Information output device, system, and information output program

Abstract

To output appropriate information for a given range of environmental controls.SOLUTION: An information output device includes: a source strength calculation unit 36 that calculates the source strength of the crop for each specified period in the past based on environmental information for a specified range and information indicating the leaf growth status of the crop growing in the specified range; a sink strength calculation unit 34 that calculates the sink strength of the crop based on environmental information for the specified range and fruit set information for a test plant, which is a part of the crop; a fruit set probability calculation unit 38 that calculates the fruit set probability of the crop growing in the specified range based on the source strength and sink strength; a harvestable number estimation unit 40 that estimates the future harvestable number within the specified range based on the fruit set probability and environmental information for the specified range; and a processing unit (44, 46) that identifies the environmental conditions necessary to adjust the future harvestable number estimated by the harvestable number estimation unit to a predetermined range and outputs the identified environmental conditions to the environmental control device.SELECTED DRAWING: Figure 20

Description

[Technical Field]

[0001] The present invention relates to an information output device, a system, and an information output program. [Background technology]

[0002] The yield of crops such as tomatoes and peppers fluctuates due to environmental factors, even when grown in facilities such as greenhouses. Peppers, in particular, have large fluctuations in yield, so if the shipping volume is high, there is a risk of unsold produce, and if the shipping volume is low, there is a risk of lost sales opportunities. Furthermore, fluctuations in shipping volume can lead to a loss of credibility for producers and the inability to properly allocate workers.

[0003] Therefore, it is desirable to accurately predict crop yields in order to systematically obtain sales through contract shipments or to appropriately allocate personnel to harvest work.

[0004] Conventionally, yield predictions for crops grown in facilities such as greenhouses have often been made based on the experience and intuition of workers. Recently, a technique has been known for estimating yields using aerial images of farm fields and time-series weather data for specific growth stages of crops (see, for example, Patent Document 1). Another technique is known for predicting growth stages based on planting time and accumulated temperature (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-49 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-53927 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in the past, it was not possible to predict probabilistically whether future fruit would be produced, and therefore there was a high possibility that yield predictions could not be made with high accuracy.

[0007] The present invention aims to provide an information output device and an information output program that can output useful information related to environmental conditions and fruit thinning, and a system that enables appropriate environmental control and fruit thinning. [Means for solving the problem]

[0008] The first information output device of the present invention comprises a source strength calculation unit that calculates the source strength of the crop for each specified period in the past based on environmental information for a specified range and information indicating the growth state of leaves of the crop growing in the specified range; a sink strength calculation unit that calculates the sink strength of the crop based on environmental information for the specified range and fruit set information for a survey plant that is part of the crop; a fruit set probability calculation unit that calculates the fruit set probability of the crop growing in the specified range based on the source strength and the sink strength; a harvestable number estimation unit that estimates the future harvestable number in the specified range based on the fruit set probability and the environmental information for the specified range; and a processing unit that identifies the environmental conditions necessary to adjust the future harvestable number estimated by the harvestable number estimation unit to within a predetermined range and output the identified environmental conditions to an environmental control device.

[0009] The second information output device of the present invention comprises a sink strength calculation unit that calculates the sink strength of a crop based on environmental information of a specified range and fruit set information of a survey plant that is part of the crop growing in the specified range; a fruit set probability calculation unit that calculates the fruit set probability of the crop growing in the specified range based on the sink strength; a harvestable number estimation unit that estimates the future harvestable number in the specified range based on the fruit set probability and the environmental information of the specified range; and a processing unit that identifies the environmental conditions necessary to adjust the future harvestable number estimated by the harvestable number estimation unit to within a predetermined range and output the identified environmental conditions to an environmental control device.

[0010] The third information output device of the present invention comprises a source intensity calculation unit that calculates the source intensity of the crop for each specified past and future period based on past and future environmental information for a specified range in which the crop grows and information indicating the growth state of the leaves of the crop; a sink intensity calculation unit that calculates the sink intensity of the crop for each specified past and future period based on the past and future environmental information for the specified range and fruit set information of the crop; and a processing unit that adjusts the fruit set information of the crop so that the ratio between the source intensity and the sink intensity for each specified future period falls within a predetermined range, and outputs information regarding fruit thinning based on the adjusted fruit set information. [Effects of the Invention]

[0011] The information output device and the information output program of the present invention have the advantage of being able to output useful information related to environmental conditions and fruit thinning. Also, the system of the present invention has the advantage of being able to realize appropriate environmental control and fruit thinning work. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a diagram showing a configuration of an agricultural system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of a control device according to the first embodiment. [Figure 3] FIG. 2 is a functional block diagram of a control device according to the first embodiment. [Figure 4] 4 is a flowchart showing the processing of the control device of the first embodiment. [Figure 5] FIG. 1 is a diagram (part 1) showing a sink strength calculation table. [Figure 6] Figure 6(a) shows the distribution of dry matter weight per fruit against accumulated temperature for the variety "Artega" and the regression curve, and Figure 6(b) shows the results of differentiating Figure 6(a). [Figure 7] FIG. 2 is a diagram (part 2) showing a sink strength calculation table. [Figure 8] FIG. 10 is a diagram showing the relationship between source-sink ratio and fruit-setting probability for the variety "Artega." [Figure 9] This is a diagram (part 1) showing a harvest forecast table. [Figure 10] This is a diagram (part 2) showing the harvest forecast table. [Figure 11] FIG. 10 is a diagram (part 3) showing a sink strength calculation table. [Figure 12] This is a diagram (part 3) showing the harvest forecast table. [Figure 13] FIG. 4 is a diagram showing a sink strength calculation table (part 4). [Figure 14] This is a diagram (part 4) showing the harvest forecast table. [Figure 15] Figure 15(a) shows the distribution of dry matter weight per fruit against accumulated temperature for the variety "Nagano" and the regression curve, and Figure 15(b) shows the relationship between the source-sink ratio and fruit-setting probability for the variety "Nagano." [Figure 16] Figure 16(a) shows the distribution of dry weight per fruit against accumulated temperature for the variety "Nesbit" and the regression curve, and Figure 16(b) shows the relationship between the source-sink ratio and fruit-setting probability for the variety "Nesbit." [Figure 17] Figure 17(a) shows the distribution of dry matter weight per fruit against accumulated temperature for the variety "Trirosso" and the regression curve, and Figure 17(b) shows the relationship between the source-sink ratio and fruit-setting probability for the variety "Trirosso." [Figure 18] Figures 18(a) to 18(d) are graphs showing the predicted and actual measured fruit set probability 120 to 130 days after planting for the varieties "Artega," "Nagano," "Nesbit," and "Trirosso." [Figure 19] Figures 19(a) to 19(d) are graphs showing the change in fruit-setting probability and the change in yield with the number of days after planting for each of the varieties "Artega," "Nagano," "Nesbit," and "Trirosso." [Figure 20] FIG. 10 is a functional block diagram of a control device according to a first modified example. [Figure 21] FIG. 10 is a diagram showing a harvest prediction table according to Modification 2. [Figure 22]FIG. 10 is a diagram for explaining the relationship between the source-sink ratio and the fruit-setting probability in the second embodiment. [Figure 23] FIG. 10 is a functional block diagram of a control device according to a second embodiment. [Figure 24] 10 is a flowchart showing the processing of the control device of the second embodiment. [Figure 25] Figure 25(a) is a graph showing the calculation results of past and future source-sink ratios, and Figure 25(b) is a diagram showing an example of deleting information about fruits to be thinned from the sink strength calculation table when it is determined that fruit thinning is necessary. [Figure 26] Figure 26(a) is a graph showing the calculation results of the past and future source-sink ratios when one fruit is thinned, and Figure 26(b) is a graph showing the calculation results of the past and future source-sink ratios when two fruits are thinned. [Figure 27] This is a graph showing the calculation results of the past and future source-sink ratios when three fruits are thinned. [Figure 28] Figures 28(a) and 28(b) are diagrams for explaining that fruit thinning was able to control yield. DETAILED DESCRIPTION OF THE INVENTION

