JP7705141B2 - Strawberry yield prediction method, yield prediction program, and yield prediction device - Google Patents

Description

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

For farms and agricultural corporations that produce crops, it is important to predict yields before harvesting and to make plans for harvesting work (labor management) and shipping in order to operate efficiently.

In particular, in fruit vegetable production, the same plant repeatedly bears fruit over a long period of time, so predicting yields by season is extremely useful for creating the above plans.

Technologies for predicting the yield of crops such as tomatoes are known (see, for example, Patent Documents 1 to 3). Technologies for predicting the harvest date, yield, size, etc. of fruits such as strawberries are also known (see, for example, Patent Document 4).

JP 2020-204829 A JP 2021-26771 A Japanese Patent Application Publication No. 8-228602 JP 2020-54289 A

However, because strawberry forced cultivation is a crop type that involves unique physiological changes, even if yield prediction techniques that can be used for other fruit vegetables such as tomatoes are used, it may not be possible to predict yields with a high degree of accuracy.

The present invention aims to provide a strawberry yield prediction method, a yield prediction program, and a yield prediction device that can accurately predict strawberry yield.

The strawberry yield prediction method of the present invention is a strawberry yield prediction method executed by a computer, which includes the steps of: estimating the amount of dry matter production in the strawberry plant until the harvest date based on at least one of actual measured values and predicted values for the growth environment of the strawberry plant during the growth period until the harvest date, and at least one of actual measured values and predicted values for the leaf area of the plant during the growth period ; and estimating the yield of the strawberries based on the amount of dry matter production, the proportion of the dry matter production translocated to the strawberry fruit, the water proportion of the fruit, and evaluation indexes for the physiological characteristics manifested in the strawberries depending on the growth environment.

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

FIG. 1(a) is a graph showing the estimated results of dry matter production versus daily accumulated temperature in the high and low temperature areas, and FIG. 1(b) is a graph showing the actual measured values of yield versus daily accumulated temperature in the high and low temperature areas. FIG. 2(a) is a graph showing the change in leaf area growth of the first leaf (new leaf) versus accumulated temperature for both the high temperature and low temperature zones, and FIG. 2(b) is a graph showing the change in leaf area of the third leaf versus accumulated temperature for both the high temperature and low temperature zones. FIG. 1 is a diagram illustrating a schematic configuration of an agricultural system. FIG. 2 is a diagram illustrating a hardware configuration of an information processing device. FIG. 2 is a functional block diagram of an information processing device. 4 is a flowchart showing a process of the information processing device. Figure 7(a) is a diagram showing the relationship between the leaf area of the third leaf and the total leaf area, Figure 7(b) is a diagram showing the relationship between the estimated total leaf area and accumulated temperature, and Figure 7(c) is a diagram explaining a method of estimating the future total leaf area. FIG. 8( a ) is a diagram showing the change in the growth amount of the leaf area of the first leaf versus the accumulated temperature, and FIG. 8( b ) is a diagram for explaining how to determine the evaluation index a. 13 is a graph showing the relationship between the estimated cumulative yield and the cumulative yield in a comparative example. FIG. 10(a) is a graph showing the results of yield estimation for the high temperature area together with the actual measured yield, and FIG. 10(b) is a graph showing the results of yield estimation for the low temperature area together with the actual measured yield. FIG. 11(a) is a graph showing the change in the leaf area of the second leaf relative to the accumulated temperature, and FIG. 11(b) is a graph showing the change in the leaf area of the third leaf relative to the accumulated temperature. FIG. 12( a ) is a graph showing the change in the petiole length of the second leaf versus the accumulated temperature, and FIG. 12 ( b ) is a graph showing the change in the petiole length of the third leaf versus the accumulated temperature. Figure 13(a) is a graph showing the change in plant height of the first leaf (new leaf) versus accumulated temperature, Figure 13(b) is a graph showing the change in plant height of the second leaf versus accumulated temperature, and Figure 13(c) is a graph showing the change in plant height of the third leaf versus accumulated temperature. Figure 14(a) is a graph showing the change in the frequency of first leaf (new leaf) emergence versus accumulated temperature, and Figure 14(b) is a graph showing the change in the growth amount of the petiole length of the first leaf (new leaf) versus accumulated temperature.

The following provides a detailed explanation of a strawberry yield prediction method according to one embodiment.

First, let us explain the premise of this embodiment.

