A tobacco pest identification method and system

Abstract

The invention discloses a tobacco pest identification method and system, and relates to the technical field of tobacco field intelligent management. The method divides a tobacco field into several sub-areas through a tobacco field intelligent management module, collects tobacco disease images, pest videos, environmental data and soil data of the sub-areas, and establishes a corresponding database after image processing; collects tobacco pest historical data and matches it with the image database to obtain the corresponding tobacco confirmed pest types. Calculate the environmental and soil impact coefficients of tobacco diseases; calculate the impact coefficients of various diseases respectively, judge the tobacco disease situation and give strategies; analyze the pest diffusion speed and direction, calculate the pest diffusion coefficient, judge the pest diffusion situation and give strategies; use the tobacco field ecological model, without manually annotating data for training, automatically learn the potential laws of tobacco pests, output the optimal yield coefficient of the tobacco field, and provide accurate and scientific pest control solutions for tobacco planting.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco field intelligent management, and in particular to a tobacco pest identification method and system. Background Art

[0002] As one of the main commodity crops in my country, the agricultural economic value of tobacco occupies an important position in the national economic income. With the rapid development of modern agriculture, tobacco planting has gradually shifted from traditional manual management to a more intelligent and efficient management model. However, in the actual application of tobacco pest and disease identification methods, there are still problems such as inaccurate diagnosis of pests and diseases and incomplete analysis of environmental and soil factors.

[0003] Traditional tobacco pest and disease management methods rely on manual inspections and empirical judgments, which often lead to untimely identification, low efficiency, and inaccurate judgments due to interference from environmental and soil factors. Although intelligent management technology has made some progress in tobacco pest and disease control, there are still many technical difficulties in practical applications. Rapid identification and precise prevention and control of tobacco pests and diseases are the core challenges currently faced in tobacco cultivation, especially in large-scale tobacco fields. How to combine real-time collected environmental data, soil data, tobacco disease data and tobacco pest data, pest and disease images, and historical pest and disease data, etc., effectively integrate and analyze multi-source data to accurately diagnose tobacco field diseases and pests, development and spread trends, and provide strategies, is still a problem to be solved. Therefore, it is necessary to propose a tobacco pest and disease identification method and system to provide a more scientific and sophisticated pest and disease identification and monitoring method for tobacco field management. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a tobacco pest and disease identification method and system to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tobacco pest identification method and system, comprising:

[0006] Preferably, in step 1, the entire tobacco field area is divided into several sub-areas of uniform area, sampling points are set in the several sub-areas, tobacco disease pictures of the sub-areas are collected and acquired, and a tobacco disease image database is established after image processing; tobacco insect pest videos of the sub-areas are collected and acquired, and a tobacco insect pest image database is established after image processing; environmental data of the sub-areas are collected to establish a tobacco environmental database; soil data of the sub-areas are collected to establish a tobacco soil database;

[0007] Step 2: Collect tobacco disease history data and match them with the data in the tobacco disease image database and the tobacco pest image database to obtain corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests;

[0008] And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Area of ​​tobacco common mosaic disease 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area ;

[0009] Step 3: Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region based on the data from the tobacco environment database , based on the data of tobacco soil database, calculate the soil impact coefficient of tobacco diseases in the ith sub-region ;

[0010] Step 4: Calculate and obtain the tobacco brown spot disease impact coefficient of the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region And evaluate, determine the disease situation of tobacco, and provide strategies;

[0011] Step 5: Calculate the pest diffusion rate of the ith sub-region based on the data of the tobacco pest image database. and the spreading direction of pests in the ith sub-region , and then calculate the tobacco aphid infestation area in the ith sub-region and tobacco moth pests , calculate the diffusion coefficient of tobacco aphid pest in the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region Analyze and determine the spread of tobacco pests and provide strategies;

[0012] Step 6: By establishing a tobacco field ecology model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotating data for training, and the influence coefficient of tobacco brown spot disease in the i-th sub-region can be used to calculate the influence coefficient of tobacco brown spot disease in the i-th sub-region. , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX.

[0013] Preferably, step one comprises:

[0014] S11, the entire tobacco field area is divided into a number of sub-areas of uniform area, and the n sub-areas are divided into a plurality of sub-areas of uniform area in the tobacco field intelligent management module according to , , ,..., Perform sequential marking;

[0015] S12. Set up sampling points in several sub-areas, and use cruise drones equipped with high-definition cameras, multi-spectral cameras and high-resolution optical sensors to collect tobacco disease images in the sub-areas, including: the color of tobacco leaf spots, tobacco leaf holes, tobacco leaf edge damage, tobacco leaf shrinkage and curling, main vein bending and root and stem deformity; use high-definition cameras to collect videos of tobacco pests in the sub-areas, including: tobacco aphids and tobacco moths;

[0016] S13, using smoothing filtering technology to perform noise reduction processing on the tobacco disease images in the sub-regions; performing geometric correction on the image space deviation of the tobacco disease image samples, obtaining tobacco disease image data after image processing, and establishing a tobacco disease image database;

[0017] S14, analyzing the tobacco pest video of the sub-region frame by frame, then using smoothing filtering technology to perform noise reduction processing on the image of each frame, using histogram equalization image enhancement technology to obtain image brightness and contrast data, and after image processing, obtaining tobacco pest image data, and establishing a tobacco pest image database;

[0018] S15, by installing temperature sensors, humidity sensors, light sensors and wind sensors in the sub-areas, respectively collecting the temperature values ​​of the sub-area environments , humidity value of the air in the sub-area , the duration of sunlight received by tobacco in each sub-area every day And the wind force value of tobacco in the sub-area , and establish a tobacco environment database;

[0019] S16. Install a soil detector in the soil of the sub-area to collect the urease content per cubic decimeter of soil in the sub-area. , Phosphatase content per cubic decimeter of soil in the sub-area , the content of cellulase in each cubic decimeter of soil in the sub-area , the content of trace elements in each cubic decimeter of soil in the sub-area and pH of the soil in the sub-area , and establish a tobacco soil database.

[0020] Preferably, step 2 includes:

[0021] S21, collecting tobacco pest and disease historical data and matching them with the data in the tobacco pest and disease image database and the tobacco pest and disease image database, obtaining corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests;

[0022] And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Area of ​​tobacco common mosaic disease 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area .

[0023] Preferably, step three includes:

[0024] S31. Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data from the tobacco environmental database. , the formula is as follows:

[0025]

[0026] In the formula, represents the temperature value of the environment in the ith sub-region, Indicates the maximum ambient temperature tolerance of tobacco. represents the humidity value of the air environment in the ith sub-area, Indicates the maximum air humidity tolerance of tobacco. represents the duration of sunlight received by the tobacco in the ith sub-area every day, Indicates the maximum light exposure duration of tobacco per day. represents the wind force value of tobacco in the ith sub-area, Indicates the maximum wind force that tobacco can withstand. , , and Expressed as weight coefficient, , , and ,and ;

[0027] S32. Calculate the soil impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data in the tobacco soil database. , the formula is as follows:

[0028]

[0029] In the formula, represents the content of urease per cubic decimeter of soil in the i-th sub-area, represents the content of phosphatase in each cubic decimeter of soil in the i-th sub-area, represents the cellulase content per cubic decimeter of soil in the ith sub-region, represents the content of trace elements in each cubic decimeter of soil in the i-th sub-area, represents the pH value of the soil in the ith sub-region, Indicates the ideal pH value of the soil in the sub-region, , , , and Expressed as weight coefficient, , , , and ,and

[0030] .

