Orchard disease and insect pest prescription map construction method based on species migration law

Abstract

The invention relates to an orchard disease and insect pest prescription map construction method based on a species migration rule. The method comprises the following steps: step 1, selecting sample points; 2, acquiring environmental parameter data; 3, establishing a diffusion model; 4, constructing an infection degree prediction map; and 5, constructing a prescription map. According to the method, migration rules of different diseases and insect pests under different temperature, humidity, wind speed and wind direction conditions are analyzed, a mathematical model and a data analysis technology are combined, and an effective prescription map is constructed.

Description

Technical Field

[0001] This invention relates to the field of orchard pest and disease control technology, specifically to a method for constructing orchard pest and disease prescription maps based on species migration patterns. Background Technology

[0002] Orchard pests and diseases are common problems in agricultural production, seriously affecting the yield and quality of orchard crops. Traditional methods of orchard pest and disease control mainly rely on chemical pesticides. While this method can control pests and diseases in the short term, it brings many negative impacts. First, long-term and excessive use of chemical pesticides leads to environmental pollution, disrupts the balance of the soil ecosystem, and affects the sustainable development of the ecological environment. Second, chemical pesticides also pose certain health risks to humans; long-term exposure to pesticides by farmers and orchard workers may cause various health problems.

[0003] Patent publication number CN1226919C discloses a device for monitoring crop diseases and pests and generating pesticide prescriptions. It uses a camera and pesticide detector to monitor crops in real time, and employs a computer program to acquire information on the type and severity of diseases and pests, as well as the concentration of pesticide residues, to generate a pesticide prescription. However, this device only analyzes existing data and does not consider the mutual influence of disease and pest spread on surrounding fruit trees, resulting in limited prescription results. Patent publication number CN103136632A discloses a crop disease prescription map generation and publishing system. It collects leaf images of crop diseases and pests, calculates a disease index, grades the diseases, and stores the grading results. Based on the location information of the data collection module and the disease index and grading results calculated by the disease calculation module, it generates a disease distribution map, resulting in a prescription map. However, this system does not consider the mutual influence during disease spread, leading to inaccurate prescription results. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for constructing orchard pest and disease prescription maps based on species migration patterns.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for constructing orchard pest and disease prescription maps based on species migration patterns, comprising the following steps: Step 1: Sample point selection: Select areas with dense peach tree planting and susceptibility to disease as sample points, determine N initial sample points, take fruit tree A as the sample center, select fruit trees in different directions around fruit tree A as sample points, and record the location information of these N fruit trees. Observe the external symptoms of the N fruit trees, compare the observed disease and pest symptoms with the known characteristics of fruit tree diseases and pests, and preliminarily determine the types of diseases and pests. Step 2: Environmental parameter data collection: Data on different temperatures (T), humidity (H), wind speed (v), and wind direction (θ) in the orchard were collected using sensor equipment. At the same time, insect traps and sticky insect boards were used to collect data on the quantity and concentration of various pests and diseases in the orchard. Step 3: Establishing the diffusion model: Based on environmental parameter data collected from sample points, a diffusion model was established to determine the relationship between environmental factors and the occurrence of pests and diseases. The diffusion model was used to calculate the pest and disease concentration C in different directions. propagate ; Step 4: Construct an infection severity prediction map: Based on the diffusion model analysis results, the spread concentration of pests and diseases in different directions was determined. Construct a prediction map of the degree of pest and disease infection; Step 5: Construct the prescription diagram: Based on the obtained results of pest and disease transmission, a prescription map is constructed.

[0006] As a preferred option, in step one, there are 9 initial sample points. The fruit trees around fruit tree A in different directions are fruit tree B in the northwest, fruit tree C in the north, fruit tree D in the northeast, fruit tree E in the west, fruit tree F in the east, fruit tree G in the southwest, fruit tree H in the south, and fruit tree I in the southeast.

[0007] As a preferred option, in step two, a temperature sensor is used to collect ambient temperature data, a humidity sensor is used to collect ambient humidity data, and an anemometer is used to collect ambient wind speed and direction data.

[0008] As a preferred option, a resistive temperature sensor is used to obtain the temperature value T, in degrees Celsius (°C). A capacitive humidity sensor is used to obtain the relative humidity RH (expressed as a percentage). Then, the humidity value H is derived from the relationship between relative humidity and humidity. An anemometer is used to obtain the instantaneous wind speed v, in meters per second (m / s), and the wind direction θ, in degrees (°).

