Agricultural Internet of Things intelligent management system and method

Abstract

The invention relates to the technical field of crop prediction management, and discloses an agricultural Internet of Things intelligent management system and method.The agricultural Internet of Things intelligent management system comprises an insect pest monitoring module, and the insect pest monitoring module comprises an image acquisition device for shooting images of leaves of crops in a greenhouse, evaluating the damage degree of the leaves of the crops and monitoring the damage degree of the leaves of the crops; when the leaf damage degree reaches an initial insect damage degree threshold value, marking as an insect damage starting day; and the primary prediction module is used for calculating the daily average growth rate of the leaf damage degree from the insect pest starting day to the day reaching the insect pest risk threshold value. By monitoring the damage degree of the leaves, the system can timely warn the potential insect pest problem, so that agricultural management personnel can take prevention and control measures before large-scale outbreak of insect pests, and can more accurately determine when and how to apply pesticides based on the assessment of the insect pest risk, so that the agricultural management personnel can take prevention and control measures before large-scale outbreak of insect pests. Therefore, unnecessary chemical use is reduced.

Description

Technical Field

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

[0002] In greenhouse agricultural planting technology, the appearance of pests in greenhouses is a relatively common phenomenon. Since greenhouses provide a relatively closed and suitable environment, pests are easy to breed and threaten crops. Pests will directly damage crop leaves and affect photosynthesis, thereby inhibiting crop growth and yield.

[0003] As for the current pest management in greenhouse agriculture, although there is technology 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 it is unable to 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, and often resort to pesticide spraying directly 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 view of 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 the greenhouse, 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 solution: an intelligent management system for agricultural Internet of Things, comprising: The pest monitoring module includes using an image acquisition device to take images of the leaves of crops in the greenhouse, assessing the degree of damage to the leaves of the crops, and marking the start date of the pest when the degree of damage to the leaves reaches an initial pest degree threshold; A primary prediction module, which includes calculating the average daily growth rate of leaf damage from the start date of the pest to the day when the pest risk threshold is reached, and combining the remaining growth cycle days of the crops in the greenhouse to obtain the leaf damage degree after the crop growth cycle ends, and taking corresponding prevention and control measures according to the leaf damage degree after the growth cycle ends; A secondary prediction module, wherein 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, and comparing the average daily growth rate of the degree of damage to the leaves with the average daily growth rate before the control strategy is implemented, and making corresponding prevention and control measures again according to the comparison results; A feedback adjustment module, wherein the feedback adjustment module combines the leaf damage degrees 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.

[0007] In some embodiments, in a prediction module, the specific method of obtaining the degree of damage to leaves after the crop growth cycle ends is: 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 estimated degree of damage to the leaves after the crop growth cycle ends .

[0008] In some embodiments, in a prediction module, if the estimated 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 growth cycle of the crop, resulting in serious damage to crop yield and quality. Pesticides should be sprayed on crops in the greenhouse for prevention and control to effectively control the spread of pests. If the estimated 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 daily average growth rate of leaf damage and control pest reproduction and growth.

[0009] In some embodiments, the control strategy includes increasing the ventilation times 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.

[0010] In some embodiments, after the regulation 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 The average daily growth rate after regulation .

[0011] 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 executed 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.

[0012] In some embodiments, the specific method of obtaining the adjustable degree is: obtaining the degree of damage to the leaves of the 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 estimated 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.

[0013] In some embodiments, the adjustable degree Divide by the remaining growth period 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 it will not exceed the growth humidity that the crops in the greenhouse are adapted to.

[0014] The present invention also provides the following technical solution: an intelligent management method for agricultural Internet of Things, comprising the following steps: Use image acquisition equipment to take images of the leaves of crops in the greenhouse to assess the degree of damage to the leaves of the crops. When the degree of damage to the leaves reaches the threshold of the initial pest level, it is marked as the pest start date. Calculate the average daily growth rate of leaf damage from the start of the pest to the day when the pest risk threshold is reached, and combine the remaining days of the crop growth cycle in the greenhouse to obtain the degree of leaf damage after the crop growth cycle ends. Take corresponding prevention and control measures based on the degree of leaf damage after the growth cycle ends. Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the regulation strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the regulation strategy is implemented, and make corresponding prevention and control measures again based on the comparison results; The degree of leaf damage after the two growth cycles before and after the control strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

[0015] The present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned agricultural Internet of Things intelligent management system and method.

