Evaluation method for integrated prevention and control of bacteria, fertilizer and pesticide based on tobacco disease state analysis

Abstract

The present application relates to the field of tobacco disease prevention and control, and discloses a fungus fertilizer and pesticide integrated prevention and control evaluation method based on tobacco disease state analysis, comprising the following steps: recording the disease type and disease grade of tobacco, calculating the disease index of tobacco, combining the disease index of tobacco to design a fungus fertilizer and pesticide composition for preventing and controlling tobacco diseases, and evaluating the prevention and control effect after the integrated prevention and control of the fungus fertilizer and pesticide composition on the diseased tobacco. The present application can improve the ecological, economic, agronomic and social benefits, replace high-toxicity chemical pesticides with the fungus fertilizer and pesticide composition, quickly suppress disease outbreaks, and repair the soil micro-ecology in the long term to realize sustainable planting.

Description

Technical Field

[0001] This invention relates to the field of tobacco disease control, and in particular to an integrated microbial-fertilizer-pesticide control and evaluation method based on tobacco disease status analysis. Background Technology

[0002] Tobacco is an annual herbaceous plant belonging to the Solanaceae family, a widely cultivated economic crop globally, primarily used for processing into cigarettes, cigars, and tobacco products. Tobacco diseases are mainly classified into three categories: fungal diseases, bacterial diseases, and viral diseases, including black shank, red spot disease, bacterial wilt, tobacco mosaic virus, and cucumber mosaic virus. Different diseases can cause similar symptoms in tobacco plants, such as black rot at the base of the stem, wilting of the entire plant, brown spots and perforations on leaves, browning of vascular bundles, bacterial wilt and death, mosaic and wrinkled leaves, deformities, and systemic yellowing. Viral outbreaks have various causes, including but not limited to continuous cropping, high temperature and humidity environments, and overuse of chemical fertilizers.

[0003] Integrated microbial-fertilizer-pesticide control combines three functional components into a single formulation or synergistic solution. Microbial agents inhibit pathogens and enhance plant resistance; functional fertilizers improve soil structure and provide balanced nutrition; and plant protectants target and kill biopesticides with low toxicity. The core advantages of integrated microbial-fertilizer-pesticide control are improved ecological, economic, agronomical, and social benefits, replacement of highly toxic chemical pesticides, rapid suppression of disease outbreaks, and, in the long term, restoration of soil microecology, enabling sustainable planting. Therefore, this paper proposes an evaluation method for integrated microbial-fertilizer-pesticide control based on tobacco disease status analysis. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides an integrated microbial-fertilizer-pesticide control and evaluation method based on tobacco disease status analysis.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides an integrated microbial-fertilizer-pesticide control and evaluation method based on tobacco disease status analysis, comprising the following steps:

[0007] Identify tobacco plants with diseases, record the disease type and severity for each plant, and calculate the real-time disease index of the tobacco.

[0008] Based on the real-time disease index of tobacco, the concentration of pathogens in tobacco with diseases is calculated, and a fertilizer-pesticide combination for the target bacteria is designed.

[0009] The control of tobacco diseases was carried out in an integrated manner by using a combination of target bacteria, fertilizer and pesticide, and the control effect was evaluated after the integrated control was carried out.

[0010] Furthermore, in a preferred embodiment of the present invention, the step of identifying diseased tobacco and recording the disease type and severity for the corresponding tobacco to calculate the real-time disease index of the tobacco specifically involves:

[0011] Identify the tobacco planting areas that require integrated microbial, fertilizer, and pesticide control, mark them as target tobacco planting areas, and divide the target tobacco planting areas into grids to obtain different target tobacco planting sub-regions;

[0012] Tobacco samples were collected and processed from all the target tobacco planting sub-regions to obtain tobacco samples;

[0013] Image acquisition and analysis were performed on all tobacco samples to extract the surface color features of the tobacco samples. At the same time, the standard color features of the tobacco samples were determined, and the Mahalanobis distance between the standard color features and the surface color features of the tobacco samples was calculated.

[0014] If the Mahalanobis distance between the standard color characteristics and the surface color characteristics of a tobacco sample is greater than a predetermined value, the corresponding tobacco sample will be classified into the disease category, labeled as a diseased tobacco sample, and the growth period of the diseased tobacco sample will be determined.

[0015] By combining the growth stage of diseased tobacco samples, the disease type and disease severity of the tobacco samples are determined, and the real-time disease index of the tobacco is calculated.

