A pesticide application intelligent monitoring method and system based on crop protection

Abstract

The application discloses a kind of intelligent monitoring method and system based on crop protection pesticide application, it is related to pesticide application monitoring technical field, the method of the present application includes transpiration detection, soil state detection, pesticide quality detection and pesticide use control, the present application is first by transpiration detection, collects historical environmental data, pesticide standard transpiration concentration variation slope interval is combined with artificial climate chamber experiment, and the transpiration state is judged in combination with current data, secondly by soil state detection, determines pesticide loss state according to the experimental prearranged pesticide standard soil concentration variation slope interval, then by pesticide quality detection, pesticide use state is determined by crop growth picture feature similarity calculation, finally by pesticide use control, carry out differentiating regulation according to three types of state, the present application improves pesticide utilization, reduces pollution, improves crop growth quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pesticide application monitoring, in particular to a pesticide application intelligent monitoring method and system based on crop protection. BACKGROUND

[0002] As an industry related to most industry chains, the development quality of agriculture directly affects the upstream and downstream fields such as agricultural product processing, logistics and sales. In addition, modern agriculture also bears the responsibility of ecological protection, and needs to reduce the impact of pesticides on the environment while ensuring yield. Therefore, a pesticide application intelligent monitoring method and system based on crop protection is needed.

[0003] In current agricultural production, pesticide application is still dominated by manual experience judgment, lacking scientific and systematic monitoring and control mechanism. On the one hand, the pesticide decision-making is not fully related to dynamic environmental factors such as temperature, humidity, light intensity and wind speed, which will directly affect the transpiration of crops, resulting in fluctuations in the retention and volatilization rate of pesticides on the leaf surface. For example, in high temperature and strong wind environment, the transpiration of pesticides is too fast, which is easy to cause the loss of pesticide efficacy. The penetration and retention of pesticides in soil will cause excessive loss of pesticides to deep soil or water, forming pollution.

[0004] Traditional methods cannot accurately evaluate the effect of pesticide application. Only by manually observing the growth of crops can it be determined whether the pesticide application is reasonable. It cannot quantify the dynamic changes of pesticides in the environment, and often causes problems such as crop pesticide damage and environmental pollution due to excessive pesticide application, or ineffective prevention and control of pests due to insufficient pesticide application, which not only wastes pesticide resources, but also aggravates agricultural non-point source pollution, and restricts the improvement of crop yield and quality, which cannot meet the green, efficient and precise production needs of modern agriculture. SUMMARY

[0005] In view of the above technical deficiencies, the purpose of the present application is to provide a pesticide application intelligent monitoring method and system based on crop protection.

[0006] To solve the above technical problems, the present application adopts the following technical scheme: the present application provides a pesticide application intelligent monitoring method based on crop protection, comprising the following steps: step one, transpiration detection: collecting historical transpiration environmental data, analyzing the historical transpiration environmental data, establishing a standard crop transpiration environmental model, and then conducting experiments on the transpiration state of various pesticides to obtain the transpiration rate change curve of various pesticides, and further collecting current transpiration environmental data and air quality data, analyzing the current transpiration environmental data and air quality data, and then judging the transpiration state of the current various pesticides.

[0007] Step two, soil state detection: collect current soil environmental data, analyze the current soil environmental data, and determine the pesticide loss state of the current various pesticides.

[0008] Step three, pesticide quality detection: collect current crop growth state data, analyze the current crop growth state data, and obtain the use state of each type of pesticide.

[0009] Step four, pesticide use control: based on the transpiration state, pesticide loss state and use state of each type of pesticide, set the current pesticide use control scheme.

[0010] Preferably, the analysis of the current transpiration environment data and air quality data is as follows: the current transpiration environment data includes but is not limited to the temperature, humidity, light intensity, wind speed and precipitation of the target crop after each application for a certain period of time, and the air quality data includes the transpiration rate of each type of pesticide after each application for a certain period of time and the drug concentration of each type of pesticide after each application for a certain period of time. The current transpiration environment data is input into the standard crop transpiration environment model to obtain the standard transpiration rate of the current environment after each application for a certain period of time. The standard transpiration rate of the current environment after each application for a certain period of time is divided by the corresponding transpiration rate to obtain the transpiration correction rate of the current environment after each application for a certain period of time. The drug concentration of each type of pesticide after each application for a certain period of time is multiplied by the corresponding environmental transpiration correction rate to obtain the converted drug concentration of each type of pesticide after each application for a certain period of time.