[0031] Preferably, step four includes:

[0032] S41, Environmental impact coefficient of tobacco disease in the ith sub-region and the soil influence coefficient of tobacco diseases in the ith sub-region , the impact on tobacco diseases, after dimensionless processing, calculate the impact coefficient of tobacco brown spot disease in the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region , the formula is as follows:

[0033]

[0034]

[0035]

[0036]

[0037] In the formula, n represents the number of sub-regions, represents the area of ​​the ith sub-region, represents the tobacco brown spot disease area in the ith sub-region, represents the tobacco downy mildew area in the ith sub-region, represents the tobacco common mosaic area in the ith sub-region, represents the tobacco wilt area in the ith sub-region, represents the environmental impact coefficient of tobacco diseases in the ith sub-region, Represents the soil impact coefficient of tobacco diseases in the i-th sub-region.

[0038] Preferably, step 4 further comprises:

[0039] S42, by presetting the first threshold Q1 and comparing it with the tobacco brown spot disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a first evaluation result;

[0040] When the influence coefficient of tobacco brown spot disease in the i-th sub-region When the value is greater than the first threshold Q1, it indicates that the tobacco in the ith sub-region is affected by brown spot disease, triggering the first warning instruction and generating the first strategy, including: spraying the ith sub-region with 90% mancozeb, 80% polyoxin and 50% sclerotinia net, once every 10 days for 3 consecutive times, and recalculating until the tobacco brown spot disease impact coefficient of the ith sub-region is ≤ the first threshold Q1;

[0041] When the influence coefficient of tobacco brown spot disease in the i-th sub-region When ≤ the first threshold Q1, it means that the tobacco in the ith sub-area is not affected by brown spot disease and continues to be monitored;

[0042] S43, by presetting the second threshold Q2 and comparing it with the tobacco downy mildew influence coefficient of the i-th sub-region , conduct comparative analysis and generate a second evaluation result;

[0043] When the influence coefficient of tobacco downy mildew in the i-th sub-region When the second threshold Q2 is reached, it indicates that the tobacco in the ith sub-region is affected by downy mildew, triggering the second warning instruction and generating the second strategy, including: spraying the ith sub-region with 80% mancozeb, 70% mancozeb and 25% metalaxyl solution, once every 10 days for 3 consecutive times, and recalculating until the tobacco downy mildew impact coefficient of the ith sub-region is ≤ the second threshold Q2;

[0044] When the influence coefficient of tobacco downy mildew in the i-th sub-region When ≤ the second threshold Q2, it means that the tobacco in the ith sub-area is not affected by downy mildew and continues to be monitored;

[0045] S44, by presetting the third threshold Q3 and comparing it with the influence coefficient of tobacco common mosaic disease in the i-th sub-region , conduct comparative analysis and generate the third evaluation result;

[0046] When the influence coefficient of tobacco mosaic disease in the i-th sub-region When the third threshold Q3 is reached, it means that the tobacco in the ith sub-region is affected by common mosaic disease, triggering the third warning instruction and generating the third strategy, including: spraying the ith sub-region with 0.1% sodium alginate, 0.1% zinc sulfate and 0.3% urea, once every 4 days for 3 times in a row, and recalculating until the tobacco common mosaic disease impact coefficient of the ith sub-region is ≤ the third threshold Q3;

[0047] When the influence coefficient of tobacco mosaic disease in the i-th sub-region When ≤ the third threshold Q3, it means that the tobacco in the ith sub-area is not affected by common mosaic disease and continues to be monitored;

[0048] S45, by presetting the fourth threshold Q4 and comparing it with the tobacco wilt disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a fourth evaluation result;

[0049] When the tobacco wilt disease impact coefficient of the i-th sub-region When the fourth threshold Q4 is reached, it indicates that the tobacco in the ith sub-region is affected by wilt disease, triggering the fourth warning instruction and generating the fourth strategy, including: spraying the ith sub-region with 50% carbendazim, 70% methyl thiophanate and 30% chlorothalonil solution for 3 consecutive times, and recalculating until the tobacco wilt disease impact coefficient of the ith sub-region is ≤ the fourth threshold Q4;

[0050] When the tobacco wilt disease impact coefficient of the i-th sub-region When ≤ the fourth threshold Q4, it indicates that the tobacco in the i-th sub-area is not affected by wilt disease and continues to be monitored.

[0051] Preferably, step five includes:

[0052] S51, extracting the movement trajectory of the pest in the ith sub-region through the data in the tobacco pest image database, including: the x-axis coordinate and y-axis coordinate of the pest, and the corresponding first frame time and the second frame time ; By calculating the displacement and time interval of pests between consecutive frames, after dimensionless processing, the pest diffusion speed of the i-th sub-area is calculated , the formula is as follows:

[0053]

[0054] In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame, Indicates the first frame time, Indicates the second frame time;

[0055] S52: extract the movement trajectory of the pests in the ith sub-region through the data in the tobacco pest image database, and calculate the diffusion direction of the pests in the ith sub-region through vector analysis. , the formula is as follows:

[0056]

[0057] In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame.

[0058] Preferably, step five also includes:

[0059] S53, the speed of pest spread through the i-th sub-region , the direction of pest spread , Tobacco aphid infestation area and tobacco moth infestation area , after dimensionless processing, calculate the tobacco aphid pest diffusion coefficient of the i-th sub-region and tobacco moth pest diffusion coefficient The formula is as follows:

[0060] ;

[0061] ;

[0062] In the formula, n represents the total number of sub-regions, represents the area of ​​the ith sub-region, represents the area of ​​tobacco aphid infestation in the ith sub-region, represents the area of ​​tobacco moth infestation in the ith sub-region;

[0063] S54, by presetting the fifth standard threshold G1 and comparing it with the tobacco aphid pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate the fifth evaluation result;

[0064] When the tobacco aphid pest diffusion coefficient in the i-th sub-region When the fifth standard threshold G1 is reached, it indicates that the tobacco aphid pest in the ith sub-region is spreading, triggering the fifth warning instruction and generating the fifth strategy to spray the ith sub-region with 50% pirimicarb wettable powder 2000 times diluted and recalculating until the tobacco aphid pest diffusion coefficient in the ith sub-region is ≤ the fifth standard threshold G1;

[0065] When the tobacco aphid pest diffusion coefficient in the i-th sub-region When ≤ the fifth standard threshold G1, it means that the tobacco aphid pest in the i-th sub-area has not spread and continues to be monitored;

[0066] S55, by presetting the sixth standard threshold G2 and comparing it with the tobacco moth pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate a sixth evaluation result;

[0067] When the tobacco moth pest diffusion coefficient of the i-th sub-region When the sixth standard threshold G2 is reached, it indicates that the tobacco moth pest in the ith sub-area is spreading, triggering the sixth warning instruction and generating the sixth strategy to spray the ith sub-area with 7500 times of 5% emamectin benzoate water dispersible granules and recalculate until the tobacco moth pest diffusion coefficient in the ith sub-area is ≤ the sixth standard threshold G2;

[0068] When the tobacco moth pest diffusion coefficient of the i-th sub-region When ≤ the sixth standard threshold G2, it indicates that the tobacco moth pest in the i-th sub-area has not spread and continues to be monitored.

