Method and device for determining pest and disease prediction data of crops and storage medium

Abstract

The invention discloses a method and device for determining pest and disease prediction data of crops and a storage medium. Comprising the following steps: determining a current growth period of a crop and a variety of the crop; obtaining predicted meteorological data of a planting area where the crops are located in a first preset future duration; processing the growth period, the variety and the predicted meteorological data, and inputting the processed data into a prediction model, so as to output pest and disease prediction data of the crops in a first preset future duration through the prediction model; acquiring a historical ecological environment image of a planting area where the crops are located; determining historical meteorological data of the planting area according to the historical ecological environment image; according to the invention, the pest and disease prediction data is calibrated according to the historical meteorological data to obtain the final pest and disease prediction data of the crops in the first preset future duration, so that the accuracy of the pest and disease prediction data is improved, and the final pest and disease prediction data is more scientific and reliable.

Description

Technical Field

[0001] The present application relates to the field of agricultural technologies, and particularly to a method, device, automated plant protection platform and storage medium for determining pest and disease prediction data of crops. Background Art

[0002] For a long time in the field of agricultural plant protection, there has been a lack of timely and accurate early warning information guarantee and real-time monitoring means for information such as plant pests and diseases and growth conditions. At present, traditional pest and disease control mainly relies on a large number of manual investigations and experience to judge the occurrence of pests and diseases, so as to issue early warning information and determine the control time. In this process, due to the high professional requirements and difficult investigation environment, there is a shortage of professional plant protection personnel, and the workload is large and the working hours are long, which often leads to inaccurate and poor timeliness of the obtained pest and disease data results, and it is impossible to accurately obtain the situation of plant pests and diseases. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, automated plant protection platform and storage medium for determining pest and disease prediction data of crops, so as to solve the problem in the prior art that the situation of plant pests and diseases cannot be accurately determined.

[0004] To achieve the above purpose, the first aspect of the present application provides a method for determining pest and disease prediction data of crops, including:

[0005] Determine the growth stage and variety of the crop currently;

[0006] Obtain the predicted meteorological data of the planting area where the crop is located within a first preset future time period;

[0007] Process the growth stage, variety and predicted meteorological data and input them into a prediction model, so as to output the pest and disease prediction data of the crop within the first preset future time period through the prediction model, where the pest and disease prediction data includes at least one of the risk level of the crop suffering from pests and diseases, the type of pests and diseases, and the occurrence time of pests and diseases;

[0008] Obtain the historical ecological environment images of the planting area where the crop is located;

[0009] Determine the historical meteorological data of the planting area according to the historical ecological environment images;

[0010] Calibrate the pest and disease prediction data according to the historical meteorological data to obtain the final pest and disease prediction data of the crop within the first preset future time period.

[0011] In an embodiment of the present application, the method further includes: after obtaining the final pest and disease prediction data of the crop within the first preset future time period, acquiring the historical pesticide application data for the crop; determining multiple pesticide formulations for the crop according to the historical pesticide application data and the types of pests and diseases; generating a target pesticide for the crop according to the multiple pesticide formulations; generating a suitable control period for the crop according to the risk level of pest and disease occurrence and the time of pest and disease occurrence of the crop; and determining a control plan for the crop according to the suitable control period and the target pesticide.

[0012] In an embodiment of the present application, the method further includes: after determining a control plan for the crop according to the suitable control period and the target pesticide, generating a control task sheet according to the control plan; and sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet.

[0013] In an embodiment of the present application, the method further includes: after the automatic operation device controls the crop according to the control task sheet, re-acquiring the predicted meteorological data of the growth period and the planting area where the crop is located within the second preset future time period; inputting the variety, the re-acquired growth period, and the predicted meteorological data into a prediction model after processing, so as to output new pest and disease prediction data of the crop within the second preset future time period through the prediction model; re-acquiring the historical ecological environment images of the planting area where the crop is located; re-determining the historical meteorological data of the planting area according to the re-acquired historical ecological environment images; calibrating the new pest and disease prediction data according to the re-determined historical meteorological data to obtain the final pest and disease prediction data of the crop within the second preset future time period; determining that the pest and disease control of the crop is completed when the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to the preset risk level; and when the risk level in the final pest and disease prediction data within the second preset future time period is greater than the preset risk level, returning to the step of acquiring the historical pesticide application data for the crop again until the risk level in the final pest and disease prediction data is less than or equal to the preset risk level.