[0013] First Embodiment A first embodiment will be described in detail below with reference to Fig. 1 to Fig. 14. Fig. 1 shows a schematic configuration of an agricultural system 100 according to the first embodiment. The agricultural system 100 of this embodiment is a system that calculates the fruit-bearing probability and estimates and outputs the future harvestable number in a facility (e.g., a greenhouse) for cultivating crops such as tomatoes and peppers.

[0014] 1, the agricultural system 100 includes a control device 10, an outdoor sensor 12, a greenhouse sensor 14 installed in a greenhouse 18, and an environmental control device (hereinafter referred to as a controlled device) 16 that adjusts the environment inside the greenhouse 18. The control device 10, the outdoor sensor 12, the greenhouse sensor 14, and the controlled device 16 are connected via a network such as the Internet, allowing information to be exchanged between the devices.

[0015] The control device 10 is an information processing device that can be used by an operator cultivating a crop (say, peppers) in a greenhouse 18, and estimates and outputs (displays, etc.) the predicted harvestable number of peppers (the number of fruits that can be harvested) for each period (each week in this embodiment) based on environmental information acquired by the outdoor sensor 12 and the greenhouse sensor 14, and information input by the operator. The control device 10 can also control the controlled device 16. The configuration and processing of the control device 10 will be described in detail below.

[0016] The outdoor sensor 12 includes a temperature sensor that detects the temperature outside the greenhouse 18 and a solar radiation sensor that detects solar radiation, and inputs the detection results to the control device 10.

[0017] The greenhouse sensor 14 includes a temperature sensor that detects the air temperature inside the greenhouse 18, a solar radiation sensor that detects the solar radiation inside the greenhouse 18, and a CO2 concentration sensor that detects the CO2 concentration inside the greenhouse 18, and inputs the detection results to the control device 10.

[0018] The controlled devices 16 include heat pumps, ventilation windows, heaters, CO2 applicators, shading and heat-retaining curtains, etc. The heat pump is a device that lowers the temperature inside the greenhouse 18, and the ventilation window is a window that introduces outside air into the greenhouse 18. The heater is a device that raises the temperature inside the greenhouse 18, and the CO2 applicator is a device that adjusts the CO2 concentration inside the greenhouse 18. The shading and heat-retaining curtains are curtains that adjust the solar radiation and temperature inside the greenhouse 18. The controlled devices 16 are capable of performing operations according to instructions from the control device 10, and the environment inside the greenhouse 18 is adjusted by the operation of the controlled devices 16.

[0019] The configuration and processing of the control device 10 will now be described in detail. FIG. 2 schematically illustrates the hardware configuration of the control device 10. As illustrated in FIG. 2, the control device 10 includes a CPU 90, a ROM 92, a RAM 94, a storage unit (here, a HDD) 96, a network interface 97, a display unit 93 as a display device, an input unit 95, and a portable storage medium drive 99. The display unit 93 includes a liquid crystal display or the like, and the input unit 95 includes a keyboard, a mouse, a touch panel, or the like. These components of the control device 10 are connected to a bus 98. In the control device 10, the CPU 90 executes a program stored in the ROM 92 or the HDD 96, or a program read by the portable storage medium drive 99 from the portable storage medium 91, thereby realizing the functions of the components illustrated in FIG. 3. Note that the functions of the components illustrated in FIG. 3 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0020] Fig. 3 shows a functional block diagram of the control device 10. In the control device 10, the CPU 90 executes a program to realize the functions of an input reception unit 30, an environmental information acquisition unit 32, a sink strength calculation unit 34, a source strength calculation unit 36, a fruit-set probability calculation unit 38, a harvestable number estimation unit 40, and an information output unit 42, as shown in Fig. 3.

[0021] The input receiving unit 30 acquires information input by the worker via the input unit 95 and transmits it to the sink strength calculation unit 34. The information input by the worker includes information on which of the paprika plants cultivated in each area have borne fruit among the predetermined target plants (investigation plants). The information input by the worker also includes information on the leaf growth state of the target plants (such as leaf area or information necessary to calculate leaf area). Note that the information on fruit bearing and leaf growth state does not have to be input by the worker. For example, if a robot patrolling the greenhouse 18 can acquire information on the plants that have borne fruit using a camera or the like, the input receiving unit 30 may acquire information input from the robot.

[0022] The environmental information acquisition unit 32 acquires the detection results of the outdoor sensor 12 and the greenhouse sensor 14 and transmits them to the sink intensity calculation unit 34 and the source intensity calculation unit 36. The environmental information acquisition unit 32 also acquires past data (normal values, etc.) and forecast data (weather forecast mesh, etc.) from an external server or the like as future environmental information and transmits them to the harvestable quantity estimation unit 40.

[0023] The sink strength calculation unit 34 calculates the sink strength (fruit bearing burden) of the crops (paprika) in the greenhouse 18 based on the information acquired by the input receiving unit 30 and the information acquired by the environmental information acquisition unit 32.

[0024] The source intensity calculation unit 36 ​​calculates the source intensity (matter production) of the crop (paprika) in the greenhouse 18 based on the information acquired by the environmental information acquisition unit 32 and information related to the growth state of the leaves.

[0025] The fruit-bearing probability calculation unit 38 calculates the fruit-bearing probability of the crop (paprika) in the greenhouse 18 based on the sink strength calculated by the sink strength calculation unit 34 and the source strength calculated by the source strength calculation unit 36. Details of the method for calculating the fruit-bearing probability will be described later.

[0026] The harvestable number estimation unit 40 estimates the number of harvestable fruits (predicted harvestable number) in the greenhouse 18 for each future period (each week in this embodiment) based on the fruit-bearing probability calculated by the fruit-bearing probability calculation unit 38.

[0027] The information output unit 42 generates a screen including the predicted harvestable yield estimated by the harvestable yield estimation unit 40, and displays (outputs) the generated screen on the display unit 93 in response to a request from the operator.

[0028] (Regarding the processing of the control device 10) Next, the processing of the control device 10 will be described in detail with reference to the flowchart of FIG. 4 and other drawings as appropriate.

