CN114202247A - Crop growth environment big data analysis system - Google Patents

Abstract

The invention discloses a crop growth environment big data analysis system, and belongs to the technical field of agriculture. A crop growth environment big data analysis system comprises a plurality of test fields and detection devices arranged in the test fields, wherein the test fields are respectively a natural growth group and a plurality of control groups; the crops in the natural growth group are exposed in the natural environment, and the growth environment of the crops in the control group is changed by a certain variable compared with that of the natural growth group; the control group comprises a fertilizer control group, an illumination control group and a temperature control group, wherein the fertilizer control group only changes the using amount and the timing of the fertilizer, the illumination control group only changes the illumination time length and the illumination intensity, and the temperature control group only changes the environmental temperature of crops. The present invention provides a system to analyze the most important environmental factors needed by different crops.

Description

Crop growth environment big data analysis system

Technical Field

The invention relates to the technical field of agriculture, in particular to a crop growth environment big data analysis system.

Background

In the process of crop growth, crops are often subjected to multiple influences of various environmental factors, different crops have different requirements and different importance for different environmental factors, and a system for analyzing the most important environmental factors needed by different crops is lacking at present.

Disclosure of Invention

1. Technical problem to be solved

The invention aims to provide a crop growth environment big data analysis system to solve the problems in the background technology:

there is a lack of a system to analyze the most important environmental factors needed for different crops.

2. Technical scheme

A crop growth environment big data analysis system comprises a cloud processor and a detection device, wherein the detection device is arranged in a test field;

the detection device comprises an air data acquisition module, a soil data acquisition module and a growth data acquisition module, wherein the air data acquisition module, the soil data acquisition module and the growth data acquisition module are all connected with the cloud processor;

the air data acquisition module comprises a wind speed detection module, an illumination detection module, a temperature detection module, a pest detection module and a carbon dioxide detection module;

the soil data acquisition module comprises a pH value detection module, a humidity detection module and a fertilizer detection module;

the growth data acquisition module comprises a height detection module, a volume detection module and a fruit detection module.

A crop growth environment big data analysis method comprises the following steps:

step 1, setting a test field, and collecting crop growth environment big data in the test field through a detection device; the test fields are respectively a natural growth group and a plurality of control groups, the crops in the natural growth group are exposed in the natural environment, and the growth environment of the crops in the control groups is changed by a certain variable compared with the natural growth group;

step 2, obtaining growth value parameters of crops through a detection device;

step 3, obtaining the optimal fertilizing amount and fertilizing time required by crops through a detection device;

step 4, obtaining the optimal illumination intensity and illumination duration required by the crops through a detection device;

and 5, obtaining the optimal growth temperature required by the crops through a detection device.

The control group comprises a fertilizer control group, an illumination control group and a temperature control group, wherein the fertilizer control group only changes the use amount and the timing of the fertilizer, the illumination control group only changes the illumination time length and the intensity, and the temperature control group only changes the environmental temperature of crops;

two gradient control groups are arranged in the fertilizer control groups, namely a fertilizer control group and a fertilization time control group respectively, and the fertilizer control group is used for sowing after the crops in the fertilizer control group are harvested;

the fertilizer quantity control group is provided with n groups, the fertilizer application time is controlled to be unchanged, the fertilizer quantity control group is arranged from low to high according to the gradient of the fertilizer application quantity and is marked as A₁……A_n；

The fertilization time control group is provided with n groups, the crop growth cycle is equally divided into n sections, then the same fertilizer is applied in different time sections, and the fertilizer is marked as B according to the fertilization time₁……B_n。

Two gradient control groups are arranged in the illumination control group, namely an illumination intensity control group and an illumination duration control group, and the illumination duration control group is used for sowing after the crops in the illumination intensity control group are harvested;

the illumination intensity control groups are provided with n groups, the illumination time length of each day is controlled to be constant, the illumination intensity control groups are arranged from low to high according to the gradient and are marked as C₁……C_n；

The illumination duration control group is provided with n groups, the illumination intensity is controlled to be unchanged, the n groups are arranged from low to high according to the number of illumination durations in each day and are marked as D₁……D_n。