[0069] Preferably, step six includes:

[0070] S61. By establishing a tobacco field ecology model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotated data for training. The influence coefficient of tobacco brown spot disease in the i-th sub-region is used , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX, the formula is as follows:

[0071]

[0072] In the formula, , , , , and is the weight coefficient, , , , , and ,and .

[0073] Preferably, a tobacco pest identification system comprises:

[0074] The area division unit is used to divide the entire tobacco field area into several sub-areas of uniform area;

[0075] The acquisition unit is used to set sampling points in several sub-areas, acquire tobacco disease images in the sub-areas, and establish a tobacco disease image database after image processing; acquire tobacco insect pest videos in the sub-areas, and establish a tobacco insect pest image database after image processing; acquire environmental data in the sub-areas to establish a tobacco environmental database; acquire soil data in the sub-areas to establish a tobacco soil database;

[0076] The disease type matching unit is used to collect tobacco disease history data and match it with the data in the tobacco disease image database to obtain the corresponding tobacco confirmed disease types, which include tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease;

[0077] The pest type matching unit is used to collect tobacco pest historical data and match it with the data in the tobacco pest image database to obtain the corresponding tobacco confirmed pest types, which include tobacco aphid pests and tobacco moth pests;

[0078] The calculation unit is used to calculate the environmental impact coefficient of tobacco diseases in the i-th sub-region based on the data of the tobacco environment database. , calculate the soil impact coefficient of tobacco diseases in the i-th sub-region through the data of the tobacco soil database ;

[0079] The first evaluation unit analyzes and determines the tobacco brown spot disease situation in the i-th sub-region to generate a first evaluation result, analyzes and determines the tobacco downy mildew situation in the i-th sub-region to generate a second evaluation result, analyzes and determines the tobacco common mosaic disease situation in the i-th sub-region to generate a third evaluation result, analyzes and determines the tobacco wilt situation in the i-th sub-region to generate a fourth evaluation result; and provides corresponding strategies;

[0080] The second evaluation unit is used to analyze and determine the spread of tobacco aphid pests in the ith sub-region to generate a fifth evaluation result, analyze and determine the spread of tobacco moth pests in the ith sub-region to generate a sixth evaluation result, and provide a corresponding strategy;

[0081] The summary unit is used to establish a tobacco field ecology model without the need for manually labeled data for training. It automatically learns the potential laws of tobacco pests and diseases and outputs the optimal profit coefficient ZYX.

[0082] The present invention provides a tobacco pest identification method and system, which has the following beneficial effects:

[0083] (1) The tobacco pest and disease identification method and system can better allocate and use resources and improve management efficiency by dividing the tobacco field into several sub-areas and collecting data from each sub-area independently.

[0084] (2) This tobacco pest and disease identification method and system can comprehensively analyze various factors affecting tobacco growth by collecting tobacco disease images, pest videos, environmental data and soil data, so as to fully grasp the growth status and pest and disease risks of tobacco and provide managers with a scientific decision-making basis.

[0085] (3) The tobacco pest and disease identification method and system can effectively eliminate external interference, improve the pest and disease identification accuracy, and ensure the accurate extraction of pest and disease characteristics by performing image processing on tobacco pest and disease image samples, such as noise reduction, geometric correction, and image enhancement; the image processing technology histogram equalization can improve the image quality, make the pest and disease characteristics more obvious, and help analysts more clearly identify tiny details such as tobacco leaf spots and pest tracks;

[0086] (4) This tobacco pest and disease identification method and system can quantify complex disease information into numerical values ​​by calculating the environmental impact coefficient and soil impact coefficient of tobacco diseases, which helps to quantitatively analyze disease risks and make disease assessment more scientific and objective. By combining environmental data and soil data to calculate the disease impact coefficient, it can clearly identify which factors have a greater impact on the occurrence of diseases, thereby providing a basis for formulating prevention and control strategies. According to the calculation results of the disease impact coefficient, the system can accurately spray pesticides for different types of tobacco diseases, avoid waste of resources, and improve prevention and control efficiency.

[0087] (5) This tobacco pest identification method and system can accurately identify the pest spread trend by calculating the spread speed and direction of tobacco pests. This accurate identification helps predict the areas where pests may spread, so that preventive measures can be taken in advance to prevent the pests from spreading on a large scale. By setting a threshold for the pest spread coefficient, when the pest spread coefficient exceeds the set value, the system automatically issues an early warning and triggers the corresponding prevention and control strategy. This mechanism can greatly improve the targeting of pest types and reduce the negative impact of pests on tobacco production.

[0088] (6) This tobacco pest and disease identification method and system establishes a tobacco field ecological model, substitutes the tobacco pest and disease impact coefficient, and obtains the optimal tobacco profit coefficient. It can provide farmers with scientific growth suggestions and management strategies, improve the comprehensive management level of tobacco fields, and help farmers achieve better economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 The present invention is a schematic diagram of the steps of a method for identifying tobacco pests and diseases.

[0090] Figure 2 The present invention is a schematic diagram of a tobacco pest identification system block diagram. DETAILED DESCRIPTION

[0091] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] Example 1

[0093] See also Figure 1 The present invention provides a method for identifying tobacco pests and diseases, comprising the following steps:

[0094] Step 1: Divide the entire tobacco field area into several sub-areas of uniform area, set sampling points in several sub-areas, collect tobacco disease pictures in the sub-areas, and establish a tobacco disease image database after image processing; collect tobacco insect pest videos in the sub-areas, and establish a tobacco insect pest image database after image processing; collect environmental data in the sub-areas to establish a tobacco environmental database; collect soil data in the sub-areas to establish a tobacco soil database;

[0095] Step 2: Collect tobacco disease history data and match them with the data in the tobacco disease image database and the tobacco pest image database to obtain corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests;

[0096] And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Tobacco common mosaic area 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area ;

[0097] Step 3: Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region based on the data from the tobacco environment database , based on the data of tobacco soil database, calculate the soil impact coefficient of tobacco diseases in the ith sub-region ;

[0098] Step 4: Calculate and obtain the tobacco brown spot disease impact coefficient of the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region And evaluate, determine the disease situation of tobacco, and provide strategies;

[0099] Step 5: Calculate the pest diffusion rate of the ith sub-region based on the data of the tobacco pest image database. and the spreading direction of pests in the ith sub-region , and then calculate the tobacco aphid infestation area in the ith sub-region and tobacco moth pests , calculate the diffusion coefficient of tobacco aphid pest in the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region Analyze and determine the spread of tobacco pests and provide strategies;

[0100] Step 6: By establishing a tobacco field ecology model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotating data for training, and the influence coefficient of tobacco brown spot disease in the i-th sub-region can be used to calculate the influence coefficient of tobacco brown spot disease in the i-th sub-region. , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX.