[0029] As a premise for the processing of Figure 4, it is assumed that the environmental information acquisition unit 32 acquires current environmental information inside and outside the greenhouse 18, past data (average values, etc.), and forecast data (weather forecast mesh, etc.), and transmits them to the sink strength calculation unit 34, source strength calculation unit 36, and harvestable quantity estimation unit 40.

[0030] 4, first, in step S10, the input receiving unit 30 waits for input of information on whether or not fruit has been set for one week, including the current day. When the operator inputs information on whether or not the plant being investigated has set fruit, the input receiving unit 30 acquires the input information and transmits it to the sink strength calculation unit 34 and the source strength calculation unit 36.

[0031] When information on whether or not fruit is set is input in step S10, the process proceeds to step S12, where the sink strength calculation unit 34 calculates the current sink strength S (for each day in the week including the current day) based on the fruit set information and the accumulated temperature. n is calculated. Figure 5 shows a table (sink strength calculation table) used to calculate sink strength. The table on the left side of Figure 5 shows information on whether fruit has set in each week (fruit set = 1, no fruit set = 0) and the accumulated temperature of the fruit after set. A node number (Node No.) is assigned to the set fruit. Figure 5 shows that fruit set of Node No. 1 was confirmed in the third week, and that the accumulated temperature of that fruit was 130°C.

[0032] Here, the sink strength calculation unit 34 calculates the sink strength Sn as follows.

[0033] The sink strength Sn can be expressed by the following equation (1). Sn={g(fr1)+g(fr2)+g(fr3)+…+g(fr n )}·Sd …(1)

[0034] In the above equation (1), g(fr n ) means the sink strength (g / fruit) of the fruit at Node No. = n, and Sd means the stem density (stems / m 2 ) and planting density (plant / m 2 ) by the number of stems per plant (stems / plant). The number of stems per plant for peppers is often 2, 3, or 4 (stems / plant), and the number chosen varies depending on the farm. For tomatoes, 1 (stems / plant) is common, but 2 to 4 (stems / plant) can also be mixed.

[0035] Figure 6(a) shows the relationship between the dry weight per fruit and the accumulated temperature, i.e., g(fr n ) and the regression curve are shown. The scatter plot in Figure 6(a) was obtained from the results of a test on the paprika variety Artega grown in rock wool. g(fr n ) is expressed by the following equation (2). g(fr n )=K·b·e -c.·xn …(2)

[0036] Here, K, b, and c are coefficients determined for each variety, and xn is the accumulated temperature.

[0037] Differentiating the above equation (2), g(fr n )' is expressed by a curve like that shown in Figure 6(b). g(fr n )' is expressed as the following equation (2)'. g(frn )'=(K·b·e -c.·xn )' …(2)'

[0038] In the case of the variety Artega, the coefficients in the above formula (2)' are K = 10.8 and b = 1.5 × 10 -3 , c=3.1×10 -3 Therefore, the above equation (2)' can be expressed as the following equation (2)". g(fr n )'=(10.8×1.5×10 -3 ×e -3.1×10-3·xn )' …(2)”

[0039] Therefore, when information indicating fruit set is input in the third week, the sink strength calculation unit 34 calculates g(fr1) by integrating the above equation (2)" between the accumulated temperature (xn) of 0°C and 130°C, as shown in the following equation (3).

[0040]

number

[0041] As a result of the above equation (3), g(fr n ) (sink strength in one week (Sn / stem / week)) is 0.14..., so the sink strength calculation unit 34 calculates the sink strength (Sn / stem / d) for each day of the week including the current day as 0.14... / 7 ≈ 0.02. Then, the sink strength calculation unit 34 updates the sink strength calculation table of FIG. 5 as shown in FIG. 7. In FIG. 7, the columns indicated by the bold lines store the sink strength for the third week and the sink strength for each day of the third week.

[0042] The curves in Figures 6(a) and 6(b) were obtained through the following tests. Research greenhouse: (Width 9m, depth 18m, eave height 4.8m, within the Vegetable and Floriculture Research Division of the National Agriculture and Food Research Organization) Sowing: June 4, 2019 Planting: July 10, 2019 Rockwool cultivation, 246 days after planting Measurement items: Weather data (temperature, CO2 concentration, solar radiation) Fruit set data (flowering date, fruit drop date, harvest date) Biometric data (LAI)

[0043] Next, in step S14, the source strength calculation unit 36 ​​calculates the source strength of the plant under investigation. The source strength means the total dry matter production amount for each day of each week.

[0044] The source intensity calculation unit 36 ​​first calculates the number of expanded leaves from the average temperature acquired from the environmental information acquisition unit 32. The number of expanded leaves means the number of expanded (spread) leaves, and the current number of expanded leaves can be calculated (estimated) from data showing the relationship between the past number of expanded leaves and the average temperature and the acquired average temperature value. Note that the number of expanded leaves may be a value that an operator actually counts the number of leaves and inputs into the user terminal 70.

[0045] The source strength calculation unit 36 ​​also calculates the individual leaf area (the area of ​​one leaf of average size and shape) from the leaf length (mm / leaf) and leaf width (mm / leaf) input from the user terminal 70. In this case, the source strength calculation unit 36 ​​calculates the individual leaf area (m 2 / leaf). The predetermined coefficient may be calculated in advance from leaf data obtained in the past.

[0046] Next, the source intensity calculation unit 36 ​​calculates the leaf area per stem (m ) from the number of expanded leaves and the leaf area. 2 / stem) and leaf area per individual (leaf area per stem × number of stems per individual, m 2 In this case, the source intensity calculation unit 36 ​​calculates the leaf area per stem by multiplying the number of expanded leaves by the area of ​​each individual leaf. Next, the source intensity calculation unit 36 ​​calculates the leaf area per individual and the stem density (stems / m 2 ) to calculate the Leaf Area Index (LAI). Here, the Leaf Area Index is calculated as the number of leaves per unit land area (1 m 2 ) to the total leaf area of ​​the crop (m 2) that is, the leaf area index (m 2 / m 2 ) can be said to be the product of leaf area per individual and planting density.

[0047] Next, the source intensity calculation unit 36 ​​calculates the leaf area index (LAI), the cumulative solar radiation (MJ / m 2 ), and the absorption coefficient. Specifically, the source intensity calculation unit 36 ​​calculates the daily integrated amount of received light (MJ / (m 2 d)) is calculated. p " is the cumulative amount of light received on the pth day after planting, "k" is the light absorption coefficient, and "LAI p ” is the leaf area index p days after planting, and “SR p " is the outdoor global solar radiation p days after planting. IL p =(1-e -k·LAIp )·0.55·0.5·SR p …(4)

[0048] Here, 0.55 means the light transmission coefficient inside the facility (inside the greenhouse), and 0.5 means the coefficient for conversion into photosynthetically active radiation (PAR).

