JP2022093884A - Growth state prediction method and growth state prediction program - Google Patents

Abstract

To accurately predict onion growth information.SOLUTION: A development index calculation unit 22 calculates an onion development rate DVRn on the basis of a day average temperature Tn and a day length Ln on the nth day (S22). A dry matter weight distribution unit 24 calculates an onion bulb dry matter ratio BDWRn on the basis of a development index DVIn, which is an integrated value of the development rate DVRn from a planting date (S26). In addition, the dry matter weight distribution unit 24 predicts an onion bulb dry matter weight on the basis of an onion total dry matter weight TDWn calculated by a total dry matter weight calculation unit 20 and the bulb dry matter ratio BDWRn (S26). Furthermore, a harvest date/yield estimation unit 30 estimates a harvest date from the development index DVIn, uses a bulb dry matter weight BDWhvs on the harvest date to calculate a bulb weight BFWhvs, and estimates a bulb living body yield (kg/m2) from the bulb weight BFWhvs (S34).SELECTED DRAWING: Figure 3

Description

The present invention relates to a growth state prediction method and a growth state prediction program.

In recent years, onion cultivation has become widespread in the Tohoku region, but stable production is difficult due to the wide area of the Tohoku region, which has various weather conditions, and the lack of experience of producers due to the emerging production areas. For this reason, it is preferable to be able to grasp the harvest time and yield for each production area in advance and formulate a cropping plan before cultivation.

Conventionally, the harvest date and yield of onions are based on the cultivation calendar based on the results of cultivation tests (planting time, variety, planting density, average yield, etc.) conducted by public testing and research institutes near the onion production areas and JA, etc. It was predicted by taking into account the weather conditions.

On the other hand, recently, although it is a part of vegetable items, efforts are being made to predict the yield using a growth model from past weather and growth data (see, for example, Patent Document 1 and the like).

Japanese Unexamined Patent Publication No. 2020-053039

With the above-mentioned prediction method based on the results of past cultivation tests, it is difficult to respond to differences in weather conditions depending on the region and year, and to consider and optimize planting dates and combinations of varieties in order to disperse work. Therefore, it is necessary to carry out cultivation tests over multiple years and create a cultivation calendar suitable for the area. In addition, it is difficult to apply the prediction method using the above-mentioned growth model to heading liliaceous crops such as onions and garlic.

Therefore, an object of the present invention is to provide a growth state prediction method and a growth state prediction program capable of accurately predicting the growth information of a heading lily family crop.

The growth state prediction method of the present invention is a growth state prediction method for predicting the growth state of a heading lily family crop, and the daily growth rate of the heading lily family crop based on the daily average temperature and the day length. Based on the second value, which is the integrated value of the first value from the planting date, the daily bulb dry matter ratio of the heading liliaceae crop is calculated, and the amount of solar radiation is calculated. Predicts the weight of the bulb of the Liliaceae crop based on the daily dry matter weight of the Liliaceae crop and the ratio of the dry matter of the bulb of the Liliaceae crop every day, which is calculated based on the values related to the leaves and the leaves. It is a growth state prediction method in which a computer executes processing.

The growth state prediction method and the growth state prediction program of the present invention have the effect of being able to accurately predict the growth information of the heading lily family crop.

It is a figure which shows roughly the structure of the growth state prediction system which concerns on one Embodiment. FIG. 2A is a diagram showing a hardware configuration of a server, and FIG. 2B is a diagram showing a hardware configuration of a user terminal. It is a figure which shows the growth model in one Embodiment. It is a graph which shows the relationship between the growth index and the leaf dry matter ratio. It is a functional block diagram of a server. It is a flowchart which shows the process of a server. It is a figure (the 1) which shows the work table. It is a figure (2) which shows a work table. It is a figure (the 3) which shows a work table. 10 (a) and 10 (b) are diagrams showing an example of a table output to the display unit of the user terminal. It is a table which shows the yield prediction accuracy in the case of determining the leaf dry matter ratio and the bulb dry matter ratio based on the growth index, and in the case of fixing.