[0011] Draw a change curve to obtain the drug concentration change slope of each type of pesticide after each application for a certain period of time.

[0012] Preferably, the judgment of the transpiration state of each type of pesticide is as follows: if the drug concentration change slope belongs to the standard transpiration concentration change slope interval, it indicates that the transpiration state is normal transpiration state, and the transpiration characteristic value is recorded as 0; if the drug concentration change slope is less than the minimum value of the standard transpiration concentration change slope interval, it indicates that the transpiration state is over-low transpiration state, and the transpiration characteristic value is recorded as -1; if the drug concentration change slope is greater than the maximum value of the standard transpiration concentration change slope interval, it indicates that the transpiration state is over-transpiration state, and the transpiration characteristic value is recorded as 1. In this way, the transpiration state and transpiration characteristic value of each type of pesticide after each application for a certain period of time are obtained.

[0013] From the database, obtain the weight factor of each type of pesticide after each application for a certain period of time, multiply the characteristic value of each type of pesticide after each application for a certain period of time by the corresponding weight factor, and sum to obtain the transpiration characteristic value of each type of pesticide. From the database, obtain the standard transpiration characteristic value interval to obtain the transpiration state of each type of pesticide.

[0014] In another aspect, the present application provides a pesticide application intelligent monitoring system based on crop protection, comprising the following modules: a transpiration detection module for collecting historical transpiration environment data, analyzing the historical transpiration environment data, establishing a standard crop transpiration environment model, and then conducting experiments on the transpiration state of various pesticides to obtain the transpiration rate change curve of various pesticides, and further collecting current transpiration environment data and air quality data, analyzing the current transpiration environment data and air quality data, and then judging the transpiration state of the current various pesticides.

[0015] A soil state detection module for collecting current soil environment data, analyzing the current soil environment data, and determining the pesticide loss state of the current various pesticides.

[0016] A pesticide quality detection module for collecting current crop growth state data, analyzing the current crop growth state data, and obtaining the use state of the current various pesticides.

[0017] A pesticide use control module for setting a current pesticide use control scheme based on the transpiration state, pesticide loss state and use state of the current various pesticides.

[0018] The beneficial effects of the present application are as follows: 1. The present application first detects transpiration, collects historical environment data, obtains the pesticide standard transpiration concentration change slope interval through artificial climate chamber experiments, judges the transpiration state based on current data, secondly detects soil state, judges the pesticide loss state based on the experimentally preset pesticide standard soil concentration change slope interval, then detects pesticide quality, determines the pesticide use state through crop growth picture feature similarity calculation, and finally controls pesticide use, differentiates and controls according to the three states. The present application improves pesticide utilization rate, reduces pollution, and improves crop growth quality.

[0019] 2. The present application reduces the waste of pesticides and solvents caused by traditional blind pesticide application by precisely regulating the application amount and solvent ratio, thereby reducing agricultural production costs, such as increasing solvents instead of additional pesticides when transpiration is excessive, reducing the application rate instead of reducing the total amount when loss is too small, improving the effect of pesticide resource utilization. In terms of ecological protection, the application height is adjusted by monitoring the pesticide loss state to reduce the penetration of pesticides into deep soil and water, reducing the risk of agricultural non-point source pollution; at the same time, the harm of excessive pesticides to beneficial organisms in the field is avoided, and the balance of the farmland ecosystem is maintained.

[0020] 3、The application is based on the similarity of crop growth picture features to determine the pesticide use state, avoiding residue accumulation caused by excessive pesticide application; the second aspect ensures that the pesticide works as expected by monitoring transpiration and soil, reducing the adsorption of excess pesticide in crop fruits, roots and stems, and ultimately controlling the pesticide residue in agricultural products from the source. Under the condition of normal pesticide application to ensure crop yield, the market competitiveness of agricultural products is improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 The present application is a method for implementing step flowchart.