[0014] In an embodiment of the present application, sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet includes: sending the control task sheet to the automatic operation device so that the automatic operation device obtains the information of the target pesticide from the control task sheet; determining the merchant selling the target pesticide, and sending the location information of the merchant to the automatic operation device so that the automatic operation device obtains the target pesticide from the merchant; and controlling the automatic operation device to apply the target pesticide to the crop according to the control plan.

[0015] In an embodiment of the present application, generating a target pesticide for a crop according to multiple pesticide formulations includes: for each pesticide formulation, obtaining multiple pesticides corresponding to the pesticide formulation, and determining whether each pesticide can be compounded according to the pesticide compounding rules; for each pesticide formulation, when all the pesticides in the pesticide formulation can be compounded, compounding all the pesticides in the pesticide formulation according to the pesticide compounding rules to obtain a candidate pesticide corresponding to the pesticide formulation; sending all the candidate pesticides to the user, and determining the candidate pesticide selected by the user as the target pesticide.

[0016] In an embodiment of the present application, the method further includes: when all the pesticides in each pesticide formulation cannot be compounded, reminding the user that the pesticide compounding fails; visualizing the pesticide compounding rules and all the pesticide formulations, and obtaining the editing operations of the user for each pesticide formulation to generate new pesticide formulations; compounding all the pesticides corresponding to each new pesticide formulation according to the pesticide compounding rules to obtain candidate pesticides corresponding to each new pesticide formulation; sending all the candidate pesticides corresponding to the new pesticide formulations to the user, and determining the candidate pesticide selected by the user as the target pesticide.

[0017] A second aspect of the present application provides a device for determining pest and disease prediction data of a crop, including:

[0018] A memory configured to store instructions; and

[0019] A processor configured to call instructions from the memory and capable of implementing the above method for determining pest and disease prediction data of a crop when executing the instructions.

[0020] A third aspect of the present application provides an automated plant protection platform, including:

[0021] An automatic operation device for receiving a prevention and control task sheet and performing prevention and control on the crop according to the prevention and control task sheet; and

[0022] The above device for determining pest and disease prediction data of a crop.

[0023] A fourth aspect of the present application provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above method for determining pest and disease prediction data of a crop.

[0024] Through the above technical solution, the growth period, variety of the crop, and predicted meteorological data of the planting area where the crop is located are processed and then input into the prediction model, so as to output the pest and disease prediction data of the crop within the first preset future time period through the prediction model, and then calibrate the pest and disease prediction data through the historical meteorological data of the planting area to obtain the final pest and disease prediction data of the crop within the first preset future time period, improving the accuracy of the pest and disease prediction data, and the scientific nature of the final pest and disease prediction data is higher and more reliable.

[0025] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation section. Description of the Drawings

[0026] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0027] Figure 1 Schematically shows a flowchart of a method for determining pest and disease prediction data of crops according to an embodiment of the present application;

[0028] Figure 2 Schematically shows a schematic diagram of an automated plant protection platform according to an embodiment of the present application;

[0029] Figure 3 Schematically shows another flowchart of a method for determining pest and disease prediction data of crops according to an embodiment of the present application;

[0030] Figure 4 Schematically shows the internal structure diagram of a computer device according to an embodiment of the present application. Specific Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation described herein is only used to explain and illustrate the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0032] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present application, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0033] In addition, if the descriptions such as "first" and "second" are involved in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0034] Figure 1 Schematically shows a flowchart of a method for determining pest and disease prediction data of crops according to an embodiment of the present application. As Figure 1 shown, the embodiments of the present application provide a method for determining pest and disease prediction data of crops, and the method may include the following steps.

