according to the program. The computer can also read the program directly from the portable storage medium and execute the processing according to the program. In addition, the computer can sequentially execute processing according to the received program each time the program is transferred from the server computer.

The embodiments described above are examples of preferred embodiments of the present invention. However, the present invention is not limited to this, and various modifications can be made without departing from the gist of the present invention.

10 servers (agricultural support equipment)
42 Environmental data acquisition department (acquisition department)
44 Simulation unit (reading unit, prediction unit)
45 Output unit 50 Parameter DB (storage unit)
100 agricultural system

Claims (9)

Read the parameters showing the characteristics of each crop variety from the storage unit,
Get information about the cultivation environment of crops,
Based on the parameters read from the storage unit and the acquired information on the cultivation environment, a growth model for each variety is created, and a prediction for growth for each variety is executed from the growth model for each variety. death,
The prediction result obtained by the above prediction is output as information to support the selection of varieties or cultivation management.
Agricultural support program to let a computer perform processing. The agricultural support program according to claim 1, wherein in the output process, prediction results regarding the growth of a plurality of varieties selected by the user are output in a comparable manner. The agricultural support program according to claim 1 or 2, wherein in the output process, the predicted results regarding the growth when the same variety is cultivated in different cultivation environments are output in a comparable manner. The agricultural support program according to any one of claims 1 to 3, wherein the parameter is updated based on the information input by the user, and the processing is further executed by the computer, which is stored in the storage unit. .. The agricultural support program according to claim 4, wherein the parameter is updated based on the cultivation information obtained as a result of the user cultivating the agricultural product. The agricultural support program according to claim 5, wherein the parameters are updated so that the prediction result approaches the cultivation information. The agricultural support program according to any one of claims 4 to 6, wherein when the reliability of the information input by the user is equal to or higher than a predetermined value, the information is used for updating the parameter. Read the parameters showing the characteristics of each crop variety from the storage unit,
Get information about the cultivation environment of crops,
Based on the parameters read from the storage unit and the acquired information on the cultivation environment, a growth model for each variety is created, and a prediction for growth for each variety is executed from the growth model for each variety. death,
The prediction result obtained by the above prediction is output as information to support the selection of varieties or cultivation management.
Agricultural support method characterized by a computer performing processing. A reading unit that reads out parameters indicating the characteristics of each crop variety from the storage unit,
The acquisition department that acquires information on the cultivation environment of agricultural products,
Based on the parameters read from the storage unit and the acquired information on the cultivation environment, a growth model for each variety is created, and a prediction for growth for each variety is executed from the growth model for each variety. Prediction section and
An output unit that outputs the prediction results obtained by the above prediction as information to support variety selection or cultivation management.
Agricultural support device equipped with.

Images