The inventor cultivated strawberries (variety: Tochiotome) in two test zones (high temperature zone: average 17.3°C, night temperature 11.9°C; low temperature zone: average 15.3°C, night temperature 8.4°C) and investigated the total leaf area and yield. In this investigation, in addition to image processing using a digital camera, measurements were also taken using a tape measure and a scale.

The inventors also calculated the estimated dry matter production volume (estimated dry matter production volume) for each test plot based on the leaf area obtained as a result of the above investigation. As a result, the calculation results shown in Figure 1(a) were obtained. Furthermore, as a result of the above investigation, the actual yield (yield volume relative to daily accumulated temperature) was obtained as shown in Figure 1(b).

The yield of fruit vegetables is generally thought to be proportional to the amount of dry matter production. This is because plants produce fruit through photosynthesis in the leaves in response to environmental conditions. Therefore, if the change in estimated dry matter production relative to daily accumulated temperature is roughly the same in the high and low temperature areas, as in Figure 1(a), then the yield should also be roughly the same in the high and low temperature areas. However, in reality, the yield was higher in the high temperature area than in the low temperature area, as shown in Figure 1(b).

After investigating the reasons for this, the inventor came to the conclusion that it may be related to "dormancy," a physiological phenomenon unique to strawberries. Forcing strawberry cultivation is a cultivation method that utilizes dormancy to suppress growth and harvest fruit over a long period of time, and is known to be affected by photoperiod and temperature. It is also known that the deeper the dormancy, the dwarf the fruit will become. However, many details about dormancy remain unknown.

The inventors hypothesized that since dormancy causes changes in the internal physiological state even in plants with the same total leaf area, accurate yield predictions could be achieved by taking into account factors other than total leaf area that are affected by dormancy.

Based on this assumption, the inventors confirmed the changes in the leaf area growth of the youngest first leaf (new leaf), the changes in the leaf area of the second leaf, which is the next youngest after the first leaf, and the changes in the leaf area of the third leaf, which is the next youngest after the second leaf, for the plants in the high temperature and low temperature zones, and found that there were different trends. Figure 2(a) shows the changes in the leaf area growth of the first leaf (new leaf) against the accumulated temperature for each of the high temperature zone and the low temperature zone. Figure 2(b) shows the changes in the leaf area of the third leaf against the accumulated temperature for each of the high temperature zone and the low temperature zone. The changes in Figures 2(a) and 2(b) are similar to the commonly known way in which the depth of dormancy changes (gradually deepening and then gradually becoming shallower when it hits bottom), so it can be assumed that the effects of temperature (i.e., the effects of the depth of dormancy) are apparent in young leaves such as the first leaf to the third leaf. Furthermore, when comparing the first and third leaves, the first leaf is shifted to the left in relation to the accumulated temperature (the most convex part is about 1500°C for the first leaf and about 1800°C for the third leaf), which suggests that younger leaves respond to dormancy more quickly. From this, the inventors came to the conclusion that if some value that represents the change in the leaf area of the first leaf (new leaf) relative to the accumulated temperature is used in yield prediction, it will be possible to predict yield with high accuracy.

(Agricultural system 100 according to this embodiment)
FIG. 3 shows an agricultural system 100 for implementing a method for predicting strawberry yield according to one embodiment.

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

The information processing device 10 is an information processing device that can be used by workers cultivating strawberries in a greenhouse 18, and predicts and outputs (displays, etc.) the strawberry yield on a target prediction date based on environmental data acquired by the outdoor sensor 12 and the greenhouse sensor 14, and information input by the worker. The information processing device 10 can also control the controlled device 16. The configuration and processing of the information processing device 10 will be described in detail later.

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.

The greenhouse sensors 14 include a temperature sensor that detects the air temperature inside the greenhouse 18 , a solar radiation sensor that detects the solar radiation inside the greenhouse 18 , and a CO ₂ concentration sensor that detects the CO ₂ concentration inside the greenhouse 18 , and input the detection results to the information processing device 10 .

The controlled devices 16 include a heat pump, a ventilation window, a heater, a _CO2 applicator, a shading/heat-retaining curtain, etc. The heat pump is a device that lowers the temperature inside 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 inside the greenhouse 18, and the _CO2 applicator is a device that adjusts the _CO2 concentration inside the greenhouse 18. The shading/heat-retaining curtain is a curtain that adjusts the solar radiation and temperature inside the greenhouse 18. The controlled devices 16 are capable of executing operations according to instructions from the information processing device 10, and the environment inside the greenhouse 18 is adjusted by the operation of the controlled devices 16.