[0023] Figure 2 The present application is a system structure connection diagram. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] According to Figure 1 As shown in the figure, the present application provides a pesticide application intelligent monitoring method based on crop protection, comprising the following steps: step one, transpiration detection: collecting historical transpiration environment data, analyzing the historical transpiration environment data, establishing a standard crop transpiration environment model, and further conducting experiments on the transpiration state of various pesticides to obtain the transpiration rate change curve of various pesticides, and further collecting current transpiration environment data and air quality data, analyzing the current transpiration environment data and air quality data, and further determining the transpiration state of the current various pesticides.

[0026] In one specific embodiment, the collection of historical transpiration environment data is as follows: the historical transpiration environment data includes but is not limited to the temperature, humidity, light intensity, wind speed and precipitation of each historical pesticide application of the target crop. The temperature is collected by a temperature sensor, the humidity is collected by a humidity sensor, the light intensity is collected by a photosynthetically active radiation sensor, the wind speed is collected by a small ultrasonic anemometer, and the precipitation is collected by a tipping bucket rain gauge.

[0027] In one specific embodiment, the analysis of historical transpiration environment data is carried out as follows: the transpiration rate of the target crop after each historical pesticide application for a preset time is obtained from the database, and the mapping relationship between the historical transpiration environment data and the transpiration rate of the target crop is established through multiple linear regression of machine learning algorithm, so as to obtain a standard crop transpiration environment model: inputting transpiration environment data of various scenarios and outputting standard transpiration rate.

[0028] It should be noted that the transpiration rate of the target crop after each historical pesticide application at a preset time can be collected using a small transpiration rate measuring instrument.

[0029] Machine learning algorithms are existing technologies and can be found on the internet, so I will not go into detail here.

[0030] In one specific embodiment, the experiment on the transpiration state of various pesticides is conducted as follows: Select the target crop, simulate the field environment in an artificial climate chamber, set up a blank control group without pesticides and experimental groups of various pesticides, and conduct the experiment.

[0031] It should be noted that the artificial climate chamber allows staff to control transpiration environmental data such as temperature, humidity, light intensity, wind speed, and precipitation.

[0032] The experimental group applied the target pesticide according to the standard dosage preset by the staff, and collected the pesticide concentration at various time points after application to obtain the pesticide concentration of each type of pesticide in the experimental group at various time points after application.

[0033] It should be noted that the standard dosage for each pesticide is preset by staff, obtained through experience or by searching on the Internet.

[0034] The staff applied water at a pre-set standard dose to the blank control group and collected the transpiration rate at a pre-set time after watering to obtain the standard transpiration rate of the blank control group. The transpiration environment data of the artificial climate chamber was then input into the standard crop transpiration environment model to obtain the standard transpiration rate of the artificial climate chamber.

[0035] It should be noted that the standard dosage of water is set by the staff and is generally the same mass of water as the standard dosage of pesticide.

[0036] The evapotranspiration data in the artificial climate chamber are preset by the staff, including evapotranspiration data such as temperature, humidity, light intensity, wind speed, and precipitation.

[0037] The standard transpiration rate of the artificial climate chamber is divided by the standard transpiration rate of the blank control group to obtain the transpiration correction rate of the experimental group. The ideal drug concentration of each post-application time length of each type of pesticide in the experimental group is obtained by multiplying the drug concentration of each post-application time length of each type of pesticide in the experimental group by the transpiration correction rate.

[0038] In one specific embodiment, the transpiration rate change curve of each type of pesticide is obtained, and the specific process is as follows: taking each post-application time length as the horizontal axis and the ideal drug concentration as the vertical axis, the ideal drug concentration change curve of each type of pesticide in the experimental group is drawn, so as to obtain the drug concentration change slope of each post-application time length of each type of pesticide in the experimental group.

[0039] The staff conducts experiments on the transpiration state of each type of pesticide according to the preset number of times, and then draws the concentration change curve of each experiment, so as to obtain the drug concentration change slope of each post-application time length of each type of pesticide in each experiment, and then obtain the standard transpiration concentration change slope interval of each post-application time length of each type of pesticide.