Two different control groups, namely a constant temperature control group and a variable temperature control group, are arranged in the temperature control group, and the variable temperature control group is used for sowing after the crops are harvested in the constant temperature control group;

the constant temperature control group is provided with n groups, the environmental temperature of each group of crops in the whole growth cycle process is kept unchanged, the crops are arranged from low to high according to different gradients of temperature and are marked as E₁……E_n；

The variable temperature control group is provided with n groups, the growth cycle of crops is equally divided into n sections, the optimal environment temperature is provided in different time periods, the temperature is changed in the other time periods, and the time sequence of providing the optimal temperature is recorded as F₁……F_n。

The method for processing the growth data by the growth data acquisition module (4) in the step 2 comprises the following steps:

step 2.1, a height detection module (401) utilizes infrared rays to detect the height and collects the height of the crops in the growth process, and the height is recorded as h;

step 2.2, searching the Internet database to obtain the average volume of the crops with the height h

Measuring the actual volume V of the crop by using ultrasonic reflection through a volume detection module (402);

step 2.3, the fruit detection module (403) finally measures the quality G of the crop fruits and searches an internet database to obtain the quality GTo average harvest

Step 2.4, obtaining the growth value parameter g of the crops

The data processing mode of the fertilizer control group in the step 3 comprises the following steps:

the fertilizer quantity control group data analysis method comprises the following steps:

step 3.11, obtaining A₁……A_nCrop growth value parameter of group, noted as a₁……a_nBuilding a coordinate set using the received data (A)_n,a_n)}；

Step 3.12, drawing a coordinate set { (A)_n,a_n) An image;

step 3.13, drawing images of the natural growth group in the same image, wherein the crops in the natural growth group are not fertilized in the whole process;

step 3.14, comparing the fertilizer amount control group with the natural growth group image to obtain the optimal fertilizer amount A_m；

The fertilization time control group data analysis method comprises the following steps:

step 3.21, recording the seeding time of the crops as 0, and recording the harvesting time of the crops as T;

step 3.22, fertilizing the crops at different time, wherein the fertilizing amount is the optimal fertilizing amount obtained in the step 2.14, and the fertilizing time of the ith group is B_i

Step 3.23, obtaining B₁……B_nCrop growth value parameter of group, noted b₁……b_nUsing the received data, a coordinate set is constructed (B)_n,b_n)}；

Step 3.24, drawing a coordinate set { (B)_n,b_n) An image;

step 3.25, controlling the internal image of the group by the fertilization time as a ratio to obtain the optimal fertilization time B_m。

The data processing mode of the illumination control group in the step 4 comprises the following steps:

the illumination intensity control group data analysis method comprises the following steps:

step 4.11, obtaining C₁……C_nCrop growth value parameter of group, noted c₁……c_nBuilding a coordinate set using the received data (C)_n,c_n)}；

Step 4.12, drawing a coordinate set { (C)_n,c_n) An image;

step 4.13, drawing a natural growth group image in the same image;

step 4.14, comparing the control group of illumination intensity with the image of the natural growth group to obtain the optimal illumination intensity C_m；

The method for analyzing the data of the illumination duration control group comprises the following steps:

step 4.21, adopting the optimal illumination intensity C_mSetting the maximum time length Delta S and the ith group illumination time length D_i

Step 4.22 obtaining D₁……D_nCrop growth value parameter of group, noted d₁……d_nUsing the received data, a coordinate set is constructed (D)_n,d_n)}；

Step 4.23, drawing a coordinate set { (D)_n,d_n) An image;

step 4.24, the illumination duration controls the internal image of the group as the ratio to obtain the optimal illumination duration D_m。

The data processing mode of the temperature control group in the step 5 comprises the following steps:

the data processing mode of the constant temperature control group comprises the following steps:

step 5.11, presetting a temperature range (K)_p～K_q) Temperature E provided by group i_i

Step 5.12 obtaining E₁……E_nCrop growth value parameter of group, noted as e₁……e_nBuilding a coordinate set using the received data (E)_n,e_n)}；

