Greenhouse pesticide application method based on dynamic regulation and control of environmental factors

Abstract

The invention belongs to the technical field of agricultural intellectualization, and provides a greenhouse pesticide application method based on dynamic regulation and control of environmental factors. Comprising the following steps: S1, establishing a pesticide application relation database; s2, obtaining the environmental influence coefficient of the environmental factors on the prevention and control effect and the lowest effective concentration and the highest safe concentration of the pesticide through laboratory testing; s3, determining the used pesticide through the pesticide application relation database; s4, actual numerical values of environmental factors are obtained through a sensor installed in a field, and the concentration of used pesticide is determined through a weighted calculation model; s5, adjusting environmental factors to enable the pesticide concentration to reach the lowest effective concentration or the highest safe concentration after calculation of the weighted calculation model; s6, pesticide is prepared and sprayed to the crops. The pesticide concentration can be adjusted according to actual environmental factors, the pesticide database can be updated according to the prevention and control effect of pesticide spraying, and efficient prevention and control of diseases and insect pests are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent agriculture, and in particular relates to a greenhouse pesticide application method based on dynamic regulation of environmental factors. Background Art

[0002] In agricultural production, pest and disease control is a crucial component in ensuring crop yield and quality. Traditional pesticide application techniques rely primarily on manual experience to determine the type and dosage of pesticides, leading to problems such as pesticide waste, environmental pollution, and unstable control effectiveness. With the development of modern agricultural technology, the impact of environmental factors on the effectiveness of pesticides has gradually gained attention. Studies have shown that environmental parameters such as temperature, humidity, and light intensity can significantly alter the physical and chemical properties of pesticides, affecting their adhesion to crop surfaces, degradation rates, and the biological activity of pests and diseases. For example, high temperatures can accelerate pesticide volatilization, while excessive humidity can easily cause the loss of the pesticide solution, which directly causes the actual effective dose to deviate from the preset value.

[0003] In recent years, precision pesticide application technology has begun to incorporate environmental monitoring data into its application decisions. Existing technologies typically use fixed thresholds to simply adjust environmental parameters, such as automatically increasing the application rate when the temperature exceeds a set range. However, this approach fails to consider the synergistic effects of multiple environmental factors.

[0004] In terms of pesticide concentration control, existing methods mostly use linear compensation models, which simply superimpose environmental parameters and basic concentrations. This approach ignores the interactive effects of environmental factors on the active ingredients of pesticides. For example, temperature and light intensity may jointly change the chemical stability of pesticides. Current technology also has limitations in database construction. Most systems use static data storage methods and fail to effectively integrate laboratory test data with actual field control effects. These problems restrict the promotion and application of precision pesticide application technology. There is an urgent need to establish a dynamic control system based on multi-source data fusion to improve pesticide utilization efficiency and ecological and environmental safety.

[0005] Chinese patent application publication number CN117441532A discloses a non-pesticide method for controlling rice bakanae disease. While this application utilizes biopesticides to control pests by leveraging natural enemies and predatory insects, thereby reducing damage to crops, it fails to tailor pesticide concentrations to specific environmental factors, nor does it update the pesticide database in real time based on control effectiveness. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the present invention provides a greenhouse pesticide application method based on dynamic environmental control. This method not only adjusts pesticide concentration based on actual environmental factors, but also updates the pesticide database based on the control effects of spraying pesticides, achieving efficient pest control.

[0007] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0008] The present invention provides a greenhouse pesticide application method based on dynamic regulation of environmental factors, comprising the following steps: S1, establishing a relational database whose attributes include pest and disease type, pesticide type, environmental factors, pesticide concentration and control effect, and establishing a pesticide application relational database with the pest and disease type as the only primary key; S2, obtaining the environmental impact coefficient of environmental factors on the control effect, as well as the minimum effective concentration and maximum safe concentration of various pesticides through laboratory tests; S3, collecting pest and disease images through unmanned aerial vehicles, using image analysis to identify the pest and disease type, and determining the type of pesticide to be used through the pesticide application relational database; S4, obtaining the actual value of the environmental factor through an integrated sensor installed in the field, and determining the pesticide concentration to be used through a weighted calculation model; S5, when the pesticide concentration is lower than the minimum effective concentration or higher than the maximum safe concentration, adjusting the environmental factors so that the pesticide concentration reaches the minimum effective concentration or the maximum safe concentration after calculation by the weighted calculation model; S6, configuring pesticides with a concentration equal to the pesticide concentration finally obtained in the above steps, and spraying the crops, collecting control effect data 48 hours after the spraying, and updating the pesticide application relational database.

