Crop growth data management method, device, computing equipment, and storage medium

Abstract

The application relates to the field of crop data management, and discloses a crop growth data management method and device, a computing device and a storage medium. The method comprises the following steps: acquiring an initial fertilization area division scheme; adjusting the initial fertilization area division scheme according to demand data corresponding to a target crop type to obtain a final fertilization area division scheme; determining target fertilization data of a plurality of fertilization areas; collecting target crop growth data of the plurality of fertilization areas fertilized according to the final fertilization area division scheme; recording the target fertilization data and the target crop growth data, and adjusting a fertilization plan according to the target crop growth data. According to the application, different fertilization areas are divided according to soil conditions and crop demand, the fertilization data and the crop growth data of the fertilization areas are analyzed and processed, the fertilization plan is further adjusted, and the effect of fertilization is enhanced and the yield of crops is improved through planning and management.

Description

Technical Field

[0001] This application relates to the field of simulated seedling raising, and more specifically to a method, apparatus, computing device, and storage medium for crop growth data management. Background Technology

[0002] Straw-based seedling cultivation is a multi-step agricultural technique. Its core lies in using straw as a seedling substrate to promote seed germination and seedling growth. During the cultivation process, attention must be paid to pest and disease control and temperature management. Regular checks on seedling growth are necessary to promptly identify and address pests and diseases. Simultaneously, the temperature of the cultivation environment should be adjusted according to weather conditions and the seedlings' growth needs to ensure healthy growth. Simulating straw-based seedling cultivation eliminates the need for actual input of large amounts of straw, seeds, and other resources. By finding the optimal cultivation scheme through simulation before actual implementation, resource waste can be effectively avoided. Simulation also allows for the identification of potential risks during seedling cultivation, enabling the development of corresponding countermeasures and reducing risks in actual seedling cultivation. Summary of the Invention

[0003] This application provides a crop growth data management method, apparatus, computing device, and storage medium. It divides different fertilization areas according to the needs of crops, analyzes and processes the fertilization data and crop growth data of the fertilization areas, and further adjusts the fertilization plan. Through planning and management, it enhances the fertilization effect and increases crop yield.

[0004] In a first aspect, embodiments of this application provide a crop growth data management method, which includes: obtaining an initial fertilization area division scheme; adjusting the initial fertilization area division scheme according to the demand data corresponding to the target crop type to obtain a final fertilization area division scheme, wherein the final fertilization area division scheme includes multiple fertilization areas; determining target fertilization data for multiple fertilization areas, wherein the target fertilization data includes multiple fertilization plans, and each fertilization area corresponds to one fertilization plan; collecting target crop growth data for multiple fertilization areas fertilized according to the final fertilization area division scheme; recording the target fertilization data and the target crop growth data; and adjusting the fertilization plan according to the target crop growth data.

[0005] In one implementation, obtaining the initial fertilization area division scheme includes:

[0006] Obtain topographic data, soil data, and climate data of the fertilization area;

[0007] Based on the terrain data, soil data, and climate data, a clustering algorithm is used to obtain the clustering results of the fertilization area.

[0008] The initial fertilization area division scheme is determined based on the clustering results.

[0009] In one implementation, the requirement data corresponding to the target crop type includes nutrient requirement data, and adjusting the initial fertilization area division scheme according to the requirement data corresponding to the target crop type to obtain the final fertilization area division scheme includes:

[0010] Obtain soil data from the multiple fertilization areas, wherein the soil data includes soil nutrient data;

[0011] Calculate the matching degree between the nutrient requirement data corresponding to the target crop type and the soil nutrient data;

[0012] The initial fertilization area division scheme is adjusted based on the matching degree to obtain the final fertilization area division scheme.

[0013] In one implementation, the collection of target crop growth data from multiple fertilization areas fertilized according to the final fertilization area division scheme includes:

[0014] Collect plant height data, leaf area data, and chlorophyll content data of the target crop;

[0015] The target crop growth data includes plant height, leaf area, and chlorophyll content data.

[0016] In one implementation, recording the target fertilization data and target crop growth data, and adjusting the fertilization plan based on the target crop growth data, includes:

[0017] The current growth stage of the target crop is determined based on the target crop growth data, and the predetermined growth data for the current growth stage is obtained.

[0018] Calculate the deviation between the target crop growth data and the predetermined growth data;

[0019] The fertilization plan is adjusted based on the deviation.

[0020] In one embodiment, the method further includes:

[0021] Acquire soil data, climate data, and crop type data for the multiple fertilization areas;

[0022] The soil data, climate data, and crop type data of the fertilization area are input into the pest and disease probability prediction model to obtain the probability of pest and disease occurrence.

[0023] The prevention and control strategy is determined based on the probability of occurrence of the pests and diseases and the transmission methods corresponding to the types of pests and diseases.

[0024] The fertilization plans for the multiple fertilization areas are adjusted according to the prevention and control strategy.

[0025] In one embodiment, the crop growth data management method is applied to a management device, the management device having a display interface, and the method further includes:

[0026] Collect fertilization data and crop growth data from the multiple fertilization areas;

[0027] Calculate the correlation between the fertilization data and the crop growth data;

[0028] The relevant data is displayed on the display interface of the management device.