Step 5.13, drawing a coordinate set { (E)_n,e_n) An image;

step 5.14, the internal image of the constant temperature control group is used as a ratio to obtain the optimal growth temperature E_m；

The data processing mode of the temperature-changing control group comprises the following steps:

step 5.21, setting the temperature change curve in each control group along with time as a sine function curve;

step 5.22, dividing the crops into cold-like crops and warm-like crops, wherein the optimum growth temperature E of the cold-like crops_mAt the lowest temperature, the internal temperature range of the control group is (E)_m～K_q) (ii) a Optimum growth temperature E of thermophilic crops_mControlling the internal temperature range of the group to be (K) at the highest temperature_p～E_m)；

Step 5.23, recording the seeding time of the crops as 0, recording the harvesting time of the crops as T, and controlling the i-th group to give the optimal temperature time T_i

Step 5.24, the temperature inside the i-th group as a function of time t is

The crops are favored to be warm:

cold-loving crops:

step 5.25, obtaining F₁……F_nCrop growth value parameter of group, noted f₁……f_nUsing the received data, a coordinate set is constructed { (F)_n,f_n)}；

Step 5.26, drawing a coordinate set { (F)_n,f_n) An image;

and 5.27, taking the images in the temperature change control group as a ratio to obtain an optimal temperature change curve.

3. Advantageous effects

Compared with the prior art, the invention has the advantages that:

1. according to the invention, the growth condition of the crops is digitized by measuring and calculating the height, the volume and the fruit weight of the crops, so that the growth condition of the crops is more visual, and the subsequent experimental data can be conveniently processed and analyzed.

2. The invention can obtain the optimal fertilizing amount and the optimal fertilizing time required by the growth of crops through data analysis.

3. According to the invention, the optimal illumination intensity and illumination duration required by crop growth can be obtained through data analysis.

4. The temperature required by the crops in different growth stages is different, and the optimal sowing time of the crops can be effectively analyzed through the method.

Drawings

FIG. 1 is a schematic diagram of the system of the present invention.

The reference numbers in the figures illustrate: 1. a cloud processor; 2. an air data acquisition module; 201. a wind speed detection module; 202. an illumination detection module; 203. a temperature detection module; 204. a pest detection module; 205. a carbon dioxide detection module; 3. a soil data acquisition module; 301. a pH value detection module; 302. a humidity detection module; 303. a fertilizer detection module; 4. a growth data acquisition module; 401. a height detection module; 402. a volume detection module; 403. and a fruit detection module.

Detailed Description

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the equipment or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.

In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like are to be construed broadly, e.g., "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

Example 1:

referring to fig. 1, a crop growth environment big data analysis system includes a cloud processor (1), and further includes a detection device, the detection device is disposed in a test field;

the detection device comprises an air data acquisition module 2, a soil data acquisition module 3 and a growth data acquisition module 4, wherein the air data acquisition module 2, the soil data acquisition module 3 and the growth data acquisition module 4 are all connected with the cloud processor 1;

the air data acquisition module 2 comprises a wind speed detection module 201, an illumination detection module 202, a temperature detection module 203, a pest detection module 204 and a carbon dioxide detection module 205;

the soil data acquisition module 3 comprises a pH value detection module 301, a humidity detection module 302 and a fertilizer detection module 303;

growth data acquisition module 4 includes a height detection module 401, a volume detection module 402, and a fruit detection module 403.

The multiple detection modules are matched with each other, and the corresponding change of other variables after a certain variable is changed can be analyzed.

A crop growth environment big data analysis method comprises the following steps:

step 1, setting a test field, and collecting crop growth environment big data in the test field through a detection device; the test fields are respectively a natural growth group and a plurality of control groups, the crops in the natural growth group are exposed in the natural environment, and the growth environment of the crops in the control groups is changed by a certain variable compared with the natural growth group;

step 2, obtaining growth value parameters of crops through a detection device;

step 3, obtaining the optimal fertilizing amount and fertilizing time required by crops through a detection device;

step 4, obtaining the optimal illumination intensity and illumination duration required by the crops through a detection device;

and 5, obtaining the optimal growth temperature required by the crops through a detection device.