[0101] In this embodiment, by dividing the tobacco field into several sub-areas and collecting data independently for each sub-area, resources can be better allocated and tobacco disease images, pest videos, environmental data and soil data can be collected, providing a scientific decision-making basis for subsequent calculations; by performing image processing on tobacco disease image samples, such as noise reduction, geometric correction and image enhancement, external interference can be effectively eliminated, the recognition accuracy of pests and diseases can be improved, and the accurate matching of disease characteristics can be ensured; the image processing technology histogram equalization can improve the image quality, make the characteristics of pests and diseases more obvious, and help analysts more clearly identify tiny details such as lesions and pest tracks on tobacco leaves; by calculating the impact coefficient of tobacco diseases and the impact coefficient of tobacco pests, and by comparing and analyzing the set thresholds, the type of tobacco pests and diseases can be accurately determined, and strategies can be given according to the type to avoid waste of resources and improve prevention and control efficiency; by establishing a tobacco field ecology model, substituting the tobacco pest and disease impact coefficient, and obtaining the optimal tobacco benefit coefficient, scientific growth suggestions and management strategies can be provided to farmers, the comprehensive management level of tobacco fields can be improved, and farmers can be helped to achieve better economic and environmental benefits.

[0102] Example 2

[0103] This embodiment is explained in Example 1. Specifically, step 1 includes:

[0104] S11, the entire tobacco field area is divided into a number of sub-areas of uniform area, and the n sub-areas are divided into a plurality of sub-areas of uniform area in the tobacco field intelligent management module according to , , ,..., Perform sequential marking;

[0105] S12. Set up sampling points in several sub-areas, and use cruise drones equipped with high-definition cameras, multi-spectral cameras and high-resolution optical sensors to collect tobacco disease images in the sub-areas, including: the color of tobacco leaf spots, tobacco leaf holes, tobacco leaf edge damage, tobacco leaf shrinkage and curling, main vein bending and root and stem deformity; use high-definition cameras to collect videos of tobacco pests in the sub-areas, including: tobacco aphids and tobacco moths;

[0106] S13, using smoothing filtering technology to perform noise reduction processing on the tobacco disease images in the sub-regions; performing geometric correction on the image space deviation of the tobacco disease image samples, obtaining tobacco disease image data after image processing, and establishing a tobacco disease image database;

[0107] S14, analyzing the tobacco pest video of the sub-region frame by frame, then using smoothing filtering technology to perform noise reduction processing on the image of each frame, using histogram equalization image enhancement technology to obtain image brightness and contrast data, and after image processing, obtaining tobacco pest image data, and establishing a tobacco pest image database;

[0108] S15, by installing temperature sensors, humidity sensors, light sensors and wind sensors in the sub-areas, respectively collecting the temperature values ​​of the sub-area environments , humidity value of the air in the sub-area , the duration of sunlight received by tobacco in each sub-area every day And the wind force value of tobacco in the sub-area , and establish a tobacco environment database;

[0109] S16. Install a soil detector in the soil of the sub-area to collect the urease content per cubic decimeter of soil in the sub-area. , Phosphatase content per cubic decimeter of soil in the sub-area , the content of cellulase in each cubic decimeter of soil in the sub-area , the content of trace elements in each cubic decimeter of soil in the sub-area and pH of the soil in the sub-area , and establish a tobacco soil database.

[0110] In this embodiment, by dividing the tobacco field into several sub-areas and collecting data independently for each sub-area, resources can be better allocated and used, and tobacco disease images, pest videos, environmental data and soil data can be collected; image processing such as noise reduction, geometric correction and image enhancement is then performed on the tobacco disease image samples, which can effectively eliminate external interference, improve the recognition accuracy of pests and diseases, and ensure the accurate extraction of disease characteristics; image processing technology histogram equalization can improve image quality, make pest and disease characteristics more obvious, and help analysts more clearly identify tiny details such as lesions on tobacco leaves and pest tracks, so as to fully grasp the growth status of tobacco and the risk of pests and diseases, and provide managers with a scientific decision-making basis.

[0111] Example 3

[0112] This embodiment is explained in Example 2. Specifically, step 2 includes:

[0113] S21, collecting tobacco pest and disease historical data and matching them with the data in the tobacco pest and disease image database and the tobacco pest and disease image database, obtaining corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests;

[0114] And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Tobacco common mosaic area 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area .

[0115] In this embodiment, by collecting tobacco pest history data and matching them with tobacco pest image data, the tobacco pest type is diagnosed and the pest area is extracted, providing a scientific basis for subsequent calculations.

[0116] Example 4

[0117] This embodiment is an explanation of the embodiment 1. Specifically, step 3 includes:

[0118] S31. Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data from the tobacco environmental database. , the formula is as follows:

[0119]

[0120] In the formula, represents the temperature value of the environment in the ith sub-region, Indicates the maximum ambient temperature tolerance of tobacco. represents the humidity value of the air environment in the ith sub-area, Indicates the maximum air humidity tolerance of tobacco. represents the duration of sunlight received by the tobacco in the ith sub-area every day, Indicates the maximum light exposure duration of tobacco per day. represents the wind force value of tobacco in the ith sub-area, Indicates the maximum wind force that tobacco can withstand. , , and Expressed as weight coefficient, , , and ,and ;

[0121] S32. Calculate the soil impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data in the tobacco soil database. , the formula is as follows:

[0122]

[0123] In the formula, represents the content of urease per cubic decimeter of soil in the i-th sub-area, represents the content of phosphatase in each cubic decimeter of soil in the i-th sub-area, represents the cellulase content per cubic decimeter of soil in the ith sub-region, represents the content of trace elements in each cubic decimeter of soil in the i-th sub-area, represents the pH value of the soil in the ith sub-region, Indicates the ideal pH value of the soil in the sub-region, , , , and Expressed as weight coefficient, , , , and ,and

[0124] .

[0125] In this embodiment, by calculating the environmental impact coefficient and soil impact coefficient of tobacco diseases, complex disease information can be quantified into numerical values, which helps to quantitatively analyze disease risks and makes disease assessment more scientific, objective and accurate.

