Strawberry Yield Prediction Method, Yield Prediction Program, and Yield Prediction Device

Abstract

To accurately estimate strawberry yield.SOLUTION: A yield estimation unit 38 estimates a dry matter production obtained by a harvest date on the basis of actual measurements and predictive values relating to a growth environment of strawberry plants during a growth period up to the harvest date and actual measurements and estimates of leaf areas of the plants during the growth period. Then, the yield estimation unit 38 estimates strawberry yield on the basis of the dry matter production, a rate b of translocation from the dry matter production to strawberry fruit, a percentage of moisture content z in fruit, and an evaluation index a.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a strawberry yield prediction method, a yield prediction program, and a yield prediction device.

Background Art

[0002] In farmers and agricultural corporations engaged in crop production, in order to conduct efficient management, it is important to predict the yield before harvest and formulate a harvest work (labor management) plan and a shipping plan.

[0003] Especially in the production of fruit vegetables, since fruits are repeatedly borne on the same plant over a long period of time, predicting the yield by time period is very effective for formulating the above plans.

[0004] Conventionally, techniques for predicting the yield of crops such as tomatoes have been known (see, for example, Patent Documents 1 to 3, etc.). In addition, techniques for predicting the harvest date, harvest quantity, size, etc. of fruits such as strawberries have been known (see, for example, Patent Document 4, etc.).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, since the forced cultivation of strawberries is a cultivation type accompanied by specific physiological state changes, there is a risk that the yield cannot be predicted with high accuracy even by using a yield prediction technique applicable to other fruit vegetables such as tomatoes.

[0007] Therefore, an object of the present invention is to provide a strawberry yield prediction method, a yield prediction program, and a yield prediction device that can accurately predict the strawberry yield.

Means for Solving the Problems

[0008] The strawberry yield prediction method of the present invention is 、 estimating the dry matter production amount of the plant up to the harvest date based on at least one of the measured value and the predicted value of the growth environment of the strawberry plant during the growth period until the harvest date and at least one of the measured value and the predicted value of the leaf area of the plant during the growth period; and 、 estimating the yield of the strawberry based on the dry matter production amount, the ratio of the dry matter production amount transferred to the strawberry fruits, the moisture ratio of the fruits, and the evaluation index of the physiological characteristics exhibited by the strawberry according to the growth environment. A method for predicting the yield of strawberries executed by a computer

Effects of the Invention

[0009] The strawberry yield prediction method, the yield prediction program, and the yield prediction device of the present invention have the effect of being able to accurately predict the strawberry yield.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] Hereinafter, a method for predicting the yield of strawberries according to an embodiment will be described in detail.

[0012] First, the premise of this embodiment will be described.

[0013] The present inventor cultivated strawberries (variety: Tochiotome) in two experimental plots (high temperature plot: average temperature 17.3°C, night temperature 11.9°C; low temperature plot: average temperature 15.3°C, night temperature 8.4°C), and investigated the leaf area of all leaves and the yield. In this investigation, in addition to image processing using a digital camera, measurements were also taken using a measuring tool and a scale.

[0014] In addition, from the leaf area obtained as a result of the above investigation, the present inventor calculated the estimated value of dry matter production (estimated dry matter production) with respect to the daily accumulated temperature for each experimental plot. As a result, the calculation results as shown in Fig. 1(a) were obtained. Also, as a result of the above investigation, the actual yield (yield with respect to the daily accumulated temperature) was as shown in Fig. 1(b).

[0015] Generally, it is considered that the yield of fruit vegetables is proportional to the dry matter production. This is because plants produce fruits by photosynthesis according to environmental conditions with their leaves. Therefore, if the change in the estimated dry matter production with respect to the daily accumulated temperature is almost the same in the high temperature plot and the low temperature plot as shown in Fig. 1(a), the yields in the high temperature plot and the low temperature plot should be about the same. However, actually, as shown in Fig. 1(b), the yield in the high temperature plot was higher than that in the low temperature plot.