The method for processing the growth data by the growth data acquisition module (4) in the step 2 comprises the following steps:

step 2.1, a height detection module (401) utilizes infrared rays to detect the height and collects the height of the crops in the growth process, and the height is recorded as h;

step 2.2, searching the Internet database to obtain the average volume of the crops with the height h

Measuring the actual volume V of the crop by using ultrasonic reflection through a volume detection module (402);

step 2.3, the fruit detection module (403) finally measures the quality G of the crop fruits, searches the Internet database to obtain the average harvest

Step 2.4, obtaining the growth value parameter g of the crops

The growth condition of the crops is digitalized by measuring and calculating the height, the volume and the fruit weight of the crops, so that the growth condition of the crops is more visual, and the subsequent experimental data can be conveniently processed and analyzed.

Example 2:

two gradient control groups are arranged in the fertilizer control groups, namely a fertilizer control group and a fertilization time control group respectively, and the fertilizer control group is used for sowing after the crops in the fertilizer control group are harvested;

the fertilizer quantity control group is provided with n groups, the fertilizer application time is controlled to be unchanged, the fertilizer is applied from sowing, the fertilizer is arranged from low to high according to the gradient of the fertilizer application quantity and is marked as A₁……A_n；

The fertilization time control group is provided with n groups, the crop growth cycle is equally divided into n sections, then the same fertilizer is applied in different time sections, and the fertilizer is marked as B according to the fertilization time₁……B_n。

The data processing mode of the fertilizer control group in the step 3 comprises the following steps:

the fertilizer quantity control group data analysis method comprises the following steps:

step 3.11, obtaining A₁……A_nCrop growth value of the groupNumber, mark as a₁……a_nBuilding a coordinate set using the received data (A)_n,a_n)}；

Step 3.12, drawing a coordinate set { (A)_n,a_n) An image;

step 3.13, drawing images of the natural growth group in the same image, wherein the crops in the natural growth group are not fertilized in the whole process;

step 3.14, comparing the fertilizer amount control group with the natural growth group image to obtain the optimal fertilizer amount A_m；

The fertilization time control group data analysis method comprises the following steps:

step 3.21, recording the seeding time of the crops as 0, and recording the harvesting time of the crops as T;

step 3.22, fertilizing the crops at different time, wherein the fertilizing amount is the optimal fertilizing amount obtained in the step 2.14, and the fertilizing time of the ith group is B_i

Step 3.23, obtaining B₁……B_nCrop growth value parameter of group, noted b₁……b_nUsing the received data, a coordinate set is constructed (B)_n,b_n)}；

Step 3.24, drawing a coordinate set { (B)_n,b_n) An image;

step 3.25, controlling the internal image of the group by the fertilization time as a ratio to obtain the optimal fertilization time B_m。

By the method, the optimal fertilizing amount and the optimal fertilizing time required by crop growth can be analyzed.

Example 3:

two gradient control groups are arranged in the illumination control group, namely an illumination intensity control group and an illumination duration control group, and the illumination duration control group is used for sowing after the crops in the illumination intensity control group are harvested;

the lightThe illumination intensity control groups are provided with n groups, the illumination time length of each day is controlled to be constant, the illumination intensity control groups are arranged from low to high according to the gradient of the illumination intensity and are marked as C₁……C_n；

The illumination duration control group is provided with n groups, the illumination intensity is controlled to be unchanged, the n groups are arranged from low to high according to the number of illumination durations in each day and are marked as D₁……D_n。

The illumination intensity control group data analysis method comprises the following steps:

step 4.11, obtaining C₁……C_nCrop growth value parameter of group, noted c₁……c_nBuilding a coordinate set using the received data (C)_n,c_n)}；

Step 4.12, drawing a coordinate set { (C)_n,c_n) An image;

step 4.13, drawing a natural growth group image in the same image;

step 4.14, comparing the control group of illumination intensity with the image of the natural growth group to obtain the optimal illumination intensity C_m；