[0029] Secondly, embodiments of this application provide a crop growth data management device, which has the function of implementing the crop growth data management method corresponding to the first aspect described above. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function, and the modules can be software and / or hardware. In one embodiment, the crop growth data management device includes:

[0030] Acquisition module: Used to acquire the initial fertilization area division plan;

[0031] The first adjustment module is used to adjust the initial fertilization area division scheme according to the demand data corresponding to the target crop type, so as to obtain the final fertilization area division scheme.

[0032] Determine module: Used to determine target fertilization data for multiple fertilization areas;

[0033] Data Acquisition Module: Used to collect target crop growth data from multiple fertilization areas that are fertilized according to the final fertilization area division plan;

[0034] The second adjustment module is used to record the target fertilization data and target crop growth data, and to adjust the fertilization plan based on the target crop growth data.

[0035] In one embodiment, the acquisition module is specifically used for:

[0036] Obtain topographic data, soil data, and climate data of the fertilization area;

[0037] Based on the terrain data, soil data, and climate data, a clustering algorithm is used to obtain the clustering results of the fertilization area.

[0038] The initial fertilization area division scheme is determined based on the clustering results.

[0039] In one implementation, the demand data corresponding to the target crop type includes nutrient demand data, and the first adjustment module is specifically used for:

[0040] Obtain soil data from the multiple fertilization areas, wherein the soil data includes soil nutrient data;

[0041] Calculate the matching degree between the nutrient requirement data corresponding to the target crop type and the soil nutrient data;

[0042] The initial fertilization area division scheme is adjusted based on the matching degree to obtain the final fertilization area division scheme.

[0043] In one embodiment, the acquisition module is specifically used for:

[0044] Collect plant height data, leaf area data, and chlorophyll content data of the target crop.

[0045] In one embodiment, the second adjustment module is specifically used for:

[0046] The current growth stage of the target crop is determined based on the target crop growth data, and the predetermined growth data for the current growth stage is obtained.

[0047] Calculate the deviation between the target crop growth data and the predetermined growth data;

[0048] The fertilization plan is adjusted based on the deviation.

[0049] In one embodiment, the crop growth data management device further includes a prevention and control module, which is specifically used for:

[0050] Acquire soil data, climate data, and crop type data for the multiple fertilization areas;

[0051] The soil data, climate data, and crop type data of the fertilization area are input into the pest and disease probability prediction model to obtain the probability of pest and disease occurrence.

[0052] The prevention and control strategy is determined based on the probability of occurrence of the pests and diseases and the transmission methods corresponding to the types of pests and diseases.

[0053] The fertilization plans for the multiple fertilization areas are adjusted according to the prevention and control strategy.

[0054] In one embodiment, the crop growth data management device further includes a display module, which is specifically used for:

[0055] Collect fertilization data and crop growth data from the multiple fertilization areas;

[0056] Calculate the correlation between the fertilization data and the crop growth data;

[0057] The relevant data is displayed on the display interface of the management device.

[0058] Thirdly, embodiments of this application provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the crop growth data management method described in the first aspect.

[0059] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the crop growth data management method as described in the first aspect.

[0060] This application divides different fertilization zones according to the needs of crops, analyzes and processes fertilization data and crop growth data of the fertilization zones, and further adjusts the fertilization plan. Through planning and management, the application aims to enhance the fertilization effect and increase crop yield. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of a scenario for the crop growth data management device provided in an embodiment of this application;

[0063] Figure 2 This is a schematic flowchart of an embodiment of the crop growth data management method provided in this application.

[0064] Figure 3 This is a flowchart illustrating another embodiment of the crop growth data management method provided in this application.

[0065] Figure 4 This is a schematic diagram of the structure of the crop growth data management method apparatus according to an embodiment of this application;

[0066] Figure 5 This is a schematic diagram of a crop growth data management device according to an embodiment of this application;

[0067] Figure 6 This is a schematic diagram of the structure of a mobile phone in one embodiment of this application;

[0068] Figure 7This is a schematic diagram of a server structure in one embodiment of this application.

[0069] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0070] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] In the following description, specific embodiments of this application will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of this application are described in the foregoing text, which is not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0072] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this application.

[0073] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0074] This application provides a method, apparatus, and storage medium for crop growth data management.

[0075] Please see Figure 1 , Figure 1 This is a schematic diagram of a crop growth data management device provided in an embodiment of this application. The crop growth data management device may include a crop growth data management system 100 and a user terminal 200. The crop growth data management system 100 is connected via a network, and the crop growth data management device is integrated within the crop growth data management system 100. In this embodiment, the crop growth data management system 100 may be a terminal device or a server, and the crop growth data management system 100 may send fertilization plans to the user terminal 200.

[0076] In this embodiment of the application, when the crop growth data management system 100 is a server, the server can be an independent server, a server network, or a server cluster. For example, the server described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment, communication between the server and the client can be achieved through any communication method, including but not limited to, mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP protocol suite (TCP / IP) and User Datagram Protocol (UDP).

[0077] It is understood that when the crop growth data management system 100 used in the embodiments of this application is a terminal device, the terminal device can be a device that includes both receiving hardware and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a terminal device may include: cellular or other communication devices, which have a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the crop growth data management system 100 may be a desktop terminal or a mobile terminal, and may be one of a mobile phone, tablet computer, laptop computer, etc.

[0078] The terminal devices involved in the embodiments of this application can also be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. Examples include mobile phones (or "cellular" phones) and computers with mobile terminals, such as portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with a wireless access network. Examples include Personal Communication Service (PC) phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), and other similar devices.