[0126] Example 5

[0127] This embodiment is an explanation of the embodiment 1. Specifically, step 4 includes:

[0128] S41, Environmental impact coefficient of tobacco disease in the ith sub-region and the soil influence coefficient of tobacco diseases in the ith sub-region , the impact on tobacco diseases, after dimensionless processing, calculate the impact coefficient of tobacco brown spot disease in the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region , the formula is as follows:

[0129]

[0130]

[0131]

[0132]

[0133] In the formula, n represents the number of sub-regions, represents the area of ​​the ith sub-region, represents the tobacco brown spot disease area in the ith sub-region, represents the tobacco downy mildew area in the ith sub-region, represents the tobacco common mosaic area in the ith sub-region, represents the tobacco wilt area in the ith sub-region, represents the environmental impact coefficient of tobacco diseases in the ith sub-region, Represents the soil impact coefficient of tobacco diseases in the i-th sub-region.

[0134] In this embodiment, by combining environmental data and soil data to calculate the impact coefficient of the disease, it is possible to clearly identify which factors have a greater impact on the occurrence of the disease, thereby providing a basis for formulating prevention and control strategies.

[0135] Example 6

[0136] This embodiment is an explanation of the embodiment 1. Specifically, step 4 also includes:

[0137] S42, by presetting the first threshold Q1 and comparing it with the tobacco brown spot disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a first evaluation result;

[0138] When the influence coefficient of tobacco brown spot disease in the i-th sub-region When the value is greater than the first threshold Q1, it indicates that the tobacco in the ith sub-region is affected by brown spot disease, triggering the first warning instruction and generating the first strategy, including: spraying the ith sub-region with 90% mancozeb, 80% polyoxin and 50% sclerotinia net, once every 10 days for 3 consecutive times, and recalculating until the tobacco brown spot disease impact coefficient of the ith sub-region is ≤ the first threshold Q1;

[0139] When the influence coefficient of tobacco brown spot disease in the i-th sub-region When ≤ the first threshold Q1, it means that the tobacco in the ith sub-area is not affected by brown spot disease and continues to be monitored;

[0140] S43, by presetting the second threshold Q2 and comparing it with the tobacco downy mildew influence coefficient of the i-th sub-region , conduct comparative analysis and generate a second evaluation result;

[0141] When the influence coefficient of tobacco downy mildew in the i-th sub-region When the second threshold Q2 is reached, it indicates that the tobacco in the ith sub-region is affected by downy mildew, triggering the second warning instruction and generating the second strategy, including: spraying the ith sub-region with 80% mancozeb, 70% mancozeb and 25% metalaxyl solution, once every 10 days for 3 consecutive times, and recalculating until the tobacco downy mildew impact coefficient of the ith sub-region is ≤ the second threshold Q2;

[0142] When the influence coefficient of tobacco downy mildew in the i-th sub-region When ≤ the second threshold Q2, it means that the tobacco in the ith sub-area is not affected by downy mildew and continues to be monitored;

[0143] S44, by presetting the third threshold Q3 and comparing it with the influence coefficient of tobacco common mosaic disease in the i-th sub-region , conduct comparative analysis and generate the third evaluation result;

[0144] When the influence coefficient of tobacco mosaic disease in the i-th sub-region When the third threshold Q3 is reached, it means that the tobacco in the ith sub-region is affected by common mosaic disease, triggering the third warning instruction and generating the third strategy, including: spraying the ith sub-region with 0.1% sodium alginate, 0.1% zinc sulfate and 0.3% urea, once every 4 days for 3 times in a row, and recalculating until the tobacco common mosaic disease impact coefficient of the ith sub-region is ≤ the third threshold Q3;

[0145] When the influence coefficient of tobacco mosaic disease in the i-th sub-region When ≤ the third threshold Q3, it means that the tobacco in the ith sub-area is not affected by common mosaic disease and continues to be monitored;

[0146] S45, by presetting the fourth threshold Q4 and comparing it with the tobacco wilt disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a fourth evaluation result;

[0147] When the tobacco wilt disease impact coefficient of the i-th sub-region When the fourth threshold Q4 is reached, it indicates that the tobacco in the ith sub-region is affected by wilt disease, triggering the fourth warning instruction and generating the fourth strategy, including: spraying the ith sub-region with 50% carbendazim, 70% methyl thiophanate and 30% chlorothalonil solution for 3 consecutive times, and recalculating until the tobacco wilt disease impact coefficient of the ith sub-region is ≤ the fourth threshold Q4;

[0148] When the tobacco wilt disease impact coefficient of the i-th sub-region When ≤ the fourth threshold Q4, it indicates that the tobacco in the i-th sub-area is not affected by wilt disease and continues to be monitored.

[0149] In this embodiment, the influence coefficient of the disease is calculated by combining environmental data and soil data, and the influence relationship between environmental factors and soil factors on the occurrence of tobacco diseases is comprehensively calculated, which improves the accuracy and objectivity of the calculation. By comparing and analyzing the preset threshold and the influence coefficient of the disease, the system can accurately spray pesticides for different types of tobacco diseases to improve the prevention and control efficiency. Spray pesticides separately in sub-areas to avoid waste of resources.

[0150] Example 7

[0151] This embodiment is an explanation of the embodiment 1. Specifically, step 5 includes:

[0152] S51, extracting the movement trajectory of the pest in the ith sub-region through the data in the tobacco pest image database, including: the x-axis coordinate and y-axis coordinate of the pest, and the corresponding first frame time and the second frame time ; By calculating the displacement and time interval of pests between consecutive frames, after dimensionless processing, the pest diffusion speed of the i-th sub-area is calculated , the formula is as follows:

[0153]

[0154] In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame, Indicates the first frame time, Indicates the second frame time;

[0155] S52: extract the movement trajectory of the pests in the ith sub-region through the data in the tobacco pest image database, and calculate the diffusion direction of the pests in the ith sub-region through vector analysis. , the formula is as follows:

[0156]

[0157] In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame.

[0158] In this embodiment, by calculating the spreading speed and direction of tobacco pests, the spreading trend of pests can be accurately identified. This accurate identification helps predict the areas where pests may spread, so that preventive measures can be taken in advance to prevent the pests from spreading over a large area.

[0159] Example 8

[0160] This embodiment is explained in Embodiment 1. Specifically, step five also includes:

[0161] S53, the speed of pest spread through the i-th sub-region , the direction of pest spread , Tobacco aphid infestation area and tobacco moth infestation area , after dimensionless processing, calculate the tobacco aphid pest diffusion coefficient of the i-th sub-region and tobacco moth pest diffusion coefficient The formula is as follows:

[0162] ;

[0163] ;

[0164] In the formula, n represents the total number of sub-regions, represents the area of ​​the ith sub-region, represents the area of ​​tobacco aphid infestation in the ith sub-region, represents the area of ​​tobacco moth infestation in the ith sub-region.

[0165] In this embodiment, the pest diffusion coefficient is calculated by using tobacco pest parameters, so that the pest type diffusion trend can be accurately calculated, thereby improving the accuracy and objectivity of the calculation.

