A tobacco leaf pruning prediction method, electronic device and storage medium

Abstract

The application provides a tobacco pruning prediction method, an electronic device and a storage medium, and relates to the field of data processing.The method comprises the following steps: obtaining a historical tobacco seedling ID list, obtaining an effective accumulated temperature list required by the historical tobacco seedling from sowing to transplanting period according to the historical tobacco seedling ID list, and obtaining the average growth rate parameter of the historical tobacco seedling and the average total tobacco leaf area of the historical tobacco seedling at the time of transplanting according to the effective accumulated temperature list; then, future temperature data list after sowing of the to-be-pruned tobacco seedling is obtained, and the future growth trend of the to-be-pruned tobacco seedling is obtained according to the future data list; and the first, second and third pruning days are predicted, so that the pruning of the to-be-pruned tobacco seedling can be scientifically controlled.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture, and in particular to a method for predicting tobacco pruning, an electronic device, and a storage medium. Background Technology

[0002] In tobacco seedling cultivation, leaf pruning is a crucial management measure, promoting plant growth and development by reducing some leaves. However, traditional leaf pruning is usually based on cultivation experience and visual judgment, typically involving three pruning operations. The first pruning is performed after the seed trays have closed (i.e., when the leaves have grown to four or five leaves covering the entire seed tray), removing half of the leaves. The second pruning is done again when the seed trays have closed, removing another half of the leaves to ensure even growth of all leaves. The third pruning is for hardening off the seedlings before transplanting, and is therefore performed about a week before transplanting.

[0003] Existing technologies have some shortcomings in predicting the timing of leaf pruning during the tobacco seedling stage, including being based on experience and subjective judgment, lacking scientific basis and quantitative guidance, lacking further regulation and prediction, and lacking prediction of the next leaf pruning time. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0005] A method for predicting tobacco pruning, comprising the following steps:

[0006] S1: Retrieve the historical tobacco seedling ID list A = {A1, A2, ..., A...} i , ..., A m}, A i Let be the ID of the i-th historical tobacco seedling, i = 1, 2, ..., m, where m is the total number of historical tobacco seedlings. Historical tobacco seedlings are those that have gone through the sowing and transplanting stages.

[0007] S2: Based on A, obtain the list of effective accumulated temperatures required for historical tobacco seedlings from sowing to transplanting, K = {K1, K2, ..., K...} i ..., K m}, K i For A i The corresponding effective accumulated temperature required for historical tobacco seedlings from sowing to transplanting.

[0008] S3: Based on K and the average total tobacco leaf area S at the time of historical tobacco seedling transplanting. t The average growth rate parameter C0 of historical tobacco seedlings was obtained.

[0009] S4: Obtain a list of future temperature data starting from the date the tobacco seedlings to be pruned are sown, Q = {Q1, Q2, ..., Q...} g Q z}, Q g is the average temperature value on the g-th day starting from the sowing date of the tobacco seedlings to be pruned, where g=1, 2...z, and z is the total number of days of the acquired future temperature data.

[0010] Wherein, Q g satisfies the following condition:

[0011] Q g =(H g 1 +H g 2 +...+H g e ) / e, H g e is the average temperature value on the same day corresponding to the date of Q in the e-th year in history, and e is the total number of years that have the average temperature value on the same day corresponding to the date of Q. g g

[0012] S5: Obtain the total leaf area LA of the tobacco seedlings to be pruned according to C0, S t and Q0, where Q0 is the biological zero temperature, and LA satisfies the following condition:

[0013] LA=S t / (1+e^(-C0*24*∑ x＝1 y (Q x -Q0))).

[0014] Wherein, when Q x <Q0, let Q x -Q0=0.

[0015] When LA=S0, let t1=y, where t1 is the first pruning date of the tobacco seedlings to be pruned, S0 is the plug tray area, and the plug tray is a tool for cultivating tobacco seedlings.

[0016] When LA=1.5S0, let t2=y, where t2 is the second pruning date of the tobacco seedlings to be pruned.

[0017] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided, where at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the aforementioned method.

[0018] According to still another aspect of the present invention, an electronic device is provided, comprising a processor and the aforementioned non-transitory computer-readable storage medium.