[0035] Step 101: Determine the growth stage and variety of the crop currently.

[0036] Step 102: Obtain the predicted meteorological data of the planting area where the crop is located within a first preset future time period.

[0037] Step 103: Input the processed growth stage, variety, and predicted meteorological data into a prediction model to output pest and disease prediction data of the crop within the first preset future time period through the prediction model, where the pest and disease prediction data includes at least one of the risk level of the crop suffering from pests and diseases, the types of pests and diseases, and the occurrence time of pests and diseases.

[0038] Step 104: Obtain the historical ecological environment images of the planting area where the crop is located.

[0039] Step 105: Determine the historical meteorological data of the planting area according to the historical ecological environment images.

[0040] Step 106: Calibrate the pest and disease prediction data according to the historical meteorological data to obtain the final pest and disease prediction data of the crop within the first preset future time period.

[0041] The processor can determine the growth stage that the crop is currently in and the variety of the crop. Among them, the growth stage refers to the time that the crop experiences from sowing to seed maturity. For example, taking rice as an example, the growth stage can include the seedling stage, the transplanting stage, the tillering stage, the jointing stage, the booting stage, the heading stage, the flowering and pollination stage, and the filling stage. The processor can also obtain the predicted meteorological data of the planting area where the crop is located within a first preset future time period. After obtaining the growth stage, variety, and predicted meteorological data of the crop, the processor can process the growth stage, variety, and predicted meteorological data and input them into a prediction model to output the pest and disease prediction data of the crop within the first preset future time period through the prediction model. Among them, the pest and disease prediction data includes at least one of the risk level of the crop suffering from pests and diseases, the type of pests and diseases, and the occurrence time of pests and diseases. The processor can obtain the historical ecological environment images of the planting area where the crop is located. And determine the historical meteorological data of the planting area according to the historical ecological environment images. After obtaining the historical meteorological data of the planting area, the processor can calibrate the pest and disease prediction data according to the historical meteorological data to obtain the final pest and disease prediction data of the crop within the first preset future time period.

[0042] In the embodiment of the present application, the method further includes: after obtaining the final pest and disease prediction data of the crop within the first preset future time period, obtaining the historical pesticide application data for the crop; determining multiple pesticide formulations for the crop according to the historical pesticide application data and the type of pests and diseases; generating a target pesticide for the crop according to the multiple pesticide formulations; generating a control suitable period for the crop according to the risk level of the crop suffering from pests and diseases and the occurrence time of pests and diseases; determining a control plan for the crop according to the control suitable period and the target pesticide.

[0043] After obtaining the final pest and disease prediction data of the crop within the first preset future time period, the processor can obtain the historical pesticide application data for the crop, such as the type of pesticide, the amount of pesticide used, etc. After obtaining the historical pesticide application data, the processor can determine multiple agricultural formulations for the crop according to the historical pesticide application data and the type of pests and diseases. After obtaining the multiple pesticide formulations, the processor can generate a target pesticide for the crop according to the multiple pesticide formulations. The processor can also generate a control suitable period for the crop according to the risk level of the crop suffering from pests and diseases and the occurrence time of pests and diseases. After obtaining the control suitable period and the target pesticide, the processor can determine a control plan for the crop according to the control suitable period and the target pesticide, where the control suitable period refers to the best period for preventing crop pests and diseases.

[0044] In the embodiments of the present application, generating a target pesticide for a crop according to multiple pesticide formulations includes: for each pesticide formulation, obtaining multiple pesticides corresponding to the pesticide formulation, and judging whether each pesticide can be compounded according to the pesticide compounding rules; for each pesticide formulation, when all the pesticides in the pesticide formulation can be compounded, compounding all the pesticides in the pesticide formulation according to the pesticide compounding rules to obtain a candidate pesticide corresponding to the pesticide formulation; sending all the candidate pesticides to the user, and determining the candidate pesticide selected by the user as the target pesticide.

