Cultivation evaluation method and cultivation evaluation program, cultivation environment control method and cultivation environment control program, and cultivation countermeasure information output method and cultivation countermeasure information output program

Abstract

To appropriately evaluate crop cultivation. [Solution] The cultivation evaluation method of the present invention is a cultivation evaluation method in which a computer performs the following processes: acquiring information on a first temperature used as a standard in crop cultivation and information on a second temperature to be evaluated; calculating the ratio of the values ​​of each of the multiple types of elements that affect the yield in the second case, when the crop is cultivated at the second temperature, to the values ​​of each of the multiple types of elements in the first case, when the crop is cultivated at the first temperature; and estimating the yield ratio of the second case to the first case based on the ratio of each of the multiple types of elements.

Description

[Technical Field]

[0001] The present invention relates to a cultivation evaluation method and cultivation evaluation program, a cultivation environment control method and cultivation environment control program, and a cultivation countermeasure information output method and cultivation countermeasure information output program. [Background technology]

[0002] In recent years, due to global warming, extremely high temperatures during the summer have become more frequent. High temperatures are a major problem for fruit and vegetable crops such as tomatoes and bell peppers, and are reducing their yields.

[0003] To establish a highly profitable production structure, it is crucial to predict yield reductions due to high temperatures in advance. Conventionally, techniques have been developed to calculate the yield of fruit clusters from fruiting vegetables and fruit trees using solar radiation, temperature, and leaf area (see, for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-120830 [Overview of the project] [Problems that the invention aims to solve]

[0005] The decrease in yield of fruit and vegetable crops due to high temperatures is thought to be influenced by various changes in the plant body caused by the high temperatures. Therefore, it is considered necessary to consider these changes in the plant body in order to estimate the decrease in yield during high temperatures. Furthermore, if we know how much each change in the plant body affects the decrease in yield, we can take appropriate countermeasures. However, Patent Document 1 and others do not take into account the changes in the plant body caused by high temperatures.

[0006] An object of the present invention is to provide a cultivation evaluation method and a cultivation evaluation program capable of appropriately evaluating the cultivation of crops, a cultivation environment control method and a cultivation environment control program capable of appropriately controlling the cultivation environment of crops, and a cultivation-time countermeasure information output method and a cultivation-time countermeasure information output program capable of outputting appropriate countermeasure information during the cultivation of crops. [Means for Solving the Problems]

[0007] The cultivation evaluation method of the present invention acquires information on a first temperature that serves as a reference in crop cultivation and information on a second temperature that is an evaluation target, calculates the ratio of each of the plurality of types of elements in the second case where the crop is cultivated at the second temperature to each of the values of the plurality of types of elements that affect the yield in the first case where the crop is cultivated at the first temperature, and estimates the yield ratio of the second case relative to the first case based on the ratio of each of the plurality of types of elements. This is a cultivation evaluation method in which a computer executes the processing. [Effects of the Invention]

[0008] The cultivation evaluation method and cultivation evaluation program of the present invention produce the effect of being able to appropriately evaluate the cultivation of crops. In addition, the cultivation environment control method and cultivation environment control program of the present invention produce the effect of being able to appropriately control the cultivation environment of crops. Further, the cultivation-time countermeasure information output method and cultivation-time countermeasure information output program of the present invention produce the effect of being able to output appropriate countermeasure information during the cultivation of crops. [Brief Description of the Drawings]

[0009] [Figure 1] Figure 1 is a diagram showing the configuration of a cultivation evaluation system according to an embodiment. [Figure 2] Figure 2(a) is a diagram showing the hardware configuration of a server, and Figure 2(b) is a diagram showing the hardware configuration of a user terminal. [Figure 3] Figure 3 is a functional block diagram of the server. [Figure 4]Figure 4 is a diagram illustrating the hierarchical relationship of yield reduction factors caused by high temperatures. [Figure 5] Figure 5 is a flowchart (Part 1) illustrating the processing of the server. [Figure 6] Figure 6 is a flowchart (Part 2) illustrating the processing of the server. [Figure 7] Figure 7 is a diagram illustrating an example of the data structure of the cultivation DB. [Figure 8] Figure 8 is a graph representing an equation for estimating the reduction rate α of the photosynthetic amount according to air temperature. [Figure 9] Figure 9 is a table showing coefficients and constants predefined for the cultivar Artega. [Figure 10] Figure 10 is a diagram illustrating an example of the estimation result table (Part 1). [Figure 11] Figure 11 is a diagram illustrating an example of the estimation result table (Part 2). [Figure 12] Figure 12 is a diagram illustrating an example of the estimation result table (Part 3). [Figure 13] Figure 13 is a diagram illustrating an example of the estimation result table (Part 4). [Figure 14] Figure 14 is a diagram illustrating an example of the estimation result table (Part 5). [Figure 15] Figure 15 is a diagram illustrating an example of the display pattern table. [Modes for Carrying Out the Invention]

