Agricultural Internet of Things Intelligent Management System and Method

Abstract

The present invention relates to the field of crop prediction and management technology, and discloses an intelligent agricultural Internet of Things (IoT) management system and method. The system includes a pest monitoring module that uses an image acquisition device to capture images of crop leaves within a greenhouse, assesses the extent of leaf damage, and marks the infestation start date when the leaf damage reaches an initial infestation threshold; and a primary prediction module that calculates the average daily growth rate of leaf damage from the infestation start date to the date the infestation risk threshold is reached. By monitoring leaf damage, the system can provide timely warnings of potential pest problems, enabling agricultural managers to take preventive measures before a large-scale outbreak. Furthermore, based on the pest risk assessment, agricultural managers can more accurately decide when and how to apply pesticides, thereby reducing unnecessary chemical use.

Description

Technical Field

[0001] The present invention relates to the technical field of crop prediction management, and in particular to an intelligent agricultural Internet of Things management system and method. Background Art

[0002] In greenhouse farming, the presence of pests is a relatively common phenomenon. Because greenhouses provide a relatively closed and suitable environment, pests can easily breed and threaten crops. Pests can directly damage crop leaves, affecting photosynthesis and thus inhibiting crop growth and yield.

[0003] At present, although there is technology for pest management in greenhouse agriculture that uses digital monitoring to collect information on pest activities in the greenhouse, its function is only to reflect the approximate number of pests and cannot effectively predict and manage the growth of pests in the greenhouse. As a result, agricultural managers are unable to take effective prevention and control measures in advance. They often directly adopt the control method of pesticide spraying when the number of pests is large. However, this also leads to problems such as pesticide residues in the later stages of crops and effects on soil microorganisms.

[0004] To this end, the present invention provides an intelligent management system and method for agricultural Internet of Things. Summary of the Invention

[0005] In response to the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an intelligent management system and method for agricultural Internet of Things, so as to predict the growth rate of pests when crops are planted in greenhouses, so as to control the number of pests within an acceptable range and take corresponding preventive measures in advance.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent management system for agricultural Internet of Things, comprising:

[0007] The pest monitoring module uses an image acquisition device to capture images of the leaves of crops in the greenhouse, assesses the degree of damage to the leaves of the crops, and marks the infestation start date when the degree of damage reaches an initial infestation threshold;

[0008] A primary prediction module, which calculates the average daily growth rate of leaf damage from the onset of pest infestation to the day when the pest risk threshold is reached, and combines the remaining growth cycle days of the crops in the greenhouse to determine the degree of leaf damage at the end of the crop growth cycle, and takes corresponding prevention and control measures based on the degree of leaf damage at the end of the growth cycle;

[0009] A secondary prediction module, which calculates the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the control strategy is implemented, compares it with the average daily growth rate of the degree of damage to the leaves before the control strategy is implemented, and makes corresponding prevention and control measures again based on the comparison results;

[0010] The feedback adjustment module combines the leaf damage degrees after the two growth cycles before and after the control strategy to obtain a controllable degree, and controls the crop growth environment in the greenhouse according to the controllable degree.

[0011] In some embodiments, in a prediction module, the specific method for obtaining the degree of leaf damage after the end of the crop growth cycle is:

[0012] Get the total number of days from the start of the pest infestation to the day when the pest risk threshold is reached , combined with the risk pest level threshold , and the average daily growth rate of leaf damage is obtained , and then obtain the remaining growth cycle days of the crops in the greenhouse from the day when the pest risk threshold is reached , get the expected degree of damage to the leaves after the crop growth cycle ends .

[0013] In some embodiments, in a prediction module, if the predicted degree of damage is greater than or equal to the maximum acceptable pest degree threshold, it means that based on the current incremental degree of leaf damage in the greenhouse, the pest degree will exceed the maximum acceptable range during the entire crop growth cycle, resulting in serious damage to crop yield and quality. Pesticides should be sprayed on the crops in the greenhouse for prevention and control to effectively control the spread of pests; if the predicted degree of damage is less than the maximum acceptable pest degree threshold, it means that based on the current incremental degree of leaf damage in the greenhouse, the growth of pests is still within a controllable range, and a control strategy is implemented to try to reduce the average daily growth rate of leaf damage and control the reproduction and growth of pests.