[0079] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computing devices shown, or the network connectivity of computing devices, for example... Figure 1 Only one computing device is shown in the diagram. It is understood that the crop growth data management device may also include one or more other computing devices, and / or one or more other computing devices that are networked with the crop growth data management system 100, which is not limited here.

[0080] In addition, such as Figure 1 As shown, the crop growth data management device may also include a memory 300 for storing data, such as target fertilization data and target crop growth data.

[0081] It should be noted that, Figure 1 The schematic diagram of the crop growth data management device shown is merely an example. The crop growth data management device and scenario described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of crop growth data management devices and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0082] This application divides different fertilization zones according to soil conditions and crop needs, analyzes and processes fertilization data and crop growth data of the fertilization zones, and further adjusts the fertilization plan. Through planning and management, the application aims to enhance fertilization effectiveness and increase crop yield.

[0083] In this embodiment, the description will be based on a crop growth data management method, which can be integrated into a crop growth data management system 100.

[0084] This application provides a crop growth data management method, which includes: obtaining an initial fertilization area division scheme; adjusting the initial fertilization area division scheme according to the demand data corresponding to the target crop type to obtain a final fertilization area division scheme, wherein the final fertilization area division scheme includes multiple fertilization areas; determining target fertilization data for the multiple fertilization areas, wherein the target fertilization data includes multiple fertilization plans, and each fertilization area corresponds to one fertilization plan; collecting target crop growth data for the multiple fertilization areas fertilized according to the final fertilization area division scheme; recording the target fertilization data and the target crop growth data, and adjusting the fertilization plan according to the target crop growth data.

[0085] Please see Figure 2 , Figure 2 This is a schematic flowchart of an embodiment of the crop growth data management method in this application, which includes the following steps 201-205:

[0086] 201. Obtain the initial fertilization area division plan.

[0087] Specifically, soil conditions and regional climate differences can affect the effectiveness of fertilization. In order to more accurately analyze the relationship between fertilization and crop growth, farmland is divided into different fertilization zones based on soil conditions, topographic conditions and corresponding climate conditions to ensure that the same fertilization zone has similar soil and climate conditions.

[0088] 202. Adjust the initial fertilization area division scheme according to the demand data corresponding to the target crop type to obtain the final fertilization area division scheme.

[0089] Optionally, the final fertilization zone division scheme includes multiple fertilization zones. Specifically, based on the crop type and growth cycle, nutrient data required by the crops can be obtained to ensure that crops in each fertilization zone have similar nutrient requirements and that the soil in each fertilization zone can meet these requirements. Optionally, the crop nutrient requirement data can include the required ratio of nutrient elements (such as nitrogen, phosphorus, and potassium) and the timing of these requirements. A matching degree is calculated by combining the initial fertilization zone and the crop's nutrient requirements. Based on this matching degree, the initial fertilization zone is fine-tuned to obtain the final fertilization zone division scheme. Optionally, different crops not only have different nutrient requirements but also different growth cycles and nutrient absorption patterns. When adjusting the division of fertilization zones, the crop's growth cycle can also be considered to adjust the fertilization zone division scheme accordingly.

[0090] 203. Determine the target fertilization data for the multiple fertilization areas.

[0091] Specifically, the target fertilization data includes multiple fertilization plans, with one plan corresponding to each fertilization area. Optionally, the fertilization data may include fertilization time, fertilizer amount, and the fertilization plan corresponding to the fertilization area. The target fertilization data can be uploaded to a cloud database.

[0092] 204. Collect target crop growth data from multiple fertilization areas that were fertilized according to the final fertilization area division plan.

[0093] Specifically, crop growth data can be collected periodically, for example, by collecting data twice a day, at 9:00 AM and 9:00 PM. Optionally, crop growth data can include plant height, leaf area, and chlorophyll content data, reflecting the crop's growth status and providing a basis for optimizing fertilization plans. Optionally, different target crops can be planted in multiple fertilization areas, or the same target crop can be planted. Optionally, target crop growth data can also include other growth data that reflects the crop's growth status; plant height, leaf area, and chlorophyll content data are only examples here.

[0094] 205. Record the target fertilization data and target crop growth data, and adjust the fertilization plan based on the target crop growth data.

[0095] Specifically, a fertilization plan can include the daily types, methods, and amounts of fertilizer. Correlation analysis algorithms can be used to obtain historical crop growth and fertilization data. Correlation analysis is then used to analyze the correlation between these two data, thereby determining the optimal fertilization plan for the crop. Optionally, historical fertilization and crop growth data can be used to train a crop growth prediction model. The trained model can output predicted crop growth data after different fertilization plans are input, thus determining the most beneficial fertilization plan for crop growth. Alternatively, historical crop growth data can be used to divide the crop into growth stages, and algorithms can be used to summarize the growth characteristics of different stages. After obtaining the target crop's growth data, the current growth stage can be determined based on the growth characteristics within that data. By summarizing historical crop growth data, the predetermined growth characteristics of the target crop at the current growth stage can be obtained. The deviation between the predetermined growth characteristics and the actual growth characteristics of the target crop is calculated, and the fertilization plan can be adjusted based on the direction of the deviation. For example, corn has a high demand for nitrogen fertilizer, especially during the tasseling and grain-filling stages. If, by calculating the deviation, it is found that the corn is currently in the tasseling stage, and by comparing the growth data of the target crop at this time with the planned growth data of the corn during the tasseling stage, it is found that the corn leaves are dark gray and curled, which indicates that the corn is deficient in nitrogen. Based on this, the amount and timing of nitrogen fertilizer in the fertilization plan can be adjusted.