Hereinafter, the method for predicting the growth state of the heading Liliaceae crop according to the embodiment will be described in detail. In this embodiment, a method of predicting the growth state of onions among the heading lily family crops will be described, but the same applies to the case of predicting the growth state of other heading lily family crops such as scallions, garlic, and scallions. Is.

FIG. 1 schematically shows the configuration of a growth state prediction system according to an embodiment. In the growth state prediction system 100 of the present embodiment, when a producer who grows onions formulates an onion planting plan or a shipping plan, when the planting is carried out at a certain cultivation point, when the harvest date arrives, and at that time. It is a system for confirming how much the yield will be.

As shown in FIG. 1, the growth state prediction system 100 includes a server 10 and a user terminal 70. The server 10 and the user terminal 70 are connected to a network 80 such as the Internet.

The server 10 is an information processing device installed in a data center or the like, and is input from environmental information (normal value, past data, forecast data, etc.) at a point (cultivation point) where onions are cultivated, or from a user terminal 70. Based on the growth model as shown in FIG. 3, changes in the growth state of onions at the cultivation site are estimated. Then, the server 10 estimates the harvest date and yield of the onion at the cultivation point, and outputs the estimation result to the user terminal 70.

FIG. 2A shows the hardware configuration of the server 10. As shown in FIG. 2A, the server 10 includes a CPU (Central Processing Unit) 90 as a computer, a ROM (Read Only Memory) 92, a RAM (Random Access Memory) 94, and a storage unit (HDD (Hard Disk Drive)). , SSD (Solid State Drive), etc.) 96, network interface 97, portable storage medium drive 99, and the like. Each component of the server 10 is connected to the bus 98. In the server 10, a program stored in the ROM 92 or the storage unit 96 (including a growth state prediction program) or a program read from the portable storage medium 91 by the portable storage medium drive 99 (including a growth state prediction program). When the CPU 90 executes the above, the functions of the respective parts shown in FIG. 5 are realized. The functions of each part of FIG. 5 may be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The details of each part of FIG. 5 will be described later.

The user terminal 70 is a terminal such as a PC (Personal Computer) that can be used by the producer or the like, and receives input of biological information of the crop by the producer or the like and transmits the input to the server 10 or estimates the server 10. Receive and display the results. The user terminal 70 has a hardware configuration as shown in FIG. 2 (b). Specifically, as shown in FIG. 2B, the user terminal 70 includes a CPU 190, a ROM 192, a RAM 194, a storage unit (HDD, etc.) 196, a network interface 197, a display unit 193, an input unit 195, and a portable type. A portable storage medium drive 199 or the like capable of reading the storage medium 191 is provided. Each component of the user terminal 70 is connected to the bus 198. The display unit 193 includes a liquid crystal display and the like, and the input unit 195 includes a keyboard, a mouse, a touch panel and the like.

(About the growth model)
In this embodiment, the growth state of the onion is predicted based on the growth model as shown in FIG. Hereinafter, the outline of the growth model will be described with reference to FIG. The equations and coefficients described below are examples, and can be changed in various ways.

In the growth model of FIG. 3, the community light receiving rate of onions is represented by a function of a leaf area index (LAI: Leaf Area Index) and an absorption coefficient (k). For example, the canopy light receiving rate is expressed by the following equation (1).
Community light reception rate = 1-e- ^{k x LAIn} ... (1)

Here, the leaf area index LAI _n means the total leaf area (m ² ) of the crop with respect to the unit land area (1 m ² ) on the nth day from the planting date. That is, it can be said that the leaf area index (m ² / m ² ) is the product of the leaf area and the planting density per individual. The leaf area and planting density on the planting date (n = 1) are input by the producer, but past average values and the like may be used. On the other hand, the leaf area index LAI _n after the planting date is obtained by the calculation described later. In the case of onions, the extinction coefficient (k) is assumed to be a value peculiar to the variety. For example, k is assumed to be 0.285 as an example.