[0010] The following describes in detail one embodiment of a cultivation evaluation system for fruit and vegetable crops, based on Figures 1 to 15. Figure 1 schematically shows the configuration of the cultivation evaluation system 100 according to one embodiment. The cultivation evaluation system 100 of this embodiment is a system used by producers of fruit and vegetable crops such as tomatoes, bell peppers, cucumbers, and strawberries (hereinafter referred to as users), and is a system that estimates the rate of decrease or amount of decrease in fruit and vegetable crop yield (the ratio or difference between the yield when cultivated at the actual temperature (e.g., high temperature) (second case) and the yield when cultivated at the temperature designated as the standard for cultivation (first case yield)), and provides the user with the estimation results and information corresponding to the estimation results. The temperature designated as the standard for cultivation is the ideal temperature or the optimal temperature for that fruit and vegetable crop. However, it is not limited to this, and the temperature designated as the standard for cultivation does not necessarily have to be the optimal temperature, and may differ slightly from the optimal temperature.

[0011] As shown in Figure 1, the cultivation evaluation system 100 comprises a server 10 and a user terminal 70. The server 10 and the user terminal 70 are connected via a network 80 such as the Internet, enabling information exchange between the devices.

[0012] Based on the information input from the user terminal 70, the server 10 performs processing to estimate the rate and amount of decrease in fruit and vegetable yields, and outputs the estimation results and information corresponding to the estimation results (such as countermeasures) to the user terminal 70.

[0013] Figure 2(a) schematically shows the hardware configuration of server 10. As shown in Figure 2(a), server 10 includes a CPU (Central Processing Unit) 90, ROM (Read Only Memory) 92, RAM (Random Access Memory) 94, storage (e.g., SSD (Solid State Drive) or HDD (Hard Disk Drive)) 96, a network interface 97, and a portable storage medium drive 99. These components of server 10 are connected to a bus 98. In server 10, the CPU 90 executes programs (including cultivation evaluation programs) stored in the ROM 92 or HDD 96, or programs read from the portable storage medium 91 by the portable storage medium drive 99, thereby realizing the functions of each component shown in Figure 3. Note that the functions of each component in Figure 3 may also be realized by integrated circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field Programmable Gate Arrays). Details of the functions of each component in Figure 3 will be described later.

[0014] The user terminal 70 is a terminal such as a smartphone or PC (Personal Computer) used by fruit and vegetable producers (users). The user terminal 70 manages information acquired in past fruit and vegetable cultivation (environmental data, LAI (Leaf Area Index), yield results, etc.) and transmits necessary information to the server 10 in response to instructions from the user. The information managed by the user terminal 70 may be information manually entered by the user, or it may be information received from sensors installed in the cultivation environment (greenhouse, etc.) (temperature sensors, CO2 concentration meters, solar radiation sensors, LAI sensors, etc.). The user terminal 70 also acquires and displays estimation results from the server 10 and information corresponding to those estimation results (countermeasure information, etc.).

[0015] Here, the user terminal 70 has a hardware configuration as shown in Figure 2(b) as an example. As shown in Figure 2(b), the user terminal 70 includes a CPU 190, ROM 192, RAM 194, storage 196, network interface 197, display unit 193, input unit 195, and a portable storage medium drive 199 capable of reading data stored in a portable storage medium 191. The display unit 193 includes a liquid crystal display, and the input unit 195 includes a touch panel, keyboard, mouse, etc. Each of these components of the user terminal 70 is connected to the bus 198.

[0016] Figure 3 shows a functional block diagram of server 10. In server 10, the CPU 90 executes a program, thereby realizing the functions of the information acquisition unit 12, estimation processing unit 14, display pattern determination unit 16, and output unit 18, as shown in Figure 3.

[0017] The information acquisition unit 12 acquires information transmitted from the user terminal 70 and stores it in the cultivation information DB 30 (see Figure 7). Details of the information stored in the cultivation information DB 30 will be described later.

[0018] The estimation processing unit 14 refers to the information stored in the cultivation information DB 30 and estimates on a daily basis how much the yield will decrease (rate of decrease or amount of decrease) when cultivated at the actual temperature (temperature obtained during the current cultivation period or predicted future temperature) compared to the yield when cultivated at a reference temperature (optimal temperature). The estimation processing unit 14 also aggregates the estimated rate of decrease or amount of decrease in yield at predetermined intervals (for example, every week).

[0019] The display pattern determination unit 16 determines a display pattern for countermeasures when the yield decreases, based on the estimation results of the estimation processing unit 14. In this case, the display pattern determination unit 16 determines the display pattern for the countermeasures by referring to the display pattern table 32 (see Figure 15).

[0020] The output unit 18 generates a screen that displays the display pattern of the countermeasure determined by the display pattern determination unit 16, outputs (transmits) it to the user terminal 70, and causes the display unit 93 of the user terminal 70 to display the information of the countermeasure.

[0021] (Regarding the processing on Server 10) Next, we will explain the processing of server 10. In the following, we will explain the case where server 10 estimates the rate and amount of yield reduction due to high summer temperatures and outputs the result accordingly, but the basic concept is the same for the rate and amount of yield reduction due to low temperatures.