[0009] According to one embodiment of the present invention, the environmental factors in S1 include temperature, humidity, light intensity and CO2 concentration.

[0010] According to one embodiment of the present invention, the method for establishing a pesticide application relational database in S1 is as follows: T1, by parsing the pesticide registration data in the China Pesticide Information Network, a combination of pest and disease type, pesticide type and pesticide concentration is obtained; T applies pesticides in different environments according to the pesticide registration data in step T1, and records the pest and disease type, pesticide type, the values of four environmental factors, pesticide concentration and the control effect achieved after application, thereby establishing a relational database whose attributes include pest and disease type, pesticide type, environmental factors, pesticide concentration and control effect, and the format of each row record in the relational database is; T3, when the same pest and disease type corresponds to multiple pesticide types, environmental factors or pesticide concentrations, only the row with the best control effect for this pest and disease type in the relational database is retained; T4, thereby establishing a pesticide application relational database with the pest and disease type as the unique primary key.

[0011] According to one embodiment of the present invention, the environmental influence coefficients in S2 include a temperature influence coefficient W, a humidity influence coefficient S, a light intensity influence coefficient G, and a CO2 concentration influence coefficient C.

[0012] According to one embodiment of the present invention, the method for obtaining each environmental impact coefficient is as follows: E1, changing any one environmental factor based on the records in the pesticide application relationship database, and controlling the type of pests and diseases, the type of pesticides, the remaining three environmental factors, and the pesticide concentration to remain unchanged; E2, obtaining the relationship coefficient between the change value of the changed environmental factor and the percentage change of the control effect, and the relationship coefficient is the impact coefficient of the environmental factor.

[0013] According to one embodiment of the present invention, the method for obtaining the minimum effective concentration in S2 is as follows: Q1. Based on the pesticide application relationship database, independent experimental groups are established for different types of pests and diseases and their corresponding pesticides, and each experimental group only contains the same type of pests and diseases and their corresponding pesticides; Q2. After each experimental group infects healthy crops with pests and diseases, the crops are sprayed with corresponding pesticides of different concentrations; Q3. After 48 hours, each experimental group is observed separately to obtain the lowest value of the pesticide concentration in each experimental group that stops the spread of lesions or causes all pests to show obvious symptoms of poisoning; Q4. The lowest value of the pesticide concentration is the minimum effective concentration of the corresponding pest and disease type and pesticide type in the experimental group.

[0014] According to one embodiment of the present invention, the method for obtaining the maximum safe concentration in S2 is: W1. Based on the pesticide application relationship database, independent experimental groups are established for different types of pesticides recorded in the pesticide application relationship database, and each experimental group only contains the same type of pesticide; W2. Each experimental group uses pesticides of different concentrations to spray healthy crops; W3. After 48 hours, the crops in each experimental group are observed separately to obtain the highest value of the pesticide concentration in each experimental group that does not cause brown spots on the crops; W4. The highest value of the pesticide concentration is the maximum safe concentration of the pesticide corresponding to the experimental group.

[0015] According to one embodiment of the present invention, the weighted calculation model in S4 is:

[0016] Cfinal=Cbase×(1+(Aa)W)×(1+(Bb)S)×(1+(Cc)G)×(1+(Dd)C)

[0017] Where Cfinal represents the final pesticide concentration, Cbase represents the pesticide concentration of the corresponding pest type in the pesticide application relationship database; W is the temperature influence coefficient, S is the humidity influence coefficient, G is the light intensity influence coefficient, and C is the CO2 concentration influence coefficient; A is the actual temperature, B is the actual humidity, C is the actual light intensity, and D is the actual CO2 concentration; a is the temperature of the corresponding pest type recorded in the pesticide application relationship database, b is the humidity of the corresponding pest type recorded in the pesticide application relationship database, c is the light intensity of the corresponding pest type recorded in the pesticide application relationship database, and d is the CO2 concentration of the corresponding pest type recorded in the pesticide application relationship database.

[0018] According to one embodiment of the present invention, the priority values of the environmental factors adjusted in S5 are temperature, humidity, light intensity, and CO2 concentration from high to low.