[0045] For each pesticide formulation, the processor can obtain multiple pesticides corresponding to the pesticide formulation, and judge whether each pesticide can be compounded according to the pesticide compounding rules. For example, the pesticide compounding rules can include the addition order of each pesticide, the addition dose, and whether each pesticide is soluble or mutually exclusive. When all the pesticides in the pesticide formulation can be compounded, the processor can compound all the pesticides in the pesticide formulation according to the pesticide compounding rules to obtain a candidate pesticide corresponding to the pesticide formulation. After obtaining all the candidate pesticides, the processor can send all the candidate pesticides to the user for the user to select from all the candidate pesticides, and determine the candidate pesticide selected by the user as the target pesticide.

[0046] In the embodiments of the present application, the method further includes: when all the pesticides in each pesticide formulation cannot be compounded, reminding the user that the pesticide compounding fails; visualizing the pesticide compounding rules and all the pesticide formulations, and obtaining the editing operations of the user for each pesticide formulation to generate a new pesticide formulation; compounding all the pesticides corresponding to each new pesticide formulation according to the pesticide compounding rules to obtain a candidate pesticide corresponding to each new pesticide formulation; sending all the candidate pesticides corresponding to the new pesticide formulations to the user, and determining the candidate pesticide selected by the user as the target pesticide.

[0047] When all the pesticides in each pesticide formulation cannot be compounded, the processor can remind the user that the pesticide compounding fails. After the pesticide compounding fails, the processor can visualize the pesticide compounding rules and all the pesticide formulations so that the user can edit the pesticide formulations. The processor can obtain the editing operations of the user for each pesticide formulation to generate a new pesticide formulation. After generating the new pesticide formulation, the processor can compound all the pesticides corresponding to each new pesticide formulation according to the pesticide compounding rules to obtain a candidate pesticide corresponding to each new pesticide formulation. The processor can send all the candidate pesticides corresponding to the new pesticide formulations to the user for the user to select from all the new candidate pesticides, and determine the candidate pesticide selected by the user as the target pesticide.

[0048] In an embodiment of the present application, the method further includes: after determining a control plan for a crop according to the control suitable period and the target pesticide, generating a control task sheet according to the control plan; sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet.

[0049] After determining a control plan for a crop according to the control suitable period and the target pesticide, the processor may generate a control task sheet according to the control plan, and send the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet.

[0050] In an embodiment of the present application, sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet includes: sending the control task sheet to the automatic operation device so that the automatic operation device obtains information on the target pesticide from the control task sheet; determining a merchant selling the target pesticide, and sending the location information of the merchant to the automatic operation device so that the automatic operation device obtains the target pesticide from the merchant; controlling the automatic operation device to apply the target pesticide to the crop according to the control plan.

[0051] The processor may send the control task sheet to the automatic operation device so that the automatic operation device obtains information on the target pesticide from the control task sheet. The processor may also determine a merchant selling the target pesticide and send the location information of the merchant to the automatic operation device so that the automatic operation device obtains the target pesticide from the merchant. After obtaining the target pesticide, the processor may control the automatic operation device to apply the target pesticide to the crop according to the control plan.

[0052] In an embodiment of the present application, the method further includes: after the automatic operation device controls the crop according to the control task sheet, re-obtaining predicted meteorological data of the growth period and the planting area where the crop is located within a second preset future time period; inputting the variety, the re-obtained growth period, and the predicted meteorological data into a prediction model to output new pest and disease prediction data of the crop within the second preset future time period through the prediction model; re-obtaining historical ecological environment images of the planting area where the crop is located; re-determining historical meteorological data of the planting area according to the re-obtained historical ecological environment images; calibrating the new pest and disease prediction data according to the re-determined historical meteorological data to obtain final pest and disease prediction data of the crop within the second preset future time period; determining that the pest and disease control of the crop is completed when the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to a preset risk level; and when the risk level in the final pest and disease prediction data within the second preset future time period is greater than the preset risk level, returning to the step of obtaining historical pesticide application data for the crop again until the risk level in the final pest and disease prediction data is less than or equal to the preset risk level.