[0096] This application's embodiments divide different fertilization zones according to soil conditions and crop needs, analyze and process fertilization data and crop growth data of the fertilization zones, further adjust the fertilization plan, and through planning and management, achieve enhanced fertilization effects and increased crop yields.

[0097] In one embodiment of this application, obtaining the initial fertilization area division scheme includes:

[0098] Obtain topographic data, soil data, and climate data of the fertilization area; based on the topographic data, soil data, and climate data, use a clustering algorithm to obtain the clustering results of the fertilization area; determine the initial fertilization area division scheme based on the clustering results.

[0099] Specifically, topographic data includes data such as farmland elevation, slope, and aspect. Optionally, topographic data can be acquired using topographic surveying instruments or remote sensing technology. Specifically, soil data can be obtained through soil sampling and analysis. Optionally, soil data can include soil texture, nutrient types and their corresponding contents, pH value, etc. Optionally, climate data can include rainfall, humidity, temperature, etc. Based on topographic data, soil data, and climate data... Specifically, clustering automatically groups data with similar characteristics within a dataset together, outputting k clusters, where features within a cluster are similar. Understandably, it's an unsupervised learning method that doesn't require a pre-labeled training set. Feature similarity is evaluated using distance; clustering algorithms calculate the distance between objects to determine if they belong to the same cluster. Specifically, K-means clustering or other clustering algorithms can be used. K-means clustering is an iterative clustering analysis algorithm that randomly selects K objects as initial cluster centers, calculates the distance between each object and the initial cluster centers, and assigns each object to the nearest cluster center. The cluster centers and the objects assigned to them represent a cluster. After all objects are assigned, the cluster centers are recalculated until the iteration is complete, yielding the clustering results for the farmland. Specifically, the clustering results include several fertilization zones. The clustering algorithm ensures that soil conditions, topographical conditions, and climatic conditions are relatively consistent within the same cluster. Based on the clustering results, an initial fertilization zone division scheme can be obtained.

[0100] This application embodiment uses a clustering algorithm to divide areas with relatively consistent soil, topographic, and climatic conditions into the same initial fertilization area division scheme, which facilitates subsequent analysis of fertilization effects.

[0101] In one embodiment of this application, the requirement data corresponding to the target crop type includes nutrient requirement data. The initial fertilization area division scheme is adjusted based on the requirement data corresponding to the target crop type to obtain the final fertilization area division scheme, including:

[0102] Soil data from multiple fertilization areas is acquired, including soil nutrient data; the matching degree between the nutrient requirement data corresponding to the target crop type and the soil nutrient data is calculated; the initial fertilization area division scheme is adjusted according to the matching degree to obtain the final fertilization area division scheme.

[0103] Specifically, multiple fertilization areas correspond to multiple fertilization plans, and soil data includes soil nutrient data. Each fertilization area corresponds to a fertilization plan and a target crop, based on the nutrient requirements of the target crop, including the required proportions and timing of major nutrient elements (such as nitrogen, phosphorus, and potassium). Combined with the actual crop types planted in the farmland, targeted nutrient requirement tables can be developed, clearly defining the nutrient requirements of each crop at different growth stages. The fertilization area and crop nutrient requirements need to be matched; therefore, the soil nutrient content of the fertilization area is compared with the crop's nutrient requirements to determine the degree of matching. Based on the degree of matching, the fertilization areas are fine-tuned, grouping crops with similar nutrient requirements into the same fertilization area and crops with significantly different nutrient requirements into different fertilization areas. The crop growth cycle can also be considered, as different crops have different growth cycles and nutrient absorption patterns. When adjusting fertilization areas, the crop growth cycle must be taken into account to ensure that the fertilization time matches the peak period of crop nutrient demand.

[0104] For example, suppose a farmland needs to be planted with both corn and wheat. Based on the clustering results of a clustering algorithm, the farmland has been divided into several fertilization zones. Next, we further adjust the fertilization zones according to the crop's nutrient requirements. Analysis of crop nutrient requirements shows that corn has a high demand for nitrogen fertilizer, especially during the tasseling and grain-filling stages; its demand for phosphorus fertilizer is mainly during the seedling stage; and its demand for potassium fertilizer is present throughout the entire growth period. Wheat's demand for nitrogen fertilizer is mainly concentrated during the tillering and jointing stages; its demand for phosphorus fertilizer is relatively stable; and its demand for potassium fertilizer is prominent during the jointing and heading stages. Optionally, using soil nutrient data, areas with higher soil nitrogen content can be selected from the already divided fertilization zones. The matching degree of the soil nutrient data of these areas with the nutrient data required by corn can be calculated, and finally, based on the matching degree, these areas are more suitable for planting corn, which has a higher demand for nitrogen fertilizer. For wheat, optionally, soil nutrient data can be used to screen areas with moderate or high phosphorus and potassium content within the already defined fertilization zones. The soil nutrient data for these areas can then be matched with the nutrient requirements of wheat to calculate the degree of compatibility. Based on this compatibility, these areas can be determined as more suitable for planting wheat with higher phosphorus and potassium requirements, ensuring sufficient nutrients for wheat at each growth stage. Alternatively, the different nutrient requirements throughout the crop's growth cycle can be considered. For corn, nitrogen fertilizer application needs to be increased during the tasseling and grain-filling stages. Therefore, when planning fertilization, it is essential to ensure sufficient nitrogen supply in the fertilization zones corresponding to these two stages and adjust the fertilization plan accordingly. For wheat, the tillering and jointing stages are peak periods for nitrogen fertilizer demand; similarly, the fertilization plan should be adjusted according to wheat's nutrient requirements.