Further, in the growth model of FIG. 3, the amount of increase in daily dry matter (ΔTDW _n ) is expressed as a function of the canopy light receiving rate, the daily integrated solar radiation amount SR _n , and the solar radiation utilization efficiency RUE _n . For example, the amount of increase in dried fish (ΔTDW _n ) is expressed by the following equation (2).
ΔTDW _n = SR _n × (1-e ^−0.285LAIn ) × RUE _n … (2)

The solar radiation utilization efficiency RUE _n can be calculated from the following equation (3).
RUE _n = a × T _n + b… (3)

Here, T _n is the daily average temperature. Further, a and b are coefficients that change depending on the daily average temperature of the previous day. As an example, if the daily average temperature T _n-1 of the previous day is less than 23 ° C., a = 0.154 and b = -1. If it is 539 and T _n-1 is 23 ° C. or higher, it is assumed that a = −0.111 and b = 4.556. In the case of onions, a and b are values peculiar to the variety.

Further, in the growth model of FIG. 3, the total dry matter weight (TDW _n ) on the nth day is expressed as the sum of the daily dry matter increase amount (ΔTDW _n ) and the total dry matter weight (TDW _n-1 ) on the previous day. That is, the total dry matter weight (TDW _n ) on the nth day is expressed by the following equation (4).
TDW _n = TDW _n-1 + ΔTDW _n … (4)

The total dry matter weight (TDW _n ) on the nth day is actually distributed to the bulb dry matter weight and the leaf dry matter weight. As a result of the present inventor's verification of the leaf dry matter ratio (LDWR _n ) used for this distribution, it was found that there is a relationship as shown by the broken line in FIG. 4 with the growth index (DVI). .. Each point in FIG. 4 shows data obtained from the cultivation test carried out by the present inventor, and the broken line is an approximate curve obtained from each point. The date in the graph means the planting date in each test.

The approximate curve of FIG. 4 is expressed by the following equation (5).
LDWR _n
= −0.144 × DVI _n ² +0.119 × DVI _n +0.675… (5)

Here, the growth index DVI _n on the nth day is an integrated value of the growth rate DVR _i on the i-day (1st to nth days) as in the following equation (6).
DVI _n = ΣDVR _i ... (6)

The DVR _i in the above equation (6) is expressed by a function of the daily average temperature on the i-day day T _i and the day length L _i on the i-day day, for example, as in the following equations (7) and (8). Can be expressed in. The following equations (7) and (8) are equations obtained from cultivation tests, and in the case of onions, they are equations peculiar to varieties.
If 0 ≤ DVI _i-1 <1,
DVR _i = -0.00084 x (T _i -5.0) x (L _i -13.0) ... (7)
When 1 ≤ DVI _i-1 ≤ 2 DVR _i = 0.033 (constant) ... (8)

Here, the value of the growth index DVI represents the actual growth state (stage) of the onion. For example, in the example of FIG. 4, when the growth index DVI is 0 <DVI <1, it indicates that it is a stage before the onion bulb hypertrophy occurs. Further, the period of 1 ≦ DVI ≦ 2 indicates that the stage is during the hypertrophy of the bulb. And DVI = 2 indicates a state in which onions can be harvested. Therefore, the present inventor has determined that the day when the DVI obtained from the above formula (6) is 2 or more is the harvest day.

When the leaf dry matter ratio LDWR _n obtained by the above equation (5) is used, the spherical dry matter ratio BDWR _n is expressed by the following equation (9).
BDWR _n = 1-LDWR _n ... (9)

Further, from the leaf dry matter ratio LDWR _n and the bulb dry matter ratio BDWR _n in the above equations (5) and (9), the leaf dry matter weight LDW _n and the bulb dry matter weight BDW _n are obtained by the following equations (10) and (11). ) Can be expressed as.
LDW _n = TDW _n × LDWR _n … (10)
BDW _n = TDW _n × BDWR _n … (11)

The leaf area index LAI _{n + 1} on the next day is expressed by a function of the leaf dry matter weight LDW _n , and is expressed by, for example, the following equation (12). In the case of onions, the coefficient (= 0.0175) in the following equation (12) is a value peculiar to the variety.
LAI _{n + 1} = 0.0175 × LDW _n
= 0.0175 × TDW _n × LDWR _n … (12)

This leaf area index LAI _{n + 1} can be used when determining the growth state of onions on the next day.