[0016] Furthermore, in a preferred embodiment of the present invention, the step of determining the disease type and severity of the diseased tobacco sample by combining the growth stage of the diseased tobacco sample, and calculating the real-time disease index of the tobacco, specifically includes:

[0017] By introducing a big data network, the disease type of the diseased tobacco sample is determined based on its growth stage and surface color characteristics, and then labeled as the target tobacco disease type.

[0018] Identify the abnormal color locations on the surface of diseased tobacco samples, mark them as the diseased areas, and calculate the lesion area and the number of diseased areas in the samples using image analysis.

[0019] Construct a tobacco disease grade knowledge comparison map, wherein the tobacco disease grade knowledge comparison map is used to determine the disease grade of a tobacco sample within the target tobacco disease type based on the area and number of lesions at the site of disease, and at the same time determine the highest disease grade of the tobacco sample;

[0020] Determine the number of tobacco plants within the target tobacco planting area, and based on the number of tobacco plants within the target tobacco planting area, determine the number of tobacco plants with diseases.

[0021] Based on the disease severity of the tobacco samples and the number of tobacco plants with disease, the number of tobacco plants with different disease severity is determined. Combined with the number of tobacco plants in the target tobacco planting area, the real-time disease index of tobacco plants with different disease severity is calculated.

[0022] Furthermore, in a preferred embodiment of the present invention, the step of calculating the concentration of pathogens in diseased tobacco based on the real-time disease index of the tobacco, and simultaneously designing a target microbial fertilizer-pesticide composition, specifically involves:

[0023] The harmfulness index of tobacco is preset for different disease levels. If the real-time disease index of tobacco is less than the harmfulness index in all disease levels, the corresponding tobacco is labeled as harmless tobacco.

[0024] If the real-time disease index of tobacco in any disease level is not less than the harmful disease index, then the corresponding tobacco will be labeled as diseased tobacco.

[0025] Based on big data networks, all pathogens present under the current surface color characteristics of diseased tobacco are retrieved and identified as potential pathogens. At the same time, based on the potential pathogens, pathogenic microorganisms are identified in the diseased tobacco and the soil corresponding to the diseased tobacco to determine the real-time concentration of different potential pathogens on the diseased tobacco.

[0026] Preset the concentration of dangerous pathogens, and mark the pathogens to be selected with a real-time concentration not less than the concentration of dangerous pathogens as key pathogens;

[0027] Based on the key pathogens and their corresponding real-time concentrations, search within the big data network for all microbial agents, fertilizers, and pesticides used to control key pathogens at known real-time concentrations.

[0028] All the retrieved microbial agents, fertilizers, and pesticides were classified and combined to obtain different types of microbial-fertilizer-pesticide combinations. Key pathogen samples with known real-time concentrations were obtained, and different types of microbial-fertilizer-pesticide combinations were applied to the key pathogen samples with known real-time concentrations.

[0029] Calculate the concentration decrease rate of key pathogen samples with known real-time concentrations, select the microbial fertilizer-pesticide composition with the lowest concentration decrease rate, and label it as the target microbial fertilizer-pesticide composition.

[0030] Furthermore, in a preferred embodiment of the present invention, the integrated control of tobacco diseases using the target microorganism-fertilizer-pesticide combination, and the evaluation of the control effect after integrated control, specifically includes:

[0031] Based on the real-time concentration of key pathogens, the optimal concentration of the target bacteria fertilizer-pesticide combination is determined, and the organic fertilizer concentration that matches the optimal concentration of the target bacteria fertilizer-pesticide combination is retrieved from the big data network and calibrated as the target organic fertilizer concentration.

[0032] Obtain organic fertilizer, wherein the concentration of organic fertilizer is equal to the target organic fertilizer concentration, and mix the organic fertilizer with the target microbial fertilizer-pesticide composition at the optimal concentration to obtain an organic fertilizer mixture;

[0033] The organic fertilizer mixture is introduced into the diseased tobacco areas of the target tobacco planting area. The introduction method is to use a drone to spray the leaves of the diseased tobacco areas in the target tobacco planting area and mix the organic fertilizer mixture into the soil of the diseased tobacco areas in the target tobacco planting area.

[0034] A pre-set control standard time is set. After the control standard time, the real-time disease index of different disease levels of tobacco is calculated in the target tobacco planting area, and it is determined whether there are still any disease levels whose real-time disease index is not less than the damage disease index.