[0040] It should be noted that the minimum drug concentration change slope of each post-application time length of each type of pesticide in each experiment is recorded as the lower limit of the standard transpiration concentration change slope interval, and the maximum drug concentration change slope of each post-application time length of each type of pesticide in each experiment is recorded as the upper limit of the standard transpiration concentration change slope interval, so as to obtain the standard transpiration concentration change slope interval of each post-application time length of each type of pesticide.

[0041] In one specific embodiment, the current transpiration environment data and air quality data are collected, and the specific collection process is as follows: the current transpiration environment data includes but is not limited to the current temperature, humidity, light intensity, wind speed and precipitation of each post-application time length of the target type of crop. The temperature is collected by a temperature sensor, the humidity is collected by a humidity sensor, the light intensity is collected by a photosynthetic active radiation sensor, the wind speed is collected by a small ultrasonic anemometer, and the precipitation is collected by a tipping bucket rain gauge.

[0042] The air quality data includes the transpiration rate of each post-application time length of the current pesticide and the drug concentration of each post-application time length of the current each type of pesticide. The transpiration rate of each post-application time length of the current pesticide is collected by a small transpiration rate tester, and the drug concentration of each post-application time length of the current each type of pesticide is collected by a gas chromatograph mass spectrometer.

[0043] In one specific embodiment, the analysis of the current transpiration environment data and the air quality data is performed as follows: the current transpiration environment data is input into a standard crop transpiration environment model to obtain the standard transpiration rate of the current environment for each application duration, the standard transpiration rate of the current environment for each application duration is divided by the corresponding transpiration rate to obtain the transpiration correction rate of the current environment for each application duration, the drug concentration of each application duration of each type of pesticide is multiplied by the corresponding environmental transpiration correction rate to obtain the converted drug concentration of each application duration of each type of pesticide.

[0044] A change curve is drawn to obtain the drug concentration change slope of each application duration of each type of pesticide.

[0045] It should be noted that the converted drug concentration change curve of each type of pesticide is drawn with the application duration as the horizontal axis and the converted drug concentration as the vertical axis, and the drug concentration change slope of each application duration of each type of pesticide is obtained.

[0046] In one specific embodiment, the determination of the transpiration state of each type of pesticide is performed as follows: if the drug concentration change slope belongs to the standard transpiration concentration change slope interval, it indicates that the transpiration state is a normal transpiration state, and the transpiration characteristic value is recorded as 0; if the drug concentration change slope is less than the minimum value of the standard transpiration concentration change slope interval, it indicates that the transpiration state is an excessively low transpiration state, and the transpiration characteristic value is recorded as -1; if the drug concentration change slope is greater than the maximum value of the standard transpiration concentration change slope interval, it indicates that the transpiration state is an excessive transpiration state, and the transpiration characteristic value is recorded as 1. In this way, the transpiration state and the transpiration characteristic value of each application duration of each type of pesticide are obtained.

[0047] The weight factor of each application duration of each type of pesticide is obtained from the database, and the transpiration characteristic value of each type of pesticide is obtained by multiplying the characteristic value of each application duration of each type of pesticide by the corresponding weight factor and summing. The standard transpiration characteristic value interval is obtained from the database, and the transpiration state of each type of pesticide is obtained in this way.

[0048] It should be noted that the weight factor of each application duration of each type of pesticide is set by the staff, and the weight factor is greater for a longer application duration.

[0049] The standard transpiration characteristic value interval is set by the staff, for example, the standard transpiration characteristic value interval is set to -0.5 to 0.5. When the transpiration characteristic value of a certain type of pesticide is less than the lower limit of the standard transpiration characteristic value interval, the transpiration state of the current type of pesticide is excessively low. When the transpiration characteristic value of a certain type of pesticide is greater than the upper limit of the standard transpiration characteristic value interval, the transpiration state of the current type of pesticide is excessive. When the transpiration characteristic value of a certain type of pesticide belongs to the standard transpiration characteristic value interval, the transpiration state of the current type of pesticide is normal. This is an example and is not the only limitation.