[0016] As a result of examining the reason for this, the present inventor came to the conclusion that it may be related to the "dormancy", which is a physiological phenomenon peculiar to strawberries. Here, the forced cultivation of strawberries is a cultivation method that suppresses growth by utilizing dormancy and harvests fruits for a long period, and it is known to be affected by day length and temperature. It is also known that the plants become dwarfed when dormancy deepens. However, there are still many unclear points about the details of dormancy.

[0017] Even in the case of plants with the same total leaf area, the internal physiological state changes due to dormancy. Therefore, the inventor speculated that by considering factors other than the total leaf area that are affected by dormancy in strawberry yield prediction, the yield can be predicted accurately.

[0018] Based on such speculation, the inventor confirmed the changes in the growth amount of the leaf area of the youngest first leaf (new leaf), the second leaf next to the first leaf, and the third leaf next to the second leaf for plants in the high-temperature area and the low-temperature area, and found that there were different trends. Fig. 2(a) shows the changes in the growth amount of the leaf area of the first leaf (new leaf) with respect to the accumulated temperature for each of the high-temperature area and the low-temperature area. Fig. 2(b) shows the changes in the leaf area of the third leaf with respect to the accumulated temperature for each of the high-temperature area and the low-temperature area. Since the trends in Fig. 2(a) and Fig. 2(b) are similar to the generally known way of changing the depth of dormancy (gradually becoming deeper and then shallower after reaching the bottom), it can be speculated that the influence of temperature (i.e., the influence due to the depth of dormancy) appears in young leaves such as the first leaf to the third leaf. Furthermore, when comparing the first leaf and the third leaf, the first leaf is shifted to the left with respect to the accumulated temperature (the most convex part is about 1500 °C for the first leaf and about 1800 °C for the third leaf), so it is considered that young leaves react faster to dormancy. From this, the inventor reached the conclusion that if some value representing the change of the leaf area of the first leaf (new leaf) with respect to the accumulated temperature is used for yield prediction, the yield can be predicted accurately.

[0019] (Regarding the agricultural system 100 according to this embodiment) Fig. 3 shows an agricultural system 100 for realizing a strawberry yield prediction method according to an embodiment.

[0020] As shown in FIG. 3, the agricultural system 100 includes an information processing device 10 as a yield prediction device, an outdoor sensor 12, an in - greenhouse sensor 14 installed in the greenhouse 18, and an environmental control device (hereinafter referred to as a control target device) 16 that adjusts the environment in the greenhouse 18. The information processing device 10, the outdoor sensor 12, the in - greenhouse sensor 14, and the control target device 16 are connected via a network such as the Internet, and information can be exchanged between the devices.

[0021] The information processing device 10 is an information processing device available to the operator who cultivates strawberries in the greenhouse 18. Based on the environmental data acquired by the outdoor sensor 12 and the in - greenhouse sensor 14, and the information input by the operator, it predicts the yield of strawberries on the prediction target day and outputs (displays, etc.). Also, the information processing device 10 can control the control target device 16. Details of the configuration and processing of the information processing device 10 will be described later.

[0022] The outdoor sensor 12 includes a temperature sensor that detects the air temperature outside the greenhouse 18 and a solar radiation sensor that detects solar radiation, and inputs the detection results to the information processing device 10.

[0023] The in - greenhouse sensor 14 includes a temperature sensor that detects the air temperature in the greenhouse 18, a solar radiation sensor that detects solar radiation in the greenhouse 18, and a CO2 concentration sensor that detects the CO2 concentration in the greenhouse 18, and inputs the detection results to the information processing device 10.