Further, the ball weight BFW _hvs on the harvest day is expressed as a function of the ball dry matter weight BDW _hvs on the harvest day, and is expressed by, for example, the following equation (13).
BFW _hvs = BDW _hvs x 0.383 x T _bulbing ... (13)

In addition, BDW _hvs means the dry matter weight of the bulb part on the day when the DVI becomes 2 or more as described above. Further, the T _bulbing in the above equation (13) means the average temperature from the start of hypertrophy to the harvest date, that is, the average temperature between 1 and 2 in DVI.

Further, the bulb bioyield _{BFY hvs} on the harvest day is expressed by, for example, the following equation (14).
BFY _hvs = BFW _hvs × planting density… (14)
It can be said that this bulb body yield BFY _hvs is the yield on the harvest day.

(About the function of server 10)
Next, the function of the server 10 will be described with reference to FIG.

As shown in FIG. 5, in the server 10, the CPU 90 executes the program to execute the total dry matter weight calculation unit 20, the growth index calculation unit 22, the dry matter weight distribution unit 24, the LAI calculation unit 28, and the harvest date / yield. The function of the estimation unit 30 is realized.

The total dry matter weight calculation unit 20 executes the processing in the range surrounded by the frame A in the growth model of FIG. 3 to calculate the total dry matter weight TDW _n on the nth day.

The growth index calculation unit 22 executes processing in the range surrounded by the frame B in FIG. 3 to calculate the growth rate DVR _n and the growth index DVI _n on the nth day.

The dry matter weight distribution unit 24 calculates the leaf dry matter ratio LDWR _n and the bulb dry matter ratio BDWR _n from the growth index DVI _n , and uses these ratios LDWR _n , BDWR _n and the total dry matter weight TDW _n to obtain the leaf portion. The dry matter weight LDW _n and the bulb dry matter weight BDW _n are calculated.

The LAI calculation unit 28 calculates the LAI (LAI _{n + 1} ) of the next day based on the leaf dry matter weight LDW _n calculated by the dry matter weight distribution unit 24.

The harvest date / yield estimation unit 30 estimates the harvest date based on the growth index DVI _n calculated by the growth index calculation unit 22. Further, the harvest date / yield estimation unit 30 calculates (estimates) the ball weight BFW _hvs on the harvest day based on the dry matter weight of the bulb on the harvest date. Further, the harvest date / yield estimation unit 30 calculates (estimates) the bulb weight BFW _hvs on the harvest day based on the ball weight BFW _hvs on the harvest day. Then, the harvest date / yield estimation unit 30 outputs the estimation result to the user terminal 70.

(About the processing of server 10)
Next, the processing of the server 10 will be described in detail with reference to other drawings according to the flowchart of FIG.

As a premise for starting the process of FIG. 6, the producer or the like obtains information on the planting date, the leaf area on the planting date, the planting density, and the total dry matter weight TDW ₀ on the previous day from the input unit 95 of the user terminal 70. It is assumed that you have entered it. The server 10 prepares a work table as shown in FIG. 7 based on the input information. As shown in Fig. 7, the work table shows the average daily temperature (° C), the length of the day (h), and the cumulative amount of solar radiation (MJ / m ² ) after the planting date. , Predicted value, etc.) are stored. In addition, the work table stores the value calculated from the leaf area information input by the producer or the like as LAI ₁ (m ₂ / m ₂ ) on the planting date. Further, the work table stores RUE (g / MJ) calculated from the daily average temperature of each day. The RUE is a value calculated from the above equation (3) using the daily average temperature T _n . From this state, the process of FIG. 6 is started.