[0014] In some embodiments, the control strategy includes increasing the ventilation frequency in the greenhouse, controlling the humidity control equipment in the greenhouse, and appropriately adjusting the irrigation frequency and water volume, and using humidity sensors to monitor the humidity conditions in the greenhouse to reduce them to the minimum acceptable standard for crops.

[0015] In some embodiments, after the control strategy is implemented, the daily average growth rate of the degree of damage to the leaves of the crops in the greenhouse is specifically obtained by setting an observation period and calculating the total growth rate of the degree of damage to the leaves of the crops in the greenhouse during the observation period. Combined with the number of days of observation period Average daily growth rate after regulation .

[0016] In some embodiments, in the secondary prediction module, when the average daily growth rate after regulation is less than the average daily growth rate of the degree of leaf damage before regulation, it indicates that the regulation strategy implemented by the system is effective in inhibiting the reproduction and growth of pests and can effectively reduce the growth rate of the degree of leaf damage; and when the average daily growth rate after regulation is greater than or equal to the average daily growth rate of the degree of leaf damage before regulation, it indicates that the pests in the greenhouse are not effectively affected by the regulation strategy, and the degree of leaf damage will further expand. Pesticide spraying should be used for prevention and control of crops in the greenhouse to effectively control the spread of pests.

[0017] In some embodiments, the specific method of obtaining the controllable degree is: obtaining the degree of damage to the leaves of the current crops in the greenhouse , combined with the average daily growth rate after regulation And the remaining growth cycle days of crops in the greenhouse , and obtain the expected degree of damage to the leaves of crops at the end of the growth cycle in the greenhouse after regulation , combined with the maximum acceptable pest level threshold Get the degree of control ,in, is the proportional adjustment factor.

[0018] In some embodiments, the degree of Divide by the remaining growth cycle days of the crops in the greenhouse , and obtain the maximum daily increase in leaf damage during the remaining growth period of the crop ,in, is the proportional adjustment factor, combined with the change of humidity value in the greenhouse Changes in the average daily growth rate of leaf damage before and after the control strategy Get the humidity environment in the greenhouse , and adjust the humidity in the greenhouse accordingly based on the humidity environment in the greenhouse, but not exceeding the growth humidity that the crops in the greenhouse are adapted to.

[0019] The present invention also provides the following technical solution: an intelligent management method for agricultural Internet of Things, comprising the following steps:

[0020] Use image acquisition equipment to capture images of crop leaves in the greenhouse to assess the degree of damage to the leaves. When the degree of damage reaches the initial pest threshold, it is marked as the pest start date.

[0021] Calculate the average daily growth rate of leaf damage from the onset of pest infestation to the day the pest risk threshold is reached. Combined with the remaining growth cycle days of the crops in the greenhouse, the degree of leaf damage at the end of the crop growth cycle is calculated. Based on the degree of leaf damage at the end of the growth cycle, appropriate prevention and control measures are taken.

[0022] Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the control strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the control strategy is implemented. Based on the comparison results, make corresponding prevention and control measures again;

[0023] The degree of leaf damage after the two growth cycles before and after the regulation strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

[0024] The present invention further provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above-mentioned agricultural Internet of Things intelligent management system and method.

[0025] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0026] First, by monitoring the degree of leaf damage, the system can provide timely warnings of potential pest problems, allowing agricultural managers to take preventive measures before large-scale pest outbreaks occur. Based on the assessment of pest risks, agricultural managers can more accurately decide when and how to apply pesticides, thereby reducing unnecessary chemical use.

[0027] Secondly, the present invention can continuously collect and analyze data on crops in the greenhouse through intelligent greenhouse pest monitoring and analysis management, which can better understand the occurrence patterns of pests, reduce the need for manual inspections, and improve management efficiency.

[0028] Third, the present invention can perform precise humidity and pest management through the feedback adjustment module, find a balance between pest suppression and growth conditions while keeping crops from being affected by pests, and reduce crop yield and quality losses caused by interference from pest suppression. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a module diagram of the intelligent management system of the agricultural Internet of Things of the present invention;

[0030] Figure 2 This is a flow chart of the intelligent management method of the agricultural Internet of Things of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0033] See also Figure 1 , the present invention provides an intelligent management system for agricultural Internet of Things, the system includes a pest monitoring module, a primary prediction module, a secondary prediction module and a feedback adjustment module;

[0034] The pest monitoring module includes using image acquisition equipment to capture images of the leaves of crops in the greenhouse, using image processing algorithms to analyze the captured images, and evaluating the degree of damage to the crop leaves. Since pests can directly damage crops, the degree of damage to the crop leaves can directly reflect the severity of the pests on the crops in the greenhouse.