[0053] After the automatic operation device has carried out pest control on the crops according to the pest control task list, the processor can re-obtain the growth stage of the crops and the predicted meteorological data of the planting area within the second preset future time period. After re-obtaining the predicted meteorological data of the growth stage and the planting area, the processor can process the variety, the re-obtained growth stage, and the predicted meteorological data and input them into the prediction model, so as to output new pest and disease prediction data of the crops within the second preset future time period through the prediction model. The processor can also re-obtain the historical ecological environment images of the planting area where the crops are located. And re-determine the historical meteorological data of the planting area according to the re-obtained historical ecological environment images. The processor can calibrate the new pest and disease prediction data according to the re-determined historical meteorological data to obtain the final pest and disease prediction data of the crops within the second preset future time period. After obtaining the final pest and disease prediction data of the crops within the second preset future time period, the processor can determine whether the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to the preset risk level. When the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to the preset risk level, the processor can determine that the pest and disease control of the crops is completed. When the risk level in the final pest and disease prediction data within the second preset future time period is greater than the preset risk level, the processor can return to the step of obtaining the historical medication data for the crops again until the risk level in the final pest and disease prediction data is less than or equal to the preset risk level.

[0054] Through the above technical solution, the accuracy of the pest and disease prediction data is improved, and the final pest and disease prediction data is more scientific and reliable.

[0055] The embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to make a machine execute the above method for determining the pest and disease prediction data of crops.

[0056] The embodiment of the present application also provides a device for determining the pest and disease prediction data of crops, including:

[0057] A memory configured to store instructions; and

[0058] A processor configured to call instructions from the memory and be able to implement the above method for determining the pest and disease prediction data of crops when executing the instructions.

[0059] The embodiment of the present application also provides an automated plant protection platform, including:

[0060] An automatic operation device for receiving a pest control task list and carrying out pest control on crops according to the pest control task list; and

[0061] The above device for determining the pest and disease prediction data of crops.

[0062] For example, as Figure 2 shown, the automated plant protection platform may include a database, decision algorithms, and automatic operations. Among them, the database includes a pest, disease, and weed database, a meteorological database, satellite remote sensing, a basic database of varieties / plot boundaries, a pesticide library, and a resource library of agricultural materials & agricultural service. The decision algorithms include pest and disease identification algorithms, pest and disease prediction algorithms, remote sensing monitoring and prediction algorithms, spraying suitability algorithms, pesticide compounding and recommendation logics, and task allocation logics.

[0063] The process of automatic operation is that after clicking on plant protection trusteeship, the data in the pest, disease, and weed database is processed by the pest, disease, and weed identification algorithm, the data in the meteorological database and the basic database of varieties / plot boundaries is processed by the pest and disease prediction algorithm, and the data in the satellite remote sensing and the meteorological database is processed by the remote sensing monitoring and prediction algorithm to judge the occurrence situation of pests, diseases, and weeds on the farm through the processed data, such as the symptoms caused by diseases, pests, or nutrient deficiencies, so as to form a regional hotspot map of the occurrence of pests, diseases, and weeds. Among them, the hotspot map of the occurrence of pests, diseases, and weeds can allocate disease types, location information, etc. The data in the pest, disease, and weed database may include the pest situation data monitored by the pest situation lamp. The satellite remote sensing can collect the images of the habitats of pests and diseases on the farm.

[0064] The data in the meteorological database and the basic database of varieties / plot boundaries is processed by the pest and disease prediction algorithm, the data in the satellite remote sensing and the meteorological database is processed by the remote sensing monitoring and prediction algorithm, the data in the satellite remote sensing is processed by the spraying suitability algorithm, and the data in the pesticide library is processed by the pesticide compounding and recommendation logic to automatically generate a personalized control plan through the processed data. For example, the data in the meteorological database may include the temperature, humidity, wind speed, rainfall, etc. monitored by the meteorological station.

