Crop growth model construction method and system, terminal and storage medium

Abstract

The invention belongs to the technical field of crop monitoring, and particularly relates to a crop growth model construction method and system, a terminal and a storage medium. The crop growth model construction method comprises the following steps: S1, obtaining an original sample set of crops and initial data corresponding to the original sample set to generate an initial sample model; s2, acquiring a real-time training set of crops and real-time data corresponding to the real-time training set; s3, generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data, and obtaining verification data; s4, constructing a real-time model according to the verification data; and S5, comparing the real-time model with the initial model to obtain an optimization model. The invention can be used.

Description

Technical Field

[0001] The present invention belongs to the technical field, and in particular relates to a method, system, terminal and storage medium for constructing a crop growth model. Background Art

[0002] In modern agricultural production, as the importance of data-driven decision-making increases, especially in the face of changing climate conditions and complex growth environments, accurate monitoring and analysis of crop growth status has become extremely critical. By accurately monitoring the growth of crops, timely discovering problems such as plant diseases, pest infestations or malnutrition, it ensures that agricultural producers can take appropriate measures to optimize growth conditions, thereby effectively improving crop yields and quality and reducing resource waste. In addition, accurate growth status monitoring can also reduce the excessive use of pesticides and fertilizers, which is also of positive significance to environmental protection.

[0003] However, the existing methods of obtaining growth data of crops at various stages require more manpower investment, and the authenticity and accuracy of the data are poor, making it impossible to obtain a better growth model. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a crop growth model to address the deficiencies of the prior art and to solve any of the above technical problems.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A method for constructing a crop growth model comprises the following steps:

[0007] S1. Obtaining the original sample set of crops and the corresponding initial data to generate an initial sample model;

[0008] S2, obtaining a real-time training set of crops and its corresponding real-time data;

[0009] S3, generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data;

[0010] S4, constructing a real-time model according to the verification data;

[0011] S5. Compare the real-time model with the initial model to obtain an optimized model.

[0012] Preferably, the step of obtaining the original sample set of crops and the initial data corresponding thereto to generate the initial sample model comprises the following:

[0013] Obtaining a first preset number of original crop samples of the same type according to the type of the crop;

[0014] respectively obtaining respective temporary data corresponding to original crop samples of the same type;

[0015] Performing weighted average conversion on all the temporary data to obtain the corresponding initial data;

[0016] An initial sample model is generated according to the initial data.

[0017] Preferably, the step of obtaining a real-time training set of crops and the real-time data corresponding thereto comprises the following:

[0018] Divide the park where the crops are located into areas to form at least two control areas;

[0019] Acquire crops of the same type and a second preset number in each of the management and control areas as a real-time training set;

[0020] Acquire real-time data corresponding to each element in the real-time training set.

[0021] Preferably, the step of obtaining the real-time data corresponding to each element in the real-time training set includes the following:

[0022] The image acquisition component and the color mark sensor component are used to obtain the real-time data corresponding to each element in the real-time training set.

[0023] Preferably, the step of generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtaining verification data comprises the following:

[0024] After receiving the real-time data, the management cloud platform generates a data verification instruction;

[0025] The data verification instruction is transmitted to the corresponding control area to control the verification operation area within the control area to perform the operation of collecting the real-time area.

[0026] Preferably, the step of building a real-time model according to the verification data comprises the following:

[0027] The management cloud platform compares the real-time collected data and the verification data to obtain the data accuracy level of the corresponding control area; the data accuracy level includes a low error level and a high error level; the low error level is a level where the error is between 0 and 10% of the real-time data; the high error level is a level where the error is greater than 10% of the real-time data;

[0028] A real-time model is constructed based on the accuracy level of the data.

[0029] Preferably, the step of comparing the real-time model with the initial model to obtain an optimized model comprises the following:

[0030] Comparing the real-time model with the initial model to obtain a model difference level; wherein the model difference level includes a better level, a similar level, and a worse level;

[0031] The optimized model is obtained according to the model difference level.