[0105] This application embodiment readjusts the fertilization plan by matching the crop's required nutrient data with the soil nutrient data to ensure that the fertilization data matches the crop's needs and thus ensures the fertilization effect.

[0106] In one embodiment of this application, the collection of target crop growth data from multiple fertilization areas fertilized according to the final fertilization area division scheme may include:

[0107] Collect plant height data, leaf area data, and chlorophyll content data of the target crop.

[0108] The target crop growth data includes plant height, leaf area, and chlorophyll content data.

[0109] Specifically, the target crop growth data may include plant height, leaf area, and chlorophyll content data. Optionally, plant height, leaf area, and chlorophyll content data can be collected in real time using a data acquisition device. After receiving a data acquisition command, the device collects the target crop's growth data in real time and uploads it to a cloud database for storage. Optionally, the fertilization plan includes historical fertilization data. Multiple historical fertilization data points collected by a smart fertilizer can also be uploaded to a cloud database for storage. Historical fertilization data and historical crop growth data can be used to analyze their correlation to determine the optimal fertilization plan for the crop. Optionally, historical fertilization data and historical crop growth data can also be used to train a crop growth prediction model. The trained model can output predicted crop growth data after different fertilization plans are input, thus determining the most beneficial fertilization plan for crop growth.

[0110] The embodiments of this application collect fertilization data and target crop growth data through intelligent fertilizer applicators and sensors, which facilitates subsequent processing of the fertilization data and crop growth data.

[0111] In one embodiment of this application, recording the target fertilization data and target crop growth data, and adjusting the fertilization plan based on the target crop growth data, includes:

[0112] The current growth stage of the target crop is determined based on the target crop growth data, and the predetermined growth data for the current growth stage is obtained; the deviation between the target crop growth data and the predetermined growth data is calculated; and the fertilization plan is adjusted based on the deviation.

[0113] Optionally, the current growth stage of the target crop can be determined by combining expert experience and historical crop growth data. Specifically, the current growth stage of the target crop can be determined based on its current growth characteristics. A training set can be constructed using historical growth data and historical fertilization data to train the crop growth prediction model, resulting in a trained model. Inputting the current target fertilization data into this model will output predicted crop growth data, i.e., the predetermined crop growth data. After obtaining the predetermined crop growth data, the deviation between the target crop growth data and the predetermined crop growth data is calculated, and the fertilization plan is adjusted based on the deviation. For example, corn has a high nitrogen requirement during the tasseling and grain-filling stages. If, by calculating the deviation, it is found that the corn is currently in the tasseling stage, and comparing the model's output of the predetermined growth data with the data predicted for the tasseling stage reveals that the corn leaves are dark and curled, this indicates a nitrogen deficiency. Based on this, the amount and timing of nitrogen fertilizer application in the fertilization plan can be adjusted.

[0114] In one embodiment of this application, please refer to Figure 3 , Figure 3 This is a schematic flowchart of another embodiment of the crop growth data management method in this application. The crop growth data management method further includes the following steps 301-304:

[0115] 301. Obtain soil data, climate data, and crop type data for the multiple fertilization areas.

[0116] Specifically, in the process of processing crop growth data, pest and disease events can be simulated separately to determine their occurrence probabilities. Then, fertilization plans can be adjusted based on the characteristics of the pests and diseases. Optionally, soil simulation data, climate simulation data, and crop growth simulation data can be input into a pest and disease probability prediction model to obtain the simulated probabilities. Optionally, soil data can include soil nutrient content data, pH data, etc., and climate data can include predicted temperature data, predicted humidity data, and predicted rainfall data, etc. Optionally, the pest and disease probability prediction model can be a probabilistic model or a machine learning model, such as a logistic regression model or a random forest model.

[0117] 302. Input the soil data, climate data and crop type data of the fertilization area into the pest and disease probability prediction model to obtain the probability of pest and disease occurrence.

[0118] Specifically, the soil data, climate data, and crop type data of the fertilization area are input into the pest and disease probability prediction model. Based on the pest and disease probability prediction model and the input parameters, the pest and disease prediction probability, i.e. the probability of pest and disease occurrence, can be obtained.

[0119] 303. Determine control strategies based on the probability of occurrence of the pests and diseases and the transmission methods corresponding to the types of pests and diseases.