Here, the configuration and processing of the information processing device 10 will be described in detail. FIG. 4 shows a schematic diagram of the hardware configuration of the information processing device 10. As shown in FIG. 4, the information processing device 10 includes a CPU 90, a ROM 92, a RAM 94, a storage unit (HDD or SSD in this case) 96, a network interface 97, a display unit 93, an input unit 95, and a portable storage medium drive 99. The display unit 93 includes a liquid crystal display, and the input unit 95 includes a keyboard, a mouse, a touch panel, and the like. These components of the information processing device 10 are connected to a bus 98. In the information processing device 10, the CPU 90 executes a program (including a yield prediction program) stored in the ROM 92 or HDD 96, or a program read by the portable storage medium drive 99 from the portable storage medium 91, thereby realizing the functions of the components shown in FIG. 5. The functions of each unit 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).

Figure 5 shows a functional block diagram of the information processing device 10. In the information processing device 10, the CPU 90 executes a program to realize the functions of an input reception unit 30, an environmental information acquisition unit 32, a leaf area estimation unit 34, an index calculation unit 36, and a yield estimation unit 38 as a first estimation unit and a second estimation unit, as shown in Figure 5.

The input reception unit 30 acquires information input by the worker via the input unit 95. The information input by the worker includes information used to estimate the total leaf area of the plant (e.g., the leaf area of the third leaf) and information used to calculate an evaluation index of the physiological state (dormancy) of the plant (in this embodiment, the leaf area of the first leaf). Note that this information does not have to be input by the worker. For example, if a robot patrolling inside 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 information input from the robot.

The environmental information acquisition unit 32 acquires the detection results of the outdoor sensor 12 and the greenhouse sensor 14. In addition, the environmental information acquisition unit 32 acquires past data (average values, etc.) and forecast data (weather forecast mesh, etc.) from an external server, etc., as future environmental information.

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

The index calculation unit 36 calculates an evaluation index for the physiological state (dormancy) of the plant to be used for yield estimation. In this embodiment, the evaluation index is calculated based on the change in the leaf area of the first leaf relative to the accumulated temperature, but details will be described later.

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. The yield estimation unit 38 also displays the estimated strawberry yield information on the display unit 93. The processing of the yield estimation unit 38 will be described in detail later.

(Regarding processing of information processing device 10)
Fig. 6 is a flowchart showing the processing of the information processing device 10. As a prerequisite for starting the processing of Fig. 6, it is assumed that information necessary for yield estimation has been input to the input receiving unit 30. It is also assumed that the environmental information acquiring unit 32 has acquired past and present environmental information, and future environmental information (predicted values).

6 is started, first, in step S10, the leaf area estimation unit 34 estimates the transition of the total leaf area of the plant (change in the total leaf area with respect to the accumulated temperature). FIG. 7(a) shows the relationship between the leaf area of the third leaf and the total leaf area. From 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 receiving unit 30 into the formula in FIG. 7(a). In addition, as shown in FIG. 7(b), the leaf area estimation unit 34 plots the relationship between the estimated total leaf area and the accumulated temperature in a graph, and obtains a formula (the formula in FIG. 7(b)) showing the relationship between the accumulated temperature and the total leaf area. Then, the leaf area estimation unit 34 uses the formula in FIG. 7(b) to estimate the total leaf area with respect to the future accumulated temperature (see FIG. 7(c)).

The leaf area estimation unit 34 may estimate the total leaf area using a method 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 total leaf area to determine the relationship between the total leaf area and the accumulated temperature as shown in FIG. 7(b), and estimate the future total leaf area as shown in FIG. 7(c). The leaf area estimation unit 34 may also estimate the total leaf area based on the projected area of the plant obtained from an image of the plant photographed from above, and from the estimated total leaf area, determine the relationship as shown in FIG. 7(b), and estimate the future relationship between the total leaf area and the accumulated temperature as shown in FIG. 7(c).

Returning to FIG. 6, in the next step S12, the index calculation unit 36 calculates the evaluation index of the physiological state (dormancy) of the plant. In this 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 accumulated 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 shown by "●" in FIG. 8(a) is obtained (high temperature zone), the index calculation unit 36 linearly approximates the approximation curve (dashed line) downward to the right, and the slope of the obtained approximation line (-1.78) is the evaluation index a. Also, when the transition of the growth amount of the leaf area of the first leaf as shown by "■" in FIG. 8(a) is obtained, the index calculation unit 36 linearly approximates the approximation curve (dashed line) downward to the right, and the slope of the obtained approximation line (-2.71) is the evaluation index a.