[0024] The control target device 16 includes a heat pump, a ventilation window, a heater, a CO2 applicator, a light - shading and heat - insulating curtain, etc. The heat pump is a device that lowers the temperature in the greenhouse 18, and the ventilation window is a window that takes in outside air into the greenhouse 18. The heater is a device that raises the temperature in the greenhouse 18, and the CO2 applicator is a device that adjusts the CO2 concentration in the greenhouse 18. Also, the light - shading and heat - insulating curtain is a curtain that adjusts solar radiation and temperature in the greenhouse 18. It is assumed that the control target device 16 can execute operations according to the instructions of the information processing device 10, and the environment in the greenhouse 18 is adjusted by the operation of the control target device 16.

[0025] Here, the configuration and processing of the information processing apparatus 10 will be described in detail. FIG. 4 schematically shows the hardware configuration of the information processing apparatus 10. As shown in FIG. 4, the information processing apparatus 10 includes a CPU 90, a ROM 92, a RAM 94, a storage unit (here, an HDD or an SSD) 96, a network interface 97, a display unit 93, an input unit 95, and a drive 99 for a portable storage medium, etc. 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. Each of these constituent parts of the information processing apparatus 10 is connected to a bus 98. In the information processing apparatus 10, the CPU 90 executes a program stored in the ROM 92 or the HDD 96 (including a yield prediction program), or a program read by the drive 99 for a portable storage medium from the portable storage medium 91, whereby the functions of each part shown in FIG. 5 are realized. Note that the functions of each part in FIG. 5 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0026] FIG. 5 shows a functional block diagram of the information processing apparatus 10. In the information processing apparatus 10, when the CPU 90 executes a program, as shown in FIG. 5, an input reception unit 30, an environmental information acquisition unit 32, a leaf area estimation unit 34, an index calculation unit 36, a yield estimation unit 38 as a first estimation unit and a second estimation unit, and functions are realized.

[0027] The input reception unit 30 acquires information input by an operator via the input unit 95. The information input by the operator includes information used when estimating the total leaf area of the stock (for example, the leaf area of the third leaf) and information used for calculating an evaluation index of the physiological state (dormancy) of the stock (in this embodiment, the leaf area of the first leaf). Note that these pieces of information do not necessarily need to be input by the operator. For example, when a robot that circulates in the greenhouse 18 can acquire information on the leaf area of the third leaf and the leaf area of the first leaf using a camera or the like, the input reception unit 30 may acquire the information input from the robot.

[0028] The environmental information acquisition unit 32 acquires the detection results of the outdoor sensor 12 and the in-greenhouse sensor 14. Further, as future environmental information, the environmental information acquisition unit 32 acquires past data (normal values, etc.) and prediction data (weather prediction meshes, etc.) from an external server or the like.

[0029] The leaf area estimation unit 34 estimates the total leaf area of each plant. Details of the processing of the leaf area estimation unit 34 will be described later.

[0030] The index calculation unit 36 calculates an evaluation index for the physiological state (dormancy) of the plant used for yield estimation. In the present embodiment, the evaluation index is calculated based on the change of the integrated temperature of the leaf area of the first leaf, but the details will be described later.

[0031] The yield estimation unit 38 estimates the strawberry yield using the total leaf area of each plant estimated by the leaf area estimation unit 34, the evaluation index calculated by the index calculation unit 36, and various environmental information acquired by the environmental information acquisition unit 32. Further, the yield estimation unit 38 displays the estimated strawberry yield information on the display unit 93. Note that details of the processing of the yield estimation unit 38 will be described later.

[0032] (Regarding the processing of the information processing apparatus 10) FIG. 6 is a flowchart showing the processing of the information processing apparatus 10. As a premise for starting the processing of FIG. 6, it is assumed that information necessary for yield estimation has been input to the input reception unit 30. Further, it is assumed that the environmental information acquisition unit 32 has acquired past and current environmental information and future environmental information (predicted values).