[0016] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: Firstly, by monitoring the degree of leaf damage, the system can promptly warn 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 use of chemicals.

[0017] 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.

[0018] Thirdly, the present invention can carry out precise humidity and pest management through the feedback adjustment module, find a balance between pest control and growth conditions while keeping crops from being affected by pests, and reduce crop yield and quality losses caused by interference from pest control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a module schematic diagram of the intelligent management system of the agricultural Internet of Things of the present invention; Figure 2 It is a schematic diagram of the process of the intelligent management method of agricultural Internet of Things of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0021] 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 element may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0022] See also Figure 1 , the present invention provides an intelligent management system for agricultural Internet of Things, the system comprising an insect pest monitoring module, a primary prediction module, a secondary prediction module and a feedback adjustment module; The pest monitoring module includes using an image acquisition device to capture images of the leaves of crops in the greenhouse, using an image processing algorithm 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 pests on the crops in the greenhouse.

[0023] In addition, the greenhouse provides a relatively closed and suitable environment, where pests can easily breed and threaten crops. In greenhouse cultivation, pest management is of course very important. Although ideally there should be no pests in the greenhouse, it is unrealistic to completely eliminate pests in actual greenhouse agricultural operations. Therefore, the key to greenhouse pest management is to control the number of pests and keep it within a reasonable range.

[0024] An initial pest level threshold should be set. When the leaf damage reaches the initial pest level threshold, the system records the timestamp of the day and marks it as the pest start date. For example, the initial pest level threshold can be set to 0.1%. When the system detects that the leaf damage of crops in the greenhouse reaches 0.1%, it means that pests have destroyed crops in the greenhouse, and the system will mark the day as the pest start date.

[0025] The primary prediction module includes setting a risk pest level threshold and a maximum acceptable pest level threshold. When the leaf damage level of the crop reaches the risk pest level threshold, the average daily growth rate of the leaf damage level 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 estimated 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 ends.

[0026] For example, the system can set the maximum acceptable pest level threshold to 10%, that is, the degree of damage to the leaves of the crops in the greenhouse should not exceed 10% before the end of the crop growth cycle, set the risk pest level threshold to 3%, and set the number of days in the greenhouse crop growth cycle to 90 days. On the 30th day, the system monitored that the degree of damage to the leaves of the crops in the greenhouse reached 0.1%, that is, the 30th day was the start day of the pest. On the 60th day, the system monitored that the degree of damage to the leaves of the crops in the greenhouse reached 3%, that is, reaching the risk pest level threshold. At this time, the total number of days from the start day of the pest to the day when the pest risk threshold is reached can be obtained as 30 days, and the average daily growth rate of the leaf damage is 0.1%. Since the 60th day is 30 days away from the 90-day growth cycle of the crops in the greenhouse, it can be obtained that the expected degree of damage to the leaves after the end of the crop growth cycle is 6%.

[0027] After obtaining the estimated degree of damage to the leaves, compare the estimated degree of damage with the maximum acceptable pest level threshold. If the estimated degree of damage is greater than or equal to the maximum acceptable pest level threshold, it means that based on the current incremental degree of leaf damage in the greenhouse, the pest level will exceed the maximum acceptable range during the entire growth cycle of the crop, resulting in serious damage to crop yield and quality. Pesticides should be used to spray crops in the greenhouse for prevention and control to effectively control the spread of pests. If the estimated degree of damage is less than the maximum acceptable pest level 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. A 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.

[0028] Since the greenhouse provides pests with a suitable humidity environment, which is particularly conducive to pest reproduction, the control strategy includes increasing the ventilation times 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%, which is suitable for crop growth, to the minimum acceptable 40%, thereby trying to inhibit the reproduction and growth rate of pests.