In step S10, the total dry matter weight calculation unit 20 sets the parameter n indicating the number of days after planting the onion seedlings to 1 (n = 1).

Next, in step S12, the total dry matter weight calculation unit 20 calculates the daily dry matter increase amount ΔTDW _n on the nth day based on the above equation (2). Further, in the next step S14, the total dry matter weight calculation unit 20 calculates the total dry matter weight TDW _n on the nth day based on the above equation (4).

Next, in step S16, the growth index calculation unit 22 refers to the value of DVI _n -1 and determines whether or not it is included in the range of 0 ≦ DVI _n-1 <1. If the determination in step S16 is affirmed, the growth index calculation unit 22 proceeds to step S18, but if it is denied, it proceeds to step S20. At the stage of n = 1, the determination in step S16 is affirmed.

When the determination in step S16 is affirmed and the process proceeds to step S18, the growth index calculation unit 22 sets the first formula (the above formula (7) in the present embodiment) as the calculation formula for the growth rate DVR. On the other hand, when the determination in step S16 is denied and the process proceeds to step S20, the growth index calculation unit 22 sets the second formula (the above formula (8) in the present embodiment) as the calculation formula for the growth rate.

When the process proceeds to step S22 via either step S18 or S20, the growth index calculation unit 22 uses the daily average temperature T _n and the day length L _n on the nth day to set the formula (the above formula (the above formula (the above formula). From any of 7) and (8), the growth rate DVR _n on the nth day is calculated.

Next, in step S24, the growth index calculation unit 22 integrates the growth rates up to the nth day as in the above equation (6), and calculates the growth index DVI _n on the nth day.

Next, in step S26, the dry matter weight distribution unit 24 calculates the leaf dry matter ratio LDWR _n and the bulb dry matter ratio BDWR _n on the nth day, and also calculates the leaf dry matter weight LDW _n and the bulb dry matter weight BDW _n . do. Here, the dry matter weight distribution unit 24 calculates the leaf dry matter ratio LDWR _n and the bulb dry matter ratio BDWR _n from the above equations (5) and (9) using the growth index DVI _n calculated in step S24. Using these ratios LDWR _n and BDWR _n , the leaf dry matter weight LDW _n and the bulb dry matter weight BDW _n on the nth day are calculated from the above equations (10) and (11).

Next, in step S28, the LAI calculation unit 28 calculates the next day's LAI (LAI _{n + 1} , here LAI ₂ ) from the above equation (12) using the leaf dry matter weight LDW _n .

When the processing up to this point is performed, the data obtained as a result of the processing is added to the work table, and the work table is as shown in FIG. Specifically, as shown in FIG. 8, in the row of the planting day of the work table, the growth rate DVR (/ d), the growth index DVI, the total dry matter weight TDW _n (g / m ² ), and the leaf dry matter weight LDW. The values of _n (g / m ² ) and the dry weight of the bulb BDW _n (g / m ² ) are added, and the LAI value of the row on the day after the planting day is also added.

Next, in step S30, the harvest date / yield estimation unit 30 determines whether or not the DVI _n is 2 or more. If the determination in step S30 is denied, the process proceeds to step S32.

When the process proceeds to step S32, the total dry matter weight calculation unit 20 increments n by 1 (n ← n + 1). That is, it shifts to the estimation processing of the next day.

After that, the above-mentioned processes (steps S12 to S32) are repeatedly executed. In this iterative process, the LAI of the next day obtained in the process (S28) of the previous day is used. Then, when the data is accumulated and the value of DVI _n becomes 2 or more as shown in the bottom row of FIG. 9, the judgment of step S30 is affirmed, and the process proceeds to step S34. In FIG. 9, since the growth index DVI on the date “June 26, 2019” exceeds 1, this date is shown as the “estimated date for starting bulb hypertrophy”.