[0019] According to one embodiment of the present invention, in S6, if the actual control effect after spraying is better than the control effect corresponding to the same type of pests and diseases in the pesticide application relationship database, the data in the pesticide application relationship database is updated; if the actual control effect after spraying is less than or equal to the control effect corresponding to the same type of pests and diseases in the pesticide application relationship database, the data in the pesticide application relationship database is not updated.

[0020] The beneficial effects of the present invention compared with the prior art solutions are:

[0021] 1. Determine the optimal pesticide for prevention and control based on the pesticide application relationship database, combine it with drones to identify pests and diseases, and use a weighted calculation model to dynamically calculate pesticide concentrations to ensure that the pesticide application plan is highly matched with the actual pests and diseases in the field and the actual environment, reducing reliance on experience.

[0022] 2. Determine the minimum effective concentration and maximum safe concentration in the laboratory, and design a dynamic adjustment mechanism for environmental factors. When the pesticide concentration is insufficient or exceeds the standard, adjust controllable parameters such as temperature and humidity first, so that the pesticide concentration just reaches the minimum effective concentration or the maximum safe concentration, reducing resource consumption while ensuring the effectiveness of pesticide application.

[0023] 3. Collect control effect data after each application and only update the better solutions to the database, forming a self-iterative learning system to gradually improve the adaptability and reliability of the application method. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow diagram of the present invention; DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following is merely a conceptual description of the specific implementation methods of the present invention, and is not a limitation on the implementation methods of the present invention. Without departing from the design spirit of the present invention, any adjustments, equivalent substitutions, improvements, etc. made by those skilled in the art to the various details of the technical solution of the present invention should fall within the scope of protection determined by the claims of the present invention.

[0026] Example 1

[0027] Figure 1 The figure shows a schematic flow chart of a greenhouse pesticide application method based on dynamic environmental control. The present invention is mainly used for spraying pesticides on blueberries in greenhouses, but is not limited to blueberries. To more clearly demonstrate and illustrate the technical solutions and principles of the present invention, the following is a further explanation with reference to specific implementation examples and accompanying drawings. The steps are as follows:

[0028] S1. Establish a relational database with attributes including pest and disease type, pesticide type, environmental factors, pesticide concentration, and control effect, and based on this, establish a pesticide application relational database with pest and disease type as the only primary key;

[0029] The method of establishing the pesticide application relationship database is:

[0030] T1. Obtain combinations of pest types, pesticide types, and pesticide concentrations by parsing pesticide registration data from the China Pesticide Information Network;

[0031] T2. Apply pesticides under different environments based on the pesticide registration data in step T1, and record the pest and disease type, pesticide type, the values of four environmental factors, pesticide concentration, and the control effect achieved after application, thereby establishing a relational database whose attributes include pest and disease type, pesticide type, environmental factor, pesticide concentration, and control effect. The format of each row of records in the relational database is (pest and disease type, pesticide type, environmental factor, pesticide concentration, control effect);

[0032] T3. When the same pest type corresponds to multiple pesticide types, environmental factors, or pesticide concentrations, only the row with the best control effect for this pest type in the relational database is retained;

[0033] T4. Thus, a pesticide application relation database is established with the pest and disease type as the only primary key.

[0034] To optimize database quality and retrieval efficiency, this paper employs a "single pest optimal screening" rule: for multiple records under the same pest type, only the one with the best control effect is retained, and all other redundant data is eliminated. This strategy significantly reduces data retrieval complexity while ensuring that only acceptable control records are included in the pesticide application database.

[0035] Environmental factors related to pesticides include temperature, humidity, light intensity, wind speed, CO2 concentration, soil pH, soil conductivity, and soil moisture. Among these environmental factors, wind speed mainly affects the amount of pesticide sprayed, but has almost no effect on the relationship between pesticide concentration and control effect. When the wind speed is too high, only additional pesticide adhesion aids need to be added. Soil pH, soil conductivity, and soil moisture only affect a small number of special pests and diseases, such as anthrax and root rot, and have little effect on other pests and diseases. Temperature, humidity, light intensity, and CO2 concentration have a significant impact on the photosynthesis and respiration of the plant itself, as well as the reproduction and activity of pathogens and pests. Therefore, these environmental factors have a significant impact on the control effect of various pests and diseases.