[0065] The agricultural materials & agricultural service are allocated through the data in the resource library of agricultural materials & agricultural service and the task allocation logic to execute the operation. After the operation is executed, the data obtained by processing the data in the satellite remote sensing and the meteorological database by the remote sensing monitoring and prediction algorithm is used to judge the imitation, and it is determined whether to end or supplement the prevention according to the imitation.

[0066] In the embodiment of the present application, as Figure 3As shown, on the automated plant protection platform, farmers can click on the plant protection service. After clicking on the plant protection service, the processor will send a signal to the ground personnel, and the ground personnel will go to investigate the farm for pests and diseases. The processor will also obtain the current data of the plant protection station. And upload the farm pests and diseases and the plant protection station data to the automated plant protection platform. And determine in the automated plant protection platform whether the farm needs to be sprayed. In the case of determining that spraying is not required, a prompt can be output that the current treatment is of low risk and no prevention and control is required, and this plant protection service ends. In the case of determining that spraying is required, the automated plant protection platform can output the prevention and control period and the chemical agent plan, and link to the drone pilot service. Farmers can click to call the drone pilot, and automatically assign the order to the target drone pilot without tasks, and link to the agricultural materials merchant with the chemical agent. The drone pilot accepts the order, obtains the chemical agent and conducts the service. 7 to 10 days after the drone pilot accepts the order and acts, the ground personnel investigate the drug effect and feedback the investigation data. And return to the step of judging whether spraying is required again on the automated plant protection platform until a prompt is output that the current is of low risk and no prevention and control is required, so as to improve the effect of the plant protection service and conduct more scientific pest and disease prevention and control on the farm.

[0067] Figure 1 and 3 is a schematic flowchart of a method for determining pest and disease prediction data of crops in an embodiment. It should be understood that although Figure 1 and 3 the steps in the flowchart are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 and 3 at least a part of the steps in can include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0068] In an embodiment, a computer device is provided. This computer device can be a server, and its internal structure diagram can be as Figure 4As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected by a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store predicted meteorological data, pest and disease prediction data, historical meteorological data, and final pest and disease prediction data. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a method for determining pest and disease prediction data of crops.

[0069] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0070] An embodiment of this application provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: determining the growth stage and variety of the crop that the crop is currently in; obtaining the predicted meteorological data of the planting area where the crop is located within a first preset future time period; inputting the growth stage, variety, and predicted meteorological data after processing into a prediction model to output the pest and disease prediction data of the crop within the first preset future time period through the prediction model, where the pest and disease prediction data includes at least one of the risk level of the crop suffering from pests and diseases, the type of pests and diseases, and the time of occurrence of pests and diseases; obtaining the historical ecological environment images of the planting area where the crop is located; determining the historical meteorological data of the planting area according to the historical ecological environment images; calibrating the pest and disease prediction data according to the historical meteorological data to obtain the final pest and disease prediction data of the crop within the first preset future time period.

[0071] In one embodiment, the method further includes: after obtaining the final pest and disease prediction data of the crop within the first preset future time period, obtaining the historical pesticide application data for the crop; determining multiple pesticide formulations for the crop according to the historical pesticide application data and the type of pests and diseases; generating a target pesticide for the crop according to the multiple pesticide formulations; generating a control suitable period for the crop according to the risk level of the crop suffering from pests and diseases and the time of occurrence of pests and diseases; determining a control plan for the crop according to the control suitable period and the target pesticide.

[0072] In one embodiment, the method further includes: after determining a control plan for the crop according to the control suitable period and the target pesticide, generating a control task sheet according to the control plan; sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet.