[0166] S54, by presetting the fifth standard threshold G1 and comparing it with the tobacco aphid pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate the fifth evaluation result;

[0167] When the tobacco aphid pest diffusion coefficient in the i-th sub-region When the fifth standard threshold G1 is reached, it indicates that the tobacco aphid pest in the ith sub-region is spreading, triggering the fifth warning instruction and generating the fifth strategy to spray the ith sub-region with 50% pirimicarb wettable powder 2000 times diluted and recalculating until the tobacco aphid pest diffusion coefficient in the ith sub-region is ≤ the fifth standard threshold G1;

[0168] When the tobacco aphid pest diffusion coefficient in the i-th sub-region When ≤ the fifth standard threshold G1, it means that the tobacco aphid pest in the i-th sub-area has not spread and continues to be monitored;

[0169] S55, by presetting the sixth standard threshold G2 and comparing it with the tobacco moth pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate a sixth evaluation result;

[0170] When the tobacco moth pest diffusion coefficient of the i-th sub-region When the sixth standard threshold G2 is reached, it indicates that the tobacco moth pest in the ith sub-area is spreading, triggering the sixth warning instruction and generating the sixth strategy to spray the ith sub-area with 7500 times of 5% emamectin benzoate water dispersible granules and recalculate until the tobacco moth pest diffusion coefficient in the ith sub-area is ≤ the sixth standard threshold G2;

[0171] When the tobacco moth pest diffusion coefficient of the i-th sub-region When ≤ the sixth standard threshold G2, it indicates that the tobacco moth pest in the i-th sub-area has not spread and continues to be monitored.

[0172] In this embodiment, by setting a threshold value of the pest diffusion coefficient, when the pest diffusion coefficient exceeds the set value, the system automatically issues an early warning and triggers a corresponding prevention and control strategy. This mechanism can greatly improve the specificity of responding to pest types and reduce the negative impact of pests on tobacco production.

[0173] Example 9

[0174] This embodiment is an explanation of the embodiment 1. Specifically, step 6 includes:

[0175] S61. By establishing a tobacco field ecology model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotated data for training. The influence coefficient of tobacco brown spot disease in the i-th sub-region is used , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX, the formula is as follows:

[0176]

[0177] In the formula, , , , , and is the weight coefficient, , , , , and ,and .

[0178] In this embodiment, by establishing a tobacco field ecology model, automatically learning the potential laws of tobacco diseases and pests, substituting the tobacco disease and pest impact coefficient, and obtaining the optimal tobacco profit coefficient, it can provide farmers with scientific growth suggestions and management strategies, improve the comprehensive management level of tobacco fields, and help farmers achieve better economic and environmental benefits.

[0179] Example 10

[0180] This example is an explanation of Example 1. Figure 2 , a tobacco pest identification system, comprising:

[0181] The area division unit is used to divide the entire tobacco field area into several sub-areas of uniform area;

[0182] The acquisition unit is used to set sampling points in several sub-areas, acquire tobacco disease images in the sub-areas, and establish a tobacco disease image database after image processing; acquire tobacco insect pest videos in the sub-areas, and establish a tobacco insect pest image database after image processing; acquire environmental data in the sub-areas to establish a tobacco environmental database; acquire soil data in the sub-areas to establish a tobacco soil database;

[0183] The disease type matching unit is used to collect tobacco disease history data and match it with the data in the tobacco disease image database to obtain the corresponding tobacco confirmed disease types, which include tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease;

[0184] The pest type matching unit is used to collect tobacco pest historical data and match it with the data in the tobacco pest image database to obtain the corresponding tobacco confirmed pest types, which include tobacco aphid pests and tobacco moth pests;

[0185] The calculation unit is used to calculate the environmental impact coefficient of tobacco diseases in the i-th sub-region based on the data of the tobacco environment database. , calculate the soil impact coefficient of tobacco diseases in the i-th sub-region through the data of the tobacco soil database ;

[0186] The first evaluation unit analyzes and determines the tobacco brown spot disease situation in the i-th sub-region to generate a first evaluation result, analyzes and determines the tobacco downy mildew situation in the i-th sub-region to generate a second evaluation result, analyzes and determines the tobacco common mosaic disease situation in the i-th sub-region to generate a third evaluation result, analyzes and determines the tobacco wilt situation in the i-th sub-region to generate a fourth evaluation result; and provides corresponding strategies;

[0187] The second evaluation unit is used to analyze and determine the spread of tobacco aphid pests in the ith sub-region to generate a fifth evaluation result, analyze and determine the spread of tobacco moth pests in the ith sub-region to generate a sixth evaluation result, and provide a corresponding strategy;

[0188] The summary unit is used to establish a tobacco field ecology model without the need for manually labeled data for training. It automatically learns the potential laws of tobacco pests and diseases and outputs the optimal profit coefficient ZYX.

[0189] In this embodiment, by establishing a tobacco pest and disease identification system, it is possible to quickly and conveniently serve farmers, improve the comprehensive management level of tobacco fields, and improve farmers' operational experience in managing tobacco fields.

[0190] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0191] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A method for identifying tobacco pests and diseases, characterized in that: The following steps are involved: Step 1: Divide the entire tobacco field area into several sub-areas of uniform area, set sampling points in several sub-areas, collect tobacco disease pictures in the sub-areas, and establish a tobacco disease image database after image processing; collect tobacco insect pest videos in the sub-areas, and establish a tobacco insect pest image database after image processing; collect environmental data in the sub-areas to establish a tobacco environmental database; collect soil data in the sub-areas to establish a tobacco soil database; Step 2: Collect tobacco disease history data and match them with the data in the tobacco disease image database and the tobacco pest image database to obtain corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests; And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Tobacco common mosaic area 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area ; Step 3: Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region based on the data from the tobacco environment database , based on the data of tobacco soil database, calculate the soil impact coefficient of tobacco diseases in the ith sub-region ; Step 4: Calculate and obtain the tobacco brown spot disease impact coefficient of the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region And evaluate, determine the disease situation of tobacco, and provide strategies; The environmental impact coefficient of tobacco disease in the i-th sub-region and the soil influence coefficient of tobacco diseases in the ith sub-region , the impact on tobacco diseases, after dimensionless processing, calculate the impact coefficient of tobacco brown spot disease in the i-th sub-region , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region and the tobacco wilt disease impact coefficient of the ith sub-region , the formula is as follows: In the formula, n represents the number of sub-regions, represents the area of ​​the ith sub-region, represents the tobacco brown spot disease area in the ith sub-region, represents the tobacco downy mildew area in the ith sub-region, represents the tobacco common mosaic area in the ith sub-region, represents the tobacco wilt area in the ith sub-region, represents the environmental impact coefficient of tobacco diseases in the ith sub-region, represents the soil impact coefficient of tobacco diseases in the ith sub-region; Step 5: Calculate the pest diffusion rate of the ith sub-region based on the data of the tobacco pest image database. and the spreading direction of pests in the ith sub-region , and then calculate the tobacco aphid infestation area in the ith sub-region and tobacco moth pests , calculate the diffusion coefficient of tobacco aphid pest in the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region Analyze and determine the spread of tobacco pests and provide strategies; Step 6: By establishing a tobacco field ecology model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotating data for training, and the influence coefficient of tobacco brown spot disease in the i-th sub-region can be used to calculate the influence coefficient of tobacco brown spot disease in the i-th sub-region. , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX.