Note that when the processing of FIG. 6 begins, in reality, only a portion of the data in FIG. 8(a) may exist, as shown in FIG. 8(b), but the evaluation index a can be found by performing curve approximation or linear approximation using the existing data.

Next, in step S14, the yield estimation unit 38 performs yield estimation. The process of step S14 is as follows.

The yield estimating unit 38 first calculates the amount of received light I _int based on the following equation (1).
I _int = I ₀ × (1-e ^{(-k × LAI)} ) × α … (1)

Here, _I0 is the amount of solar radiation, k is the light absorption coefficient, and LAI is the leaf area index (leaf area per land area). When calculating the LAI, the yield estimation unit 38 uses the total leaf area of each plant estimated by the leaf area estimation unit 34. Also, α means the proportion (%) of photosynthetically active radiation.

Next, the yield estimator 38 calculates the light utilization efficiency RUE based on the following equation (2).
RUE=3.2×(1-e ^{(-0.0032×CO2)} ) …(2)

In the above formula (2), "CO2" is the _CO2 concentration, for example, 400 ppm.

Next, the yield estimation unit 38 estimates the dry matter production CGR based on the following equation (3).
CGR=I _int ×RUE…(3)

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

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

In the above formula (5), b is the translocation ratio to the fruit, for example 60%. z is the moisture ratio of the fruit (1-sugar content), for example 90%. a is the evaluation index calculated by the index calculation unit 36. The evaluation index a can be determined in a manner other than the method described with reference to Figures 8(a) and 8(b) based on the change in the growth amount of the leaf area of the first leaf. n in the above formula (5) is a coefficient for converting an evaluation index determined by another method into an evaluation index determined based on the change in the growth amount of the leaf area of the first leaf. Therefore, in the present embodiment, n=1.

Then, the yield estimation unit 38 predicts the yield by period based on the accumulated yield ΣH, the relationship between the accumulated temperature ΣT and the date D, and the harvest start date d. The harvest start date d may be (1) input by the worker, or (2) estimated by detecting flower buds from photographed images of the plants. It may also be (3) estimated by observing fruit set and fruits from photographed images of the plants, or (4) estimated from environmental information (temperature and solar radiation). Furthermore, the above (2) to (4) may be combined to estimate the harvest start date d.

Returning to FIG. 6, in the next step S16, the yield estimation unit 38 displays (outputs) the yield by period (estimated result) estimated in step S14 on the display unit 93. In addition, in step S14, when a graph of the estimated value of the accumulated yield for each day as shown in FIG. 10(a) and FIG. 10(b) is obtained, the yield estimation unit 38 may display the graph of the estimated value of the accumulated yield as it is on the display unit 93. The worker can check the accumulated yield up to a specific day and the yield expected in a specific period from the displayed graph. In addition, when the worker specifies a period (called a confirmation period) for which the worker wants to know the yield, the yield estimation unit 38 can obtain the difference between the estimated value of the accumulated yield up to the last day of the confirmation period and the estimated value of the accumulated yield up to just before the confirmation period, and can display the difference on the display unit 93 as the yield in the confirmation period. In other words, the yield estimation unit 38 can display the information obtained in step S14 as it is or after processing it, and can also display information calculated from the information obtained in step S14. This completes the entire process in Figure 6.

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

As shown in Figure 9, in the comparative example, in both the high temperature and low temperature zones, it was found that the estimated cumulative yield and the cumulative yield (actual measurement value) deviated significantly from the case shown by the dashed line in Figure 9 where they match. In other words, it was found that in the comparative example, in both the high temperature and low temperature zones, a difference occurred between the estimated cumulative yield and the cumulative yield (actual measurement value).

Meanwhile, FIG. 10(a) shows the results of estimating the yield of strawberries grown in a high temperature zone using the method of this embodiment, together with the actual measured yield. Also, FIG. 10(b) shows the results of estimating the yield of strawberries grown in a low temperature zone using the method of this embodiment, together with the actual measured yield. From FIG. 10(a) and FIG. 10(b), it can be seen that by using the method of this embodiment, it is possible to accurately estimate the yield of strawberries in both high and low temperature zones.