[0120] Furthermore, the transmission methods of pests and diseases are determined by analyzing transmission factors. Specifically, factors influencing pest and disease transmission are analyzed, including crop layout, wind direction, wind speed, rainfall, insect activity, and crop health. For example, diseases may spread via wind or insects to adjacent crops. Based on the transmission method and the probability of pest and disease occurrence, at least two control strategies can be determined. For instance, based on the probability of pest and disease occurrence, a high probability of rust disease is found in areas where wheat is grown. A pest and disease prediction model yields an initial rust disease occurrence probability of 5%. The wheat fields are contiguous and have recently experienced continuous southeasterly winds. Rust disease can spread via wind, and insect activity will accelerate its spread. Based on the transmission method, it can be determined that rust disease will spread rapidly within a few days, first affecting the southeastern part of the field and then gradually spreading in other directions. Further, control strategies for pests and diseases are determined based on this situation. Correspondingly, commonly used methods in agricultural practice can be considered, such as biological control (introducing natural enemies), chemical control (spraying pesticides), and physical control (removing diseased plants). Different combinations of prevention and control strategies can be set up to evaluate the effectiveness of different strategies.

[0121] 304. Adjust the fertilization plan for the multiple fertilization areas according to the prevention and control strategy.

[0122] Specifically, because pest control strategies involve multiple levels, whether biological, chemical, or physical, they can all affect crop growth. Therefore, after obtaining the control strategy, the fertilization plan for the fertilization area can be adjusted accordingly. For example, when chemical pesticides are sprayed to control wheat rust, soil data can be re-analyzed based on the type and amount of pesticide used, updating the soil nutrient content data. The nutrients required for crop growth can then be recalculated, and the fertilization plan for that area can be readjusted based on this nutrient data.

[0123] In this embodiment, by taking into account the impact of pest and disease control on crop growth, the fertilization plan for the fertilization area is readjusted through control strategies to ensure normal crop growth.

[0124] In one embodiment of this application, the crop growth data management method is applied to a management device, the management device having a display interface, and the method further includes:

[0125] Collect fertilization data and crop growth data from the multiple fertilization areas; calculate the correlation data between the fertilization data and the crop growth data; and display the correlation data on the display interface of the management device.

[0126] Optionally, datasets can be visualized using charts, images, etc., to display fertilization data and crop growth data from multiple fertilization areas. By calculating and displaying the correlation between fertilization and crop growth data, the relationship between fertilization and crop growth can be understood more intuitively. Optionally, correlation analysis algorithms can be used to calculate correlation data, thereby determining the degree of association between fertilization and crop growth data. For example, the impact of different fertilization amounts on crop yield can be analyzed and displayed on the device in chart form. Optionally, machine learning algorithms can be used to train historical fertilization data to build a fertilization prediction model. This model can automatically adjust the fertilization plan based on historical fertilization and crop growth data to improve crop yield. In practice, machine learning algorithms can be used to continuously optimize and adjust the model to adapt to fertilization needs under different seasons, climates, and soil conditions. Through continuous data collection and analysis, a complete data management system can be gradually formed to achieve precision fertilization and improved crop yield.

[0127] In this embodiment of the application, the correlation between fertilization data and crop growth is visualized, which allows for a more intuitive understanding of the relationship between fertilization and crop growth.

[0128] To facilitate better implementation of the crop growth data management method provided in this application, this application also provides an apparatus based on the above-described crop growth data management method. The meanings of the terms used are the same as in the above-described crop growth data management method, and specific implementation details can be found in the descriptions of the crop growth data management method embodiments.

[0129] The crop growth data management device in this application embodiment has the function of implementing the crop growth data management method corresponding to the above embodiment. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function, and the modules can be software and / or hardware.

[0130] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of the crop growth data management device provided in the embodiment of this application. The crop growth data management device can be applied to computing devices in scenarios requiring content push. Specifically, the crop growth data management method device 400 may include an acquisition module 401, a first adjustment module 402, a determination module 403, a collection module 404, and a second adjustment module 405, as detailed below:

[0131] Acquisition module: Used to acquire the initial fertilization area division plan;

[0132] The first adjustment module is used to adjust the initial fertilization area division scheme according to the demand data corresponding to the target crop type, so as to obtain the final fertilization area division scheme.

[0133] Determine module: Used to determine target fertilization data for multiple fertilization areas;

[0134] Data Acquisition Module: Used to collect target crop growth data from multiple fertilization areas that are fertilized according to the final fertilization area division plan;

[0135] The second adjustment module is used to record the target fertilization data and target crop growth data, and to adjust the fertilization plan based on the target crop growth data.

[0136] In one embodiment, the acquisition module is specifically used for:

[0137] Obtain topographic data, soil data, and climate data of the fertilization area;

[0138] Based on the terrain data, soil data, and climate data, a clustering algorithm is used to obtain the clustering results of the fertilization area.

[0139] The initial fertilization area division scheme is determined based on the clustering results.

[0140] In one implementation, the demand data corresponding to the target crop type includes nutrient demand data, and the first adjustment module is specifically used for:

[0141] Obtain soil data from the multiple fertilization areas, wherein the soil data includes soil nutrient data;

[0142] Calculate the matching degree between the nutrient requirement data corresponding to the target crop type and the soil nutrient data;

[0143] The initial fertilization area division scheme is adjusted based on the matching degree to obtain the final fertilization area division scheme.

[0144] In one embodiment, the acquisition module is specifically used for:

[0145] The target crop's plant height, leaf area, and chlorophyll content data are collected using sensors. In one embodiment, the second adjustment module is specifically used for:

[0146] The current growth stage of the target crop is determined based on the target crop growth data, and the predetermined growth data for the current growth stage is obtained.

[0147] Calculate the deviation between the target crop growth data and the predetermined growth data;

[0148] The fertilization plan is adjusted based on the deviation.