[0049] Next, the source intensity calculation unit 36 ​​calculates the light use efficiency from the average CO2 concentration. Specifically, the source intensity calculation unit 36 ​​calculates the light use efficiency (g / MJ) based on the following formula (5). Note that the "LUE" in the following formula (5) p ” is the light utilization efficiency on day p, and “CO 2p " is the daytime CO2 concentration on day p (average CO2 concentration on day p). Also, "m" and "o" are coefficients obtained from the actual measured values. LUE p =m CO 2p -o …(5)

[0050] Next, the source intensity calculation unit 36 ​​calculates the integrated amount of received light for one day IL p and light utilization efficiency (LUE) pSpecifically, the source strength calculation unit 36 ​​calculates the total dry matter production DM for one day (day p) based on the following equation (6): p Calculate (g / (m² d)). DM p =IL p LUE p …(6)

[0051] Then, the source intensity calculation unit 36 ​​calculates the source intensity Sr from the following equation (7) using a predetermined relational expression f. Sr=f(DM p ) …(7)

[0052] It is assumed that the source strength Sr for each day in the third week (average source strength for each day in the third week (Sr / stem / d)) is Sr=0.42.

[0053] In the next step S16, the fruit-setting probability calculation unit 38 calculates the ratio of the sink strength Sn to the source strength Sr (source-sink ratio) R SS is calculated from the following equation (8). R SS =Sr / Sn …(8)

[0054] For example, in the third week, Sn = 0.02 and Sr = 0.42, so R SS can be calculated as 0.42 / 0.02=21.0.

[0055] Next, in step S18, the fruit-setting probability calculation unit 38 calculates the fruit-setting probability P FS is calculated using the following equation (9). P FS =0.2455×ln(R SS )+0.1769 …(9)

[0056] The coefficients in the above formula (9) are determined in advance for each type of crop, variety, and cultivation method. For example, the above formula (9) is calculated using the ratio R of the sink strength Sn to the source strength Sr, as shown in Figure 8, which was obtained from a test conducted in advance on the variety "Artega." SS (Horizontal axis: Source-sink ratio) and fruit-setting probability P FS(vertical axis) is the regression curve showing the relationship between

[0057] From the above equation (9), R SS = 21.0, P FS =0.924.

[0058] Next, in step S20, the harvestable number estimation unit 40 calculates the fruit-setting probability P FS Here, the predicted number of fruits is the number of fruits that will be set in the third week and can be harvested later, assuming that the measurement range of the fruit set information is one plot and that one plot has 40 stems. The harvestable number estimation unit 40 calculates the predicted number of fruits based on the fruit set probability P FS If is 0.924, the expected number of fruits in one plot is calculated as 40 x 0.924 = 37.0.

[0059] The table in Figure 9 (harvest forecast table) shows the accumulated temperature (measured and predicted values) ΣTw, calculated Sr, Sn, and R for each week. SS , information of each node (fruit) (fruit-setting probability P FS , predicted fruit number (Fr / plot), and fruit cumulative temperature ΣT) are stored together. The harvest prediction table of FIG. 9 also stores the predicted harvestable number for each plot in each week and the predicted harvestable number for each area (in this embodiment, the predicted harvestable number for one greenhouse 18). In the third week, as shown in FIG. 9, data up to the third week will be entered into the harvest prediction table.

[0060] Next, in step S22, the harvestable number estimation unit 40 identifies the harvestable week for the fruit that has borne fruit this week (week 3) based on the predicted value of the accumulated temperature for each future week. In the example of Fig. 9, the harvestable number estimation unit 40 accumulates the accumulated temperatures ΣTw from week 4 onwards and sequentially enters them into the ΣT field of Node 1 as shown in Fig. 10, and identifies the week in which ΣT reaches a predetermined temperature (e.g., 1000°C) as the harvestable week. In the case of Fig. 10, the harvestable week for the fruit that borne fruit in week 3 is identified as week 10.

[0061] Next, in step S24, the harvestable quantity estimation unit 40 determines the predicted harvestable quantity for each future week. In the example of FIG. 10, it is predicted that 37.0 fruit will be harvested per plot in week 10, and based on this, the predicted harvestable quantity in greenhouse 18 is determined. For example, if there are 1,000 stalks in greenhouse 18, there are 1,000 / 40 = 25 plots of stalks in greenhouse 18, and therefore the predicted harvestable quantity in greenhouse 18 is 37.0 × 25 = 925.0. Note that this predicted harvestable quantity for week 10 is a provisional value as of week 3, and therefore the predicted harvestable quantity for week 10 will fluctuate with each passing week (each time fruit bearing is confirmed).

[0062] Thereafter, the process returns to step S10, and the above processing is repeated.

[0063] For example, suppose that fruit bearing at Node No. 2 is confirmed in the fourth week. In this case, the sink strength calculation unit 34 uses the accumulated temperatures at Node Nos. 1 and 2 to calculate g(fr1) and g(fr2) using the following equations (10) and (11), as shown in the sink strength calculation table in FIG.

[0064]

number

[0065] Then, the sink strength calculation unit 34 determines the sum of g(fr1) and g(fr2) (for example, 0.62...) as the sink strength for one week, and calculates the daily sink strength (Sn / stem / d) as 0.62... / 7 ≒ 0.09 (step S12).

[0066] The source strength calculation unit 36 ​​also calculates the source strength Sr in the same manner as above (step S14). For example, assume that the source strength Sr (Sr / stem / d) for each day in the fourth week is 0.71.

[0067] Furthermore, the fruit-setting probability calculation unit 38 calculates R SS R SS = 0.71 / 0.09 ≒ 8.0, and the fruit-setting probability PFS is calculated from the above equation (9) (steps S16 and S18). FS Let's say it was ≒0.69.

[0068] The harvestable number estimation unit 40 then calculates the fruit-bearing probability P FS Based on this, the expected number of fruits in one plot is calculated as 27.5 (≒40 × 0.69). Furthermore, the harvestable number estimation unit 40 uses the accumulated temperature from the fifth week onwards to identify the harvestable week for the fruits that were set in the fourth week. Here, it is assumed that the harvestable week for the fruits that were set in the fourth week is identified as week 10.

[0069] In this case, the harvest prediction table in Figure 10 is updated in the fourth week as shown in Figure 12. Note that since both the fruit at Node 1 and the fruit at Node 2 are predicted to be harvested in the tenth week, the predicted harvest quantity for the tenth week is the sum of the predicted harvest quantities for the fruit at Node 1 and Node 2.

[0070] Furthermore, for example, suppose that a table such as that shown in Fig. 13 is obtained in the sixth week. In this case, the sync strength calculation unit 34 calculates g(fr1), g(fr2), g(fr3), and g(fr4) using the following equations (12) to (15).

[0071]

number

[0072] In this case, if g(fr1) = 1.49, g(fr2) = 1.13, g(fr3) = 0.59, and g(fr4) = 0.18, the sum of these = 3.39 becomes the sink strength for the 6th week, and the sink strength for each day in the 6th week (Sn / stem / d) is calculated as 3.39 / 7 ≒ 0.48 (step S12).

[0073] The source strength calculation unit 36 ​​also calculates the source strength Sr in the same manner as above (step S14). For example, it is assumed that the source strength Sr (Sr / stem / d) for each day in the sixth week is 0.97.