[0033] When the process of FIG. 6 starts, first, in step S10, the leaf area estimation unit 34 estimates the transition of the total leaf area of the plant (the change in the total leaf area with respect to the integrated temperature). FIG. 7(a) shows the relationship between the leaf area of the third leaf and the total leaf area. From this FIG. 7(a), it can be seen that there is a correlation between the leaf area of the third leaf and the total leaf area. Therefore, the leaf area estimation unit 34 estimates the total leaf area by substituting the leaf area of the third leaf input to the input reception unit 30 into the formula of FIG. 7(a). Also, as shown in FIG. 7(b), the leaf area estimation unit 34 represents the relationship between the estimated total leaf area and the integrated temperature in a graph and obtains an equation (the equation of FIG. 7(b)) showing the relationship between the integrated temperature and the total leaf area. Then, the leaf area estimation unit 34 estimates the total leaf area with respect to the future integrated temperature using the equation of FIG. 7(b) (see FIG. 7(c)).

[0034] Note that the leaf area estimation unit 34 may estimate the total leaf area by methods other than the above. For example, when the past total leaf area has been measured, the leaf area estimation unit 34 may use the measured value of the total leaf area to obtain the relationship between the total leaf area and the integrated temperature as shown in FIG. 7(b), and estimate the future total leaf area as shown in FIG. 7(c). Also, for example, the leaf area estimation unit 34 may estimate the total leaf area based on the projected area of the plant obtained from an image taken of the plant from above, obtain the relationship as shown in FIG. 7(b) from the estimated total leaf area, and estimate the relationship between the future total leaf area and the integrated temperature as shown in FIG. 7(c).

[0035] Returning to FIG. 6, in the next step S12, the index calculation unit 36 calculates an evaluation index for the physiological state (dormancy) of the stock. In the present embodiment, the index calculation unit 36 calculates the evaluation index a using the transition of the growth amount of the leaf area of the first leaf with respect to the integrated temperature as shown in FIG. 8(a). For example, when the transition of the growth amount of the leaf area of the first leaf as indicated by "●" in FIG. 8(a) is obtained (high temperature region), the index calculation unit 36 linearly approximates the period during which the approximate curve (broken line) slopes downward to the right, and uses the slope (-1.78) of the obtained approximate straight line as the evaluation index a. Further, when the transition of the growth amount of the leaf area of the first leaf as indicated by "■" in FIG. 8(a) is obtained, the index calculation unit 36 linearly approximates the period during which the approximate curve (broken line) slopes downward to the right, and uses the slope (-2.71) of the obtained approximate straight line as the evaluation index a.

[0036] Note that, actually, at the stage when the process of FIG. 6 is started, as shown in FIG. 8(b), there may be only a part of the data in FIG. 8(a). However, by performing curve approximation or linear approximation using the existing data, the evaluation index a can be obtained.

[0037] Next, in step S14, the yield estimation unit 38 executes yield estimation. The process of step S14 is as follows.

[0038] The yield estimation unit 38 first calculates the light reception amount I int based on the following equation (1). I int = I0×(1 - e (-k×LAI) )×α …(1)

[0039] Here, I0 is the solar radiation amount, k is the light absorption coefficient, and LAI is the leaf area index (leaf area per unit land area). When obtaining the LAI, the yield estimation unit 38 uses the total leaf area of each stock estimated by the leaf area estimation unit 34. Further, α means the ratio (%) of the photosynthetically active radiation.

[0040] Next, the yield estimation unit 38 calculates the light use efficiency RUE based on the following equation (2). RUE = 3.2×(1 - e (-0.0032×CO2) ) …(2)

[0041] "CO2" in the above formula (2) is the CO2 concentration, which is assumed to be, for example, 400 ppm.