[0149] In one embodiment, the crop growth data management device further includes a prevention and control module, which is specifically used for:

[0150] Acquire soil data, climate data, and crop type data for the multiple fertilization areas;

[0151] The soil data, climate data, and crop type data of the fertilization area are input into the pest and disease probability prediction model to obtain the probability of pest and disease occurrence.

[0152] The prevention and control strategy is determined based on the probability of occurrence of the pests and diseases and the transmission methods corresponding to the types of pests and diseases.

[0153] The fertilization plans for the multiple fertilization areas are adjusted according to the prevention and control strategy.

[0154] In one embodiment, the crop growth data management device further includes a display module, which is specifically used for:

[0155] Collect fertilization data and crop growth data from the multiple fertilization areas;

[0156] Calculate the correlation between the fertilization data and the crop growth data;

[0157] The relevant data is displayed on the display interface of the management device.

[0158] This application's embodiments divide different fertilization zones according to soil conditions and crop needs, analyze and process fertilization data and crop growth data of the fertilization zones, further adjust the fertilization plan, and through planning and management, achieve enhanced fertilization effects and increased crop yields.

[0159] The crop growth data management device in the embodiments of this application has been described above from the perspective of modular functional entities. The crop growth data management device in the embodiments of this application will be described below from the perspective of hardware processing.

[0160] It should be noted that, Figure 4 The physical device corresponding to the first acquisition module 401 shown can be a transceiver, radio frequency circuit, communication module and input / output (I / O) interface, etc., and the physical device corresponding to the second adjustment module 405 can be a processor.

[0161] Figure 5 The devices shown can all have the following characteristics: Figure 4 The structure shown, when Figure 4 The crop growth data management method device shown has the following features: Figure 5 When the structure shown is used, Figure 5 The processor and transceiver in the device can perform the same or similar functions as the acquisition module 401 and the second adjustment module 405 provided in the aforementioned device embodiments. Figure 5 The memory in the processor is the computer program that needs to be called when executing the above crop growth data management method.

[0162] When the computing device in this application embodiment is a terminal device, this application embodiment also provides a terminal device, such as... Figure 6 As shown, for ease of explanation, only the parts related to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. The terminal device can be any terminal device including mobile phones, tablets, personal digital assistants (PDAs), point-of-sale (POs), in-vehicle computers, etc. Taking a mobile phone as an example:

[0163] Figure 6 This diagram illustrates a partial structural representation of a mobile phone related to the terminal device provided in this embodiment. (Reference) Figure 6 The mobile phone includes components such as a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a wireless fidelity (WiFi) module 1070, a processor 1080, and a power supply 1090. Those skilled in the art will understand that... Figure 6 The mobile phone structure shown does not constitute a limitation on the mobile phone and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0164] The following is combined with Figure 6 A detailed introduction to each component of a mobile phone:

[0165] The RF circuit 1010 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and processes it with the processor 1080; additionally, it transmits uplink data to the base station. Typically, the RF circuit 1010 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Furthermore, the RF circuit 1010 can also communicate wirelessly with networks and other devices. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile Communications (GSM), General Packet Radio Service (GPRs), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, and Short Messaging Service (SMS).

[0166] The memory 1020 can be used to store software programs and modules. The processor 1080 executes various mobile phone functions and data processing by running the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 1020 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0167] The input unit 1030 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the mobile phone. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 1031), and drive the corresponding connection devices according to a pre-set program. Optionally, the touch panel 1031 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1080, and can also receive and execute commands sent by the processor 1080. In addition, the touch panel 1031 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 1031, the input unit 1030 may also include other input devices 1032. Specifically, other input devices 1032 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0168] The display unit 1040 can be used to display information input by the user or information provided to the user, as well as various menus of the mobile phone. The display unit 1040 may include a display panel 1041, which may optionally be configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar display. Further, a touch panel 1031 may cover the display panel 1041. When the touch panel 1031 detects a touch operation on or near it, it transmits the information to the processor 1080 to determine the type of touch event. Subsequently, the processor 1080 provides corresponding visual output on the display panel 1041 based on the type of touch event. Although in Figure 6 In this embodiment, the touch panel 1031 and the display panel 1041 are two separate components to realize the input and output functions of the mobile phone. However, in some embodiments, the touch panel 1031 and the display panel 1041 can be integrated to realize the input and output functions of the mobile phone.

[0169] The mobile phone may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 1041 according to the ambient light level, and the proximity sensor can turn off the display panel 1041 and / or the backlight when the phone is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, taps), etc. Other sensors that may be configured in the mobile phone, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0170] The audio circuit 1060, speaker 1061, and microphone 1062 provide an audio interface between the user and the mobile phone. The audio circuit 1060 converts the received audio data into electrical signals and transmits them to the speaker 1061, where the speaker 1061 converts them into sound signals for output. On the other hand, the microphone 1062 converts the collected sound signals into electrical signals, which are then received by the audio circuit 1060, converted into audio data, and then processed by the processor 1080 before being transmitted via the RF circuit 1010 to, for example, another mobile phone, or the audio data can be output to the memory 1020 for further processing.

[0171] Wi-Fi is a short-range wireless transmission technology. Through the Wi-Fi module 1070, mobile phones can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 6 The Wi-Fi module 1070 is shown, but it is understood that it is not an essential component of a mobile phone and can be omitted as needed without changing the essence of the invention.