[0074] Furthermore, the fruit-setting probability calculation unit 38 calculates R SS R SS = 0.97 / 0.48 ≒ 2.0, and the fruit-setting probability P FS is calculated from the above equation (9) (steps S16 and S18). FS Let's say it was ≒0.35.

[0075] The harvestable number estimation unit 40 then calculates the fruit-bearing probability P FS Based on this, the expected number of fruits per plot is calculated as 13.9 (≒40 × 0.35). Furthermore, the harvestable number estimation unit 40 uses the accumulated temperature from the 7th week onwards to identify the harvestable week for the fruits that were set in the 6th week. Here, it is assumed that the harvestable week for the fruits that were set in the 6th week is identified as the 12th week.

[0076] In this case, the harvest forecast table is updated in the sixth week as shown in FIG.

[0077] By repeating the above process, the harvest prediction table is updated every week. Here, the information output unit 42 generates a screen including all or part of the harvest prediction table in response to a request from the operator, and displays (outputs) it on the display unit 93. This allows the operator to confirm what harvest amount can be expected in which week in the greenhouse 18.

[0078] As described above in detail, according to the first embodiment, the source strength calculation unit 36 ​​calculates the source strength Sr for each predetermined period in the past based on the environmental information in the greenhouse 18 and information indicating the growth state of the leaves of the crops growing in the greenhouse 18, and the sink strength calculation unit 34 calculates the sink strength Sn based on the environmental information in the greenhouse 18 and whether or not the plants under investigation have set fruit. Then, the fruit-set probability calculation unit 38 calculates the ratio R of the source strength Sr to the sink strength Sn. SS Using the above equation (9), the fruit-bearing probability P FS In this way, in this embodiment, the fruit-setting probability is calculated from the source strength and sink strength obtained from the state of the crop and environmental information, so that the fruit-setting probability can be calculated with high accuracy.

[0079] In this embodiment, when the fruit-bearing probability calculation unit 38 calculates the fruit-bearing probability, it uses a calculation formula that is predetermined for each type of crop, variety, and cultivation method. This allows the fruit-bearing probability to be calculated with high accuracy using a calculation formula that suits each type of crop and cultivation method.

[0080] In this embodiment, the harvestable number estimation unit 40 estimates the fruit-bearing probability P FS Based on the accumulated temperature and the temperature, the predicted harvest yield for each future week in the greenhouse 18 is determined, and the information output unit 42 generates a screen displaying the predicted harvest yield and displays it on the display unit 93. This allows the worker to check the predicted harvest yield for each future week, thereby accurately understanding the shipping volume. It also enables the worker to appropriately adjust the allocation of personnel performing harvesting work. Furthermore, in this embodiment, simply by inputting whether or not the plants being surveyed have fruit, the fruit-bearing probability and predicted harvest yield can be calculated. Therefore, even if there are only a few plants being surveyed or the number of surveys is small, the predicted harvest yield for the entire greenhouse 18 can be accurately estimated.

[0081] Figure 6(a) shows the regression curve of the dry weight per fruit versus the accumulated temperature obtained from the test of the paprika variety "Artega" grown under rock wool, and Figure 8 shows the source-sink ratio R SS and fruit set probability P FS This figure shows the relationship between the ordinate and abscissa, but similar test results were obtained for other varieties, so they will be explained below. The curves in Figures 15(a), 15(b), 16(a), 16(b), 17(a), and 17(b) were obtained by the following tests. Research greenhouse: (18m wide, 14m deep, 5.1m high eave, within the Vegetable and Floriculture Research Division of the National Agriculture and Food Research Organization) Sowing: July 17, 2020 Planting: August 20, 2020 Rockwool cultivation, 245-250 days from planting Measurement items: Weather data (temperature, CO2 concentration, solar radiation) Fruit set data (flowering date, fruit drop date, harvest date) Biometric data (LAI)

[0082] Figure 15(a) shows the regression curve of the dry weight per fruit against the accumulated temperature for the paprika variety "Nagano." This regression curve is also calculated using the above formula (2) (g(fr n )=K·b·e -c.·xn ), and the parameters K, b, and c are as shown in Fig. 15(a), K = 12.7, b = 8.3 × 10 -5 , c=4.0×10 -3 Therefore, in the case of the variety "Nagano," the values ​​in FIG. 15(a) should be used as the parameters K, b, and c in the above formula (2).

[0083] Figure 15(b) shows the ratio R of the sink strength Sn to the source strength Sr obtained from a previous test on the variety "Nagano." SS (Horizontal axis: Source-sink ratio) and fruit-setting probability P FS Therefore, in the case of the variety "Nagano," the following formula (9a) can be used instead of the above formula (9). P FS =0.109×R SS -0.075 …(9a)

[0084] Figure 16(a) shows the regression curve of the dry weight per fruit versus the accumulated temperature for the paprika variety "Nesbitt." This regression curve is also expressed by the above formula (2), and the parameters K, b, and c are K = 15.0 and b = 3.1 × 10 as shown in Figure 16(a). -6 , c=4.0×10 -3 Therefore, in the case of the variety "Nesbit," the values ​​shown in FIG. 16(a) should be used as the parameters K, b, and c in the above formula (2).

[0085] Figure 16(b) shows the ratio R of the sink strength Sn to the source strength Sr for the variety "Nesbitt". SS (Horizontal axis: Source-sink ratio) and fruit-setting probability P FS Therefore, in the case of the variety "Nesbitt," the following formula (9b) can be used instead of the above formula (9). P FS =0.176×ln(R SS )+0.0055 …(9b)

[0086] Figure 17(a) shows the regression curve of the dry weight per fruit versus the accumulated temperature for the paprika variety "Trirosso." This regression curve is also expressed by the above formula (2), and the parameters K, b, and c are K = 8.6 and b = 2.7 × 10 as shown in Figure 17(a). -4 , c=3.6×10 -3 Therefore, in the case of the variety "Trirosso," the values ​​shown in FIG. 17(a) should be used as the parameters K, b, and c in the above formula (2).

[0087] Figure 17(b) shows the ratio R of the sink strength Sn to the source strength Sr for the variety "Trirosso." SS (Horizontal axis: Source-sink ratio) and fruit-setting probability P FS Therefore, in the case of the variety "Trirosso," the following formula (9c) can be used instead of the above formula (9). P FS =0.259×ln(R SS )+0.199 …(9c)

[0088] Figures 18(a) to 18(d) are graphs showing the predicted fruit set probability 120 to 130 days after planting for the cultivars "Artega," "Nagano," "Nesbit," and "Trirosso" using the above method, as well as the actual measurement results. As shown in Figures 18(a) to 18(d), we were able to predict fruit set probability accurately (with a prediction error of approximately ±10% or less) for all cultivars. Figures 19(a) to 19(d) show the change in fruit set probability and yield with the number of days after planting for the cultivars "Artega," "Nagano," "Nesbit," and "Trirosso." As shown in Figures 19(a) to 19(d), each cultivar has different fruit set characteristics, and the yield of each cultivar varies due to these differences. In this embodiment, even when there are differences in yield due to differences in fruit set characteristics between cultivars, the fruit set probability can be predicted accurately, making it possible to accurately predict yield as well.