As described above in detail, in this embodiment, the yield estimation unit 38 estimates the dry matter production until the harvest date based on the actual and predicted values of the growing environment of the strawberry plants during the growing period until the harvest date, and the actual and estimated values of the plant's leaf area during the growing period (S14, formulas (1) to (4)). The yield estimation unit 38 then estimates the strawberry yield based on the dry matter production, the rate b of the dry matter production translocated to the strawberry fruit, the moisture rate z of the fruit, and the evaluation index a (S14, formula (5)). As a result, in this embodiment, the strawberry yield can be accurately estimated taking into account the effects of dormancy (see Figures 10(a) and 10(b)).

In addition, in this embodiment, the index calculation unit 36 calculates the evaluation index a from the change in the characteristic amount obtained from the actual strawberry plants (in this embodiment, the change in the leaf area of the first leaf with respect to the accumulated temperature), so the strawberry yield can be estimated with high accuracy. For example, even within the same greenhouse, environmental conditions may vary, which may result in differences in the physiological state of the plants. Even in such cases, the yield of each plant can be estimated with high accuracy by calculating the evaluation index a from the characteristic amount of each plant.

The index calculation unit 36 may calculate the evaluation index a using past data of plants cultivated under the same conditions (e.g., data from one year ago).

In the above embodiment, the index calculation unit 36 calculates the evaluation index a using the change in the growth amount of the leaf area of the first leaf ( FIG. 8( a )), but the present invention is not limited to this. For example, in the above embodiment, the change in the leaf area of the first leaf is large, so the evaluation index a is calculated using the change in the growth amount, but the evaluation index a may be calculated using the change in the leaf area of the first leaf itself.
Moreover, the evaluation index a can be calculated using the transition of the feature amount as follows.

(a) Change in 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 zone is −2.77, which is the slope of the straight line approximating the downward sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −4.33, which is the slope of the straight line approximating the downward sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −4.33/−2.77=1.56. This value is approximate to the value of −2.71/−1.78=1.52 in the above embodiment (when the change in the leaf area of the first leaf is used), so even if the change in the leaf area of the second leaf is used, the strawberry yield can be estimated with high accuracy, as in the above embodiment. Note that the value of the evaluation index a in this example is different from the value of the evaluation index a in the above embodiment, so the value of n in the above formula (5) may be determined in advance to match the evaluation index a in the above embodiment.

(b) Changes in the leaf area of the third leaf FIG. 11(b) shows changes in the leaf area of the third leaf with respect to the accumulated temperature. The evaluation index a in the high temperature zone is −3.37, which is the slope of the straight line approximating the downward sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −5.18, which is the slope of the straight line approximating the downward sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −5.18/−3.37=1.53. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the changes in the leaf area of the third leaf, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(c) Change in the petiole length of the second leaf FIG. 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 −0.015, which is the slope of the straight line approximating the right-downward portion of the approximation curve, and the evaluation index a in the low temperature zone is −0.022, which is the slope of the straight line approximating the right-downward portion of the approximation 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. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in the petiole length of the second leaf, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(d) Change in petiole length of the third leaf FIG. 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 −0.018, which is the slope of the straight line approximating the right-sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −0.027, which is the slope of the straight line approximating the right-sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.027/−0.018=1.50. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in the petiole length of the third leaf, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(e) Change in plant height of the first leaf (new leaf) FIG. 13(a) shows the change in plant height of the first leaf (new leaf) with respect to the accumulated temperature. The evaluation index a in the high temperature zone is −0.0106, which is the slope of the straight line approximating the right-sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −0.0180, which is the slope of the straight line approximating the right-sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.0180/−0.0106=1.70. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in plant height of the first leaf (new leaf) as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(f) Change in plant height of second leaf FIG. 13(b) shows the change in plant height of second leaf with respect to the accumulated temperature. The evaluation index a in the high temperature zone is −0.0267, which is the slope of the straight line approximating the downward sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −0.0406, which is the slope of the straight line approximating the downward sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.0406/−0.0267=1.52. This value is approximate to the value 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in plant height of the second leaf, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(g) Change in plant height of the third leaf FIG. 13(c) shows the change in plant height of the third leaf with respect to the accumulated temperature. The evaluation index a in the high temperature zone is −0.0268, which is the slope of the straight line approximating the downward sloping part of the approximation curve, and the evaluation index a in the low temperature zone is −0.0431, which is the slope of the straight line approximating the downward sloping part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.0431/−0.0268=1.61. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in plant height of the third leaf, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(h) Change in frequency of first leaf (new leaf) occurrence FIG. 14(a) shows the change in frequency of first leaf (new leaf) occurrence with respect to the accumulated temperature. The evaluation index a in the high temperature zone is the slope of the straight line approximating the right-sloping part of the approximation curve, −0.00030840, and the evaluation index a in the low temperature zone is the slope of the straight line approximating the right-sloping part of the approximation curve, −0.00047384. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.00047384/−0.00030840=1.54. This value is approximate to the value 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the change in frequency of first leaf (new leaf) occurrence, as in the above embodiment. In this example, the value of n in the above formula (5) must be determined in advance.