[0042] Next, the yield estimation unit 38 estimates the dry matter production amount CGR based on the following formula (3). CGR = I int × RUE …(3)

[0043] Next, the yield estimation unit 38 calculates the increase rate C of the dry matter production amount with respect to the accumulated temperature based on the following formula (4). C = ΣCGR / ΣT …(4)

[0044] Then, the yield estimation unit 38 estimates the integrated yield ΣH based on the following formula (5). ΣH = {(b × C) / (-a · n × z)} × ΣT …(5)

[0045] In the above formula (5), b is the transfer ratio to the fruit, which is assumed to be, for example, 60%. Also, z is the moisture ratio of the fruit (1 - sugar content), which is assumed to be, for example, 90%. Also, a is the evaluation index calculated by the index calculation unit 36. Note that there are other methods for determining the evaluation index a other than the method based on the transition of the growth amount of the leaf area of the first leaf as described with reference to FIGS. 8(a) and 8(b). In the case of the present embodiment, n = 1.

[0046] Next, the yield estimation unit 38 predicts the yield by period based on the relationship between the integrated yield ΣH, the integrated temperature ΣT, and the date D, and the harvest start date d. Note that the harvest start date d may be (1) input by the operator, or (2) estimated by detecting flower buds from the photographed image of the plant. Also, (3) it may be estimated by observing fruit set or fruits from the photographed image of the plant, or (4) estimated from environmental information (temperature and solar radiation amount). Furthermore, the harvest start date d may be estimated by combining the above (2) to (4).

[0047] Returning to FIG. 6, in the next step S16, the yield estimation unit 38 displays (outputs) the yield by time period (estimation result) estimated in step S14 on the display unit 93. In step S14, when graphs of the estimated values of the integrated yield for each day as shown in FIGS. 10(a) and 10(b) are obtained, the yield estimation unit 38 may display the graphs of the estimated values of the integrated yield as they are on the display unit 93. The operator can check the integrated yield up to a specific day and the yield assumed for a specific period from the displayed graphs. Also, when the operator designates a period (referred to as a confirmation period) for which the operator wants to know the yield, the yield estimation unit 38 calculates the difference between the estimated value of the integrated yield up to the last day of the confirmation period and the estimated value of the integrated yield up to immediately before the confirmation period, and can display the difference as the yield in the confirmation period on the display unit 93. That is, the yield estimation unit 38 can display the information obtained in step S14 as it is or after processing, or can display the information calculated from the information obtained in step S14. Thus, all the processing in FIG. 6 is completed.

[0048] FIG. 9 shows, as a comparative example, the relationship between the estimated integrated yield when estimating the yield without using the evaluation index a and the actual integrated yield (measured value). In the case of the comparative example, the integrated yield ΣH is estimated using the following formula (6) instead of the above formula (5). ΣH={(b×C) / z}×ΣT …(6)

[0049] As shown in FIG. 9, in the comparative example, it was found that in both the high-temperature region and the low-temperature region, there is a large deviation from the case where the estimated integrated yield and the integrated yield (measured value), indicated by the broken line in FIG. 9, match. That is, in the comparative example, it was found that a difference occurs between the estimated integrated yield and the integrated yield (measured value) in both the high-temperature region and the low-temperature region.

[0050] On the one hand, Fig. 10(a) shows the result of estimating the yield of strawberries cultivated in the high-temperature area by the method of this embodiment, together with the actually measured value of the yield. Fig. 10(b) shows the result of estimating the yield of strawberries cultivated in the low-temperature area by the method of this embodiment, together with the actually measured value of the yield. From these Figs. 10(a) and 10(b), it can be seen that by using the method of this embodiment, the yield of strawberries can be accurately estimated both in the high-temperature area and in the low-temperature area.

[0051] As described in detail above, according to this embodiment, the yield estimation unit 38 estimates the dry matter production amount until the harvest date based on the actually measured values or predicted values of the growth environment of the strawberry plants during the growth period until the harvest date, and the actually measured values or estimated values of the leaf area of the plants during the growth period (S14, formulas (1) to (4)). Then, the yield estimation unit 38 estimates the yield of strawberries based on the dry matter production amount, the ratio b of the dry matter production amount transferred to the fruits of strawberries, the moisture ratio z of the fruits, and the evaluation index a (S14, formula (5)). Thereby, in this embodiment, considering the influence of dormancy, the yield of strawberries can be accurately estimated (see Figs. 10(a) and 10(b)).