[0009] As a preferred approach, step three involves establishing a diffusion model based on sample points. Starting from the central sample point, the model diffuses outwards in all directions. The theory of aerodynamics is used to describe the spread of pests and diseases in different directions. Assuming that the spread of pests and diseases is influenced by wind, a convection-diffusion equation is used to describe the propagation process. Considering eight different wind directions, each direction needs to be treated individually. The convection-diffusion equation can be expressed as: Where C(x,y,t) is the concentration of pests and diseases at a certain location on tree A, t is time, and D is the diffusion coefficient. It is the Laplace operator, where V is the wind speed vector. It's the gradient operator, the second term. This represents the convection term, indicating the propagation caused by wind speed; Considering the influence of temperature, humidity, wind speed, and wind direction on the diffusion coefficient D, D is treated as a function of these factors, i.e., D = f(T,H,v,θ). The diffusion coefficient can then be expressed as: Where D0 is the basic diffusion coefficient, k1, k2, and k3 are adjustment factors, and θ0 is the reference angle of wind direction, in degrees (°). Considering the influence of different wind directions, the corresponding wind speed vector V is calculated based on the wind speed and wind direction in each direction. The wind direction angle θ is measured with due east as the reference, where north is 0 degrees, east is 90 degrees, and so on. The wind speed vector can be represented as: V=(vcos(θ),vsin(θ)) (3) By substituting the wind speed vector into the convection-diffusion equation, we obtain the pest and disease transmission model in each direction. Based on different wind angles, we obtain models in eight different directions. We use concentration C and location (x, y) and represent the Laplace operator and gradient operator in discrete form. Assume the concentration C of pests and diseases at a certain location on fruit tree A is known. (x,y) , where (x,y) represents the location information of pests and diseases on fruit tree A. The concentration values ​​around the affected area are used to approximate the discrete forms of the Laplace operator and the gradient operator. The approximate value of the Laplace operator is expressed as: The gradient operator is approximated as follows: Where h is the distance between fruit tree A and other fruit trees, in meters (m).

[0010] The beneficial effects of this application are as follows: By analyzing environmental factors and pest and disease data within the orchard, and combining the established mathematical model and species migration model, it is possible to predict the spread speed and concentration of pests and diseases in different directions. The constructed prescription map can help orchard managers take timely and targeted management measures, effectively reducing the spread and impact of pests and diseases, ensuring the growth and yield of orchard crops, improving the sustainability of orchard production, and promoting environmentally friendly cultivation. Simultaneously, it can reduce dependence on chemical pesticides, reducing environmental pollution and its impact on human health. This method of constructing orchard pest and disease prescription maps based on species migration patterns has significant advantages in terms of environmental protection and efficiency, and has positive significance and important value in promoting the sustainable development of orchard production. Attached Figure Description

[0011] Figure 1 This application presents a flowchart illustrating the workflow for constructing orchard pest and disease prescription maps.

[0012] Figure 2 This application includes a map showing the selection of orchard pest and disease sample points.

[0013] Figure 3 The first schematic diagram of the orchard pest and disease infection degree prediction map in this application;

[0014] Figure 4 The second schematic diagram of the orchard pest and disease infection degree prediction map in this application;

[0015] Figure 5 This application includes pesticide treatment prescription diagrams for different migration speeds and directions of pests and diseases. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0017] This application provides a method for constructing orchard pest and disease prescription maps based on species migration patterns, including the following steps:

[0018] Step 1: Sample point selection: Select areas with dense peach tree planting and susceptibility to disease as sample points, determine N initial sample points, take fruit tree A as the sample center, select fruit trees in different directions around fruit tree A as sample points, and record the location information of these N fruit trees. Observe the external symptoms of the N fruit trees, compare the observed disease and pest symptoms with the known characteristics of fruit tree diseases and pests, and preliminarily determine the types of diseases and pests.

[0019] When selecting sampling points and diagnosing diseases and pests in orchards, it is essential to first carefully consider the orchard's geographical distribution and the planting conditions of the fruit trees. When selecting sampling points, priority should be given to representative geographical locations within the orchard, covering fruit trees of different varieties, growth stages, and growing environments. Simultaneously, factors such as the orchard's altitude, soil type, and climate characteristics should be considered to ensure that the sampling points comprehensively reflect the pest and disease situation within the orchard. After selecting sampling points, the type of pest or disease should be determined by carefully observing the external symptoms of the fruit trees, or samples should be collected from suspected infected areas for pathogen detection. Comprehensive analysis and diagnosis are then conducted to determine the type and severity of the disease, providing accurate data support for subsequent pest and disease control.