[0032] The present invention also discloses a crop growth model construction system, which is used to execute the crop growth model construction method described above; the crop growth model construction system comprises:

[0033] A data acquisition module, the data acquisition module is used to obtain an original sample set of crops and the initial data corresponding thereto, and to obtain a real-time training set of crops and the real-time data corresponding thereto;

[0034] A verification module, the verification module is used to generate a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data;

[0035] A model building module, the model building module is used to generate an initial sample model, build a real-time model according to the verification data, and optimize the model;

[0036] A comparison module is used to compare the real-time model with the initial model.

[0037] The present invention also discloses a crop growth model construction terminal, which includes: a memory, a processor, and a crop growth model construction program stored in the memory and executable on the processor. When the crop growth model construction program is executed by the processor, the steps of the crop growth model construction method are implemented.

[0038] The present invention also discloses a storage medium, on which a crop growth model construction program is stored. When the crop growth model construction program is executed by a processor, the steps of the crop growth model construction method are implemented.

[0039] The beneficial effect of the present invention lies in that the technical solution first obtains the original sample set of crops and the initial data corresponding thereto to generate an initial sample model to form relatively perfect growth and breeding data of crops before this, so as to facilitate the subsequent comparison, verification and rapid adjustment; then obtains the real-time training set of crops and the real-time data corresponding thereto to facilitate the subsequent construction of the real-time model as a basis; then generates a data verification instruction according to the real-time data to verify the authenticity of the real-time data, and obtains verification data to ensure the authenticity of the obtained real-time data, so as to improve the accuracy of the real-time model; then constructs the real-time model according to the verification data, and finally compares the real-time model with the initial model to obtain an optimized model, so as to quickly obtain real-time growth and breeding data of crops, and quickly optimize the relatively perfect growth and breeding data of crops obtained previously; thereby improving the authenticity and accuracy of model construction, and improving the efficiency of supervision and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The following will refer to the attached Figures 1 to 3 To describe the features, advantages and technical effects of exemplary embodiments of the present invention.

[0041] Figure 1 A flowchart of a method for constructing a crop growth model according to an embodiment of the present invention;

[0042] Figure 2 A structural block diagram of a system for constructing a crop growth model according to an embodiment of the present invention;

[0043] Figure 3 This is a structural block diagram of a terminal for constructing a crop growth model according to an embodiment of the present invention.

[0044] In the figure: 1001 - processor; 1002 - communication bus; 1003 - user interface; 1004 - network interface; 1005 - memory. DETAILED DESCRIPTION

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and multiple situations exist alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0049] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0050] The present invention provides a crop growth model construction method, system, terminal and storage medium.

[0051] like Figure 3 As shown, Figure 3 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0052] The terminal of the embodiment of the present invention can be a PC, or it can be a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a portable computer, or other portable terminal devices with display function.

[0053] like Figure 3 As shown, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0054] Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can monitor the magnitude of acceleration in all directions (generally three axes), and can monitor the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0055] Those skilled in the art will appreciate that the terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0056] Reference Figure 1 One embodiment of the present invention provides a method for constructing a crop growth model, a system, a terminal and a storage medium. Figure 1 As shown, the crop growth model construction method includes:

[0057] S1. Obtaining the original sample set of crops and the corresponding initial data to generate an initial sample model;

[0058] S2, obtaining a real-time training set of crops and its corresponding real-time data;

[0059] S3, generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data;

[0060] S4, constructing a real-time model according to the verification data;

[0061] S5. Compare the real-time model with the initial model to obtain an optimized model.

[0062] In this embodiment, the technical solution first obtains the original sample set of crops and the initial data corresponding to them to generate an initial sample model to form relatively perfect growth and breeding data of crops before this, so as to facilitate the subsequent comparison, verification and rapid adjustment; then obtains the real-time training set of crops and the real-time data corresponding to them to serve as the basis for the subsequent construction of the real-time model; then generates a data verification instruction based on the real-time data to verify the authenticity of the real-time data, and obtains verification data to ensure the authenticity of the obtained real-time data, so as to improve the accuracy of the real-time model; then constructs the real-time model based on the verification data, and finally compares the real-time model with the initial model to obtain an optimized model, so that the real-time growth and breeding data of crops can be quickly obtained, and the relatively perfect growth and breeding data of crops obtained previously can be quickly optimized; thereby improving the authenticity and accuracy of model construction, and improving the efficiency of supervision and control.