2. A tobacco pest identification method according to claim 1, characterized in that: Step one includes: S11, the entire tobacco field area is divided into a number of sub-areas of uniform area, and the n sub-areas are divided into a plurality of sub-areas of uniform area in the tobacco field intelligent management module according to , , ,..., Perform sequential marking; S12. Sampling points are set in several sub-areas, and pictures of tobacco diseases in the sub-areas are collected by cruise drones equipped with high-definition cameras, multi-spectral cameras and high-resolution optical sensors, including: the color of tobacco leaf spots, tobacco leaf holes, tobacco leaf edge damage, tobacco leaf shrinkage and curling, main vein bending and rhizome deformity; videos of tobacco insect pests in the sub-areas are collected by using high-definition cameras; S13, using smoothing filtering technology to perform noise reduction processing on the tobacco disease images in the sub-regions; performing geometric correction on the image space deviation of the tobacco disease image samples, obtaining tobacco disease image data after image processing, and establishing a tobacco disease image database; S14, analyzing the tobacco pest video of the sub-region frame by frame, then using smoothing filtering technology to perform noise reduction processing on the image of each frame, using histogram equalization image enhancement technology to obtain image brightness and contrast data, and after image processing, obtaining tobacco pest image data, and establishing a tobacco pest image database; S15, by installing temperature sensors, humidity sensors, light sensors and wind sensors in the sub-areas, respectively collecting the temperature values ​​of the sub-area environments , humidity value of the air in the sub-area , the duration of sunlight received by tobacco in each sub-area every day And the wind force value of tobacco in the sub-area , and establish a tobacco environment database; S16. Install a soil detector in the soil of the sub-area to collect the urease content per cubic decimeter of soil in the sub-area. , Phosphatase content per cubic decimeter of soil in the sub-area , the content of cellulase in each cubic decimeter of soil in the sub-area , the content of trace elements in each cubic decimeter of soil in the sub-area and pH of the soil in the sub-area , and establish a tobacco soil database.

3. A tobacco pest identification method according to claim 2, characterized in that: Step 2 includes: S21, collecting tobacco pest and disease historical data and matching them with the data in the tobacco pest and disease image database and the tobacco pest and disease image database, obtaining corresponding confirmed tobacco disease types and confirmed pest types, wherein the confirmed tobacco disease types include: tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; the confirmed pest types include: tobacco aphid pests and tobacco moth pests; And extract the tobacco brown spot area in the i-th sub-region 、Area of ​​tobacco downy mildew 、Tobacco common mosaic area 、Tobacco wilt area , Tobacco aphid infestation area and tobacco moth infestation area .

4. A tobacco pest identification method according to claim 2, characterized in that: Step three includes: S31. Calculate the environmental impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data from the tobacco environmental database. , the formula is as follows: In the formula, represents the temperature value of the environment in the ith sub-region, Indicates the maximum ambient temperature tolerance of tobacco. represents the humidity value of the air environment in the ith sub-area, Indicates the maximum air humidity tolerance of tobacco. represents the duration of sunlight received by the tobacco in the ith sub-area every day, Indicates the maximum light exposure duration of tobacco per day. represents the wind force value of tobacco in the ith sub-area, Indicates the maximum wind force that tobacco can withstand. , , and Expressed as weight coefficient; S32. Calculate the soil impact coefficient of tobacco diseases in the ith sub-region after dimensionless processing based on the data in the tobacco soil database. , the formula is as follows: In the formula, represents the content of urease per cubic decimeter of soil in the i-th sub-area, represents the content of phosphatase in each cubic decimeter of soil in the i-th sub-area, represents the cellulase content per cubic decimeter of soil in the ith sub-region, represents the content of trace elements in each cubic decimeter of soil in the i-th sub-area, represents the pH value of the soil in the ith sub-region, Indicates the ideal pH value of the soil in the sub-region, , , , and Expressed as weight coefficient.

5. A tobacco pest identification method according to claim 1, characterized in that: Step 4 includes: S42, by presetting the first threshold Q1 and comparing it with the tobacco brown spot disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a first evaluation result; When the influence coefficient of tobacco brown spot disease in the i-th sub-region When the value is greater than the first threshold Q1, it indicates that the tobacco in the ith sub-region is affected by brown spot disease, triggering the first warning instruction and generating the first strategy, including: spraying the ith sub-region with 90% mancozeb, 80% polyoxin and 50% sclerotinia net, once every 10 days for 3 consecutive times, and recalculating until the tobacco brown spot disease impact coefficient of the ith sub-region is ≤ the first threshold Q1; When the influence coefficient of tobacco brown spot disease in the i-th sub-region When ≤ the first threshold Q1, it means that the tobacco in the ith sub-area is not affected by brown spot disease and continues to be monitored; S43, by presetting the second threshold Q2 and comparing it with the tobacco downy mildew influence coefficient of the i-th sub-region , conduct comparative analysis and generate a second evaluation result; When the influence coefficient of tobacco downy mildew in the i-th sub-region When the second threshold Q2 is reached, it indicates that the tobacco in the ith sub-region is affected by downy mildew, triggering the second warning instruction and generating the second strategy, including: spraying the ith sub-region with 80% mancozeb, 70% mancozeb and 25% metalaxyl solution, once every 10 days for 3 consecutive times, and recalculating until the tobacco downy mildew impact coefficient of the ith sub-region is ≤ the second threshold Q2; When the influence coefficient of tobacco downy mildew in the i-th sub-region When ≤ the second threshold Q2, it means that the tobacco in the ith sub-area is not affected by downy mildew and continues to be monitored; S44, by presetting the third threshold Q3 and comparing it with the influence coefficient of tobacco common mosaic disease in the i-th sub-region , conduct comparative analysis and generate the third evaluation result; When the influence coefficient of tobacco mosaic disease in the i-th sub-region When the third threshold Q3 is reached, it means that the tobacco in the ith sub-region is affected by common mosaic disease, triggering the third warning instruction and generating the third strategy, including: spraying the ith sub-region with 0.1% sodium alginate, 0.1% zinc sulfate and 0.3% urea, once every 4 days for 3 times in a row, and recalculating until the tobacco common mosaic disease impact coefficient of the ith sub-region is ≤ the third threshold Q3; When the influence coefficient of tobacco mosaic disease in the i-th sub-region When ≤ the third threshold Q3, it means that the tobacco in the ith sub-area is not affected by common mosaic disease and continues to be monitored; S45, by presetting the fourth threshold Q4 and comparing it with the tobacco wilt disease influence coefficient of the i-th sub-region , conduct comparative analysis and generate a fourth evaluation result; When the tobacco wilt disease impact coefficient of the i-th sub-region When the fourth threshold Q4 is reached, it indicates that the tobacco in the ith sub-region is affected by wilt disease, triggering the fourth warning instruction and generating the fourth strategy, including: spraying the ith sub-region with 50% carbendazim, 70% methyl thiophanate and 30% chlorothalonil solution for 3 consecutive times, and recalculating until the tobacco wilt disease impact coefficient of the ith sub-region is ≤ the fourth threshold Q4; When the tobacco wilt disease impact coefficient of the i-th sub-region When ≤ the fourth threshold Q4, it indicates that the tobacco in the i-th sub-area is not affected by wilt disease and continues to be monitored.