After shifting to step S34, the harvest date / yield estimation unit 30 estimates the nth day as the harvest date. Further, the harvest date / yield estimation unit 30 calculates the ball weight BFW _hvs (g / m ² ) from the above equation (13) using the ball weight BDW _n (= BDW _hvs ) on the nth day. do. Then, the harvest date / yield estimation unit 30 multiplies the bulb weight BFW _hvs (g / m ² ) by the planting density of the field as in the above equation (14), thereby multiplying the bulb weight (kg / m) in the field. ² ) is estimated.

With the above, all the processing of FIG. 6 is completed. After the processing of FIG. 6, the harvest date / yield estimation unit 30 obtains the estimated harvest date information, the yield information on the harvest date, and the necessary information from the work table of FIG. 9 in the user terminal 70. Output to. In this case, the information may be output to the user terminal 70 immediately after the process of FIG. 6, or the information may be output to the user terminal 70 in response to a request from the user terminal 70 side. May be.

By confirming the information displayed on the user terminal 70, the producer or the like can formulate an onion planting plan and a shipping plan. For example, as shown in FIG. 10A, it is possible to output a table describing the estimated bulb hypertrophy start date, harvest date, bulb dry matter weight, bulb dry matter ratio, and bulb bioyield. In this case, as shown in FIG. 10B, the operator or the like can also compare the predicted value and the actually measured value by inputting the actually measured value into the table.

In FIG. 11, the leaf dry matter ratio and the bulb dry matter ratio are obtained from the growth index DVI as in the present embodiment, and the bulb dry matter weight is estimated, and the leaf dry matter ratio and the bulb dry matter ratio are fixed values. A table comparing the cases (comparative example) is shown. When the leaf dry matter ratio and the bulb dry matter ratio were obtained using the growth index DVI as in the present embodiment, the bulb dry matter weight at the time of harvest could be predicted with almost no error from the measured value. On the other hand, when the leaf dry matter ratio and the bulb dry matter ratio were set to fixed values as in the comparative example, a large error occurred between the predicted value and the measured value of the bulb dry matter weight at the time of harvesting. It is considered that such a result is because if the leaf dry matter ratio and the bulb dry matter ratio are set to fixed values, it is not possible to cope with the bulb enlargement and the bulb dry matter weight is underestimated.

As described above, in the present embodiment, by determining the leaf dry matter ratio and the bulb dry matter ratio using the growth index DVI, it is possible to accurately estimate the yield (bullet bioyield BFY _hvs ). There is.

As described above in detail, according to the present embodiment, the growth index calculation unit 22 has a first value regarding the growth rate of the onion based on the daily average temperature T _n and the day length L _n on the nth day. The growth rate DVR _n ) was calculated (S22), and the dry matter weight distribution unit 24 was based on a second value (growth index DVI _n ) which is an integrated value of the growth rate DVR _n from the planting date, and the onion bulb part. The dry matter ratio BDWR _n is calculated (S26). Further, the dry matter weight distribution unit 24 predicts the ball portion dry matter weight of the onion based on the total dry matter weight TDW _n of the onion and the bulb dry matter ratio BDWR _n calculated by the total dry matter weight calculation unit 20 (S26). ). As a result, in the present embodiment, it is possible to predict the time of bulb enlargement in consideration of the time, so that the weight of the bulb dry matter can be predicted more accurately than in the case where the bulb dry matter ratio is set to a fixed value (Fig.). 11).

Further, in the present embodiment, the day when the growth index DVI _n reaches a predetermined value (“2” in the present embodiment) is estimated as the harvest date (S30: affirmative, S34), and the dry matter weight of the bulb on the estimated harvest date. Based on BDW _n (= BDW _hvs ), the yield in the field (biological yield of bulbs BFY _hvs ) is estimated (S34). As a result, in the present embodiment, the harvest date can be estimated accurately by using the growth index DVI _n . Moreover, since the harvest date is estimated accurately, the yield on the harvest date can also be estimated accurately.