[0022] The server 10 of this embodiment performs processing based on the hierarchical relationship of factors causing revenue loss due to high temperature, as shown in Figure 4.

[0023] Referring to the hierarchical relationships in Figure 4, it can be seen that various factors are involved in the decline of fruit yield. Of these factors, photosynthesis, respiration, number of fruits, number of nodes, and weight per fruit are known to be affected by high temperatures.

[0024] For example, high temperatures can decrease photosynthesis and increase respiration, leading to a decrease in total dry matter production (TDM), which in turn affects fruit yield. Furthermore, high temperatures can reduce the weight per fruit (the sink in one fruit), reducing the amount distributed to the fruit and thus impacting fruit yield. Additionally, high temperatures can increase the number of nodes and thus the number of flowers, or decrease the fruit set rate, resulting in a decrease in the number of fruits and impacting fruit yield.

[0025] Therefore, in this embodiment, we focus on how the total dry matter production, weight per fruit, and number of fruits change with temperature, and based on these changes, we estimate the rate and amount of decrease in yield when cultivated at actual temperatures (high temperature) compared to the yield when cultivated at ideal temperatures (optimal temperature).

[0026] Figures 5 and 6 show the processing of server 10 in flowcharts. When the processing in Figure 5 begins, first, in step S10, the information acquisition unit 12 waits until cultivation data is input from the user terminal 70 and a processing start instruction is given. Here, the user inputs information about the fruits and vegetables being cultivated (item, variety, etc.) and information obtained during cultivation from the start of cultivation to the present (environmental data, LAI, yield results), etc. as cultivation data. The cultivation data may also include information about the cultivation facility (facility light transmittance, curtain shading rate, etc.). The cultivation data may also include fruit setting information (information on when and which fruits set). As an example, the user inputs that the fruits and vegetables being cultivated are paprika (variety: Artega). Furthermore, in the processing start instruction, the user queries server 10 about what high-temperature countermeasures should be taken from now on.

[0027] Next, in step S12, the information acquisition unit 12 stores the acquired cultivation data in the cultivation DB 30. Figure 7 shows an example of the data structure of the cultivation DB 30. As shown in Figure 7, the cultivation DB 30 stores various cultivation data for each day after planting. Specifically, the cultivation DB 30 stores "average daily CO2 concentration (ppm)" and "cumulative daily solar radiation (SR) (MJ / m²)". 2 )", LAI(m 2 / m 2 )", "Actual temperature (Temp1) (°C)", "Yield results (FW) (g / m 2 ) is stored. Also, the cultivation DB30 contains "optimal temperature (Temp opt The "(°C)" value is also stored, but this optimal temperature is the ideal cultivation temperature (first temperature) for the fruit and vegetable being grown, and in the case of Artega, it is set to 20°C. On the other hand, "Actual temperature (Temp1) (°C)" is the temperature when the fruit and vegetable are actually being grown (second temperature). For data other than the optimal temperature, if the data is up to the present, it will be an actual measured value, and if the data is for the present or later days, it will be a predicted value (a predicted value based on weather forecasts, or an average value). The predicted value may be obtained by the information acquisition unit 12 via the internet or other means.

[0028] Returning to Figure 5, in the next step S14, the estimation processing unit 14 selects an unselected day. In the initial stage, the estimation processing unit 14 selects the first day after planting.

[0029] Next, in step S16, the estimation processing unit 14 calculates the rate of decrease R1 of total dry matter production (TDM) (the rate of decrease in total dry matter production when grown at the actual temperature compared to the total dry matter production when grown at the optimal temperature). Here, the estimation processing unit 14 refers to a table as shown in Figure 8 when calculating the rate of decrease R1. Figure 8 is a graph of the formula for estimating the rate of decrease α of photosynthesis according to temperature. Note that the rate of decrease R1 of total dry matter production (TDM) according to temperature is considered to be the same as the rate of decrease α of photosynthesis, so R1 = α.

[0030] Figure 8 shows the values ​​of photosynthesis at each temperature as α, with the photosynthesis rate at the optimal temperature set to 1. The relationship in Figure 8 is assumed to have been obtained in advance for each type and variety of fruit and vegetable crop. For example, in a chamber where environmental control is possible, the optimal temperature (e.g., 20°C) is set, and fruit and vegetable crops (artega) are grown for a predetermined time, and the change in crop weight at that time is obtained as the baseline (=1). Then, the change in crop weight is measured when the chamber is set to a different temperature and the crop is grown for a predetermined time, and the ratio of this weight change to the baseline is plotted on the coordinate system of Figure 8 as the value α for the other temperature. By repeating this process while varying the temperature inside the chamber, multiple values ​​of α are plotted, and the relationship in Figure 8 is obtained by linear approximation using the least squares method or similar. Note that the procedure for obtaining the relationship in Figure 8 is not limited to the procedure described above.

[0031] The estimation processing unit 14 calculates the value α for each day by considering the temperature Temp1 (actual temperature in Figure 7) of the selected day as follows: i) to iv).

[0032] i) When Temp1 < 12℃ or Temp1 ≥ 40℃ The estimation processing unit 14 sets α = 0.