[0019] The present invention has at least the following beneficial effects: ​​

[0020] First, the growth dates of historical tobacco seedlings, the hourly temperature each day during the growth period, the time of the first pruning, the time of the second pruning, and the total leaf area each day during the growth period are obtained. Based on these data, the growth rate of historical tobacco seedlings and the effective accumulated temperature required for transplanting are calculated, and this data serves as the basis for predicting the pruning time of the seedlings to be pruned. Then, based on historical temperature data, the dates of the first and second potting, and the final transplanting of the seedlings to be pruned are predicted. The three pruning dates are then derived from the potting and transplanting dates, thus completing the prediction of the three pruning dates and improving accuracy. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a tobacco leaf pruning prediction method provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] like Figure 1 As shown in the figure, this invention provides a method for predicting tobacco pruning, the method comprising the following steps:

[0025] S1: Retrieve the historical tobacco seedling ID list A = {A1, A2, ..., A...} i , ..., A m}, A i Let be the ID of the i-th historical tobacco seedling, i = 1, 2, ..., m, where m is the total number of historical tobacco seedlings. Historical tobacco seedlings are those that have gone through the sowing and transplanting stages.

[0026] Specifically, the historical tobacco seedling ID is the unique identifier for each historical tobacco seedling. Each historical tobacco seedling ID consists of letters, numbers, and underscores, and each historical tobacco seedling ID is unique.

[0027] S2: Based on A, obtain the list of effective accumulated temperatures required for historical tobacco seedlings from sowing to transplanting, K = {K1, K2, ..., K...} i..., K m}, K i For A i The corresponding effective accumulated temperature required for historical tobacco seedlings from sowing to transplanting.

[0028] Specifically, the accumulated temperature model is a commonly used agricultural model that predicts plant growth processes based on accumulated temperature. By utilizing the relationship between temperature data and the physiological responses of plant growth, the accumulated temperature model can calculate the accumulated temperature and growth metrics of the plant over a certain period. By applying the accumulated temperature model, the growth rate of tobacco seedlings during the seedling stage can be quantitatively predicted.

[0029] S2 further includes the following steps to obtain K. i :

[0030] S21: Obtain each A i The corresponding growth date sequence t from sowing to transplanting i ={t i 1 , t i 2 , ..., t i j , ..., t i n(i)}, t i j For A i The corresponding historical tobacco seedlings are from the j-th day after sowing, j = 1, 2, ..., n(i), where n(i) is A. i The corresponding historical number of total growing days of tobacco seedlings from sowing to transplanting.

[0031] Specifically, t i The method of obtaining the data is manual recording by those skilled in the art.

[0032] Specifically, because the environment during the tobacco seedling growth period is relatively uniform and the growth rate is stable, this embodiment adopts a linear growth hypothesis accumulated temperature model. The model uses the number of growth days n(i) required from sowing to transplanting in the seed shed based on historical data as the independent variable and the effective accumulated temperature during the growth period as the dependent variable to perform linear fitting, thereby obtaining the effective accumulated temperature K. i .

[0033] S22: Obtain each t i j The corresponding hourly average temperature list T i j ={T i j 1, T i j 2……T i j 24}, T ij 1 to T i j 24 is t i j the corresponding average temperature value per hour in a day.

[0034] S23: According to t i j and T i j obtain K i , where K i satisfies the following condition:

[0035] K i =∑ j＝1 n(i) (∑ p＝1 24 (T i j p -Q0))(T i j p >Q0).

[0036] Wherein, when T i j p <Q0, set T i j p -Q0=0.

[0037] Specifically, in this embodiment, the value of Q0 is 10 degrees Celsius.

[0038] As described above, by collecting historical tobacco seedling data, the growth time of each historical tobacco seedling from sowing to the transplanting period and the average temperature value per hour of each day in the growth period are obtained, so as to obtain the effective accumulated temperature required by each historical tobacco seedling from sowing to transplanting, which facilitates subsequent prediction of the effective accumulated temperature required for tobacco seedling growth.

[0039] S3: According to K and the average total leaf area S of historical tobacco seedlings at the time of transplanting t obtain the average growth rate parameter C0 of the historical tobacco seedlings.

[0040] Wherein, S3 further comprises the following steps to obtain C0:

[0041] S31: According to K, obtain the growth rate list C of the historical tobacco seedlings C={C1, C2, ……, C i , ……, C m}, C i is the growth rate parameter of the historical tobacco seedling corresponding to A i .

[0042] S32:C0=(∑ i＝1 m C i ) / m.