[0050] Step two, soil state detection: collect current soil environment data, analyze the current soil environment data, and determine the current pesticide loss state of each type of pesticide.

[0051] In one specific embodiment, the current soil environment data is collected, and the specific collection process is as follows: the current soil environment data includes the concentration of each type of pesticide in the soil after each application time, the current soil is sampled, and the pesticide extract is prepared, then the concentration of each type of pesticide is detected by a spectrum sensor, which is recorded as the standard soil concentration of each type of pesticide.

[0052] In one specific embodiment, the current soil environment data is analyzed, and the specific analysis process is as follows: the standard soil concentration change slope interval of each type of pesticide after each application time is obtained through experiments, and the current pesticide loss state of each type of pesticide is determined according to the judgment method of the transpiration state of each type of pesticide.

[0053] In one specific embodiment, the standard soil concentration change slope interval of each type of pesticide after each application time is obtained through experiments, and the specific acquisition process is as follows: a target crop is selected, the field environment is simulated in a artificial climate chamber, and experimental groups of each type of pesticide are set up, the experimental groups are applied with the target pesticide at the standard dose preset by the staff, and the concentration of each type of pesticide in the soil is collected at the time point corresponding to each application time, thereby obtaining the soil pesticide concentration of each type of pesticide in the experimental group after each application time.

[0054] The soil pesticide concentration change curve of each type of pesticide in the experimental group is drawn with each application time as the horizontal axis and the soil pesticide concentration as the vertical axis, thereby obtaining the soil pesticide concentration change slope of each type of pesticide in the experimental group after each application time.

[0055] The staff conducts experiments on the soil of each type of pesticide according to the preset number of times, and then draws the soil pesticide concentration change curve of each experiment, thereby obtaining the soil pesticide concentration change slope of each type of pesticide in each experiment after each application time. The minimum soil pesticide concentration change slope of each type of pesticide in each experiment after each application time is recorded as the lower limit of the standard soil concentration change slope interval, and the maximum soil pesticide concentration change slope of each type of pesticide in each experiment after each application time is recorded as the upper limit of the standard soil concentration change slope interval, thereby obtaining the standard soil concentration change slope interval of each type of pesticide after each application time.

[0056] In one specific embodiment, the current pesticide loss state of each type of pesticide is determined, and the specific determination process is as follows: the soil pesticide concentration change curve of each type of pesticide in the current soil is drawn with each application time as the horizontal axis and the soil pesticide concentration as the vertical axis, thereby obtaining the soil pesticide concentration change slope of each type of pesticide in the current soil after each application time.

[0057] The soil pesticide concentration change slope belongs to the standard soil concentration change slope interval, indicating that the pesticide loss state is a normal pesticide loss state, the pesticide loss characteristic value is recorded as 0, the soil pesticide concentration change slope is less than the minimum value of the standard soil concentration change slope interval, indicating that the pesticide loss state is an excessively low pesticide loss state, the pesticide loss characteristic value is recorded as-1, and the soil pesticide concentration change slope is greater than the maximum value of the standard soil concentration change slope interval, indicating that the pesticide loss state is an excessive pesticide loss state, and the pesticide loss characteristic value is recorded as 1. Thus, the pesticide loss state and the pesticide loss characteristic value of each type of pesticide at each length of time after application are obtained.

[0058] The weight factor of each type of pesticide at each length of time after application is obtained from the database, the pesticide loss characteristic value of each type of pesticide at each length of time after application is multiplied by the corresponding weight factor, and then summed to obtain the pesticide loss characteristic value of each type of pesticide. The standard pesticide loss characteristic value interval is obtained from the database, and thus the pesticide loss state of each type of pesticide is obtained.

[0059] It should be noted that the standard pesticide loss characteristic value interval is set by the staff, for example, the standard pesticide loss characteristic value interval is set to-0.3 to 0.3, when the pesticide loss characteristic value of a certain type of pesticide is less than the lower limit of the standard pesticide loss characteristic value interval, the current pesticide loss state of the pesticide is excessively low, when the pesticide loss characteristic value of a certain type of pesticide is greater than the upper limit of the standard pesticide loss characteristic value interval, the current pesticide loss state of the pesticide is excessive, and when the pesticide loss characteristic value of a certain type of pesticide belongs to the standard evaporation characteristic value interval, the current pesticide loss state of the pesticide is normal. This is an example and not the only limitation.