[0036] S2. Obtain the environmental impact coefficient of each environmental factor on the control effect, as well as the minimum effective concentration and maximum safe concentration of each type of pesticide through laboratory testing;

[0037] The method for obtaining the temperature influence coefficient W is as follows: based on the records in the pesticide application relationship database, the pests, pesticides, humidity, light intensity, CO2 concentration, and pesticide concentration are kept constant, and by changing the temperature, the relationship coefficient between the temperature change value and the percentage change of the control effect is obtained. This relationship coefficient is the temperature influence coefficient W;

[0038] The method for obtaining the humidity influence coefficient S is as follows: based on the records in the pesticide application relationship database, the pests, pesticides, temperature, light intensity, CO2 concentration, and pesticide concentration are kept constant, and by changing the humidity, the relationship coefficient between the humidity change value and the percentage change of the control effect is obtained. This relationship coefficient is the humidity influence coefficient S;

[0039] The method for obtaining the light intensity influence coefficient G is as follows: based on the records in the pesticide application relationship database, the pests, pesticides, temperature, humidity, CO2 concentration, and pesticide concentration are kept constant, and by changing the light intensity, the relationship coefficient between the light intensity change value and the percentage change of the control effect is obtained. This relationship coefficient is the light intensity influence coefficient G;

[0040] The method for obtaining the CO2 concentration influence coefficient C is: based on the records in the pesticide application relationship database, control the pests and diseases, pesticides, temperature, humidity, light intensity, and pesticide concentration to remain unchanged, and by changing the CO2 concentration, obtain the relationship coefficient between the value of the CO2 concentration change and the percentage value of the control effect change. This relationship coefficient is the CO2 concentration influence coefficient C.

[0041] The method for obtaining the minimum effective concentration is:

[0042] Q1. Based on the pesticide application relationship database, independent experimental groups are established for different types of pests and diseases and their corresponding pesticides. Each experimental group only contains the same type of pests and diseases and their corresponding pesticides.

[0043] Q2. After each experimental group infected healthy crops with pests and diseases, they were sprayed with corresponding pesticides at different concentrations.

[0044] After Q3 and 48 hours, observe each experimental group and obtain the lowest pesticide concentration in each experimental group that stops the spread of lesions or causes all pests to show obvious poisoning symptoms;

[0045] Q4. The lowest value among the pesticide concentrations is the minimum effective concentration for the corresponding pest and disease type and pesticide type in the experimental group.

[0046] Since when the minimum effective concentration is measured in the laboratory, it is generally considered that the concentration can still be effective even in an adverse environment, that is, the minimum effective concentration itself has already covered the impact of environmental fluctuations, the present invention does not need to make special provisions for environmental factors when obtaining the minimum effective concentration.

[0047] The method for obtaining the maximum safe concentration is:

[0048] W1. Based on the pesticide application relationship database, establish independent experimental groups for different types of pesticides recorded in the pesticide application relationship database, and each experimental group only contains the same type of pesticide;

[0049] W2, each experimental group sprayed healthy crops with different concentrations of pesticides;

[0050] After 3 and 48 hours, the crops in each experimental group were observed to obtain the highest pesticide concentration in each experimental group that did not cause brown spots on the crops.

[0051] W4. The highest value among the pesticide concentrations is the maximum safe concentration of the pesticide corresponding to the experimental group.

[0052] Since when the maximum safe concentration is measured in the laboratory, it is also taken into account that the concentration can still be effective even in an adverse environment, that is, the maximum safe concentration itself has covered the impact of environmental fluctuations. Therefore, the present invention does not require special provisions for environmental factors when obtaining the maximum safe concentration.

[0053] S3: Use drones to collect pest and disease images, use image analysis to identify pests and diseases, and determine the type of pesticide to use through the pesticide application relationship database;

[0054] Equipped with high-resolution cameras, drones cruise farmland along pre-set routes, capturing high-definition images of crop leaves, stems, and other parts. Image analysis uses AI visual algorithm models to analyze the images and identify the type of pest or disease by identifying the characteristics of lesions or insect bite marks. The unique pesticide type is then retrieved from the pesticide application database to determine the type of pesticide to use.

[0055] S4. Obtain the actual values of various environmental factors through integrated sensors installed in the field, and determine the pesticide concentration to be used through a weighted calculation model;

[0056] The integrated sensors include a temperature sensor, a humidity sensor, a light intensity sensor, and a CO2 concentration sensor. The actual values include the actual temperature, humidity, light intensity, and CO2 concentration.