[0089] (Variation 1) In the first embodiment, the environment inside the greenhouse 18 may be adjusted to be appropriate based on the predicted harvest yield estimated by the harvest yield estimation unit 40. FIG. 20 shows a functional block diagram of the control device 10 in this first modification. As shown in FIG. 20, the control device 10 in this first modification has an adjustment unit 44 and an equipment control unit 46 in addition to the configuration of FIG. 3.

[0090] The adjustment unit 44 adjusts the set temperature to bring the estimation result (predicted harvest number) of the harvest number estimation unit 40 closer to the target value (target harvest number). It is assumed that the target harvest number is input in advance by the operator via the input unit 95. When the set temperature is changed, the adjustment unit 44 notifies the environmental information acquisition unit 32. As a result, the accumulated temperature for each week (ΣTw in the harvest prediction table) is changed, and the predicted harvest number for each week estimated by the harvest number estimation unit 40 is corrected.

[0091] The equipment control unit 46 outputs the set temperature adjusted by the adjustment unit 44 to the control-target equipment 16, and controls the control-target equipment 16. In this case, the equipment control unit 46 determines the control-target equipment 16 to be controlled and the control method based on the detection results of the outdoor sensor 12 and the greenhouse sensor 14, and controls the control-target equipment 16.

[0092] Specifically, the adjustment unit 44 adjusts the set temperature as follows. (1) When the predicted harvest quantity exceeds the target value and the adjustment unit 44 wants to move the harvest quantity for the week being adjusted to the previous week, the adjustment unit 44 increases the set temperature for the week being adjusted. (2) If the predicted harvest quantity exceeds the target value and it is desired to move the harvest quantity for the week being adjusted to a later week, the adjustment unit 44 lowers the set temperature for the week being adjusted. (3) If the predicted harvestable yield is below the target value and the peak harvest occurs in a week after the week being adjusted, the adjustment unit 44 increases the set temperature for the week being adjusted. (4) If the predicted harvestable quantity is lower than the target value and the peak harvest occurs in the week prior to the week being adjusted, the adjustment unit 44 lowers the set temperature for the week prior to the week being adjusted.

[0093] In this first modification, as described above, the control information is adjusted based on the predicted harvestable quantity and output to the control-target device 16, so that the control-target device 16 can be precisely controlled to achieve the target harvest quantity. This reduces over- or under-shipment, unsold goods, and lost sales opportunities, and prevents a decline in credibility.

[0094] In the first modification, the adjustment unit 44 adjusts the set temperature so that the predicted harvest yield approaches the target value, but this is not limited to this. For example, the operator may refer to the predicted harvest yield estimated by the harvest yield estimation unit 40 and manually adjust the settings of the control-target devices 16 as needed so that the predicted harvest yield approaches the target value.

[0095] (Variation 2) In the first embodiment, the fruit-setting probability calculation unit 38 calculates the ratio R SS Calculate R SS Using this, the fruit set probability P FS However, the present invention is not limited to this. For example, the fruit-setting probability P FS It is also possible to calculate the following.

[0096] For example, if the sink strength Sn up to the sixth week is calculated as shown in FIG. 13, the fruit-setting probability calculation unit 38 calculates the fruit-setting probability P FS Based on the equation showing the relationship between the sink strength Sn and the seed yield probability P FS You may ask for: P FS =-0.191×ln(Sn)+0.2248 …(16)

[0097] In this case, P FS can be calculated as approximately 0.36. Note that the above formula (16) was derived from the results of a preliminary investigation into the relationship between fruit set probability and sink strength, and is a formula determined for each type of crop, variety, and cultivation method.

[0098] FIG. 21 shows the results of calculating the predicted harvestable number using this modified example (harvest prediction table). FS The predicted harvestable yield can also be calculated by using the relationship between sink strength and fruit set probability when calculating the yield.

[0099] Second Embodiment The second embodiment will be described below.

[0100] The inventors have estimated the future source-sink ratio R SS In the case where the source-sink ratio R is on a declining trend, by carrying out appropriate fruit thinning, the future source-sink ratio R SSIt was noted that it is possible to manipulate the value of to an appropriate value, and that by performing such manipulation, it is possible to control the fruit-setting probability. For example, when a test was conducted on the cultivar "Artega," in the non-thinning area, the source-sink ratio was 2.6 when the fruit-setting probability (actual value) was 0.45. On the other hand, in the thinning area, the source-sink ratio was 3.7 when the fruit-setting probability (actual value) was 0.56. It was found that the behavior of the change in fruit-setting probability in response to a change in this source-sink ratio is close to the curve (Figure 22) expressed by the above formula (9). Therefore, the inventors used this curve (Figure 22) as an index to calculate the fruit-setting probability P FS The source-sink ratio R is the target value. SS If the adjustment target value of is set and fruit thinning is carried out based on this, the fruit set probability P FS We came to the conclusion that this could be done to accurately approach the target value.

[0101] For example, the fruit set probability P FS If the target value of is 52%, then from the above equation (9), 0.52=0.2455×ln(R SS )+0.1769 R SS ≒4.0 The source-sink ratio is R SS The adjustment target value of R is 4.0. SS The adjustment target value of (1) will vary depending on the variety, the target value of fruit set probability, etc. For example, for the varieties "Nagano," "Nesbit," and "Trirosso," the above formulas (9b), (9c), and (9d) will be used instead of the above formula (9).

[0102] The agricultural system of the second embodiment is based on the above-mentioned knowledge, and the future source-sink ratio R SS The purpose is to estimate the probability of future fruit set and, based on the estimated results, output information regarding fruit thinning so that the probability of future fruit set will be an appropriate value (close to the target value).

[0103] The system configuration and hardware configuration of the agricultural system of this second embodiment are the same as those of the first embodiment. FIG. 23 shows a functional block diagram of the control device 10 in this second embodiment. As shown in FIG. 23, the control device 10 of this second embodiment has a source-sink ratio calculation unit 37A and a fruit thinning determination unit 37B in addition to the functions of FIG. 3. In the following, explanations of matters common to those described in the first embodiment will be omitted or simplified.

[0104] The sink strength calculation unit 34 calculates the sink strength for each predetermined period in the past and each predetermined period in the future based on the information acquired by the input receiving unit 30 and the information acquired by the environmental information acquisition unit 32. When calculating the sink strength for each predetermined period in the future, it is assumed that there are no new fruits (they are not taken into account) because newly set fruits are small and are considered to have little effect on the sink strength. However, this is not limiting, and the timing of new fruit set may also be predicted using, for example, accumulated temperature.

[0105] The source intensity calculation unit 36 ​​calculates the source intensity for each predetermined period in the past and each predetermined period in the future based on the information acquired by the environmental information acquisition unit 32 and information related to the growth state of the leaves.

[0106] The source-sink ratio calculation unit 37A calculates a source-sink ratio R for each predetermined period in the past from the sink strength for each predetermined period in the past and the source strength for each predetermined period in the past. SS Furthermore, the source-sink ratio calculation unit 37A calculates a source-sink ratio R for each predetermined future period from the sink strength for each predetermined future period and the source strength for each predetermined future period. SS Calculate.