[0052] Also, in this embodiment, the index calculation unit 36 calculates the evaluation index a from the change in the feature amount obtained from the actual strawberry plants (in this embodiment, the change in the integrated temperature of the leaf area of the first leaf), so the yield of strawberries can be accurately estimated. For example, even within the same greenhouse, the environmental conditions may vary, and there may be differences in the physiological states of the plants. Even in such a case, by obtaining the evaluation index a from the feature amounts of each plant, the yield of each plant can be accurately estimated.

[0053] Note that the index calculation unit 36 may calculate the evaluation index a using the data of past plants cultivated under the same conditions (for example, data from one year ago).

[0054] In the above embodiment, the case where the index calculation unit 36 calculates the evaluation index a using the transition of the growth amount of the leaf area of the first leaf (Fig. 8(a)) has been described, but the present invention is not limited to this. For example, in the above embodiment, since the change in the leaf area of the first leaf is large, the evaluation index a is calculated using the transition of the growth amount. However, the evaluation index a may be calculated using the transition of the leaf area of the first leaf itself. In addition, the evaluation index a can also be calculated using the transition of the following feature amounts.

[0055] (a) Transition of the leaf area of the second leaf Fig. 11(a) shows the change in the leaf area of the second leaf with respect to the accumulated temperature. The evaluation index a in the high-temperature region is the slope -2.77 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature region is the slope -4.33 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature region / the evaluation index in the high-temperature region is -4.33 / -2.77 = 1.56. Since this value is approximated to the value -2.71 / -1.78 = 1.52 in the above embodiment (when using the transition of the leaf area of the first leaf), even when using the transition of the leaf area of the second leaf, the yield of strawberries can be accurately estimated in the same manner as in the above embodiment. Note that since the value of the evaluation index a in this example is different from the value of the evaluation index a in the above embodiment, in order to match the evaluation index a in the above embodiment, the value of n in the above formula (5) may be determined in advance.

[0056] (b) Transition of the leaf area of the third leaf Fig. 11(b) shows the change in the leaf area of the third leaf with respect to the accumulated temperature. The evaluation index a in the high-temperature region is the slope -3.37 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature region is the slope -5.18 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature region / the evaluation index in the high-temperature region is -5.18 / -3.37 = 1.53. Since this value is approximated to the value 1.52 in the above embodiment, even when using the transition of the leaf area of the third leaf, the yield of strawberries can be accurately estimated in the same manner as in the above embodiment. Note that also in this example, it is necessary to determine the value of n in the above formula (5) in advance.

[0057] (c) Transition of petiole length of the second leaf Figure 12(a) shows the change in the petiole length of the second leaf with respect to the accumulated temperature. The evaluation index a in the high-temperature zone is the slope -0.015 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature zone is the slope -0.022 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature zone / the evaluation index in the high-temperature zone is -0.022 / -0.015 = 1.47. Since this value is approximated to the value 1.52 in the above embodiment, even when using the transition of the petiole length of the second leaf, it is possible to accurately estimate the strawberry yield in the same manner as in the above embodiment. Note that also in this example, it is necessary to determine in advance the value of n in the above formula (5).

[0058] (d) Transition of petiole length of the third leaf Figure 12(b) shows the change in the petiole length of the third leaf with respect to the accumulated temperature. The evaluation index a in the high-temperature zone is the slope -0.018 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature zone is the slope -0.027 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature zone / the evaluation index in the high-temperature zone is -0.027 / -0.018 = 1.50. Since this value is approximated to the value 1.52 in the above embodiment, even when using the transition of the petiole length of the third leaf, it is possible to accurately estimate the strawberry yield in the same manner as in the above embodiment. Note that also in this example, it is necessary to determine in advance the value of n in the above formula (5).