[0060] Step three, pesticide quality detection: collecting current crop growth state data, analyzing the current crop growth state data, and obtaining the use state of each type of pesticide.

[0061] In one specific embodiment, the crop growth state data is collected, and the specific collection process is as follows: the crop growth state data is crop picture feature data, the crop picture is collected through a camera, and the crop picture feature data is obtained from the crop picture through machine vision.

[0062] In one specific embodiment, the current crop growth state data is analyzed, and the specific analysis process is as follows: the excessive crop growth picture feature data, the normal crop growth picture feature data, and the excessively low crop growth picture feature data of each type of pesticide are obtained from the database, the crop growth state data is crop picture feature data, the similarity is calculated, the excessive similarity, the normal similarity, and the excessively low similarity of each type of pesticide of the current crop are obtained, and the state corresponding to the maximum similarity is recorded as the use state of each type of pesticide of the current crop.

[0063] It should be noted that the excess crop growth picture feature data of various types of pesticides can be obtained from the excess crop growth pictures of various types of pesticides, which can be queried from the Internet, entered into the database by the staff, and similarly, the normal crop growth picture feature data and the low crop growth picture feature data of various types of pesticides are obtained.

[0064] Step four, pesticide use control: based on the transpiration state, pesticide loss state and use state of the current various types of pesticides, the current pesticide use control scheme is set.

[0065] In one specific embodiment, the current pesticide use control scheme is set, and the specific control process is as follows: if the use state of the current type of pesticide is excessive, the preset use amount of the pesticide of this type is reduced, and if the use state of the current type of pesticide is insufficient, the preset use amount of the pesticide of this type is increased.

[0066] It should be noted that the preset use amount is set by the staff.

[0067] If the transpiration state of the current type of pesticide is excessive, the preset unit amount of pesticide solvent of this type is increased, and if the transpiration state of the current type of pesticide is insufficient, the preset unit amount of pesticide solvent of this type is reduced.

[0068] It should be noted that the preset unit amount of pesticide solvent is set by the staff.

[0069] If the pesticide loss state of the current type of pesticide is excessive, the preset unit amount of pesticide application height of this type is increased, and if the pesticide loss state of the current type of pesticide is insufficient, the preset unit amount of pesticide application height of this type is reduced.

[0070] It should be noted that the preset unit amount of pesticide application height is set by the staff.

[0071] According to Figure 2 As shown in the figure, the present application provides a pesticide application intelligent monitoring system based on crop protection, which comprises the following modules: transpiration detection module, soil state detection module, pesticide quality detection module, pesticide use control module and database.

[0072] The soil state detection module is connected with the transpiration detection module and the pesticide quality detection module, respectively, the pesticide use control module is connected with the pesticide quality detection module, and the transpiration detection module, the soil state detection module, the pesticide quality detection module and the pesticide use control module are all connected with the database.

[0073] The transpiration detection module is configured to collect historical transpiration environment data, analyze the historical transpiration environment data, establish a standard crop transpiration environment model, and then experiment on transpiration states of various types of pesticides to obtain transpiration rate change curves of the various types of pesticides. Further, the transpiration detection module is configured to collect current transpiration environment data and air quality data, analyze the current transpiration environment data and the air quality data, and then determine transpiration states of the various types of pesticides.

[0074] The soil state detection module is configured to collect current soil environment data, analyze the current soil environment data, and determine pesticide loss states of the various types of pesticides.

[0075] The pesticide quality detection module is configured to collect current crop growth state data, analyze the current crop growth state data, and obtain use states of the various types of pesticides.

[0076] The pesticide use control module is configured to set a current pesticide use control scheme based on the transpiration states, the pesticide loss states, and the use states of the various types of pesticides.