[0033] ii) When 12℃ ≤ Temp1 < 18℃ The estimation processing unit 14 calculates α from the following equation (1). α = 1 / 6 × Temp 1 - 2 …(1)

[0034] iii) When 18℃ ≤ Temp1 < 28℃ The estimation processing unit 14 sets α=1.

[0035] iv) When 28℃ ≤ Temp1 < 40℃ The estimation processing unit 14 calculates α from the following equation (2). α = -(1 / 12) × Temp 1 + 10 / 3 …(2)

[0036] For example, in the case of the data for day 1 after planting in Figure 7 (actual temperature Temp1 = 23.2℃), α = 1 according to iii) above. Therefore, the rate of decrease R1 in total dry matter production (TDM) on day 1 is R1 = α = 1.

[0037] Here, the estimation processing unit 14 calculates (estimates) various values ​​in addition to the R1 value for subsequent processing. Specifically, the estimation processing unit 14 calculates the light use efficiency (LUE) (g / MJ) and the amount of light received by the plant community (IL) (MJ / m) for each day based on the following equations (3) and (4). 2 ) is calculated. Note that CO2 in equation (3) is the average daily CO2 concentration (ppm), and SR in equation (4) is the daily cumulative solar radiation (MJ / m²). 2 ) LUE = a × ln(CO2) + b …(3) IL=(1-e -k×LAI ) × SR × Tg × Rp …(4)

[0038] Here, a and b are coefficients in the LUE equation. k is the extinction coefficient, Tg is the photosynthetically active radiation (MJ / MJ) as a percentage of solar radiation, and Rp is the light transmittance of the facility. These values ​​are determined for each variety, and in the case of Artega, they are as shown in Figure 9. That is, equations (3) and (4) above can be expressed as equations (3)' and (4)' below. LUE=2.09×ln(CO2)-9.12 …(3)' IL=(1-e -0.55×LAI )×SR×0.5×0.45 …(4)'

[0039] Then, the estimation processing unit 14 calculates the dry matter production (DM1) (g / m at the optimal temperature (at suitable temperature) 2 ) from the following equation (5). DM1=LUE×IL …(5)

[0040] Furthermore, the estimation processing unit 14 calculates the total dry matter production (DM2) (g / m at the actual temperature) 2 ) from the following equation (6). DM2=DM1×α …(6)

[0041] The estimation processing unit 14 stores each value calculated in this step S16 into the estimation result table 34. FIG. 10 shows the estimation result table 34 (Part 1) that stores each value calculated in step S16. As shown in FIG. 10, in the estimation result table 34 (Part 1), respective values of R1, LUE, IL, DM1, and DM2 are stored for each number of days after planting.

[0042] Returning to FIG. 5, in the next step S18, the estimation processing unit 14 executes a process of calculating the single-fruit weight reduction rate R2 (the reduction rate of the single-fruit weight when cultivated at the actual temperature relative to the single-fruit weight when cultivated at the optimal temperature).

[0043] Specifically, first, the estimation processing unit 14 calculates the standard single-fruit weight (C1) (g / fruit) for each cultivar and the standard single-fruit weight (C2) (g / fruit) at the actual temperature from the following equations (7) and (8). Note that Temp opt is the optimal temperature (20°C) as described above, and Temp1 is the temperature of the selected day. C1=c×(Temp opt )+d …(7) C2=c×(Temp1)+d …(8)

[0044] As shown in Figure 9, c and d are coefficients of the single-fruit weight estimation formula. For the cultivar Artega, c=-0.41 and d=20.28, so the above equations (7) and (8) can be expressed as the following equations (7)' and (8)'. C1=(-0.41)×(Temp opt )+20.28 …(7)' C2=(-0.41)×(Temp1)+20.28 …(8)'

[0045] Next, the estimation processing unit 14 calculates the integrated value (DMf) (g / m of the dry matter production amount of the fruit at the actual temperature 2 ) based on the following equation (9). DMf=Σ(DM2×DMp) …(9)

[0046] Here, DMp means the distribution ratio to the fruit (g / g), and for Artega, DMp=0.7 as shown in Figure 9. Therefore, the above equation (9) can be expressed by the following equation (9)'. DMf=Σ(DM2×0.7) …(9)'

[0047] Next, the estimation processing unit 14 obtains a coefficient β indicating the sufficiency rate of photosynthetic products to the fruit. This β can be obtained from the integrated value (SS1) of the dry matter increase amount of the fruit at the actual temperature and DMf as follows.

[0048] i) When DMf≧SS1, β is β=1.

[0049] ii) When DMf<SS1, β is β=DMf / SS1.