[0035] Furthermore, greenhouses provide a relatively enclosed and suitable environment, where pests can easily breed and pose a threat to crops. Therefore, pest management is crucial in greenhouse cultivation. While ideally, greenhouses should be completely pest-free, completely eliminating insects in actual greenhouse farming operations is unrealistic. Therefore, the key to greenhouse pest management lies in controlling the pest population and keeping it within a reasonable range.

[0036] An initial pest infestation threshold should be set. When the leaf damage reaches this threshold, the system records the current day's timestamp and marks it as the infestation start date. For example, the initial infestation threshold could be set to 0.1%. When the system detects 0.1% leaf damage on greenhouse crops, it indicates that pests are destroying the crops, and the system will mark that day as the infestation start date.

[0037] The primary prediction module includes setting a risk pest degree threshold and a maximum acceptable pest degree threshold. When the degree of damage to the leaves of the crop reaches the risk pest degree threshold, the average daily growth rate of the leaf damage from the start of the pest to the day when the pest risk threshold is reached is calculated. Specifically, the total number of days from the start of the pest to the day when the pest risk threshold is reached is obtained. , combined with the risk pest level threshold , and the average daily growth rate of leaf damage is obtained , and then obtain the remaining growth cycle days of the crops in the greenhouse from the day when the pest risk threshold is reached , get the expected degree of damage to the leaves after the crop growth cycle ends , and then take corresponding prevention and control measures according to the degree of damage to the leaves after the growth cycle.

[0038] For example, the system can set the maximum acceptable pest level threshold to 10%, meaning that the leaf damage of greenhouse crops should not exceed 10% before the end of the crop growth cycle. The risk pest level threshold is set to 3%, and the number of days in the greenhouse crop growth cycle is set to 90. On day 30, the system detects that the leaf damage of the greenhouse crops has reached 0.1%, meaning that the 30th day is the start date of the pest infestation. On day 60, the system detects that the leaf damage of the greenhouse crops has reached 3%, meaning that the risk pest level threshold has been reached. At this point, the total number of days from the start date of the pest infestation to the day the pest risk threshold is reached is 30, and the average daily growth rate of leaf damage is obtained as 0.1%. Since the 60th day is 30 days away from the 90-day greenhouse crop growth cycle, the estimated leaf damage after the end of the crop growth cycle is 6%.

[0039] After obtaining the estimated degree of damage to the leaves, compare the estimated degree of damage with the maximum acceptable insect pest threshold. If the estimated degree of damage is greater than or equal to the maximum acceptable insect pest threshold, it means that based on the current incremental degree of leaf damage in the greenhouse, the insect pest level will exceed the maximum acceptable range during the entire crop growth cycle, resulting in serious damage to crop yield and quality. Pesticides should be sprayed on the crops in the greenhouse for prevention and control to effectively control the spread of insect pests. If the estimated degree of damage is less than the maximum acceptable insect pest threshold, it means that based on the current incremental degree of leaf damage in the greenhouse, the growth of insect pests is still within a controllable range. The control strategy should be implemented to try to reduce the average daily growth rate of leaf damage and control the reproduction and growth of pests.

[0040] Since the greenhouse provides a suitable humidity environment for pests, which is especially conducive to pest reproduction, the control strategy includes increasing the ventilation frequency in the greenhouse, controlling the humidity control equipment in the greenhouse, and appropriately adjusting the irrigation frequency and water volume. The humidity sensor is used to monitor the humidity conditions in the greenhouse and reduce them to the minimum acceptable standard for crops. For example, the suitable growth humidity of crops in the greenhouse is 60%, and the minimum growth humidity is 40%. When the system obtains an estimated degree of damage to the leaves of 6%, which is less than the maximum acceptable pest level threshold, the system will use the control strategy to reduce the humidity in the greenhouse from 60% suitable for crop growth to the minimum acceptable 40%, thereby trying to inhibit the reproduction and growth rate of pests.