[0029] However, it should be noted that the growth rate of leaf damage caused by pests and the reproduction rate of pests are generally not linear. This is affected by the different feeding habits and reproduction rates of different types of pests, which will cause the daily average growth rate of leaf damage to change accordingly. Therefore, after the system obtains the estimated degree of leaf damage after the crop growth cycle ends for the first time and implements a control strategy, it is still necessary to further verify the actual changes in the degree of leaf damage after the control strategy.

[0030] In order to further verify the influence 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 leaf damage of 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 leaf damage of crops in the greenhouse during the observation period is calculated. Combined with the number of days of observation period The average daily growth rate after regulation and compared it with the daily average 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; More specifically, when the average daily growth rate after regulation is less than the average daily growth rate of the degree of damage to the leaves before regulation, it indicates that the regulation strategy executed by the system is effective in inhibiting the reproduction and growth of pests, and can effectively reduce the growth rate of the degree of damage to the leaves; and when the average daily growth rate after regulation is greater than or equal to the average daily growth rate of the degree of damage to the leaves before regulation, it indicates that the pests in the greenhouse are not effectively affected by the regulation strategy, and the degree of damage to the leaves will further expand. 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 executed to reduce the humidity in the greenhouse to 40%, the degree of damage to the leaves of the crops in the greenhouse increases from 3% to 3.3% in a total of 5 days, that is, the total increase in the degree of damage to the leaves during the observation period is 0.3%, and the average daily growth rate after regulation is 0.06%, which is less than the average daily growth rate of the degree of damage to the leaves before regulation, which is 0.1%, thereby verifying that the regulation strategy executed by the system has an effect on inhibiting pests.

[0031] Although the above embodiment evaluates the insect pests in the greenhouse through two predictions, even if the pests can be suppressed after the control strategy is executed, the suitable humidity growth environment for crops in the greenhouse is sacrificed to a large extent. 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 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 estimated 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.

[0032] Moreover, the specific method of controlling the growth environment of crops in the greenhouse according to the adjustable degree is to adjust the adjustable degree Divide by the remaining growth period 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 it will not exceed the growth humidity that the crops in the greenhouse are adapted to.

[0033] 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 increase in leaf damage during the remaining growth period of the crop is , and under the implementation of the control strategy, the humidity in the greenhouse was reduced from 60% to 40%, with a 20% humidity value change. At the same time, under the implementation of the control strategy, the average daily growth rate of leaf damage was reduced from 0.1% to 0.06%, with 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 adjustable degree and the maximum daily increase in leaf damage. 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 are all less than 1, so that the adjustable degree and the maximum daily increase in 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 is still within the maximum acceptable pest level threshold after the crop growth cycle ends.

[0034] In general, the present invention aims to design an intelligent management system for agricultural Internet of Things, which is used to address the problem that there is no effective prediction method for pest management in the current greenhouse crop planting, and it is impossible to take preventive measures in advance. The present invention combines the existing image acquisition equipment to take images of the leaves of the greenhouse crops, and evaluates the degree of damage to the leaves of the greenhouse crops by monitoring and analyzing the image data to reflect the number and severity of pests on the greenhouse crops. When the degree of damage to the leaves of the greenhouse crops reaches the risk pest level threshold, the potential pest problem is timely warned. The system can estimate whether the impact of the pests will exceed the reasonable range according to the change rate of the leaf damage, and can make corresponding processing judgments according to 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 risks, agricultural managers can more accurately decide when and how to apply pesticides, thereby reducing unnecessary chemical use and reducing the efficiency of pesticide use. 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 control and growth conditions while keeping crops from being affected by pests, thereby reducing crop yield and quality losses caused by pest control interference.