[0050] Here, SS1 can be obtained from the following equation (10). SS1=(g(fr_1)+g(fr_2)+g(fr_3)+…+g(fr_n))·Sd …(10)

[0051] In equation (10) above, g(fr_1), ..., g(fr_n) represent the fruit growth rate (g / fruit) for each of the fruits from No. 1 to No. n, and Sd is the planting density (stem / m 2 This means ). Furthermore, a method using fruit growth curves, as disclosed in Japanese Patent Publication No. 2022-104601, can be used to determine g(fr_1), ..., g(fr_n). Also, the planting density is predetermined as shown in Figure 9, and Sd = 7.2. Therefore, equation (10) above can be expressed as equation (10)'. SS1=(g(fr_1)+g(fr_2)+g(fr_3)+…+g(fr_n))·7.2 …(10)'

[0052] Next, the estimation processing unit 14 calculates the fruit weight at the optimal temperature (Fp1) (g / fruit) and the fruit weight at the actual temperature (FP2) (g / fruit) from the following equations (11) and (12). Note that the fruit weight at the optimal temperature (Fp1) is the same as the standard fruit weight (C1) for each variety. Fp1 = C1 …(11) Fp2 = C2 × β …(12)

[0053] Then, the estimation processing unit 14 calculates the rate of decrease R2 of the fruit weight at the actual temperature based on the following equation (13), using the fruit weight at the optimal temperature (Fp1) and the fruit weight at the actual temperature (FP2). R2 = Fp2 / Fp1 …(13)

[0054] The estimation processing unit 14 stores each value calculated in step S18 in the estimation result table 34. Figure 11 shows the estimation result table 34 (part 2) which stores each value calculated in step S18. As shown in Figure 11, the estimation result table 34 (part 2) stores the values ​​of C1, C2, DMf, SS1, β, Fp1, Fp2, and R2 for each number of days after planting.

[0055] In other words, the estimation result table 34 (part 2) contains the data for day 1, including C1 and C2 obtained by equations (7)' and (8)' above, DMf obtained by equation (9)' above, β obtained by equation (10)' above, Fp1 and Fp2 obtained by equations (11) and (12) above, and R2 obtained by equation (13) above.

[0056] Returning to Figure 5, in the next step S20, the estimation processing unit 14 performs the process of calculating the fruit number reduction rate R3 (the reduction rate of the fruit number when grown at the actual temperature compared to the fruit number when grown at the optimal temperature).

[0057] Specifically, first, the estimation processing unit 14 calculates the number of flowers (FL1) (flowers / m) at the optimal temperature. 2 ) and the number of flowers (FL2) (flowers / m) at the actual temperature. 2 ) and can be found from the following equations (14) and (15). FL1=(Temp opt ) / i×Sd …(14) FL2 = (Temp1) / i × Sd …(15)

[0058] Here, i is a coefficient, and as shown in Figure 9, for the Artega variety, i is predetermined to be 120. Therefore, equations (14) and (15) above can be expressed as equations (14)' and (15)'. FL1=(Temp opt ) / 120×7.2 …(14)' FL2 = (Temp1) / 120 × 7.2 …(15)'

[0059] Next, the estimation processing unit 14 calculates the fruit set rate (FRS1) (%) at the optimal temperature and the fruit set rate (FRS2) (%) at the actual temperature using the following equations (16) and (17). FRS1 = j × ln(DM1 / SS) opt ) + l …(16) FRS2=θ×ln(DM2 / SS1)+l …(17)

[0060] Here, SS optThis is the cumulative value of the increase in dry matter content of fruit at optimal temperature (g / m³). 2 ) means the same method as SS1 mentioned above, to reach the optimal temperature Temp opt It can be calculated using (=20℃). Also, j and l are coefficients of the fruit set rate estimation formula at the optimal temperature, and are predetermined as shown in Figure 9 (j=0.23, l=0.18). Furthermore, θ is a coefficient of the fruit set rate estimation curve at the actual temperature, and can be obtained from the following equation (18). θ = m × (Temp1) + n …(18)

[0061] Note that m and n are coefficients in the fruit set rate estimation formula for actual temperatures, and are predetermined as shown in Figure 9 (m = -0.018, l = 0.59).

[0062] From these, equations (16) and (17) above can be expressed as equations (16)' and (17)'. FRS1 = 0.23 × ln(DM1 / SS) opt )+0.18 …(16)' FRS2={-0.018×(Temp1)+0.59}×ln(DM2 / SS1)+0.18 …(17)'

[0063] Next, the estimation processing unit 14 uses FL1, FL2, FRS1, and FRS2 calculated using the above equations (14)', (15)', (16)', and (17)' to determine the number of fruits set at the optimal temperature (Fr1) and the number of fruits set at the actual temperature (Fr2) from the following equations (19A) and (19B). Fr1 = FL1 × FRS1 …(19A) Fr² = FL² × FRS² …(19B)

[0064] Then, the estimation processing unit 14 uses the obtained Fr1 and Fr2 to calculate the reduction rate R3 of the number of fruits set at the actual temperature using the following equation (20). R3 = Fr2 / Fr1 …(20)

[0065] The estimation processing unit 14 stores each value calculated in step S20 in the estimation result table 34. Figure 12 shows the estimation result table 34 (part 3) which stores each value calculated in step S20. As shown in Figure 12, in the estimation result table 34 (part 3), FL1, FL2, and SS are listed for each number of days after planting. opt The values ​​of θ, FRS1, FRS2, Fr1, Fr2, and R3 are stored.