[0041] However, it's important to note that the growth rate of leaf damage from pests, as well as the pest's reproduction rate, is generally not linear. This is affected by the different feeding habits and reproduction rates of different pests, which can cause the average daily growth rate of leaf damage to vary accordingly. Therefore, after the system first estimates the extent of leaf damage at the end of the crop growth cycle and implements a control strategy, further verification of the actual changes in leaf damage after the control strategy is implemented is still necessary.

[0042] In order to further verify the effect of the control strategy on the degree of leaf damage, the secondary prediction module includes calculating the average daily growth rate of the degree of damage to the leaves of the crops in the greenhouse after the control strategy is implemented. Specifically, an observation period is set and the total growth rate of the degree of damage to the leaves of the crops in the greenhouse during the observation period is calculated. Combined with the number of days of observation period Average daily growth rate after regulation and compared it with the average daily growth rate of leaf damage before the control strategy Make a size comparison and make corresponding prevention and control measures based on the comparison results;

[0043] More specifically, when the average daily growth rate after regulation is less than the average daily growth rate of leaf damage before regulation, it indicates that the regulation strategy implemented by the system is effective in inhibiting the reproduction and growth of pests and can effectively reduce the growth rate of leaf damage; and when the average daily growth rate after regulation is greater than or equal to the average daily growth rate of leaf damage before regulation, it indicates that the pests in the greenhouse are not effectively affected by the regulation strategy, the degree of leaf damage will further increase, and pesticide spraying should be used to prevent and control the crops in the greenhouse to effectively control the spread of pests. For example, in combination with the settings in the above embodiment, the system can set the observation period to 5 days. After the regulation strategy is implemented to reduce the humidity in the greenhouse to 40%, the leaf damage degree of the crops in the greenhouse increases from 3% to 3.3% in a total of 5 days, that is, the total increase in leaf damage during the observation period is 0.3%. The average daily growth rate after regulation is 0.06%, which is less than the average daily growth rate of leaf damage before regulation of 0.1%, thereby verifying that the regulation strategy implemented by the system is effective in inhibiting pests.

[0044] Although the above embodiment evaluates the insect pests in the greenhouse through two predictions, even if the control strategy can suppress the insect pests, it also sacrifices the suitable humidity growth environment of the crops in the greenhouse. Even within the minimum acceptable humidity range of the crops, it will have a certain impact on the quality and yield of the crops after maturity. Therefore, as another preferred embodiment of this scheme, the purpose is to reduce this impact: the feedback adjustment module includes obtaining the degree of leaf damage after the end of the crop growth cycle according to the average daily growth rate of the degree of leaf damage of the crops in the greenhouse after the control strategy, and combining the two degrees of leaf damage after the end of the growth cycle before and after the control strategy to obtain the controllable degree, specifically obtaining the degree of leaf damage of the crops in the current greenhouse. , combined with the average daily growth rate after regulation And the remaining growth cycle days of crops in the greenhouse , and obtain the expected degree of damage to the leaves of crops at the end of the growth cycle in the greenhouse after regulation , combined with the maximum acceptable pest level threshold Get the degree of control ,in, is the proportional adjustment factor.

[0045] Moreover, the specific method of controlling the crop growth environment in the greenhouse according to the controllable degree is to adjust the controllable degree Divide by the remaining growth cycle days of the crops in the greenhouse , and obtain the maximum daily increase in leaf damage during the remaining growth period of the crop ,in, is the proportional adjustment factor, combined with the change of humidity value in the greenhouse Changes in the average daily growth rate of leaf damage before and after the control strategy Get the humidity environment in the greenhouse , and adjust the humidity in the greenhouse accordingly based on the humidity environment in the greenhouse, but not exceeding the growth humidity that the crops in the greenhouse are adapted to.