[0043] S31 further includes the following steps to obtain C. i :

[0044] S311: Obtain S i ={S i 1 S i 2 , ..., S i j S i n(i)}, S i j For A i The total leaf area of ​​the corresponding historical tobacco seedlings from sowing to day j.

[0045] Specifically, S t The following conditions must be met:

[0046] S t =(∑ i＝1 m S i n(i) ) / m.

[0047] S311 further includes the following steps to obtain S i j :

[0048] S3111: Obtain A i The corresponding historical tobacco seedlings' first pruning date t0 i Second pruning day t0′ i The third pruning day t0″ i And the area S′ of the third pruning i .

[0049] S3112: When t i j ≤t0 i At that time, S i j The following conditions must be met:

[0050] S i j =∑ b＝1 ai c i j b *d i j b *Ct;ci j b For A i The corresponding historical tobacco seedling's length of leaf b on day j from sowing, d i j b For A i The width of the b-th tobacco leaf on the j-th day after sowing, where Ct is the area coefficient of the tobacco seedling.

[0051] Specifically, in this embodiment, Ct is set to 0.6345.

[0052] Specifically, b = 1, 2, ..., ai, where ai is A i The corresponding number of tobacco leaves from historical tobacco seedlings.

[0053] When t0 i <t i j ≤t0′ i At that time, S i j The following conditions must be met:

[0054] S i j =0.5*S0+∑ b＝1 ai c i j b *d i j b *Ct.

[0055] Specifically, S0 represents the area of ​​the acupoint plate.

[0056] Specifically, plug trays, also known as seedling trays, are an important tool in the industrialized seedling production process. Plug tray seedling cultivation is a modern seedling cultivation method that uses plug trays as containers, lightweight non-soil materials as substrates, and high-density intensive production of vegetable seedlings.

[0057] Specifically, the acupoints are divided into rectangular and circular shapes. The methods for calculating rectangular or circular shapes are existing technologies well known to those skilled in the art, and will not be elaborated here.

[0058] Specifically, when pruning the leaves of tobacco seedlings for the first time, 0.5S0 area of ​​the leaves should be removed when the leaf area of ​​the seedling is equal to the area of ​​the seedling tray. When the leaf area grows back to S0, a second pruning should be carried out, and 0.5S0 area of ​​the leaves should be removed in the second pruning as well.

[0059] When t0′ i <t i j ≤t0″i , S i j satisfies the following condition:

[0060] S i j =S0+∑ b＝1 ai c i j b *d i j b *Ct.

[0061] When t0″ i <t i j ≤n(i), S i j satisfies the following condition:

[0062] S i j =S′ i +S0+∑ b＝1 ai c i j b *d i j b *Ct.

[0063] S312: When j<n(i), based on S i j C is obtained i j , C i j satisfies the following condition:

[0064] C i j =(-ln(S i n(i ) / S i j -1)) / K i .

[0065] S313: C i =(∑ j＝1 n(i) C i j ) / n(i).

[0066] S4: Acquire a future temperature data list Q starting from the sowing date of tobacco seedlings to be pruned, where Q={Q1, Q2, ……, Q g , ……, Q z}, Q gis the average temperature value on the g-th day starting from the sowing date of the to-be-pruned tobacco seedling, where g=1, 2...z, and z is the total number of days of the acquired future temperature data.

[0067] Specifically, the value of z is set by those skilled in the art on their own, and will not be repeated here.

[0068] As described above, when it is necessary to predict the pruning date of the to-be-pruned tobacco seedling, it is first required to obtain the growth environment of the to-be-pruned tobacco seedling in a future period of time.

[0069] Wherein, Q g satisfies the following condition:

[0070] Q g =(H g 1 +H g 2 +……+H g e ) / e, H g e is the average temperature value on the same day corresponding to the date of Q in the e-th year in history, and e is the total number of years that have the average temperature value on the same day corresponding to the date of Q. g corresponding date in the same day of the average temperature value, e is the total number of years that have the average temperature value on the same day corresponding to the date of Q. g corresponding date in the same day of the average temperature value.

[0071] Specifically, H g e is obtained from the temperature data recorded by the temperature sensor in the seedling greenhouse.

[0072] As described above, since the geographical climate has certain stability, during the process of plug seedling raising, appropriate window opening operations are performed according to the weather conditions to stabilize the temperature in the greenhouse within the temperature range suitable for tobacco seedling growth. Therefore, the daily average temperature in the historical temperature data is used as reference data for predicting future temperature data, and the daily average temperature is taken as the hourly average temperature for subsequent acquisition of the temperature on the pruning day.