[0172] The processor 1080 is the control center of the mobile phone, connecting various parts of the phone through various interfaces and lines. It executes software programs and / or modules stored in the memory 1020 and calls data stored in the memory 1020 to perform various functions and process data, thereby providing overall monitoring of the phone. Optionally, the processor 1080 may include one or more processing units; optionally, the processor 1080 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may also not be integrated into the processor 1080.

[0173] The mobile phone also includes a power supply 1090 (such as a battery) that supplies power to various components. Optionally, the power supply can be logically connected to the processor 1080 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0174] Although not shown, mobile phones may also include a camera, Bluetooth module, etc., which will not be described in detail here.

[0175] In this embodiment of the application, the processor 1080 included in the mobile phone also has the function of controlling the execution of the crop growth data management method process executed by the crop growth data management method device.

[0176] This application also provides a server; please refer to [link / reference]. Figure 7 , Figure 7 This is a schematic diagram of a server structure provided in an embodiment of this application. The server 1100 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 1122 (e.g., one or more processors) and memory 1132, and one or more storage media 1130 (e.g., one or more mass storage devices) for storing application programs 1142 or data 1144. The memory 1132 and storage media 1130 may be temporary or persistent storage. The program stored in the storage media 1130 may include one or more modules (not shown in the figure), each module may include a series of instruction operations on the server. Furthermore, the CPU 1122 may be configured to communicate with the storage media 1130 and execute the series of instruction operations in the storage media 1130 on the server 1100.

[0177] Server 1100 may also include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input / output interfaces 1158, and / or one or more operating systems 1141, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0178] The steps in the crop growth data management method in the above embodiments can be based on this Figure 7 The structure of server 1100 is shown.

[0179] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0180] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0181] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0182] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0184] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0185] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0186] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for managing crop growth data, characterized in that, The crop growth data management method, applied to computing devices, includes: Obtain the initial fertilization area division plan; The initial fertilization area division scheme is adjusted according to the demand data corresponding to the target crop type to obtain the final fertilization area division scheme, wherein the final fertilization area division scheme includes multiple fertilization areas; Determine target fertilization data for the multiple fertilization areas, wherein the target fertilization data includes multiple fertilization plans, and each fertilization area corresponds to one fertilization plan; Collect target crop growth data from multiple fertilization areas that were fertilized according to the final fertilization area division plan; Record the target fertilization data and target crop growth data, and adjust the fertilization plan based on the target crop growth data; The requirement data corresponding to the target crop type includes nutrient requirement data. The step of adjusting the initial fertilization area division scheme according to the requirement data corresponding to the target crop type to obtain the final fertilization area division scheme includes: acquiring soil data of the multiple fertilization areas, wherein the soil data includes soil nutrient data; calculating the matching degree between the nutrient requirement data corresponding to the target crop type and the soil nutrient data; and fine-tuning the initial fertilization area according to the matching degree, dividing crops with similar nutrient requirements into the same fertilization area and dividing crops with large differences in nutrient requirements into different fertilization areas to obtain the final fertilization area division scheme. The method further includes: acquiring soil data, climate data, and crop type data of the multiple fertilization areas; inputting the soil data, climate data, and crop type data of the fertilization areas into a pest and disease probability prediction model to obtain the probability of pest and disease occurrence; determining a control strategy based on the probability of pest and disease occurrence and the transmission mode corresponding to the pest and disease type; and adjusting the fertilization plan of the multiple fertilization areas according to the control strategy. The control strategy includes spraying pesticides in chemical control. Adjusting the fertilization plan of the multiple fertilization areas according to the control strategy includes: detecting and analyzing soil data based on the type and amount of pesticide sprayed, updating soil nutrient content data, recalculating the nutrients required for crop growth, and readjusting the fertilization plan of the multiple fertilization areas based on the nutrient data required for crop growth.

2. The crop growth data management method according to claim 1, characterized in that, The method for obtaining the initial fertilization area division scheme includes: Obtain topographic data, soil data, and climate data of the fertilization area; Based on the terrain data, soil data, and climate data, a clustering algorithm is used to obtain the clustering results of the fertilization area. The initial fertilization area division scheme is determined based on the clustering results.

3. The crop growth data management method according to claim 1, characterized in that, The collection of target crop growth data from multiple fertilization areas fertilized according to the final fertilization area division scheme includes: Collect plant height data, leaf area data, and chlorophyll content data of the target crop; The target crop growth data includes plant height, leaf area, and chlorophyll content data.

4. The crop growth data management method according to claim 1, characterized in that, The process of recording the target fertilization data and target crop growth data, and adjusting the fertilization plan based on the target crop growth data, includes: The current growth stage of the target crop is determined based on the target crop growth data, and the predetermined growth data for the current growth stage is obtained. Calculate the deviation between the target crop growth data and the predetermined growth data; The fertilization plan is adjusted based on the deviation.

5. The crop growth data management method according to claim 1, characterized in that, The crop growth data management method is applied to a management device, the management device having a display interface, and the method further includes: Collect fertilization data and crop growth data from the multiple fertilization areas; Calculate the correlation between the fertilization data and the crop growth data; The relevant data is displayed on the display interface of the management device.

6. A crop growth data management device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the crop growth data management method as described in any one of claims 1 to 5.

7. A computing device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the crop growth data management method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It includes instructions that, when run on a computer, cause the computer to perform the crop growth data management method as described in any one of claims 1 to 5.

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