The method for analyzing the data of the illumination duration control group comprises the following steps:

step 4.21, adopting the optimal illumination intensity C_mSetting the maximum time length Delta S and the ith group illumination time length D_i

Step 4.22 obtaining D₁……D_nCrop growth value parameter of group, noted d₁……d_nUsing the received data, a coordinate set is constructed (D)_n,d_n)}；

Step 4.23, drawing a coordinate set { (D)_n,d_n) An image;

step 4.24, the illumination duration controls the internal image of the group as the ratio to obtain the optimal illumination duration D_m。

By the method, the optimal illumination intensity and illumination duration required by crop growth can be analyzed and obtained.

Example 4:

two different control groups, namely a constant temperature control group and a variable temperature control group, are arranged in the temperature control group, and the variable temperature control group is used for sowing after the crops are harvested in the constant temperature control group;

the constant temperature control group is provided with n groups, the environmental temperature of each group of crops in the whole growth cycle process is kept unchanged, the crops are arranged from low to high according to different gradients of temperature and are marked as E₁……E_n；

The variable temperature control group is provided with n groups, the growth cycle of crops is equally divided into n sections, the optimal environment temperature is provided in different time periods, the temperature is changed in the other time periods, and the time sequence of providing the optimal temperature is recorded as F₁……F_n。

The data processing mode of the temperature control group in the step 5 comprises the following steps:

the data processing mode of the constant temperature control group comprises the following steps:

step 5.11, presetting a temperature range (K)_p～K_q) Temperature E provided by group i_i

Step 5.12 obtaining E₁……E_nCrop growth value parameter of group, noted as e₁……e_nBuilding a coordinate set using the received data (E)_n,e_n)}；

Step 5.13, drawing a coordinate set { (E)_n,e_n) An image;

step 5.14, the internal image of the constant temperature control group is used as a ratio to obtain the optimal growth temperature E_m；

The data processing mode of the temperature-changing control group comprises the following steps:

step 5.21, setting the temperature change curve in each control group along with time as a sine function curve;

step 5.22, dividing the crops into cold-like crops and warm-like crops, wherein the optimum growth temperature E of the cold-like crops_mAt the lowest temperature, the internal temperature range of the control group is (E)_m～K_q) (ii) a Optimum growth temperature E of thermophilic crops_mControlling the internal temperature range of the group to be (K) at the highest temperature_p～E_m)；

Step 5.23, recording the seeding time of the crops as 0, recording the harvesting time of the crops as T, and controlling the i-th group to give the optimal temperature time T_i

Step 5.24, the temperature inside the i-th group as a function of time t is

The crops are favored to be warm:

cold-loving crops:

step 5.25, obtaining F₁……F_nCrop growth value parameter of group, noted f₁……f_nUsing the received data, a coordinate set is constructed { (F)_n,f_n)}；

Step 5.26, drawing a coordinate set { (F)_n,f_n) An image;

and 5.27, taking the images in the temperature change control group as a ratio to obtain an optimal temperature change curve.

The temperature required by the crops in different growth stages is different, and the optimal seeding season of the crops can be effectively analyzed by the method.

The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. The utility model provides a crops growing environment big data analysis system, includes high in the clouds processor (1), its characterized in that: the device also comprises a detection device, and the detection device is arranged in the test field;

the detection device comprises an air data acquisition module (2), a soil data acquisition module (3) and a growth data acquisition module (4), wherein the air data acquisition module (2), the soil data acquisition module (3) and the growth data acquisition module (4) are all connected with the cloud processor (1);

the air data acquisition module (2) comprises a wind speed detection module (201), an illumination detection module (202), a temperature detection module (203), a pest detection module (204) and a carbon dioxide detection module (205);

the soil data acquisition module (3) comprises a pH value detection module (301), a humidity detection module (302) and a fertilizer detection module (303);

the growth data acquisition module (4) comprises a height detection module (401), a volume detection module (402) and a fruit detection module (403).