[0057] The weighted calculation model is:

[0058] Cfinal=Cbase×(1+(Aa)W)×(1+(Bb)S)×(1+(Cc)G)×(1+(Dd)C)

[0059] Where Cfinal represents the final pesticide concentration, and Cbase represents the pesticide concentration corresponding to the pest type in the pesticide application relationship database;

[0060] W is the temperature influence coefficient, S is the humidity influence coefficient, G is the light intensity influence coefficient, and C is the CO2 concentration influence coefficient;

[0061] A is the actual temperature, B is the actual humidity, C is the actual light intensity, and D is the actual CO2 concentration;

[0062] a is the temperature of the corresponding pest type recorded in the pesticide application relationship database, b is the humidity of the corresponding pest type recorded in the pesticide application relationship database, c is the light intensity of the corresponding pest type recorded in the pesticide application relationship database, and d is the CO2 concentration of the corresponding pest type recorded in the pesticide application relationship database.

[0063] The above-mentioned change value can be negative in actual use, and when the change value is negative, it indicates a decrease. The entire model uses only simple multiplication and addition operations, which greatly reduces the complexity of the calculation while ensuring that the final pesticide concentration is acceptable.

[0064] S5. When the pesticide concentration is lower than the minimum effective concentration or higher than the maximum safe concentration, adjust the environmental factors so that the pesticide concentration reaches the minimum effective concentration or the maximum safe concentration after calculation by the weighted calculation model;

[0065] The present invention is applicable to crops grown in greenhouses. Different farmers may not necessarily have the tools to adjust temperature, humidity, light intensity and CO2 concentration. Therefore, when farmers have the corresponding tools, the priority values of adjusting environmental factors from high to low are temperature, humidity, light intensity and CO2 concentration.

[0066] When the pesticide concentration is lower than the minimum effective concentration, the temperature is adjusted first through the heater, followed by the humidity through the humidifier and dehumidifier, and then the light intensity is adjusted by adjusting the light source power. Finally, the CO2 concentration is adjusted by activated carbon adsorbing CO2 or baking soda and citric acid reacting to generate CO2, so that the pesticide concentration is exactly equal to the minimum effective concentration, thereby reducing resource consumption while ensuring the effectiveness of pesticide application.

[0067] When the pesticide concentration is higher than the maximum safe concentration, by adjusting the temperature, humidity, light intensity and CO2 concentration, the pesticide concentration is ultimately made exactly equal to the maximum safe concentration, thereby reducing resource consumption while ensuring the effectiveness of pesticide application.

[0068] S6. Prepare pesticides and spray the crops. After 48 hours of spraying, collect control effect data and update the pesticide application relationship database.

[0069] The configured pesticide concentration is equal to the pesticide concentration ultimately obtained in the above steps. Although the actual pests and diseases and pesticides used during spraying are consistent with the data in the pesticide application database, the environmental factors and pesticide concentrations used during spraying may vary, resulting in different control effects. If the control effect after spraying is better than the control effect for the same pest and disease type in the pesticide application database, the data in the pesticide application database will be updated. If the control effect after spraying is less than or equal to the control effect for the same pest and disease type in the pesticide application database, the data in the pesticide application database will not be updated. This ensures that the pesticide application database contains high-quality data.

[0070] The above-described embodiments are only preferred embodiments of the present invention, and are not intended to be all feasible embodiments of the present invention. Any obvious modifications made by a person skilled in the art without departing from the principles and spirit of the present invention should be considered to be included within the scope of protection of the claims of the present invention.

Claims

1. A greenhouse pesticide application method based on dynamic regulation of environmental factors, characterized in that: The following steps are included: S1. Establish a relational database with attributes including pest and disease type, pesticide type, environmental factors, pesticide concentration, and control effect, and based on this, establish a pesticide application relational database with pest and disease type as the only primary key; S2. Obtain the environmental impact coefficient of environmental factors on the control effect, as well as the minimum effective concentration and maximum safe concentration of various pesticides through laboratory testing; S3: Use drones to collect pest and disease images, use image analysis to identify pest types, and determine the type of pesticide to use through the pesticide application relationship database; S4. Obtain the actual values of environmental factors through integrated sensors installed in the field, and determine the pesticide concentration to be used through a weighted calculation model; S5. When the pesticide concentration is lower than the minimum effective concentration or higher than the maximum safe concentration, adjust the environmental factors so that the pesticide concentration reaches the minimum effective concentration or the maximum safe concentration after calculation by the weighted calculation model; S6. Prepare pesticide with a concentration equal to the pesticide concentration finally obtained in the above steps, and spray the crops. After 48 hours of spraying, collect control effect data and update the pesticide application relationship database.

2. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 1, characterized in that: Environmental factors in S1 include temperature, humidity, light intensity and CO2 concentration.

3. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 2, characterized in that: The method for establishing the pesticide application relationship database in S1 is: T1. Obtain combinations of pest types, pesticide types, and pesticide concentrations by parsing pesticide registration data from the China Pesticide Information Network; T2. Apply pesticides under different environments based on the pesticide registration data in step T1, and record the pest and disease type, pesticide type, the values of four environmental factors, pesticide concentration, and the control effect achieved after application, thereby establishing a relational database whose attributes include pest and disease type, pesticide type, environmental factor, pesticide concentration, and control effect. The format of each row of records in the relational database is (pest and disease type, pesticide type, environmental factor, pesticide concentration, control effect); T3. When the same pest type corresponds to multiple pesticide types, environmental factors, or pesticide concentrations, only the row with the best control effect for this pest type in the relational database is retained; T4. Thus, a pesticide application relation database is established with the pest and disease type as the only primary key.

4. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 3, characterized in that: The environmental influence coefficients in S2 include the temperature influence coefficient W, the humidity influence coefficient S, the light intensity influence coefficient G and the CO2 concentration influence coefficient C.

5. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 4, characterized in that: The method for obtaining each environmental impact coefficient is as follows: E1. Change any one environmental factor based on the records in the pesticide application relationship database, while keeping the pest type, pesticide type, the remaining three environmental factors, and pesticide concentration unchanged; E2. Obtain the relationship coefficient between the change value of the environmental factor and the percentage change of the control effect. This relationship coefficient is the influence coefficient of the environmental factor.

6. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 5, characterized in that: The method for obtaining the minimum effective concentration in S2 is: Q1. Based on the pesticide application relationship database, independent experimental groups are established for different types of pests and diseases and their corresponding pesticides. Each experimental group only contains the same type of pests and diseases and their corresponding pesticides. Q2. After each experimental group infected healthy crops with pests and diseases, they were sprayed with corresponding pesticides at different concentrations. After Q3 and 48 hours, observe each experimental group and obtain the lowest pesticide concentration in each experimental group that stops the spread of lesions or causes all pests to show obvious poisoning symptoms; Q4. The lowest value among the pesticide concentrations is the minimum effective concentration for the corresponding pest and disease type and pesticide type in the experimental group.

7. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 6, characterized in that: The method for obtaining the maximum safe concentration in S2 is: W1. Based on the pesticide application relationship database, establish independent experimental groups for different types of pesticides recorded in the pesticide application relationship database, and each experimental group only contains the same type of pesticide; W2, each experimental group sprayed healthy crops with different concentrations of pesticides; After 3 and 48 hours, the crops in each experimental group were observed to obtain the highest pesticide concentration in each experimental group that did not cause brown spots on the crops. W4. The highest value among the pesticide concentrations is the maximum safe concentration of the pesticide corresponding to the experimental group.

8. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 7, characterized in that: The weighted calculation model in S4 is: Cfinal=Cbase×(1+(Aa)W)×(1+(Bb)S)×(1+(Cc)G)×(1+(Dd)C) Where Cfinal represents the final pesticide concentration, and Cbase represents the pesticide concentration corresponding to the pest type in the pesticide application relationship database; W is the temperature influence coefficient, S is the humidity influence coefficient, G is the light intensity influence coefficient, and C is the CO2 concentration influence coefficient; A is the actual temperature, B is the actual humidity, C is the actual light intensity, and D is the actual CO2 concentration; a is the temperature of the corresponding pest type recorded in the pesticide application relationship database, b is the humidity of the corresponding pest type recorded in the pesticide application relationship database, c is the light intensity of the corresponding pest type recorded in the pesticide application relationship database, and d is the CO2 concentration of the corresponding pest type recorded in the pesticide application relationship database.

9. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 8, characterized in that: In S5, the priority values of environmental factors are adjusted from high to low as temperature, humidity, light intensity, and CO2 concentration.

10. A greenhouse pesticide application method based on dynamic regulation of environmental factors according to claim 9, characterized in that: In S6, if the actual control effect after spraying is better than the control effect corresponding to the same type of pests and diseases in the pesticide application relationship database, the data in the pesticide application relationship database is updated; if the actual control effect after spraying is less than or equal to the control effect corresponding to the same type of pests and diseases in the pesticide application relationship database, the data in the pesticide application relationship database is not updated.

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