[0063] Specifically, in some embodiments, in S1, the step of obtaining the original sample set of crops and the initial data corresponding thereto to generate the initial sample model includes the following:

[0064] Obtaining a first preset number of original crop samples of the same type according to the type of the crop; wherein the first preset number is 50, 100 or 150, etc.;

[0065] respectively obtaining respective temporary data corresponding to original crop samples of the same type;

[0066] Performing weighted average conversion on all the temporary data to obtain the corresponding initial data;

[0067] An initial sample model is generated according to the initial data.

[0068] That is to say, all crops in the crop garden are classified according to their types, and a first preset number of crops of the same type are randomly selected as original crop samples; then the corresponding temporary data of the corresponding original crop samples are obtained respectively, so as to facilitate the subsequent calculation of relatively excellent crops; then all the temporary data are weighted averaged to obtain the corresponding initial data, so as to obtain relatively reasonable and accurate initial data; finally, an initial sample model is generated according to the initial data for subsequent comparison; thereby improving operational efficiency and ensuring the rationality and accuracy of the data.

[0069] Specifically, in some embodiments, in S2, the step of obtaining a real-time training set of crops and corresponding real-time data includes the following:

[0070] Divide the park where the crops are located into areas to form at least two control areas;

[0071] Constructing a second preset number of crops of the same type in each of the control areas as a real-time training set; the first preset number is 5, 10 or 15, etc.;

[0072] Acquire real-time data corresponding to each element in the real-time training set.

[0073] In other words, monitoring by partitioning can achieve orderly management, thereby improving supervision efficiency, ensuring the accuracy and rationality of the model data, and achieving optimization efficiency.

[0074] The second preset number is less than the first preset number, so as to avoid the phenomenon that the collected data is too concentrated and too large to cause calculation confusion.

[0075] The step of obtaining the real-time data corresponding to each element in the real-time training set includes the following:

[0076] The image acquisition component and the color mark sensor component are used to obtain the real-time data corresponding to each element in the real-time training set.

[0077] That is to say, due to the high-precision sensing and collection of the surface of each element by the color mark sensing component (color mark sensor), the real-time data of each element itself can be quickly and accurately acquired. Combined with the view information of the image acquisition component, the accuracy of the real-time data corresponding to each element can be further verified.

[0078] Specifically, in some implementations, in S3, the step of generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtaining verification data includes the following:

[0079] After receiving the real-time data, the management cloud platform generates a data verification instruction;

[0080] Transmitting the data verification instruction to the corresponding control area to control the verification operation area within the control area to perform the operation of collecting the real-time area;

[0081] The verification data is obtained. There is a partial overlap between two adjacent control areas; and the partial overlap is a verification operation area; the orderliness and convenience of subsequent verification operations are achieved through the overlap.

[0082] That is to say, when the management cloud platform receives the real-time data, it generates a data verification instruction to verify the real-time data; then transmits the data verification instruction to the corresponding control area to control the image acquisition component and the color mark sensor component in the verification operation area inside the control area to perform the operation of collecting the real-time area. Among them, the image acquisition component and the color mark sensor component in the verification operation area inside the control area are the same devices as the image acquisition component and the color mark sensor component in the previous control area. In order to prevent them from being tampered with, the two functions cooperate with each other.

[0083] Specifically, in some implementations, in S4, the step of building a real-time model according to the verification data includes the following:

[0084] The management cloud platform compares the real-time collected data and the verification data to obtain the data accuracy level of the corresponding control area; the data accuracy level includes a low error level and a high error level; the low error level is a level where the error is between 0 and 10% of the real-time data; the high error level is a level where the error is greater than 10% of the real-time data;

[0085] A real-time model is constructed based on the accuracy level of the data.

[0086] Wherein, the step of obtaining the data accuracy level of the corresponding control area includes:

[0087] If the data accuracy level is the low error level, the real-time data is determined to be data that can be used for model construction;

[0088] If the data accuracy level is a high error level, the step S2 is executed again.

[0089] That is to say, when the data error is small, the real-time data collected in real time is assumed to be accurate and is a reasonable objective error of the instrument, so no adjustment is made, and the real-time model is built based on this; when the data error is too large, the real-time data collected in real time is assumed to be inaccurate and is an unreasonable error caused by instrument damage, etc., and needs to be re-collected and processed.