[0059] (e) Transition of the plant height of the first leaf (new leaf) Figure 13(a) shows the change in the cumulative temperature of the height of the first leaf (new leaf). The evaluation index a in the high-temperature region is the slope -0.0106 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature region is the slope -0.0180 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature region / the evaluation index in the high-temperature region is -0.0180 / -0.0106 = 1.70. Since this value is approximated to the value 1.52 in the above embodiment, even by using the transition of the height of the first leaf (new leaf), the strawberry yield can be accurately estimated in the same manner as in the above embodiment. In this example as well, it is necessary to determine the value of n in the above formula (5) in advance.

[0060] (f) Transition of the height of the second leaf Figure 13(b) shows the change in the cumulative temperature of the height of the second leaf. The evaluation index a in the high-temperature region is the slope -0.0267 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature region is the slope -0.0406 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature region / the evaluation index in the high-temperature region is -0.0406 / -0.0267 = 1.52. Since this value is approximated to the value 1.52 in the above embodiment, even by using the transition of the height of the second leaf, the strawberry yield can be accurately estimated in the same manner as in the above embodiment. In this example as well, it is necessary to determine the value of n in the above formula (5) in advance.

[0061] (g) Transition of the height of the third leaf Figure 13(c) shows the change in the cumulative temperature of the height of the third leaf. The evaluation index a in the high-temperature region is the slope -0.0268 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature region is the slope -0.0431 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature region / the evaluation index in the high-temperature region is -0.0431 / -0.0268 = 1.61. Since this value is approximated to the value 1.52 in the above embodiment, even by using the transition of the height of the third leaf, the strawberry yield can be accurately estimated in the same manner as in the above embodiment. In this example as well, it is necessary to determine the value of n in the above formula (5) in advance.

[0062] (h) Transition of the emergence frequency of the first leaf (new leaf) Fig. 14(a) shows the change in the accumulated temperature of the emergence frequency of the first leaf (new leaf). The evaluation index a in the high-temperature zone is the slope -0.00030840 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature zone is the slope -0.00047384 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature zone / the evaluation index in the high-temperature zone is -0.00047384 / -0.00030840 = 1.54. Since this value is approximated to the value 1.52 in the above embodiment, even when using the transition of the emergence frequency of the first leaf (new leaf), the strawberry yield can be accurately estimated in the same manner as in the above embodiment. In this example as well, it is necessary to determine the value of n in the above formula (5) in advance.

[0063] (i) Transition of the growth amount of the petiole length of the first leaf (new leaf) Fig. 14(b) shows the transition of the growth amount of the petiole length of the first leaf (new leaf) with respect to the accumulated temperature. The evaluation index a in the high-temperature zone is the slope -0.0006 of the straight line approximating the downward-sloping part of the approximate curve, and the evaluation index a in the low-temperature zone is the slope -0.0009 of the straight line approximating the downward-sloping part of the approximate curve. In this case, the evaluation index in the low-temperature zone / the evaluation index in the high-temperature zone is -0.0009 / -0.0006 = 1.50. Since this value is approximated to the value 1.52 in the above embodiment, even when using the transition of the growth amount of the petiole length of the first leaf (new leaf), the strawberry yield can be accurately estimated in the same manner as in the above embodiment. In this example as well, it is necessary to determine the value of n in the above formula (5) in advance. In the above, since the change in the petiole length of the first leaf is large, the evaluation index a is calculated using the transition of the growth amount of the petiole length, but the evaluation index a may also be calculated using the transition of the petiole length of the first leaf itself.

[0064] Incidentally, the above processing functions can be realized by a computer. In that case, a program describing the processing contents of the functions that the processing device should have is provided. By executing that program on a computer, the above processing functions are realized on the computer. The program describing the processing contents can be recorded on a computer-readable storage medium (excluding carrier waves).