(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 −0.0006, which is the slope of the straight line approximating the right-downward part of the approximation curve, and the evaluation index a in the low temperature zone is −0.0009, which is the slope of the straight line approximating the right-downward part of the approximation curve. In this case, the evaluation index of the low temperature zone/the evaluation index of the high temperature zone is −0.0009/−0.0006=1.50. This value is approximate to the value of 1.52 in the above embodiment, so that the strawberry yield can be estimated with high accuracy using the transition of the growth amount of the petiole length of the first leaf (new leaf) as in the above embodiment. In this example, the value of n in the above formula (5) must be determined 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 change in the growth amount of the petiole length, but the evaluation index a may also be calculated using the change in the petiole length of the first leaf itself.

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

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

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

The above-described embodiment is a preferred example of the present invention. However, the present invention is not limited to this embodiment, and various modifications are possible without departing from the spirit of the present invention.

10 Information processing device (yield prediction device)
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 Systems

Claims (7)

A step of estimating the amount of dry matter production in the strawberry plant until the harvest date based on at least one of an actual measurement value and a predicted value regarding the growth environment of the strawberry plant during the growth period until the harvest date and at least one of an actual measurement value and a predicted value of the leaf area of the plant during the growth period;
A method for predicting strawberry yield, characterized in that a computer executes the steps of: estimating the yield of the strawberries based on the dry matter production amount, the proportion of the dry matter production amount translocated to the strawberry fruit, the water proportion of the fruit, and evaluation indexes of physiological characteristics exhibited by the strawberries depending on the growth environment. The strawberry yield prediction method according to claim 1, characterized in that the evaluation index of the physiological characteristics is a value indicating the degree of change in a predetermined characteristic amount of a strawberry plant relative to the change in the accumulated temperature until the harvest date. The strawberry yield prediction method according to claim 2, characterized in that the predetermined characteristic amount is any one of the leaf area growth amount, petiole length growth amount, occurrence frequency, or plant height of the first leaf, which is the youngest in the plant, the leaf area, petiole length, or plant height of the second leaf, which is the next youngest after the first leaf, and the leaf area, petiole length, or plant height of the third leaf, which is the next youngest after the second leaf. The strawberry yield prediction method according to any one of claims 1 to 3, characterized in that the evaluation index of the physiological characteristics is calculated from data obtained during the cultivation of the strawberry plant for which the yield is to be estimated. The method for predicting strawberry yield according to any one of claims 1 to 3, characterized in that the evaluation index of the physiological characteristics is a coefficient calculated from data obtained while the strawberry plant was cultivated in a growth environment similar to that of the strawberry plant for which the yield is to be estimated. On the computer,
A process of estimating the amount of dry matter production in the strawberry plant until the harvest date based on at least one of an actual measurement value and a predicted value regarding the growth environment of the strawberry plant during the growth period until the harvest date and at least one of an actual measurement value and a predicted value of the leaf area of the plant during the growth period;
A process of estimating a yield of the strawberry based on the dry matter production amount, the ratio of the dry matter production amount translocated to the strawberry fruit, the water content of the fruit, and an evaluation index of physiological characteristics of the strawberry depending on the growing environment;
A strawberry yield prediction program comprising: A first estimation unit that estimates a dry matter production amount of the strawberry plant until the harvest date based on at least one of an actual measurement value and a predicted value regarding a growth environment of the strawberry plant during a growth period until the harvest date and at least one of an actual measurement value and a predicted value of a leaf area of the plant during the growth period;
A second estimation unit that estimates the yield of the strawberry based on the dry matter production amount, the ratio of the dry matter production amount translocated to the strawberry fruit, the water content of the fruit, and an evaluation index of physiological characteristics appearing in the strawberry depending on the growth environment;
A strawberry yield prediction device comprising:

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