[0046] For example, in combination with the above embodiment, after the observation period, the degree of damage to the leaves of the current crop in the greenhouse (i.e., the 65th day of crop growth) is 3.3%. After the control strategy, the average daily growth rate is 0.06%. At this time, there are still 25 days left in the remaining growth cycle of the crops in the greenhouse. The estimated degree of damage to the leaves at the end of the crop growth cycle can be obtained as , set the proportional adjustment factor is 0.3. Since the maximum acceptable pest level threshold is set at 10%, the controllable level can be obtained as , set the proportional adjustment factor is 0.34, and the maximum daily increase in leaf damage during the remaining growth period of the crop is Under the implementation of the control strategy, the humidity in the greenhouse is reduced from 60% to 40%, and there is a 20% humidity value change. At the same time, under the implementation of the control strategy, the average daily growth rate of the leaf damage degree is reduced from 0.1% to 0.06%, and there is a 0.04% daily growth rate change. Therefore, the control strategy can be used to determine the humidity environment in the greenhouse. In summary, the system can increase the humidity in the greenhouse by 10.5% by adjusting the control strategy, so that the crops in the greenhouse can grow in a humidity environment of 50.5%. In addition, it should be noted that the proportional adjustment factor and They are used to adjust the controllable degree and the maximum daily leaf damage value. In order to ensure that when the humidity environment is increased, the corresponding increase in leaf damage rate and insect pest growth will not exceed the expected range, the and Both are less than 1, so that the adjustable degree and the maximum daily leaf damage can be reduced according to the actual crop growth and planting conditions. The purpose of this is that the system needs to adjust the humidity in the greenhouse conservatively to ensure that the adjusted leaf damage level can still be within the maximum acceptable insect pest threshold after the crop growth cycle ends.

[0047] In general, the present invention aims to design an intelligent management system for the Internet of Things (IoT) of agriculture to address the current problem of a lack of effective prediction methods for pest management in greenhouse crops, making it impossible to take preventive measures in advance. The present invention combines existing image acquisition equipment to capture images of greenhouse crop leaves, and through monitoring and analyzing the image data, assesses the degree of leaf damage to the greenhouse crops, thereby reflecting the number and severity of pests on the greenhouse crops. When the degree of leaf damage to the greenhouse crops reaches the risk pest threshold, a timely warning of potential pest problems is issued. The system can estimate whether the impact of pests will exceed a reasonable range based on the rate of change of leaf damage, and can make corresponding treatment decisions based on the estimated results. Through timely pest management, crop losses can be effectively reduced, and the yield and quality of crops can be improved. Based on the assessment of pest risk, agricultural managers can more accurately decide when and how to apply pesticides, thereby reducing unnecessary chemical use and reducing pesticide efficiency. Intelligent greenhouse pest monitoring and analysis management reduces the need for manual inspections and improves management efficiency. At the same time, under the action of the feedback adjustment module, through precise humidity and pest management, it is possible to find a balance between pest suppression and growth conditions while keeping crops from being affected by pests, thereby reducing crop yield and quality losses caused by pest suppression interference.

[0048] See also Figure 2 The present invention provides an intelligent management method for agricultural Internet of Things, which comprises the following steps:

[0049] Use image acquisition equipment to capture images of crop leaves in the greenhouse to assess the degree of damage to the leaves. When the degree of damage reaches the initial pest threshold, it is marked as the pest start date.

[0050] Calculate the average daily growth rate of leaf damage from the onset of pest infestation to the day the pest risk threshold is reached. Combined with the remaining growth cycle days of the crops in the greenhouse, the degree of leaf damage at the end of the crop growth cycle is calculated. Based on the degree of leaf damage at the end of the growth cycle, appropriate prevention and control measures are taken.

[0051] Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the control strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the control strategy is implemented. Based on the comparison results, make corresponding prevention and control measures again;

[0052] The degree of leaf damage after the two growth cycles before and after the regulation strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

[0053] In the embodiments disclosed herein, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed herein include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component and / or installed from removable media. When the computer program is executed by a central processing unit, the functions defined in the methods of this application are performed. It should be noted that the computer-readable medium referred to herein can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, electrical, optical, RF, or any suitable combination thereof.

[0054] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0055] Those skilled in the art should understand that the above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the scope of protection of the present application.