[0035] See also Figure 2 The present invention provides an intelligent management method for agricultural Internet of Things, the method comprising the following steps: Use image acquisition equipment to take images of the leaves of crops in the greenhouse to assess the degree of damage to the leaves of the crops. When the degree of damage to the leaves reaches the threshold of the initial pest level, it is marked as the pest start date. Calculate the average daily growth rate of leaf damage from the start of the pest to the day when the pest risk threshold is reached, and combine the remaining days of the crop growth cycle in the greenhouse to obtain the degree of leaf damage after the crop growth cycle ends. Take corresponding prevention and control measures based on the degree of leaf damage after the growth cycle ends. Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the regulation strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the regulation strategy is implemented, and make corresponding prevention and control measures again based on the comparison results; The degree of leaf damage after the two growth cycles before and after the control strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

[0036] The embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit, the above functions defined in the method of the present application are executed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of an electrical, magnetic, optical, electromagnetic, infrared segment, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wire segments, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination 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 segments, wire segments, optical cables, RF, etc., or any suitable combination of the above.

[0037] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the 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 square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square 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.

[0038] Those skilled in the art should understand that the above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. The intelligent management system of agricultural Internet of Things is characterized by: include: The pest monitoring module includes using an image acquisition device to take images of the leaves of crops in the greenhouse, assessing the degree of damage to the leaves of the crops, and marking the start date of the pest when the degree of damage to the leaves reaches an initial pest degree threshold; A primary prediction module, which includes calculating the average daily growth rate of leaf damage from the start date of the pest to the day when the pest risk threshold is reached, and combining the remaining growth cycle days of the crops in the greenhouse to obtain the leaf damage degree after the crop growth cycle ends, and taking corresponding prevention and control measures according to the leaf damage degree after the growth cycle ends; A secondary prediction module, wherein 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, and comparing the average daily growth rate of the degree of damage to the leaves with the average daily growth rate before the control strategy is implemented, and making corresponding prevention and control measures again according to the comparison results; A feedback adjustment module, wherein the feedback adjustment module combines the leaf damage degrees 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.

2. The intelligent management system of agricultural Internet of Things according to claim 1 is characterized in that: In a prediction module, the specific method of 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 estimated degree of damage to the leaves after the crop growth cycle ends .

3. The intelligent management system of agricultural Internet of Things according to claim 2 is characterized in that: In a prediction module, if the estimated 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 growth cycle of the crop, resulting in serious damage to crop yield and quality. Pesticide spraying should be used for crops in the greenhouse to effectively control the spread of pests. If the estimated 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. A 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.

4. The intelligent management system of agricultural Internet of Things according to claim 3 is characterized in that: The control strategy includes increasing the ventilation times 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.

5. The intelligent management system of agricultural Internet of Things according to claim 4 is characterized in that: After implementing the control strategy, the daily average growth rate of the degree of damage to the leaves of the crops in the greenhouse is 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 The average daily growth rate after regulation .

6. The intelligent management system of agricultural Internet of Things according to claim 5 is characterized in that: 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 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.

7. The intelligent management system of agricultural Internet of Things according to claim 1, characterized in that: The specific way to obtain the adjustable degree is: obtain the degree of leaf damage of the current crop 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 estimated 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.

8. The intelligent management system of agricultural Internet of Things according to claim 7 is characterized in that: The degree of controllability Divide by the remaining growth period 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 it will not exceed the growth humidity that the crops in the greenhouse are adapted to.

9. The intelligent management method of agricultural Internet of Things is characterized by: According to any one of claims 1 to 8, the method comprises the following steps: Use image acquisition equipment to take images of the leaves of crops in the greenhouse to assess the degree of damage to the leaves of the crops. When the degree of damage to the leaves reaches the threshold of the initial pest level, it is marked as the pest start date. Calculate the average daily growth rate of leaf damage from the start of the pest to the day when the pest risk threshold is reached, and combine the remaining days of the crop growth cycle in the greenhouse to obtain the degree of leaf damage after the crop growth cycle ends. Take corresponding prevention and control measures based on the degree of leaf damage after the growth cycle ends. Calculate the average daily growth rate of the degree of damage to the leaves of crops in the greenhouse after the regulation strategy is implemented, and compare it with the average daily growth rate of the degree of damage to the leaves before the regulation strategy is implemented, and make corresponding prevention and control measures again based on the comparison results; The degree of leaf damage after the two growth cycles before and after the control strategy is combined to obtain the controllable degree, and the crop growth environment in the greenhouse is controlled according to the controllable degree.

10. 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 described in any one of claims 1 to 8.

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