[0077] The database is configured to store a standard transpiration characteristic value interval, transpiration rates of a target crop at a preset time length after each historical pesticide application, weight factors of each type of pesticide at each time length after application, a standard transpiration characteristic value interval, a standard pesticide loss characteristic value interval, excessive crop growth picture characteristic data of each type of pesticide, normal crop growth picture characteristic data of each type of pesticide, and too-low crop growth picture characteristic data of each type of pesticide.

[0078] The above content is merely an example and a description of the present application, and those skilled in the art can make various modifications, supplements, or substitutions to the described specific embodiments or use similar ways to replace them, as long as the modifications, supplements, or substitutions do not deviate from the concept of the present application or exceed the scope defined in the specification, and should be within the protection scope of the present application.

Claims

1. A smart monitoring method for pesticide application based on crop protection, characterized in that, Includes the following steps: Step 1: Transpiration detection: Collect historical transpiration environmental data, analyze the historical transpiration environmental data to establish a standard crop transpiration environment model, and then conduct experiments on the transpiration state of various pesticides to obtain the transpiration rate change curves of various pesticides. Further collect current transpiration environmental data and air quality data, analyze the current transpiration environmental data and air quality data, and then determine the current transpiration state of various pesticides. Step 2: Soil condition detection: Collect current soil environmental data, analyze the current soil environmental data, and determine the current pesticide loss status of various pesticides; Step 3: Pesticide quality testing: Collect data on the current crop growth status, analyze the data to determine the current usage status of various pesticides; Step 4: Pesticide use control: Based on the current transpiration, pesticide loss, and usage status of various pesticides, set up the current pesticide use control plan; The analysis of historical transpiration environment data is as follows: Historical transpiration environment data includes temperature, humidity, light intensity, wind speed, and precipitation for each historical pesticide application of the target crop. The transpiration rate of the target crop after each historical pesticide application for a preset time is obtained from the database. Through multiple linear regression using machine learning algorithms, a mapping relationship between the historical transpiration environment data and the transpiration rate of the target crop is established, thereby obtaining a standard crop transpiration environment model: inputting transpiration environment data for various scenarios and outputting the standard transpiration rate; The experiment on the transpiration state of various pesticides was conducted as follows: Select the target crop, simulate the field environment in an artificial climate chamber, set up a blank control group without pesticides and experimental groups of various pesticides, and conduct the experiment. The experimental group applied the target pesticide according to the standard dosage preset by the staff, and collected the pesticide concentration at each time point after application to obtain the pesticide concentration of each type of pesticide in the experimental group at each time point after application. The staff applied water at a pre-set standard dose to the blank control group and collected the transpiration rate at a pre-set time after watering to obtain the standard transpiration rate of the blank control group. The transpiration environment data of the artificial climate chamber was then input into the standard crop transpiration environment model to obtain the standard transpiration rate of the artificial climate chamber. Divide the standard transpiration rate of the artificial climate chamber by the standard transpiration rate of the blank control group to obtain the transpiration correction rate of the experimental group. Multiply the drug concentration of each pesticide at each time after application in the experimental group by the transpiration correction rate to obtain the ideal drug concentration of each pesticide at each time after application in the experimental group. The specific process for obtaining the transpiration rate change curves of various pesticides is as follows: plot the ideal pesticide concentration change curves of various pesticides in the experimental group with the time after each application as the horizontal axis and the ideal pesticide concentration as the vertical axis, thereby obtaining the slope of the pesticide concentration change of various pesticides in the experimental group at each time after application. Staff conducted experiments on the transpiration state of various pesticides according to a preset number of times, and then plotted the concentration change curves of each experiment. This yielded the slope of the pesticide concentration change at each application time for each pesticide in each experiment, and thus obtained the standard transpiration concentration change slope range for each pesticide at each application time. The analysis of current evapotranspiration environmental data and air quality data is as follows: Current evapotranspiration environmental data includes temperature, humidity, light intensity, wind speed, and precipitation at each application time for the target crop. Air quality data includes evapotranspiration rate at each application time for the pesticide and pesticide concentration at each application time for each type of pesticide. The current evapotranspiration environmental data is input into a standard crop evapotranspiration environmental model to obtain the standard evapotranspiration rate at each application time for the current environment. The standard evapotranspiration rate at each application time for the current environment is divided by the corresponding evapotranspiration rate to obtain the evapotranspiration correction rate at each application time for the current environment. The pesticide concentration at each application time for each type of pesticide is multiplied by the corresponding environmental evapotranspiration correction rate to obtain the converted pesticide concentration at each application time for each type of pesticide. Plot the change curves to obtain the slope of the drug concentration change at various post-application times for different types of pesticides.