[0073] S5: Obtain the total leaf area LA of the to-be-pruned tobacco seedling according to C0 and S t and Q0, where Q0 is the biological zero temperature, and LA satisfies the following condition:

[0074] LA=S t / (1+e^(-C0*24*∑ x＝1 y (Q x -Q0))).

[0075] Wherein, when Q x <Q0, let Q x -Q0=0.

[0076] When LA=S0, let t1=y, where t1 is the first pruning day of the tobacco seedling to be pruned.

[0077] When LA=1.5S0, let t2=y, where t2 is the second pruning day of the tobacco seedling to be pruned.

[0078] As described above, after obtaining C0, S t and Q0, the total leaf area LA of the tobacco seedling to be pruned becomes a univariate function related to the number of planting days. When LA=S0, that is, when the leaves of the tobacco seedling reach the first tray-sealing state, the first pruning is performed to cut off 0.5S0 of the leaf area. When the leaves of the tobacco seedling reach the second tray-sealing state, the second pruning is performed, and another 0.5S0 of the leaf area is cut off, so as to complete the prediction of two pruning operations on the tobacco seedling.

[0079] S6: Obtain the third pruning day t3 of the tobacco seedling to be pruned according to K;

[0080] Wherein, S6 further includes the following steps to obtain t3:

[0081] S61: The average effective accumulated temperature K0 of historical tobacco seedlings, where K0 satisfies the following condition:

[0082] K0=(∑ i＝1 m K i ) / m;

[0083] S62: t3 satisfies the following condition:

[0084] When 24*∑ x＝1 y (Q x -Q0)=K0, let t3=y-7.

[0085] Wherein, when Q x <Q0, let Q x -Q0=0.

[0086] As described above, the average accumulated temperature K0 of tobacco seedlings from sowing to the transplanting stage is obtained through historical tobacco seedlings, K0 is used as the total accumulated temperature data required for the tobacco seedling to be pruned from sowing to final transplanting, the date when the tobacco seedling reaches the transplanting stage is predicted through future temperature data, and 7 days before the transplanting stage is taken as the third pruning day, so as to complete the prediction of three pruning operations.

[0087] This embodiment provides a method for predicting tobacco pruning. First, it obtains the historical growth dates of tobacco seedlings, the hourly temperature each day during the growth period, the time of the first pruning, the time of the second pruning, and the total leaf area each day during the growth period. Based on this data, it calculates the historical growth rate of the seedlings and the effective accumulated temperature required for transplanting, using this as the basis for predicting the pruning time of the seedlings to be pruned. Then, based on historical temperature data, it predicts the dates of the first and second potting, as well as the final transplanting of the seedlings. The three pruning dates are then obtained based on the potting and transplanting dates, thus completing the prediction of the three pruning dates and improving accuracy.