6. A tobacco pest identification method according to claim 2, characterized in that: Step five includes: S51, extracting the movement trajectory of the pest in the ith sub-region through the data in the tobacco pest image database, including: the x-axis coordinate and y-axis coordinate of the pest, and the corresponding first frame time and the second frame time ; By calculating the displacement and time interval of pests between consecutive frames, after dimensionless processing, the pest diffusion speed of the i-th sub-area is calculated , the formula is as follows: In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame, Indicates the first frame time, Indicates the second frame time; S52: extract the movement trajectory of the pests in the ith sub-region through the data in the tobacco pest image database, and calculate the diffusion direction of the pests in the ith sub-region through vector analysis. , the formula is as follows: In the formula, Indicates the x-axis coordinate of the pest position in the first frame, Indicates the y-axis coordinate of the pest position in the first frame, Indicates the x-axis coordinate of the pest position in the second frame, Indicates the y-axis coordinate of the pest position in the second frame.

7. A tobacco pest identification method according to claim 6, characterized in that: Step five also includes: S53, the speed of pest spread through the i-th sub-region , the direction of pest spread , Tobacco aphid infestation area and tobacco moth infestation area , after dimensionless processing, calculate the tobacco aphid pest diffusion coefficient of the ith sub-region and tobacco moth pest diffusion coefficient The formula is as follows: ； ； In the formula, n represents the total number of sub-regions, represents the area of ​​the ith sub-region, represents the area of ​​tobacco aphid infestation in the ith sub-region, represents the area of ​​tobacco moth infestation in the ith sub-region; S54, by presetting the fifth standard threshold G1 and comparing it with the tobacco aphid pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate the fifth evaluation result; When the tobacco aphid pest diffusion coefficient in the i-th sub-region When the fifth standard threshold G1 is reached, it indicates that the tobacco aphid pest in the ith sub-region is spreading, triggering the fifth warning instruction and generating the fifth strategy to spray the ith sub-region with 50% pirimicarb wettable powder 2000 times diluted and recalculating until the tobacco aphid pest diffusion coefficient in the ith sub-region is ≤ the fifth standard threshold G1; When the tobacco aphid pest diffusion coefficient in the i-th sub-region When ≤ the fifth standard threshold G1, it means that the tobacco aphid pest in the i-th sub-area has not spread and continues to be monitored; S55, by presetting the sixth standard threshold G2 and comparing it with the tobacco moth pest diffusion coefficient of the i-th sub-region , conduct comparative analysis and generate a sixth evaluation result; When the tobacco moth pest diffusion coefficient of the i-th sub-region When the sixth standard threshold G2 is reached, it indicates that the tobacco moth pest in the ith sub-area is spreading, triggering the sixth warning instruction and generating the sixth strategy to spray the ith sub-area with 7500 times of 5% emamectin benzoate water dispersible granules and recalculate until the tobacco moth pest diffusion coefficient in the ith sub-area is ≤ the sixth standard threshold G2; When the tobacco moth pest diffusion coefficient of the i-th sub-region When ≤ the sixth standard threshold G2, it indicates that the tobacco moth pest in the i-th sub-area has not spread and continues to be monitored.

8. A tobacco pest identification method according to claim 7, characterized in that: Step six includes: S61. By establishing a tobacco field ecological model, the potential laws of tobacco pests and diseases can be automatically learned without manually annotating data for training. The influence coefficient of tobacco brown spot disease in the i-th sub-region is used , the influence coefficient of tobacco downy mildew in the ith sub-region , the impact coefficient of tobacco mosaic disease in the ith sub-region , the tobacco wilt disease impact coefficient of the ith sub-region , the tobacco aphid pest diffusion coefficient of the ith sub-region and the tobacco moth pest diffusion coefficient of the ith sub-region , output the optimal profit coefficient ZYX, the formula is as follows: In the formula, , , , , and is the weight coefficient.

9. A tobacco pest identification system, comprising a tobacco pest identification method according to any one of claims 1 to 8, characterized in that: include: The area division unit is used to divide the entire tobacco field area into several sub-areas of uniform area; The acquisition unit is used to set sampling points in several sub-areas, acquire tobacco disease images in the sub-areas, and establish a tobacco disease image database after image processing; acquire tobacco insect pest videos in the sub-areas, and establish a tobacco insect pest image database after image processing; acquire environmental data in the sub-areas to establish a tobacco environmental database; acquire soil data in the sub-areas to establish a tobacco soil database; The disease type matching unit is used to collect tobacco disease history data and match it with the data in the tobacco disease image database to obtain the corresponding tobacco confirmed disease types, which include tobacco brown spot disease, tobacco downy mildew, tobacco common mosaic disease and tobacco wilt disease; The pest type matching unit is used to collect tobacco pest historical data and match it with the data in the tobacco pest image database to obtain the corresponding tobacco confirmed pest types, which include tobacco aphid pests and tobacco moth pests; The calculation unit is used to calculate the environmental impact coefficient of tobacco diseases in the i-th sub-region based on the data of the tobacco environment database. , calculate the soil impact coefficient of tobacco diseases in the i-th sub-region through the data of the tobacco soil database ; The first evaluation unit analyzes and determines the tobacco brown spot disease situation in the i-th sub-region to generate a first evaluation result, analyzes and determines the tobacco downy mildew situation in the i-th sub-region to generate a second evaluation result, analyzes and determines the tobacco common mosaic disease situation in the i-th sub-region to generate a third evaluation result, analyzes and determines the tobacco wilt situation in the i-th sub-region to generate a fourth evaluation result; and provides corresponding strategies; The second evaluation unit is used to analyze and determine the spread of tobacco aphid pests in the ith sub-region to generate a fifth evaluation result, analyze and determine the spread of tobacco moth pests in the ith sub-region to generate a sixth evaluation result, and provide a corresponding strategy; The summary unit is used to establish a tobacco field ecology model without the need for manually labeled data for training. It automatically learns the potential laws of tobacco pests and diseases and outputs the optimal profit coefficient ZYX.

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