Claims

1. The intelligent management system of agricultural Internet of Things is characterized by: include: The pest monitoring module uses an image acquisition device to capture images of the leaves of crops in the greenhouse, assesses the degree of damage to the leaves of the crops, and marks the infestation start date when the degree of damage reaches an initial infestation threshold; A primary prediction module, which calculates the average daily growth rate of leaf damage from the onset of pest infestation to the day when the pest risk threshold is reached, and combines the remaining growth cycle days of the crops in the greenhouse to determine the degree of leaf damage at the end of the crop growth cycle, and takes corresponding prevention and control measures based on the degree of leaf damage at the end of the growth cycle; A secondary prediction module, which calculates the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the control strategy is implemented, compares it with the average daily growth rate of the degree of damage to the leaves before the control strategy is implemented, and makes corresponding prevention and control measures again based on the comparison results; A feedback adjustment module, wherein the feedback adjustment module combines the leaf damage degree after the two growth cycles before and after the control strategy to obtain an adjustable degree, and controls the crop growth environment in the greenhouse according to the adjustable degree; In a prediction module, if the predicted damage level is greater than or equal to the maximum acceptable pest level threshold, pesticide spraying is used on the crops in the greenhouse to effectively control the spread of pests. If the estimated damage level is less than the maximum acceptable pest level threshold, a control strategy is implemented to try to reduce the average daily growth rate of leaf damage and control pest reproduction and growth; In the primary prediction module, the average daily growth rate of leaf damage is calculated. In the secondary prediction module, when the average daily growth rate after regulation is less than the average daily growth rate of leaf damage before regulation, it indicates that the regulation strategy implemented by the system is effective in inhibiting the reproduction and growth of pests and can effectively reduce the growth rate of leaf damage. When the average daily growth rate after regulation is greater than or equal to the average daily growth rate of the leaf damage before regulation, the crops in the greenhouse should be sprayed with pesticides for prevention and control to effectively control the spread of pests.

2. The intelligent management system for agricultural Internet of Things according to claim 1, characterized in that: In a prediction module, the specific method for obtaining the degree of leaf damage after the crop growth cycle is as follows: Get the total number of days from the start of the pest infestation to the day when the pest risk threshold is reached , combined with the risk pest level threshold , and the average daily growth rate of leaf damage is obtained , and then obtain the remaining growth cycle days of the crops in the greenhouse from the day when the pest risk threshold is reached , get the expected degree of damage to the leaves after the crop growth cycle ends .

3. The intelligent management system for agricultural Internet of Things according to claim 2, characterized in that: The control strategy includes increasing the ventilation frequency in the greenhouse, controlling the humidity control equipment in the greenhouse, and appropriately adjusting the irrigation frequency and water volume, and using humidity sensors to monitor the humidity conditions in the greenhouse to reduce them to the minimum acceptable standard for crops.

4. The intelligent management system for agricultural Internet of Things according to claim 3, characterized in that: After implementing the control strategy, the specific method to obtain the daily average growth rate of the degree of damage to the leaves of the crops in the greenhouse is as follows: set an observation period, and calculate the total growth rate of the degree of damage to the leaves of the crops in the greenhouse during the observation period. Combined with the number of days of observation period Average daily growth rate after regulation .

5. The intelligent management system for agricultural Internet of Things according to claim 1, characterized in that: The specific way to obtain the controllable degree is to obtain the degree of damage to the leaves of the crops in the current greenhouse. , combined with the average daily growth rate after regulation And the remaining growth cycle days of crops in the greenhouse , and obtain the expected degree of damage to the leaves of crops at the end of the growth cycle in the greenhouse after regulation , combined with the maximum acceptable pest level threshold Get the degree of control ,in, is the proportional adjustment factor.

6. The intelligent management system for agricultural Internet of Things according to claim 5, characterized in that: The degree of controllability Divide by the remaining growth cycle days of the crops in the greenhouse , and obtain the maximum daily increase in leaf damage during the remaining growth period of the crop ,in, is the proportional adjustment factor, combined with the change of humidity value in the greenhouse Changes in the average daily growth rate of leaf damage before and after the control strategy Get the humidity environment in the greenhouse , and adjust the humidity in the greenhouse accordingly based on the humidity environment in the greenhouse, but not exceeding the growth humidity that the crops in the greenhouse are adapted to.

7. The intelligent management method of agricultural Internet of Things is characterized by: According to any one of claims 1 to 6, the method for the intelligent management system of the agricultural Internet of Things comprises the following steps: Use image acquisition equipment to capture images of crop leaves in the greenhouse to assess the degree of damage to the leaves. When the degree of damage reaches the initial pest threshold, it is marked as the pest start date. Calculate the average daily growth rate of leaf damage from the onset of pest infestation to the day the pest risk threshold is reached. Combined with the remaining growth cycle days of the crops in the greenhouse, the degree of leaf damage at the end of the crop growth cycle is calculated. Based on the degree of leaf damage at the end of the growth cycle, appropriate prevention and control measures are taken. Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the control strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the control strategy is implemented. Based on the comparison results, make corresponding prevention and control measures again; The degree of leaf damage after the two growth cycles before and after the regulation strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the agricultural Internet of Things intelligent management system according to any one of claims 1 to 6.

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