[0088] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0089] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0090] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method for predicting tobacco pruning, characterized in that, The method includes the following steps: S1: Retrieve the historical tobacco seedling ID list A = {A1, A2, ..., A...} i , ..., A m }, A i Let i be the ID of the i-th historical tobacco seedling, i = 1, 2, ..., m, where m is the total number of historical tobacco seedlings. Historical tobacco seedlings are those that have gone through the sowing to transplanting period. S2: Based on A, obtain the list of effective accumulated temperatures required for historical tobacco seedlings from sowing to transplanting, K={K1, K2, ..., K...} i , ..., K m }, K i For A i The corresponding effective accumulated temperature required for historical tobacco seedlings from sowing to transplanting; S3: Based on K and the average total tobacco leaf area S at the time of historical tobacco seedling transplanting. t The average growth rate parameter C0 of historical tobacco seedlings is obtained; step S3 further includes the following step to obtain C0: S31: Obtain a list of historical tobacco seedling growth rates C={C1, C2, ..., C...} based on K. i , ..., C m }, C i For A i The corresponding historical growth rate parameters of tobacco seedlings; S32:C0=(∑ i=1 m C i ) / m; S31 further includes the following steps to obtain C. i : S311: Obtain S i ={S i 1 S i 2 S i j S i n（i） }, S i j For A i The corresponding historical tobacco seedling's total leaf area from sowing to day j. S312: When j<n(i), according to S i j acquire C i j , C i j satisfies the following condition: C i j =(-ln(S i n（i) ) / S i j -1) / K i ; S313:C i =(∑ j=1 n（i） C i j ) / n(i); S311 further includes the following steps to obtain S i j : S3111: Obtain A i The corresponding historical tobacco seedling first pruning date t0 i Second pruning day t0ʹ i The third pruning day t0ʺ i And the area Sʹ of the third trimming i ; S3112: When t i j ≤t0 i At that time, S i j The following conditions must be met: S i j =∑ b=1 ai c i j b *d i j b *Ct;c i j b For A i The corresponding historical tobacco seedling's b-th leaf length on day j from sowing, d i j b For A i The width of the b-th tobacco leaf on the j-th day after sowing in the corresponding historical tobacco seedling, where Ct is the area coefficient of the tobacco seedling, b = 1, 2, ..., ai, and ai is the area coefficient of A. i The corresponding historical number of tobacco leaves from tobacco seedlings; When t0 i <t i j ≤t0ʹ i At that time, S i j The following conditions must be met: S i j =0.5*S0+∑ b=1 ai c i j b *d i j b *Ct; When t0ʹ i <t i j ≤t0ʺ i At that time, S i j The following conditions must be met: S i j =S0+∑ b=1 ai c i j b *d i j b *Ct; When t0ʺ i <t i j When ≤n(i), S i j The following conditions must be met: S i j =Sʹ i +S0+∑ b=1 ai c i j b *d i j b *Ct; S4: Obtain a list of future temperature data starting from the date the tobacco seedlings to be pruned are sown, Q = {Q1, Q2, ..., Q...} g Q z }, Q g Let g be the average temperature value on the g-th day from the date of sowing of the tobacco seedlings to be pruned, where g = 1, 2, ..., z, and z is the total number of days of future temperature data obtained. Among them, Q g The following conditions must be met: Q g =(H g 1 +H g 2 +……+H g e ) / e,H g e For the e-th year in history and Q g The corresponding date is the average temperature value of the same day, where e is the value containing Q. g The total number of years whose corresponding dates fall within the average temperature value of the same day; S5: Based on C0, S t And Q0 is the total tobacco leaf area LA of the tobacco seedlings to be pruned, where Q0 is the biological zero temperature, and LA satisfies the following conditions: LA=S t / (1+e^(-C0*24*∑ g=1 z (Q) g -Q0))); wherein, when Q g <Q0, set Q g -Q0=0; When LA=S0, let t1=g, where t1 is the first pruning day of the tobacco seedlings to be pruned, and S0 is the area of ​​the seedling tray, which is the device for cultivating tobacco seedlings. When LA=1.5S0, let t2=g, where t2 is the second pruning day for the tobacco seedlings to be pruned.

2. The method according to claim 1, characterized in that, S2 includes the following steps to obtain K. i : S21: For each A i Obtain the growth date sequence t corresponding to Ai from sowing to transplanting. i ={t i 1 , t i 2 , ..., t i j , ..., t i n（i） }, t i j For A i The corresponding historical tobacco seedlings are from the j-th day after sowing, j=1,2……n(i), and n(i) is the total number of growing days from sowing to transplanting for the historical tobacco seedlings; S22: Obtain each t i j The corresponding hourly average temperature list T i j ={T i j 1, T i j 2……T i j 24 }, T i j 1 to T i j 24 For t i j The corresponding average temperature value for each hour of the day; S23: According to t i j and T i j Get K i K i The following conditions must be met: TO i =∑ j=1 n（i） (∑ p=1 24 (T) i j p -Q0)); Wherein, when T i j p <Q0, let T i j p -Q0=0.

3. The method according to claim 1, characterized in that, The value of Ct is 0.6345.

4. The method according to claim 1, characterized in that, The value of Q0 is 10 degrees Celsius.

5. The method according to claim 1, characterized in that, The method further includes the following steps: S6: Obtain the third pruning day t3 for the tobacco seedlings to be pruned based on K; S6 also includes the following steps to obtain t3: S61: Obtain the average effective accumulated temperature K0 of historical tobacco seedlings, where K0 satisfies the following conditions: K0=(∑ i=1 m K i ) / m; S62: t3 satisfies the following condition: When 24*∑ g=1 z (Q) g When -Q0) = K0, let t3 = z-7; Wherein, when Q g <Q0, Q g -Q0 is set to 0.

6. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the method as described in any one of claims 1-5.

7. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 6.

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