2. A crop growth environment big data analysis method performed by the big data analysis system according to claim 1, comprising the steps of:

step 1, setting a test field, and collecting crop growth environment big data in the test field through a detection device; the test fields are respectively a natural growth group and a plurality of control groups, the crops in the natural growth group are exposed in the natural environment, and the growth environment of the crops in the control groups is changed by a certain variable compared with the natural growth group;

step 2, obtaining growth value parameters of crops through a detection device;

step 3, obtaining the optimal fertilizing amount and fertilizing time required by crops through a detection device;

step 4, obtaining the optimal illumination intensity and illumination duration required by the crops through a detection device;

and 5, obtaining the optimal growth temperature required by the crops through a detection device.

3. The crop growth environment big data analysis method according to claim 2, characterized in that: the control group comprises a fertilizer control group, an illumination control group and a temperature control group, wherein the fertilizer control group only changes the using amount and the timing of the fertilizer, the illumination control group only changes the illumination time length and the illumination intensity, and the temperature control group only changes the environmental temperature of crops.

4. The crop growth environment big data analysis method according to claim 3, characterized in that: two gradient control groups are arranged in the fertilizer control groups, namely a fertilizer control group and a fertilization time control group respectively, and the fertilizer control group is used for sowing after the crops in the fertilizer control group are harvested;

the fertilizer quantity control group is provided with n groups, the fertilizer application time is controlled to be unchanged, the fertilizer quantity control group is arranged from low to high according to the gradient of the fertilizer application quantity and is marked as A₁……A_n；

The fertilization time control group is provided with n groups, the crop growth cycle is equally divided into n sections, then the same fertilizer is applied in different time sections, and the fertilizer is marked as B according to the fertilization time₁……B_n。

5. The crop growth environment big data analysis method according to claim 3, characterized in that: two gradient control groups are arranged in the illumination control group, namely an illumination intensity control group and an illumination duration control group, and the illumination duration control group is used for sowing after the crops in the illumination intensity control group are harvested;

the illumination intensity control groups are provided with n groups, the illumination time length of each day is controlled to be constant, the illumination intensity control groups are arranged from low to high according to the gradient and are marked as C₁……C_n；

The illumination duration control group is provided with n groups, the illumination intensity is controlled to be unchanged, the n groups are arranged from low to high according to the number of illumination durations in each day and are marked as D₁……D_n。

6. The crop growth environment big data analysis method according to claim 3, characterized in that: two different control groups, namely a constant temperature control group and a variable temperature control group, are arranged in the temperature control group, and the variable temperature control group is used for sowing after the crops are harvested in the constant temperature control group;

the constant temperature control group is provided with n groups, the environmental temperature of each group of crops in the whole growth cycle process is kept unchanged, the crops are arranged from low to high according to different gradients of temperature and are marked as E₁……E_n；

The variable temperature control group is provided with n groups, the growth cycle of crops is equally divided into n sections, the optimal environment temperature is provided in different time periods, the temperature is changed in the other time periods, and the time sequence of providing the optimal temperature is recorded as F₁……F_n。

7. The crop growth environment big data analysis method according to claim 2, characterized in that: the growth value parameters of the crops obtained in the step 2 are processed by a growth data acquisition module (4), and the specific processing method comprises the following steps:

step 2.1, a height detection module (401) utilizes infrared rays to detect the height and collects the height of the crops in the growth process, and the height is recorded as h;

step 2.2, searching the Internet database to obtain the average volume of the crops with the height h

Measuring the actual volume V of the crop by using ultrasonic reflection through a volume detection module (402);

step 2.3, the fruit detection module (403) finally measures the quality G of the crop fruits, searches the Internet database to obtain the average harvest

Step 2.4, obtaining the growth value parameter g of the crops

8. The crop growth environment big data analysis method according to claim 4, characterized in that: in the step 3, the optimal fertilizing amount and time required by the crops are obtained through data processing of the fertilizer control groups, and the method comprises the following steps:

the fertilizer quantity control group data analysis method comprises the following steps:

step 3.11, obtaining A₁……A_nCrop growth value parameter of group, noted as a₁……a_nBuilding a coordinate set using the received data (A)_n,a_n)}；