[0073] In one embodiment, the method further includes: after the automatic operation device controls the crop according to the control task sheet, re-obtaining the growth period of the crop and the predicted meteorological data of the planting area within a second preset future time period; inputting the variety, the re-obtained growth period, and the predicted meteorological data into a prediction model after processing, so as to output new pest and disease prediction data of the crop within the second preset future time period through the prediction model; re-obtaining the historical ecological environment image of the planting area where the crop is located; re-determining the historical meteorological data of the planting area according to the re-obtained historical ecological environment image; calibrating the new pest and disease prediction data according to the re-determined historical meteorological data to obtain the final pest and disease prediction data of the crop within the second preset future time period; determining that the pest and disease control of the crop is completed when the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to a preset risk level; and when the risk level in the final pest and disease prediction data within the second preset future time period is greater than the preset risk level, returning to the step of obtaining the historical pesticide application data for the crop again until the risk level in the final pest and disease prediction data is less than or equal to the preset risk level.

[0074] In one embodiment, sending the control task sheet to an automatic operation device so that the automatic operation device controls the crop according to the control task sheet includes: sending the control task sheet to the automatic operation device so that the automatic operation device obtains the information of the target pesticide from the control task sheet; determining the merchant selling the target pesticide and sending the location information of the merchant to the automatic operation device so that the automatic operation device obtains the target pesticide from the merchant; and controlling the automatic operation device to apply the target pesticide to the crop according to the control plan.

[0075] In one embodiment, generating a target pesticide for the crop according to multiple pesticide formulations includes: for each pesticide formulation, obtaining multiple pesticides corresponding to the pesticide formulation and determining whether each pesticide can be compounded according to the pesticide compounding rule; for each pesticide formulation, when all the pesticides in the pesticide formulation can be compounded, compounding all the pesticides in the pesticide formulation according to the pesticide compounding rule to obtain a candidate pesticide corresponding to the pesticide formulation; sending all the candidate pesticides to the user and determining the candidate pesticide selected by the user as the target pesticide.

[0076] In one embodiment, the method further includes: when all the pesticides in each pesticide formulation cannot be compounded, reminding the user that the pesticide compounding fails; visualizing the pesticide compounding rules and all the pesticide formulations, and obtaining the editing operations of the user for each pesticide formulation to generate new pesticide formulations; compounding all the pesticides corresponding to each new pesticide formulation according to the pesticide compounding rules to obtain candidate pesticides corresponding to each new pesticide formulation; sending the candidate pesticides corresponding to all the new pesticide formulations to the user, and determining the newly selected candidate pesticides by the user as the target pesticides.

[0077] The present application also provides a computer program product, which is suitable for executing a program initialized with method steps for determining pest prediction data of crops when executed on a data processing device.

[0078] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0080] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0082] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0083] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0084] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0085] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising the element.

[0086] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for determining pest and disease prediction data of crops, characterized in that, The method includes: Determining the growth stage of the crop and the variety of the crop; Obtaining the predicted meteorological data of the planting area where the crop is located within a first preset future time period; Processing the growth stage, the variety, and the predicted meteorological data and inputting them into a prediction model to output, through the prediction model, the pest and disease prediction data of the crop within the first preset future time period, where the pest and disease prediction data includes at least one of the risk level of the crop suffering from pests and diseases, the types of pests and diseases, and the occurrence time of pests and diseases; Obtaining the historical ecological environment images of the planting area where the crop is located; Determining the historical meteorological data of the planting area according to the historical ecological environment images; Calibrating the pest and disease prediction data according to the historical meteorological data to obtain the final pest and disease prediction data of the crop within the first preset future time period.

2. The method for determining pest and disease prediction data of crops according to claim 1, characterized in that, The method further includes: After obtaining the final pest and disease prediction data of the crop within the first preset future time period, obtaining the historical pesticide application data for the crop; Determining multiple pesticide formulations for the crop according to the historical pesticide application data and the types of pests and diseases; Generating a target pesticide for the crop according to the multiple pesticide formulations; Generating a control suitable period for the crop according to the risk level of the crop suffering from pests and diseases and the occurrence time of pests and diseases; Determining a control plan for the crop according to the control suitable period and the target pesticide.