[0107] The fruit thinning determination unit 37B calculates the source-sink ratio R SS The need for fruit thinning is determined based on the source-sink ratio R SSis not within the predetermined range, it is determined that one immature fruit (a fruit in the middle of thickening, for example, a fruit weighing about 70 to 140 g) needs to be thinned. The fruit thinning determination unit 37 transmits the determination result to the sink strength calculation unit 34, the fruit-setting probability calculation unit 38, and the information output unit 42.

[0108] The fruit-setting probability calculation unit 38 calculates the source-sink ratio R SS The harvestable number estimation unit 40 is the same as that in the first embodiment.

[0109] The information output unit 42 generates a screen including the predicted harvestable number estimated by the harvestable number estimation unit 40, and displays (outputs) the generated screen on the display unit 93 in response to a request from the operator. The information output unit 42 also displays a screen showing the number of unripe fruits that the fruit thinning determination unit 37B has determined to require fruit thinning.

[0110] (Regarding the processing of the control device 10) Next, the processing of the control device 10 of the second embodiment will be described with reference to the flowchart of FIG.

[0111] 24 is started, the input receiving unit 30 is assumed to have acquired past crop information (such as fruit-bearing information on the plant being investigated), and the environmental information acquiring unit 32 is assumed to have acquired past and future environmental information.

[0112] 24, first, in step S102, the source intensity calculation unit 36 ​​calculates the source intensity for each predetermined period in the past (e.g., each week) and also calculates the source intensity for each predetermined period in the future (e.g., each week). The method of calculating the source intensity in step S102 is the same as in the first embodiment.

[0113] Next, in step S104, the sink strength calculation unit 34 calculates the sink strength for each predetermined period in the past. Also, in step S106, the sink strength calculation unit 34 calculates the sink strength for each predetermined period in the future. The method of calculating the sink strength in steps S104 and S106 is the same as in the first embodiment.

[0114] Next, in step S108, the source-sink ratio calculation unit 37A calculates the source-sink ratio R SS , and the source-sink ratio R for each predetermined period in the future SS is calculated using the above equation (8).

[0115] Next, in step S110, the fruit thinning determination unit 37B determines whether or not fruit thinning is necessary. For example, at present, the past and future source-sink ratio R SS is calculated as shown in Figure 25. In this case, the future source-sink ratio R SS To ensure that the source-sink ratio R is around 4.0, SS It is assumed that it is determined that the future source-sink ratio R SS If the future source-sink ratio R is equal to or greater than the threshold, it is determined that there is no need for fruit thinning (step S110: Yes). SS If is not equal to or greater than the threshold value (as in FIG. 25(a)), it is determined that fruit thinning is necessary (step S110: No), and the process proceeds to step S112.

[0116] In step S112, fruit thinning determination unit 37B determines that one immature fruit should be thinned, and notifies sink strength calculation unit 34 of this. Thereafter, the process returns to step S106.

[0117] Returning to step S106, the sink strength calculation unit 34 deletes one unripe fruit from the sink strength calculation table and recalculates future sink strengths. For example, in the case of the sink strength calculation table of Figure 13, one unripe fruit is deleted and the sink strength is recalculated as shown in Figure 25(b).

[0118] Next, in step S108, the source-sink ratio calculation unit 37A calculates the source-sink ratio R SS is calculated again using the above formula (8). As a result, it is assumed that the result shown in FIG. 26(a) is obtained.

[0119] Next, in step S110, the fruit thinning determination unit 37B calculates the future source-sink ratio R SS is greater than or equal to the threshold value of 3.5. In the case of Fig. 26(a), the determination in step S110 is negative, so the fruit thinning determination unit 37B proceeds to step S112, determines that one more immature fruit should be thinned, and notifies the sink strength calculation unit 34 of this. Thereafter, the process returns to step S106.

[0120] Returning to step S106, the sink strength calculation unit 34 deletes one more unripe fruit from the sink strength calculation table and recalculates future sink strengths.

[0121] Next, in step S108, the source-sink ratio calculation unit 37A calculates the source-sink ratio R SS is calculated again using the above formula (8). As a result, it is assumed that the result shown in FIG. 26(b) is obtained.

[0122] Next, in step S110, the fruit thinning determination unit 37B calculates the future source-sink ratio R SS is greater than or equal to the threshold value of 3.5. In the case of FIG. 26(b), the determination in step S110 is negative, so the fruit thinning determination unit 37B proceeds to step S112, determines that one more immature fruit should be thinned, and notifies the sink strength calculation unit 34 of this. Thereafter, the process returns to step S106.

[0123] Returning to step S106, the sink strength calculation unit 34 deletes one more unripe fruit from the sink strength calculation table and recalculates future sink strengths.

[0124] Next, in step S108, the source-sink ratio calculation unit 37A calculates the source-sink ratio R SS is calculated again using the above formula (8). As a result, it is assumed that the results shown in FIG. 27 are obtained.

[0125] Next, in step S110, the fruit thinning determination unit 37B calculates the future source-sink ratio R SS is equal to or greater than the threshold value of 3.5. In the case of FIG. 27, the determination in step S110 is affirmative, so the fruit thinning determination unit 37B proceeds to step S114.

[0126] When proceeding to step S114, the fruit thinning determination unit 37B determines that no further fruit thinning is necessary and notifies the fruit-set probability calculation unit 38 and the information output unit 42 of the number of fruits thinned so far.

[0127] Next, in step S116, the fruit-setting probability calculation unit 38 calculates the past and future fruit-setting probabilities P FS Next, in step S118, the harvestable number estimation unit 40 calculates the fruit-setting probability P FS The expected number of fruits is calculated based on this.

[0128] Next, in step S120, the information output unit 42 outputs information. For example, the information output unit 42 displays a list of Figures 25(a), 26(a), 26(b), and 27, and outputs information such as the fact that thinning three immature fruits can bring the source-sink ratio closer to an appropriate value, thereby enabling appropriate control of the yield. Furthermore, the information output unit 42 generates and outputs a screen including the predicted harvestable number estimated by the harvestable number estimation unit 40, as in the first embodiment.

[0129] Figures 28(a) and 28(b) are graphs showing the difference in yield when fruit thinning is performed using the agricultural system of the second embodiment and when fruit thinning is not performed. Figure 28(a) shows the yield on February 8th, and Figure 28(b) shows the yield on February 12th. Figures 28(a) and 28(b) show that fruit thinning made it possible to control the yield during the period when the number of harvested paprika fruits is concentrated (early February). Furthermore, because there is an upper limit to the amount that can be harvested within working hours, it can be seen that performing fruit thinning enables appropriate labor management.