Step 3.12, drawing a coordinate set { (A)_n,a_n) An image;

step 3.13, drawing images of the natural growth group in the same image, wherein the crops in the natural growth group are not fertilized in the whole process;

step 3.14, comparing the fertilizer amount control group with the natural growth group image to obtain the optimal fertilizer amount A_m；

The fertilization time control group data analysis method comprises the following steps:

step 3.21, recording the seeding time of the crops as 0, and recording the harvesting time of the crops as T;

step 3.22, the farmers are aligned at different timesFertilizing the crops, wherein the fertilizing amount is the optimal fertilizing amount obtained in the step 3.14, and the fertilizing time of the i group is B_i

Step 3.23, obtaining B₁……B_nCrop growth value parameter of group, noted b₁……b_nUsing the received data, a coordinate set is constructed (B)_n,b_n)}；

Step 3.24, drawing a coordinate set { (B)_n,b_n) An image;

step 3.25, the images in the fertilization time control group are used as comparison to obtain the optimal fertilization time B_m。

9. The crop growth environment big data analysis method according to claim 5, characterized in that: in the step 4, the optimal illumination intensity and illumination duration required by the crops are obtained through data processing of the illumination control group, and the method comprises the following steps:

the illumination intensity control group data analysis method comprises the following steps:

step 4.11, obtaining C₁……C_nCrop growth value parameter of group, noted c₁……c_nBuilding a coordinate set using the received data (C)_n,c_n)}；

Step 4.12, drawing a coordinate set { (C)_n,c_n) An image;

step 4.13, drawing a natural growth group image in the same image;

step 4.14, comparing the control group of illumination intensity with the image of the natural growth group to obtain the optimal illumination intensity C_m；

The method for analyzing the data of the illumination duration control group comprises the following steps:

step 4.21, adopting the optimal illumination intensity C_mSetting the maximum time length Delta S and the ith group illumination time length D_i

Step 4.22 obtaining D₁……D_nCrop growth value parameter of group, noted d₁……d_nUsing the received data, a coordinate set is constructed (D)_n,d_n)}；

Step 4.23, drawing a coordinate set { (D)_n,d_n) An image;

step 4.24, the internal images of the illumination duration control group are compared to obtain the optimal illumination duration D_m。

10. The method for analyzing big data of crop growing environment according to claim 6, wherein the method comprises the following steps: in the step 5, the optimal growth temperature required by the crops is obtained through data processing of the temperature control group, and the method comprises the following steps:

the data processing mode of the constant temperature control group comprises the following steps:

step 5.11, presetting a temperature range (K)_p～K_q) Temperature E provided by group i_i

Step 5.12 obtaining E₁……E_nCrop growth value parameter of group, noted as e₁……e_nBuilding a coordinate set using the received data (E)_n,e_n)}；

Step 5.13, drawing a coordinate set { (E)_n,e_n) An image;

step 5.14, comparing the internal images of the constant temperature control group to obtain the optimal growth temperature E_m；

The data processing mode of the temperature-changing control group comprises the following steps:

step 5.21, setting the temperature change curve in each control group along with time as a sine function curve;

step 5.22, dividing the crops into cold-like crops and warm-like crops, wherein the optimum growth temperature E of the cold-like crops_mAt the lowest temperature, the internal temperature range of the control group is (E)_m～K_q) (ii) a Optimum growth temperature E of thermophilic crops_mControlling the internal temperature range of the group to be (K) at the highest temperature_p～E_m)；

Step 5.23, recording the seeding time of the crops as 0, recording the harvesting time of the crops as T, and controlling the i-th group to give the optimal temperature time T_i

Step 5.24, the temperature inside the i-th group as a function of time t is

The crops are favored to be warm:

cold-loving crops:

step 5.25, obtaining F₁……F_nCrop growth value parameter of group, noted f₁……f_nUsing the received data, a coordinate set is constructed { (F)_n,f_n)}；

Step 5.26, drawing a coordinate set { (F)_n,f_n) An image;

and 5.27, taking the images in the temperature change control group as a ratio to obtain an optimal temperature change curve.

Images