3. The method for determining pest and disease prediction data of crops according to claim 2, characterized in that, The method further includes: After determining the control plan for the crop according to the control suitable period and the target pesticide, generating a control task list according to the control plan; Sending the control task list to an automatic operation device so that the automatic operation device controls the crop according to the control task list.

4. The method for determining pest and disease prediction data of crops according to claim 3, wherein, The method further includes: After the automatic operation device controls the crop according to the control task list, re-obtaining the growth stage of the crop and the predicted meteorological data of the planting area within a second preset future time period; Processing the variety, the re-obtained growth stage, and the predicted meteorological data and inputting them into the prediction model to output, through the prediction model, new pest and disease prediction data of the crop within the second preset future time period; Re-obtaining the historical ecological environment images of the planting area where the crop is located; Re-determining the historical meteorological data of the planting area according to the re-obtained historical ecological environment images; Calibrating the new pest and disease prediction data according to the re-determined historical meteorological data to obtain the final pest and disease prediction data of the crop within the second preset future time period; When the risk level in the final pest and disease prediction data within the second preset future time period is less than or equal to a preset risk level, determining that the pest and disease control of the crop is completed; When the risk level in the final pest and disease prediction data within the second preset future time period is greater than the preset risk level, returning to the step of obtaining the historical pesticide application data for the crop again until the risk level in the final pest and disease prediction data is less than or equal to the preset risk level.

5. The method for determining pest and disease prediction data of crops according to claim 3, characterized in that, Sending the prevention and control task sheet to the automatic operation device so that the automatic operation device conducts prevention and control on the crop according to the prevention and control task sheet includes: Sending the prevention and control task sheet to the automatic operation device so that the automatic operation device obtains information on the target pesticide from the prevention and control task sheet; Determining the merchant selling the target pesticide and sending the location information of the merchant to the automatic operation device so that the automatic operation device obtains the target pesticide from the merchant; Controlling the automatic operation device to apply the target pesticide to the crop according to the prevention and control plan.

6. The method for determining pest and disease prediction data of crops according to claim 2, wherein Generating the target pesticide for the crop according to the multiple pesticide formulations includes: For each pesticide formulation, obtaining the multiple pesticides corresponding to the pesticide formulation and judging whether each pesticide can be compounded according to the pesticide compounding rules; For each pesticide formulation, when all the pesticides in the pesticide formulation can be compounded, compounding all the pesticides in the pesticide formulation according to the pesticide compounding rules to obtain the candidate pesticide corresponding to the pesticide formulation; Sending all the candidate pesticides to the user and determining the candidate pesticide selected by the user as the target pesticide.

7. The method for determining pest and disease prediction data of crops according to claim 6, wherein The method further includes: When all the pesticides in each pesticide formulation cannot be compounded, reminding the user that the pesticide compounding fails; Visualizing the pesticide compounding rules and all the pesticide formulations and obtaining the editing operations of the user for each pesticide formulation to generate new pesticide formulations; Compounding all the pesticides corresponding to each new pesticide formulation according to the pesticide compounding rules to obtain the candidate pesticides corresponding to each new pesticide formulation; Sending the candidate pesticides corresponding to all the new pesticide formulations to the user and determining the candidate pesticide selected by the user as the target pesticide.

8. A device for determining pest and disease prediction data of crops, characterized in that, Includes: A memory configured to store instructions; And A processor configured to call the instructions from the memory and capable of implementing the method for determining the pest and disease prediction data of crops according to any one of claims 1 to 7 when executing the instructions.

9. An automated plant protection platform, characterized in that, Includes: An automatic operation device for receiving a prevention and control task sheet and conducting prevention and control on a crop according to the prevention and control task sheet; And The device for determining the pest and disease prediction data of crops according to claim 8.

10. A machine-readable storage medium, characterized in that, Instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the method for determining the pest and disease prediction data of crops according to any one of claims 1 to 7.

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