[0066] In other words, the estimation result table 34 (part 3) contains, as data for day 1, FL1 and FL2 obtained by equations (14)' and (15)' above, FRS1 and FRS2 obtained by equations (16)' and (17)' above, Fr1 and Fr2 obtained by equations (19A) and (19B) above, and R3 obtained by equation (20) above.

[0067] Returning to Figure 5, in the next step S22, the estimation processing unit 14 calculates the yield reduction rate (yield reduction rate) ε at the actual temperature. In this case, the estimation processing unit 14 calculates ε based on the following equation (21). ε = R1 × R2 × R3 …(21)

[0068] Next, in step S24, the estimation processing unit 14 calculates the potential yield (pFW) (g / m³). 2 ) is calculated, and the yield loss (ΔFW) (g / m 2 Specifically, the estimation processing unit 14 calculates the actual yield (FW) (g / m²) for the selected day from the cultivation DB 30. 2 The potential yield pFW is obtained from the following equation (22). pFW = FW / ε …(22)

[0069] Furthermore, the estimation processing unit 14 calculates the yield loss ΔFW from the following equation (23). ΔFW = pFW - FW …(23)

[0070] The estimation processing unit 14 stores the values ​​used and calculated in steps S22 and S24 in the estimation result table 34. Figure 13 shows the estimation result table 34 (part 4) which stores the values ​​used and calculated in steps S22 and S24. As shown in Figure 13, the estimation result table 34 (part 4) stores the values ​​of R1, R2, R3, ε, FW, pFW, and ΔFW for each number of days after planting.

[0071] In other words, the estimation result table 34 (part 4) stores the data for day 1, including R1, R2, R3 used in equation (21) above, ε obtained from equation (21), FW used in equation (22) above, pFW obtained from equation (22), and ΔFW obtained from equation (23) above.

[0072] Returning to Figure 5, in the next step S26, the estimation processing unit 14 determines whether all days have been selected or not. If the determination in step S26 is negative (i.e., there are still days to be processed), the process returns to step S14. Thereafter, the processing and determination in steps S14 to S26 are repeatedly executed until the processing in steps S16 to S24 has been performed for all days.

[0073] On the other hand, if the judgment in step S26 is affirmed, the process proceeds to step S28.

[0074] When the process moves to step S28, the estimation processing unit 14 aggregates the data in the estimation result table 34 (part 4) in Figure 13 on a weekly basis and generates data as shown in Figure 14 (estimation result table 34 (part 5)). The estimation processing unit 14 divides the data in Figure 13 into 7-day (1-week) portions, calculates the average value of each value for each week, and stores it in Figure 14. After that, the process moves to step S30 in Figure 6.

[0075] When the system moves to step S30 in Figure 6, the display pattern determination unit 16 determines whether there is a week in which the yield reduction amount > 0. If the determination in step S30 is denied, it means that there is no possibility of a yield reduction due to the influence of actual temperature (influence of high temperature). In this case, the system moves to step S32, where the display pattern determination unit 16 selects display pattern 4 from the display pattern table 32 shown in Figure 15. The output unit 18 then generates a display screen based on display pattern 4 and transmits it to the user terminal 70. At this time, the output unit 18 may also transmit all or part of the various data stored in the estimation result table 34 together with the display screen to the user terminal 70.

[0076] The display unit 193 of the user terminal 70 displays, based on the display pattern 4, "No fruit count or reduction in fruit count occurs due to high temperatures." Yield reduction: 0 kg / m 2 Workaround: None The following screen will be displayed. If the process in step S32 is completed, all processes shown in Figures 5 and 6 will be finished.

[0077] On the other hand, if the judgment in step S30 is affirmed, that is, if there is a week in which the yield reduction amount > 0 and there is a possibility of a yield reduction in the future due to the effects of high temperatures, the display pattern determination unit 16 proceeds to step S36.

[0078] When the process moves to step S36, the display pattern determination unit 16 calculates the average value of R3 from the present until 140°C of daily accumulated temperature has passed (approximately 7 days later). In this case, the display pattern determination unit 16 should refer to the estimation result table 34 (part 3) in Figure 12 to calculate the average value of R3 for the relevant day.

[0079] Next, in step S38, the display pattern determination unit 16 calculates the average value of R1 and the average value of R2 from the present to after the cumulative daily temperature reaches 1200°C (after approximately 50 to 60 days). In this case, the display pattern determination unit 16 may refer to the estimation result tables 34 (Part 1) and 34 (Part 2) in FIG. 10 and FIG. 11 to calculate the average values of R1 and R2 for the corresponding day.

[0080] Next, in step S40, the display pattern determination unit 16 determines whether the average value of R3 is the minimum as a result of steps S36 and S38 (whether R3 < R1 and R3 < R2). If the determination in step S40 is affirmative, the process proceeds to step S42, and the display pattern determination unit 16 refers to the display pattern table 32 (FIG. 15) to select display pattern 1.

[0081] Then, when the process proceeds to step S50, the output unit 18 generates a display screen based on display pattern 1 and transmits it to the user terminal 70. At this time, the output unit 18 may transmit all or part of various data stored in the estimation result table 34 to the user terminal 70 together with the display screen.