[0020] Taking peach tree bacterial leaf spot and brown spot as examples, firstly, areas with dense peach tree planting and high susceptibility to disease are selected as sample points, determining nine initial sample points. Tree A is used as the sample center, and the surrounding eight trees are also selected as sample points. These eight trees are: tree B (northwest), tree C (north), tree D (northeast), tree E (west), tree F (east), tree G (southwest), tree H (south), and tree I (southeast). The location information of these nine trees is recorded. Then, the external symptoms of the trees are observed, including color changes, spots, wilting, and deformities. The observed disease symptoms are compared with known characteristics of fruit tree diseases to preliminarily determine the possible disease type. Alternatively, diseased leaf samples can be collected for pathogen detection. PCR technology or pathogen culture methods can also be used to identify the specific infecting pathogen, such as fungi, bacteria, or viruses, to accurately determine whether the disease type is bacterial leaf spot or brown spot. The sample point selection diagram is shown below. Figure 2 As shown.

[0021] Step Two: Environmental Parameter Data Acquisition. Sensor devices are used to collect data on different temperatures (T), humidity (H), wind speed (v), and wind direction (θ) within the orchard. Simultaneously, insect traps and sticky insect boards are used to collect data on the quantity and concentration of various pests and diseases within the orchard. It should be noted that any parts not detailed in this application are prior art.

[0022] A temperature sensor is used to collect ambient temperature data, a humidity sensor is used to collect ambient humidity data, and an anemometer is used to collect ambient wind speed and direction data. The temperature sensor is a resistive temperature sensor, which obtains the temperature value T in degrees Celsius (°C). The humidity sensor is a capacitive humidity sensor, which obtains the relative humidity RH (expressed as a percentage). Then, the humidity value H is derived by using the relationship between relative humidity and humidity. The anemometer obtains the instantaneous wind speed v in meters per second (m / s) and the wind direction θ in degrees (°).

[0023] Step 3: Establishing the diffusion model: Based on the environmental parameter data collected from the sample points, establish a diffusion model to determine the relationship between environmental factors (temperature, humidity, wind speed, wind direction) and the occurrence of pests and diseases. Calculate the concentration C of pests and diseases spreading in different directions using the diffusion model. propagate .

[0024] A diffusion model based on sample points is established, starting from the central sample point and spreading outwards in all directions. The theory of aerodynamics is used to describe the spread of pests and diseases in different directions. It is assumed that the spread of pests and diseases is affected by wind, and the convection diffusion equation is used to describe its spread process. Considering eight different wind directions, each direction needs to be treated separately.

[0025] The convection-diffusion equation can be expressed as: Where C(x,y,t) is the concentration of pests and diseases at a certain location on tree A, t is time, and D is the diffusion coefficient. It is the Laplace operator, where V is the wind speed vector. It's the gradient operator, the second term. This represents the convection term, indicating the propagation caused by wind speed. Considering the influence of temperature, humidity, wind speed, and wind direction on the diffusion coefficient D, D is treated as a function of these factors, i.e., D = f(T,H,v,θ). The diffusion coefficient can then be expressed as: Where D0 is the basic diffusion coefficient, k1, k2, and k3 are adjustment factors, and θ0 is the reference angle of wind direction, in degrees (°).

[0026] Considering the influence of different wind directions, the corresponding wind speed vector V is calculated based on the wind speed and direction in each direction. The wind direction angle θ is measured with due east as the reference, where north is 0 degrees, east is 90 degrees, and so on. The wind speed vector can be represented as: V=(vcos(θ),vsin(θ)) (8) By substituting the wind speed vector into the convection-diffusion equation, we obtain the pest and disease transmission model in each direction. Based on different wind angles, we obtain models in eight different directions.

[0027] We use concentration C and location (x, y) and represent the Laplace operator and gradient operator in discrete form. Assume the concentration C of pests and diseases at a certain location on fruit tree A is known. (x,y) , where (x,y) represents the location information of pests and diseases on fruit tree A. The concentration values ​​around the affected area are used to approximate the discrete forms of the Laplace operator and the gradient operator. The approximate value of the Laplace operator is expressed as: The gradient operator is approximated as follows: Where h is the distance between fruit tree A and other fruit trees, in meters (m).

[0028] Step 4: Construct an infection severity prediction map: Based on the diffusion model analysis results, the spread concentration of pests and diseases in different directions was determined. Construct a prediction map of the degree of pest and disease infection.

[0029] Step 5: Construct a prescription map: Based on the obtained results of pest and disease transmission, construct a prescription map.