[0065] When distributing the program, for example, it is sold in the form of a portable storage medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. Also, 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.

[0066] A computer that executes the program stores, for example, the program recorded on a portable storage medium or the program transferred from a server computer in its own storage device. Then, the computer reads the program from its own storage device and executes the processing according to the program. Incidentally, the computer can also directly read the program from the portable storage medium and execute the processing according to the program. Also, the computer can sequentially execute the processing according to the received program each time the program is transferred from the server computer.

[0067] The above-described embodiments are preferred examples of the present invention. However, the present invention is not limited thereto, and various modifications can be made without departing from the gist of the present invention.

Explanation of Reference Numerals

[0068] 10 Information Processing Apparatus (Yield Prediction Apparatus) 30 Input Reception Unit 32 Environmental Information Acquisition Unit 34 Leaf Area Estimation Unit 36 Index Calculation Unit 38 Yield estimation unit (first estimation unit, second estimation unit) 100 Agricultural system

Claims

Step of estimating the dry matter production amount in the said strain until the harvest date based on at least one of the measured values and predicted values regarding the growth environment of the strawberry strain during the growth period until the harvest date, and at least one of the measured values and predicted values of the leaf area of the said strain during the said growth period. A method for predicting the yield of strawberries, characterized in that a computer executes a step of estimating the yield of the strawberries based on the dry matter production amount, the ratio at which the dry matter production amount is diverted to the fruits of the strawberries, the moisture ratio of the fruits, and an evaluation index of the physiological characteristics exhibited by the strawberries according to the growth environment.

2. The method for predicting the yield of strawberries according to Claim 1, wherein the evaluation index of the physiological characteristics is a value indicating the degree of change in a predetermined characteristic amount of the strawberry strain with respect to the change in the accumulated temperature until the harvest date.

3. The method for predicting the yield of strawberries according to Claim 2, wherein the predetermined characteristic amount is any one of the growth amount of the leaf area of the youngest first leaf in the strain, the growth amount of the petiole length, the occurrence frequency or the plant height, the leaf area of the second leaf younger than the first leaf, the petiole length or the plant height, and the leaf area of the third leaf younger than the second leaf, the petiole length or the plant height.

4. The method for predicting the yield of strawberries according to any one of Claims 1 to 3, wherein the evaluation index of the physiological characteristics is calculated from data obtained while cultivating the strawberry strain for which the yield is estimated.

5. The method for predicting the yield of strawberries according to any one of Claims 1 to 3, wherein the evaluation index of the physiological characteristics is a coefficient calculated from data obtained while cultivating a strawberry strain in a growth environment similar to the growth environment of the strawberry strain for which the yield is estimated.

6. On a computer, A process of estimating the dry matter production amount in the said strain until the harvest date based on at least one of the measured values and predicted values regarding the growth environment of the strawberry strain during the growth period until the harvest date, and at least one of the measured values and predicted values of the leaf area of the said strain during the said growth period. A process of estimating the yield of the strawberries based on the dry matter production amount, the ratio at which the dry matter production amount is diverted to the fruits of the strawberries, the moisture ratio of the fruits, and an evaluation index of the physiological characteristics exhibited by the strawberries according to the growth environment. A strawberry yield prediction program, characterized in that the above processes are executed.

7. Based on at least one of the measured values and predicted values of the growth environment of strawberry plants during the growth period until the harvest date, and at least one of the measured values and predicted values of the leaf area of the plants during the growth period, a first estimation unit that estimates the dry matter production amount of the plants until the harvest date; A second estimation unit that estimates the yield of the strawberries based on the dry matter production amount, the ratio of the dry matter production amount transferred to the strawberry fruits, the moisture ratio of the fruits, and an evaluation index of the physiological characteristics exhibited by the strawberries according to the growth environment; A strawberry yield prediction device comprising the above.

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