[0082] On the display unit 193 of the user terminal 70, based on display pattern 1, "A decrease in the number of fruits is expected due to high temperatures. Yield decrease: 0.1kg / m 2 Countermeasure: Immediately lower the daily average temperature to 20 degrees for the next 7 days Immediately increase the CO2 concentration to 500ppm for the next 7 days Yield decrease after improvement: 0kg / m 2 , 0.1kg / m 2 improvement effect" A screen as described will be displayed. Note that the output unit 18 can appropriately modify each numerical value on the display screen. That is, when the daily average temperature or CO2 concentration is changed as in a workaround, the output unit 18 may cause the estimation processing unit 14 to simulate how much the yield decrease is improved, and display the result in the column of "Yield decrease after improvement". In addition, if a desired improvement effect is not obtained as a result of the simulation, the values of the daily average temperature and CO2 concentration may be adjusted, and the result of the re-simulation may be displayed in the column of "Yield decrease after improvement".

[0083] After the process of step S50 is performed, all the processes in FIG. 5 and FIG. 6 are completed.

[0084] Incidentally, if the determination in step S40 of FIG. 6 is negative, the process proceeds to step S44, and the display pattern determining unit 16 determines whether or not the average value of R2 is the minimum as a result of steps S36 and S38 (whether R2<R1 and R2<R3). If the determination in step S44 is affirmative, the process proceeds to step S46, and the display pattern determining unit 16 refers to the display pattern table 32 (FIG. 15) to select display pattern 2.

[0085] Then, when the process proceeds to step S50, the output unit 18 generates a display screen based on the display pattern 2 and transmits it to the user terminal 70. At this time, the output unit 18 may transmit all or part of various data stored in the estimation result table 34 to the user terminal 70 together with the display screen.

[0086] On the display unit 193 of the user terminal 70, based on the display pattern 2, "A decrease in single fruit weight is expected due to high temperatures. Yield decrease: 2.1 kg / m 2 Workaround: Immediately lower the daily average temperature to 20 degrees until 50 days later Immediately raise the CO2 concentration to 500 ppm until 50 days later Yield decrease after improvement: 1.3 kg / m 2 , 1.0 kg / m2 "Improvement effect" The following screen will be displayed. As mentioned above, the output unit 18 can change each numerical value on the display screen as appropriate. After the processing in step S50 is completed, all the processing shown in Figures 5 and 6 is finished.

[0087] On the other hand, if the judgment in step S44 is rejected (i.e., the average value of R1 is the minimum), the process proceeds to step S48, where the display pattern determination unit 16 refers to the display pattern table 32 (Figure 15) and selects display pattern 3.

[0088] Then, when the system moves to step S50, the output unit 18 generates a display screen based on the display pattern 3 and transmits it to the user terminal 70. At this time, the output unit 18 may also transmit all or part of the various data stored in the estimation result table 34 together with the display screen to the user terminal 70.

[0089] The display unit 193 of the user terminal 70 displays, based on the display pattern 3, "High temperatures are expected to reduce the number of fruits and the weight of each fruit." Yield reduction: 0.2kg / m 2 Workaround: Lower the average daily temperature to 20 degrees Celsius for the next 50 days immediately. Increase the CO2 concentration to 500 ppm immediately, for the next 50 days. Yield reduction after improvement: 0.1 kg / m 2 , 0.1 kg / m 2 "Improvement effect" The following screen will be displayed. As mentioned above, the output unit 18 can change each numerical value on the display screen as appropriate. After the processing in step S50 is completed, all the processing shown in Figures 5 and 6 is finished.

[0090] As described in detail above, according to this embodiment, the information acquisition unit 12 acquires information on the standard temperature for cultivating fruit and vegetable crops (optimal temperature in this embodiment) and information on the actual temperature during cultivation that is the subject of evaluation (actual temperature). The estimation processing unit 14 calculates the ratio (R1, R2, R3) of each of the values ​​of multiple elements (total dry matter production, fruit weight, number of fruits) that affect the yield when cultivated at the actual temperature, compared to the values ​​of each of these elements when cultivated at the optimal temperature. Then, based on the calculated ratios, the estimation processing unit 14 estimates the yield ratio (reduction rate ε) when cultivated at the actual temperature compared to when cultivated at the optimal temperature. As a result, in this embodiment, the effect of temperature (e.g., high temperature) on yield can be estimated with high accuracy.

[0091] Furthermore, in this embodiment, since multiple types of elements are total dry matter production, fruit weight, and number of fruits, the yield reduction can be accurately estimated based on the hierarchical relationship of yield reduction factors due to high temperature shown in Figure 4.

[0092] Furthermore, in this embodiment, since the yield reduction rate ε is calculated from the product of the decrease rate R1 of total dry matter production, the decrease rate R2 of fruit weight, and the decrease rate R3 of the number of fruits, the yield reduction rate ε can be calculated using a simple calculation method.