[0090] Specifically, in some implementations, in S5, the step of comparing the real-time model with the initial model to obtain an optimized model includes the following:

[0091] Comparing the real-time model with the initial model to obtain a model difference level; wherein the model difference level includes a better level, a similar level, and a worse level;

[0092] The optimized model is obtained according to the model difference level.

[0093] The step of comparing the real-time model with the initial model to obtain a model difference level includes:

[0094] When the appearance data of the real-time model is less than 0% to 15% of the appearance data of the initial model, a similarity level is generated; at the same time, the real-time model is merged into the initial model, and the initial model continues to be used as the standard model;

[0095] When the appearance data of the real-time model is less than 15% of the appearance data of the initial model, a worse grade is generated; at the same time, the real-time model is merged into the initial model, and the initial model continues to be used as the standard model; and feedback instructions are generated to the crop park manager to change the breeding habits and environment of the area;

[0096] When the appearance data of the real-time model is greater than the appearance data of the initial model, a better grade is generated. At the same time, the real-time model is used as the standard model, and the previous initial model is optimized and deleted.

[0097] Among them, appearance data includes size, shape, color and other external shape data.

[0098] The invention also provides a crop growth model construction system.

[0099] Specifically, if Figure 3 As shown, the crop growth model construction system includes:

[0100] A data acquisition module 610, which is used to obtain an original sample set of crops and initial data corresponding thereto, and to obtain a real-time training set of crops and real-time data corresponding thereto;

[0101] A verification module 620, the verification module 620 is used to generate a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data;

[0102] A model building module 640, the model building module 640 is used to generate an initial sample model, build a real-time model according to the verification data, and optimize the model;

[0103] The comparison module 630 is used to compare the real-time model with the initial model.

[0104] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a crop growth model construction program is stored. When the crop growth model construction program is executed by a processor, the following operations are implemented:

[0105] Obtaining the original sample set of crops and the initial data corresponding thereto to generate an initial sample model;

[0106] Obtain real-time training sets of crops and their corresponding real-time data;

[0107] Generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data;

[0108] Building a real-time model based on the verification data;

[0109] The real-time model is compared with the initial model to obtain an optimized model.

[0110] Furthermore, the step of obtaining the original sample set of crops and the initial data corresponding thereto to generate the initial sample model includes the following:

[0111] Obtaining a first preset number of original crop samples of the same type according to the type of the crop; wherein the first preset number is 50, 100 or 150, etc.;

[0112] respectively obtaining respective temporary data corresponding to original crop samples of the same type;

[0113] Performing weighted average conversion on all the temporary data to obtain the corresponding initial data;

[0114] An initial sample model is generated according to the initial data.

[0115] Furthermore, the steps of obtaining the real-time training set of crops and the corresponding real-time data include the following:

[0116] Divide the park where the crops are located into areas to form at least two control areas;

[0117] Constructing a second preset number of crops of the same type in each of the control areas as a real-time training set; the first preset number is 5, 10 or 15, etc.;

[0118] Acquire real-time data corresponding to each element in the real-time training set.

[0119] Furthermore, the step of obtaining the real-time data corresponding to each element in the real-time training set includes the following:

[0120] The image acquisition component and the color mark sensor component are used to obtain the real-time data corresponding to each element in the real-time training set.

[0121] Further, the step of generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtaining the verification data includes the following:

[0122] After receiving the real-time data, the management cloud platform generates a data verification instruction;

[0123] The data verification instruction is transmitted to the corresponding control area to control the verification operation area within the control area to perform the operation of collecting the real-time area.

[0124] Furthermore, the step of constructing a real-time model according to the verification data includes the following:

[0125] The management cloud platform compares the real-time collected data and the verification data to obtain the data accuracy level of the corresponding control area; the data accuracy level includes a low error level and a high error level; the low error level is a level where the error is between 0 and 10% of the real-time data; the high error level is a level where the error is greater than 10% of the real-time data;

[0126] A real-time model is constructed based on the accuracy level of the data.