2. The intelligent monitoring method for pesticide application based on crop protection according to claim 1, characterized in that, The specific process for determining the current transpiration status of various pesticides is as follows: If the slope of the drug concentration change falls within the standard transpiration concentration change slope range, it indicates that the transpiration state is normal, and the transpiration characteristic value is recorded as 0. If the slope of the drug concentration change is less than the minimum value of the standard transpiration concentration change slope range, it indicates that the transpiration state is too low, and the transpiration characteristic value is recorded as -1. If the slope of the drug concentration change is greater than the maximum value of the standard transpiration concentration change slope range, it indicates that the transpiration state is excessive, and the transpiration characteristic value is recorded as 1. In this way, the transpiration state and transpiration characteristic value of various pesticides at different application times can be obtained. The weighting factors for each application duration of various pesticides are obtained from the database. The characteristic values ​​of each application duration of various pesticides are multiplied by the corresponding weighting factors and summed to obtain the transpiration characteristic values ​​of various pesticides. The standard transpiration characteristic value range is obtained from the database to obtain the transpiration status of various pesticides.

3. The intelligent monitoring method for pesticide application based on crop protection according to claim 2, characterized in that, The analysis of the current soil environmental data is as follows: The standard soil concentration change slope range of various pesticides at different application times was obtained through experiments. The current soil environmental data includes the concentration of various pesticides at different application times in the current soil. Based on the method for judging the transpiration state of various pesticides, the pesticide loss state of various pesticides at the current time was determined.

4. The intelligent monitoring method for pesticide application based on crop protection according to claim 3, characterized in that, The analysis of current crop growth status data is performed as follows: The database retrieves image feature data of crops with excessive, normal, and low pesticide levels, as well as crop growth status data. Similarity calculations are performed to obtain the current similarity scores for excessive, normal, and low pesticide levels for various crops. The status corresponding to the highest similarity score is recorded as the current pesticide application status for various crops.

5. The intelligent monitoring method for pesticide application based on crop protection according to claim 4, characterized in that, The specific control process for setting the current pesticide use control scheme is as follows: If the current application status of a certain type of pesticide is excessive, reduce the preset application amount of that type of pesticide; if the current application status of a certain type of pesticide is insufficient, increase the preset application amount of that type of pesticide. If the current transpiration state of a certain type of pesticide is excessive, increase the preset unit amount of pesticide solvent for that type of pesticide; if the current transpiration state of a certain type of pesticide is insufficient, decrease the preset unit amount of pesticide solvent for that type of pesticide. If the current pesticide loss status of a certain type of pesticide is excessive, increase the pesticide application height of the preset unit amount of that type of pesticide; if the current pesticide loss status of a certain type of pesticide is insufficient, decrease the pesticide application height of the preset unit amount of that type of pesticide.

6. An intelligent monitoring system applying the intelligent monitoring method for pesticide application based on crop protection as described in any one of claims 1-5, characterized in that, Includes the following modules: The transpiration detection module is used to collect historical transpiration environmental data, analyze the historical transpiration environmental data to establish a standard crop transpiration environment model, and then conduct experiments on the transpiration state of various pesticides to obtain the transpiration rate change curves of various pesticides. It further collects current transpiration environmental data and air quality data, analyzes the current transpiration environmental data and air quality data, and then determines the current transpiration state of various pesticides. The soil condition detection module is used to collect current soil environmental data, analyze the current soil environmental data, and determine the current pesticide loss status of various pesticides. The pesticide quality testing module is used to collect data on the current crop growth status, analyze the data, and obtain the current usage status of various pesticides. The pesticide use control module is used to set the current pesticide use control scheme based on the current transpiration status, pesticide loss status, and usage status of various pesticides.

Images