[0093] Furthermore, in this embodiment, based on the decrease rate R1 of total dry matter production, the decrease rate R2 of fruit weight, and the decrease rate R3 of the number of fruits, countermeasures (countermeasure information) to reduce the yield loss are selected from the display patterns and output. This makes it possible to provide appropriate countermeasure information to the user.

[0094] In the above embodiment, the case was described in which the reduction rate R1 of total dry matter production, the reduction rate R2 of fruit weight, and the reduction rate R3 of the number of fruits were all calculated, and the reduction rate ε was determined using all reduction rates R1 to R3. However, the invention is not limited to this. For example, one or two of the reduction rates R1 to R3 may be calculated, and the reduction rate ε may be determined based on the calculated reduction rates.

[0095] In the above embodiment, the output unit 18 was described as transmitting the display pattern (countermeasure) determined by the display pattern determination unit 16 to the user terminal 70, but it is not limited to this. The output unit 18 may also transmit control information included in the display pattern (countermeasure) to equipment (such as an air conditioner) installed in the cultivation area (such as a greenhouse) to control the equipment.

[0096] In the above embodiment, measures to adjust the daily average temperature and CO2 concentration are illustrated in each display pattern in Figure 15. However, the display patterns are not limited to these, and measures to increase the amount of light received by the plant community by supplementing with LED light may also be included.

[0097] The above processing functions can be implemented by a computer. In this case, a program describing the processing content of the functions that the processing unit should have is provided. By executing this program on a computer, the above processing functions are implemented on the computer. The program describing the processing content can be recorded on a storage medium that can be read by a computer (except for carrier waves).

[0098] When distributing a program, it may be sold in the form of a portable storage medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. Alternatively, the program can be stored in the storage device of a server computer and transferred from the server computer to other computers via a network.

[0099] A computer executing a program stores programs, for example, those recorded on a portable storage medium or transferred from a server computer, in its own memory. The computer then reads the program from its memory and executes the processing according to the program. Alternatively, the computer can directly read the program from the portable storage medium and execute the processing according to that program. Furthermore, the computer can sequentially execute the processing according to the programs received as they are transferred from the server computer.

[0100] The embodiments described above are preferred examples of the present invention. However, the invention is not limited thereto, and various modifications are possible without departing from the spirit of the invention. [Explanation of Symbols]

[0101] 10 servers 12 Information acquisition department 14 Estimation Processing Unit 16 Display Pattern Determination Unit 18 Output section 30 Cultivation information DB 32 Display Pattern Table 34 Estimated Result Table 70 User terminals 100 Cultivation Evaluation Systems

Claims

1. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportions of each of the aforementioned multiple types of elements, the yield ratio of the second case to the first case is estimated. A cultivation evaluation method characterized by the processing being performed by a computer.

2. The cultivation evaluation method according to claim 1, characterized in that the proportion of each of the aforementioned multiple types of elements includes at least one of the decrease rate of total dry matter production, the decrease rate of fruit weight, and the decrease rate of the number of fruits.

3. The cultivation evaluation method according to claim 1, characterized in that the yield ratio is the product of the ratios of each of the multiple types of elements.

4. Based on the proportion of each of the aforementioned multiple types of elements, information on measures to increase the yield ratio is determined and output. The cultivation evaluation method according to claim 1, characterized in that the processing is performed by the computer.

5. The cultivation evaluation method according to claim 4, characterized in that countermeasure information is determined based on the element with the smallest value among the proportions of each of the aforementioned multiple types of elements.

6. The cultivation evaluation method according to claim 1, characterized in that the first temperature is an ideal temperature for cultivating the crop.

7. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportions of each of the aforementioned multiple types of elements, the yield ratio of the second case to the first case is estimated. A cultivation evaluation program characterized by having a computer perform the processing.

8. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportions of each of the aforementioned multiple types of elements, control information is determined to increase the yield ratio in the second case compared to the first case, and this information is output to the environmental control device in the cultivation facility. A method for controlling the cultivation environment, characterized in that the processing is performed by a computer.

9. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportions of each of the aforementioned multiple types of elements, control information is determined to increase the yield ratio in the second case compared to the first case, and this information is output to the environmental control device in the cultivation facility. A cultivation environment control program characterized by having a computer perform the processing.

10. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportion of each of the aforementioned multiple types of elements, information on measures to increase the yield ratio in the second case compared to the first case is determined and output. The computer performs the process, The aforementioned countermeasures include measures to increase the amount of light received by the canopy using LED supplemental lighting. A method for outputting information on measures taken during cultivation, characterized by the following.

11. We obtain information on the first temperature, which is used as a standard in crop cultivation, and information on the second temperature, which is the subject of evaluation. The ratio of the values ​​of each of the multiple elements that affect the yield in the first case, when the crop is grown at the first temperature, to the values ​​of each of the multiple elements in the second case, when the crop is grown at the second temperature, is calculated. Based on the proportion of each of the aforementioned multiple types of elements, information on measures to increase the yield ratio in the second case compared to the first case is determined and output. Let the computer perform the process, The aforementioned countermeasures include measures to increase the amount of light received by the canopy using LED supplemental lighting. A cultivation-related information output program characterized by the following:

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