[0130] As described above in detail, according to the second embodiment, the control device 10 calculates the source strength for each predetermined period in the past and future (S102), and calculates the strength for each predetermined period in the past and future (S104, S106). The control device 10 then calculates the source-sink ratio for each predetermined period in the future. If the calculated source-sink ratio does not fall within a predetermined range (the range of source-sink ratios when the fruit-set probability is the target value), the control device 10 adjusts the sink strength calculation table and recalculates the source-sink ratio. This process is repeated (S106-S112), and the number of fruits to be thinned is determined and output (S120). This allows the output of the number of fruits to be thinned so that the fruit-set probability approaches the target value. Therefore, if an operator thins fruits based on the output, the fruit-set probability can be accurately controlled to an appropriate value.

[0131] In the above first and second embodiments and variants, we have described the calculation of the fruit set probability and predicted harvest yield for peppers and tomatoes, but this is not limited to this and the present invention can also be applied to other fruit vegetables such as eggplants, strawberries, melons, watermelons, etc.

[0132] The above processing functions can be realized by a computer. In this case, a program is provided that describes the processing contents 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 contents can be recorded on a computer-readable storage medium (excluding carrier waves).

[0133] When distributing a program, it is sold 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.

[0134] 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 program received each time a program is transferred from the server computer.

[0135] The above-described embodiment is a preferred example of the present invention, but the present invention is not limited to this and can be modified in various ways without departing from the spirit of the present invention. [Explanation of symbols]

[0136] 10 Control device 12 Outdoor Sensors 14 Greenhouse Sensor 16 Controlled equipment (environmental control equipment) 34 Sink strength calculation unit 36 Source strength calculation unit 38 Fruiting probability calculation section 40 Harvestable quantity estimation section 42 Information output section 44 Adjustment part 46 Equipment control section 90 CPU (computer) 93 Display section 100 Farming Systems

Claims

1. a source intensity calculation unit that calculates source intensities of the crops for each predetermined period in the past based on environmental information of a predetermined range and information indicating the growth state of leaves of the crops growing in the predetermined range; a sink strength calculation unit that calculates a sink strength of the crop based on the environmental information within the predetermined range and fruit-bearing information of an investigation plant that is part of the crop; a fruit-bearing probability calculation unit that calculates a fruit-bearing probability of the crop growing in the predetermined range based on the source intensity and the sink intensity; a harvestable yield estimation unit that estimates a future harvestable yield in the predetermined range based on the fruit-bearing probability and environmental information in the predetermined range; a processing unit that identifies environmental conditions necessary to adjust the future harvestable yield estimated by the harvestable yield estimation unit to a predetermined range, and outputs the identified environmental conditions to an environmental control device; An information output device comprising:

2. a sink strength calculation unit that calculates a sink strength of the crop based on environmental information of a predetermined range and fruit-bearing information of a survey plant that is a part of the crop growing in the predetermined range; a fruit-bearing probability calculation unit that calculates a fruit-bearing probability of the crop growing in the predetermined range based on the sink strength; a harvestable yield estimation unit that estimates a future harvestable yield in the predetermined range based on the fruit-bearing probability and environmental information in the predetermined range; a processing unit that identifies environmental conditions necessary to adjust the future harvestable yield estimated by the harvestable yield estimation unit to a predetermined range, and outputs the identified environmental conditions to an environmental control device; An information output device comprising:

3. 3. The information output device according to claim 1, wherein the fruit-bearing probability calculation unit calculates the fruit-bearing probability using a calculation formula that is predetermined for each type, variety, and cultivation method of the crop.

4. a source intensity calculation unit that calculates the source intensity of the crop for each predetermined period in the past and future based on past and future environmental information of a predetermined range in which the crop grows and information indicating the growth state of the leaves of the crop; a sink strength calculation unit that calculates a sink strength of the crop for each predetermined period in the past and future based on the past and future environmental information within the predetermined range and the fruit set information of the crop; a processing unit that adjusts the crop fruit set information so that the ratio between the source intensity and the sink intensity for each future predetermined period falls within a predetermined range, and outputs information about fruit thinning based on the adjusted fruit set information; An information output device comprising:

5. 5. The information output device according to claim 4, wherein the predetermined range is a range of the ratio of the source intensity to the sink intensity when the fruit-setting probability of the crop reaches a target value.

6. The fruit-bearing probability calculation unit calculates the fruit-bearing probability using a calculation formula that is predetermined for each type, variety, and cultivation method of the crop, The information output device described in claim 1, characterized in that the calculation formula is an equation showing the relationship between the ratio of the source strength to the sink strength and the fruit-setting probability, or an equation showing the relationship between the natural logarithm of the ratio of the source strength to the sink strength and the fruit-setting probability.

7. an environmental information acquisition device that acquires environmental information of a predetermined area where crops grow; a growth state acquisition device that acquires information indicating the growth state of leaves of crops growing in the predetermined range; a fruit-bearing information acquisition device for acquiring fruit-bearing information on a survey plant that is part of the crop; an environmental control device that controls the environmental conditions within the predetermined range; The information output device according to claim 1; A system comprising:

8. an environmental information acquisition device that acquires environmental information of a predetermined area where crops grow; a fruit-bearing information acquisition device that acquires fruit-bearing information for a survey plant that is part of a crop growing in the predetermined range; an environmental control device that controls the environmental conditions within the predetermined range; an information output device according to claim 2; A system comprising:

9. an environmental information acquisition device that acquires past and future environmental information for a predetermined area where crops grow; a growth state acquisition device that acquires information indicating the growth state of leaves of crops growing in the predetermined range; a fruit-bearing information acquisition device for acquiring fruit-bearing information of the crop; an information output device according to claim 4; A system comprising:

10. calculating a source intensity of the crop for each predetermined period in the past based on environmental information of a predetermined range and information indicating a growth state of leaves of the crop growing in the predetermined range; calculating a sink strength of the crop based on the environmental information in the predetermined range and fruit-bearing information of an investigation plant that is a part of the crop; calculating a fruit-bearing probability of the crop growing in the predetermined range based on the calculated source strength and sink strength; Estimating the future harvestable number in the predetermined range based on the fruit-setting probability and the environmental information in the predetermined range; Identifying environmental conditions necessary to adjust the estimated future harvestable yield to a predetermined range, and outputting the identified environmental conditions to an environmental control device. An information output program that causes a computer to execute a process.

11. calculating a sink strength of the crop based on environmental information of a predetermined range and fruit-bearing information of a survey plant that is a part of the crop growing in the predetermined range; calculating a fruit-bearing probability for the crop growing in the predetermined range based on the calculated sink strength; Estimating the future harvestable number in the predetermined range based on the fruit-setting probability and the environmental information in the predetermined range; Identifying environmental conditions necessary to adjust the estimated future harvestable yield to a predetermined range, and outputting the identified environmental conditions to an environmental control device. An information output program that causes a computer to execute a process.

12. calculating a source strength of the crop for each predetermined period in the past and future based on past and future environmental information of a predetermined range in which the crop grows and information indicating the growth state of the leaves of the crop; calculating a sink strength of the crop for each predetermined period in the past and future based on the past and future environmental information within the predetermined range and the fruit set information of the crop; adjusting the fruit set information of the crop so that the ratio between the source intensity and the sink intensity for each predetermined future period falls within a predetermined range, and outputting information regarding fruit thinning based on the adjusted fruit set information; An information output program that causes a computer to execute a process.

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