[0127] Furthermore, the step of comparing the real-time model with the initial model to obtain an optimized model includes the following:

[0128] Comparing the real-time model with the initial model to obtain a model difference level; wherein the model difference level includes a better level, a similar level, and a worse level;

[0129] The optimized model is obtained according to the model difference level.

[0130] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode includes only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0131] According to the disclosure and teaching of the above description, those skilled in the art to which the present invention belongs can also change and modify the above embodiment. Therefore, the present invention is not limited to the above specific embodiment, and any obvious improvement, replacement or modification made by those skilled in the art on the basis of the present invention belongs to the protection scope of the present invention. In addition, although some specific terms are used in this specification, these terms are only for the convenience of description and do not constitute any limitation to the present invention.

Claims

1. A method for constructing a crop growth model, characterized in that: The following steps are involved: S1. Obtaining the original sample set of crops and the corresponding initial data to generate an initial sample model; S2, obtaining a real-time training set of crops and their corresponding real-time data; S3, generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data; S4, constructing a real-time model according to the verification data; S5. Compare the real-time model with the initial model to obtain an optimized model.

2. The method for constructing a crop growth model according to claim 1, characterized in that: The step of obtaining the original sample set of crops and the initial data corresponding thereto to generate the initial sample model comprises the following steps: Obtaining a first preset number of original crop samples of the same type according to the type of the crop; respectively obtaining respective temporary data corresponding to original crop samples of the same type; Performing weighted average conversion on all the temporary data to obtain the corresponding initial data; An initial sample model is generated according to the initial data.

3. The method for constructing a crop growth model according to claim 1, characterized in that: The step of obtaining a real-time training set of crops and corresponding real-time data includes the following: Divide the park where the crops are located into areas to form at least two control areas; Acquire crops of the same type and a second preset number in each of the management and control areas as a real-time training set; Acquire real-time data corresponding to each element in the real-time training set.

4. The method for constructing a crop growth model according to claim 3, characterized in that: The step of obtaining the real-time data corresponding to each element in the real-time training set includes the following: The image acquisition component and the color mark sensor component are used to obtain the real-time data corresponding to each element in the real-time training set.

5. The method for constructing a crop growth model according to claim 3, characterized in that: The step of generating a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtaining verification data includes the following: After receiving the real-time data, the management cloud platform generates a data verification instruction; The data verification instruction is transmitted to the corresponding control area to control the verification operation area within the control area to perform the operation of collecting the real-time area.

6. The method for constructing a crop growth model according to claim 1, characterized in that: The step of constructing a real-time model according to the verification data includes the following: The management cloud platform compares the real-time collected data and the verification data to obtain the data accuracy level of the corresponding control area; the data accuracy level includes a low error level and a high error level; the low error level is a level where the error is between 0 and 10% of the real-time data; the high error level is a level where the error is greater than 10% of the real-time data; A real-time model is constructed based on the accuracy level of the data.

7. The method for constructing a crop growth model according to claim 1, characterized in that: The step of comparing the real-time model with the initial model to obtain an optimized model comprises the following: Comparing the real-time model with the initial model to obtain a model difference level; wherein the model difference level includes a better level, a similar level, and a worse level; The optimized model is obtained according to the model difference level.

8. A crop growth model construction system, characterized in that: Used to execute the crop growth model construction method according to any one of claims 1 to 7 above; the crop growth model construction system comprises: A data acquisition module, the data acquisition module is used to obtain an original sample set of crops and the initial data corresponding thereto, and to obtain a real-time training set of crops and the real-time data corresponding thereto; A verification module, the verification module is used to generate a data verification instruction according to the real-time data to verify the authenticity of the real-time data and obtain verification data; A model building module, the model building module is used to generate an initial sample model, build a real-time model according to the verification data, and optimize the model; A comparison module is used to compare the real-time model with the initial model.

9. A crop growth model construction terminal, characterized in that: The crop growth model construction terminal includes: a memory, a processor, and a crop growth model construction program stored in the memory and executable on the processor. When the crop growth model construction program is executed by the processor, the steps of the crop growth model construction method according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a crop growth model construction program, and when the crop growth model construction program is executed by the processor, the steps of the crop growth model construction method according to any one of claims 1 to 7 are implemented.

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