By outputting the accurately estimated harvest date and yield to the user terminal 70 in this way, the producer and the like can appropriately formulate an onion planting plan and a shipping plan. In this case, even at a site where onions have never been produced, it is possible to appropriately formulate a planting plan and a shipping plan by inputting environmental conditions and planting dates.

Further, in the present embodiment, when calculating the growth rate DVR _n , the formulas used (the above formulas (7) and (8)) are different based on the value of the growth index DVI _n-1 on the previous day. As a result, the growth rate DVR _n can be calculated more accurately in consideration of the growth state (whether before or after the bulb enlargement) as compared with the case of using one formula.

Further, in the present embodiment, the leaf area index (LAI _{n + 1} ) of the next day is calculated using the leaf area dry matter ratio LDWR _n calculated from the growth index DVI _n , and the calculated LAI _{n + 1} is used for the next day. Calculate the amount of increase in dry matter. This makes it possible to accurately calculate the total dry matter weight of the next day using the leaf area index LAI _{n + 1} of the next day predicted from the leaf dry matter ratio LDWR _n .

In the above embodiment, the case where the server 10 has the functions of each part of FIG. 5 has been described, but the present invention is not limited to this. For example, the user terminal 70 may have the functions of each part of FIG. In this case, the user terminal 70 does not have to be connected to the network 80. Further, the user terminal 70 may acquire various data stored in the server 10 via the network 80 and execute the process of FIG.

The embodiments described above are examples of preferred embodiments of the present invention. However, the present invention is not limited to this, and various modifications can be made without departing from the gist of the present invention. For example, various modifications can be made within a range that does not deviate from the gist of the present invention in part of the growth model and formula shown in FIG.

10 Server 20 Total dry matter weight calculation unit 22 Growth index calculation unit 24 Dry matter weight distribution unit 28 LAI calculation unit 30 Harvest date / yield estimation unit 90 CPU (computer)
100 Growth condition prediction system

Claims (5)

It is a growth state prediction method for predicting the growth state of heading liliaceous crops.
The first value regarding the daily growth rate of the heading lily family crop was calculated based on the daily average temperature and the day length.
Based on the second value, which is the integrated value of the first value from the planting date, the daily bulb dry matter ratio of the heading liliaceae crop was calculated.
The weight of the bulb of the heading lily crop based on the daily dry matter weight of the heading lily crop and the daily bulb dry matter ratio calculated based on the amount of solar radiation and the value related to the leaves. Predict,
A growth state prediction method characterized by a computer performing processing. The day when the second value reaches a predetermined value is estimated as the harvest day, and the harvest date is estimated.
Yield is predicted based on the weight of the bulb on the estimated harvest date.
The growth state prediction method according to claim 1, wherein the processing is further executed by the computer. In the process of calculating the first value,
Which of the plurality of growth stages each day belongs to is determined from the second value.
Using the formula according to the growth stage to which it belongs, the first value for each day is calculated.
The growth state prediction method according to claim 1 or 2, wherein the method is characterized by the above. Based on the second value, the daily leaf dry matter ratio of the heading lily family crop was calculated.
Based on the leaf dry matter ratio, the value for the leaf on the next day was calculated.
Based on the calculated values for the leaves on the next day, the amount of increase in dry matter on the next day of the heading lily family crop is calculated.
The growth state prediction method according to any one of claims 1 to 3, wherein the computer further executes the process. It is a growth state prediction program that predicts the growth state of heading liliaceous crops.
The first value regarding the daily growth rate of the heading lily family crop was calculated based on the daily average temperature and the day length.
Based on the second value, which is the integrated value of the first value from the planting date, the daily bulb dry matter ratio of the heading liliaceae crop was calculated.
The weight of the bulb of the heading lily crop based on the daily dry matter weight of the heading lily crop and the daily bulb dry matter ratio calculated based on the amount of solar radiation and the value related to the leaves. Predict,
A growth state prediction program characterized by having a computer execute processing.

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