[0030] Specifically, in step two, the relative humidity (RH) can be derived using the following formula: Where T is the air temperature, measured in degrees Celsius (°C), and P... sat (T) is the saturated water vapor pressure at temperature T, expressed in Pascals (Pa) or other pressure units. sat (T)=6.1078×10^((7.5×T)÷(237.3+T)), where RH is relative humidity, expressed as a percentage.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for constructing orchard pest and disease prescription maps based on species migration patterns, characterized in that, Includes the following steps: Step 1: Sample point selection: Select areas with dense peach tree planting and susceptibility to disease as sample points, determine N initial sample points, take fruit tree A as the sample center, select fruit trees in different directions around fruit tree A as sample points, and record the location information of these N fruit trees. Observe the external symptoms of the N fruit trees, compare the observed disease and pest symptoms with the known characteristics of fruit tree diseases and pests, and preliminarily determine the types of diseases and pests. Step 2: Environmental parameter data collection: Data on different temperatures (T), humidity (H), wind speed (v), and wind direction (θ) in the orchard were collected using sensor equipment. At the same time, insect traps and sticky insect boards were used to collect data on the quantity and concentration of various pests and diseases in the orchard. Step 3: Establishing the diffusion model: Based on environmental parameter data collected from sample points, a diffusion model was established to determine the relationship between environmental factors and the occurrence of pests and diseases. The diffusion model was used to calculate the concentration C of pests and diseases spreading in different directions. propagate ; Step 4: Construct an infection severity prediction map: Based on the diffusion model analysis results, the spread concentration C of pests and diseases in different directions was determined. propagate Construct a prediction map of the degree of pest and disease infection; Step 5: Construct the prescription diagram: Based on the obtained results of pest and disease transmission, a prescription map is constructed.

2. The method for constructing orchard pest and disease prescription maps based on species migration patterns according to claim 1, characterized in that, In step one, there are 9 initial sample points. The fruit trees around fruit tree A in different directions are fruit tree B in the northwest, fruit tree C in the north, fruit tree D in the northeast, fruit tree E in the west, fruit tree F in the east, fruit tree G in the southwest, fruit tree H in the south, and fruit tree I in the southeast.

3. The method for constructing orchard pest and disease prescription maps based on species migration patterns according to claim 1, characterized in that, In step two, a temperature sensor is used to collect ambient temperature data, a humidity sensor is used to collect ambient humidity data, and an anemometer is used to collect ambient wind speed and direction data.

4. The method for constructing orchard pest and disease prescription maps based on species migration patterns according to claim 3, characterized in that, The temperature sensor is a resistive temperature sensor, which yields a temperature value of T in degrees Celsius (°C). The humidity sensor is a capacitive humidity sensor, which yields a relative humidity of RH (expressed as a percentage). The humidity value of H is derived from the relationship between relative humidity and humidity. The instantaneous wind speed is v in meters per second (m / s) and the wind direction is θ in degrees (°).

5. The method for constructing orchard pest and disease prescription maps based on species migration patterns according to claim 1, characterized in that, In step three, a diffusion model based on sample points is established, starting from the central sample point and spreading outwards in all directions. The theory of aerodynamics is used to describe the spread of pests and diseases in different directions. Assuming that the spread of pests and diseases is influenced by wind, a convection-diffusion equation is used to describe the propagation process. Considering eight different wind directions, each direction needs to be treated separately. The convection-diffusion equation can be expressed as: Where C(x,y,t) is the concentration of pests and diseases at a certain location on tree A, t is time, and D is the diffusion coefficient. It is the Laplace operator, where V is the wind speed vector. It's the gradient operator, the second term. This represents the convection term, indicating the propagation caused by wind speed; Considering the influence of temperature, humidity, wind speed, and wind direction on the diffusion coefficient D, D is treated as a function of these factors, i.e., D = f(T,H,v,θ). The diffusion coefficient can then be expressed as: Where D0 is the basic diffusion coefficient, k1, k2, and k3 are adjustment factors, and θ0 is the reference angle of wind direction, in degrees (°). Considering the influence of different wind directions, the corresponding wind speed vector V is calculated based on the wind speed and wind direction in each direction. The wind direction angle θ is measured with due east as the reference, where north is 0 degrees, east is 90 degrees, and so on. The wind speed vector can be represented as: V=(vcos(θ),vsin(θ)) (3) By substituting the wind speed vector into the convection-diffusion equation, we obtain the pest and disease transmission model in each direction. Based on different wind angles, we obtain models in eight different directions. We use concentration C and location (x, y) and represent the Laplace operator and gradient operator in discrete form. Assume the concentration C of pests and diseases at a certain location on fruit tree A is known. (x,y) , where (x,y) represents the location information of pests and diseases at a certain point on fruit tree A. The discrete forms of the Laplace operator and gradient operator are approximated using the surrounding concentration values; The approximate value of the Laplace operator is expressed as: The gradient operator is approximated as follows: Where h is the distance between fruit tree A and other fruit trees, in meters (m).

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