[0035] If so, continue to use the target microbial fertilizer and pesticide combination to carry out integrated prevention and control of diseased tobacco in the target tobacco planting area until the real-time disease index of all disease levels of all tobacco is less than the disease damage index, and obtain disease-controlled tobacco.

[0036] A quantitative scoring model was constructed to score the tobacco disease control.

[0037] Furthermore, in a preferred embodiment of the present invention, the construction of a quantitative scoring model to score the control of tobacco diseases specifically includes:

[0038] The tobacco plants under disease control and those without harmful effects within the target tobacco planting area are collectively referred to as tobacco to be evaluated. The ratio of beneficial bacteria to pathogens is calculated for all tobacco plants to be evaluated. At the same time, the residual concentration of the target bacteria fertilizer and pesticide combination in the soil within the target tobacco planting area is also calculated.

[0039] A quantitative scoring model was established based on different beneficial bacteria / pathogen ratios and different residual concentrations of the target bacteria fertilizer-pesticide combination. Different combinations of beneficial bacteria / pathogen ratios and residual concentrations of the target bacteria fertilizer-pesticide combination resulted in different scores for the tobacco being evaluated in the quantitative scoring model.

[0040] Based on the quantitative scoring model, all tobacco products to be evaluated are scored and output with different prevention and control scores. A non-compliance score is preset, and tobacco products with a prevention and control score not greater than the non-compliance score are marked as waste tobacco. Meanwhile, tobacco products with a prevention and control score greater than the non-compliance score are marked as qualified tobacco.

[0041] A second aspect of the present invention also provides an integrated microbial-fertilizer-pesticide control evaluation system based on tobacco disease status analysis. The integrated microbial-fertilizer-pesticide control evaluation system includes a memory and a processor. The memory stores an integrated microbial-fertilizer-pesticide control evaluation method. When the processor executes the integrated microbial-fertilizer-pesticide control evaluation method, it performs the following steps:

[0042] Identify tobacco plants with diseases, record the disease type and severity for each plant, and calculate the real-time disease index of the tobacco.

[0043] Based on the real-time disease index of tobacco, the concentration of pathogens in tobacco with diseases is calculated, and a fertilizer-pesticide combination for the target bacteria is designed.

[0044] The control of tobacco diseases was carried out in an integrated manner by using a combination of target bacteria, fertilizer and pesticide, and the control effect was evaluated after the integrated control was carried out.

[0045] This invention addresses the technical deficiencies in the prior art and offers the following beneficial effects: It records tobacco disease types and severity, calculates the tobacco disease index, and uses this index to design a microbial-fertilizer-pesticide combination for disease control. The effectiveness of this combination in integrated disease control of tobacco is then evaluated. This invention improves ecological, economic, agronomic, and social benefits. By replacing highly toxic chemical pesticides with the microbial-fertilizer-pesticide combination, it rapidly curbs disease outbreaks and, in the long term, restores the soil microecology, enabling sustainable farming. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0047] Figure 1 A flowchart of an evaluation method for integrated microbial-fertilizer-pesticide control based on tobacco disease status analysis is shown.

[0048] Figure 2 A flowchart illustrating a method for integrated control of tobacco diseases using a combination of target microorganisms and fertilizers is provided.

[0049] Figure 3 A program view of an integrated microbial-fertilizer-pesticide control and evaluation system based on tobacco disease status analysis is shown. Detailed Implementation

[0050] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0052] Figure 1 The flowchart illustrates an evaluation method for integrated microbial-fertilizer-pesticide control based on tobacco disease status analysis, including the following steps:

[0053] S102: Identify tobacco plants with diseases, record the disease type and severity for the corresponding tobacco plants, and calculate the real-time disease index of the tobacco plants.

[0054] S104: Based on the real-time disease index of tobacco, calculate the concentration of pathogens in tobacco with diseases, and design a fertilizer-pesticide combination for the target bacteria.

[0055] S106: To implement integrated control of tobacco diseases using a combination of target microorganisms, fertilizers, and pesticides, and to evaluate the control effect after integrated control.

[0056] Furthermore, in a preferred embodiment of the present invention, the step of identifying diseased tobacco and recording the disease type and severity for the corresponding tobacco to calculate the real-time disease index of the tobacco specifically involves:

[0057] Identify the tobacco planting areas that require integrated microbial, fertilizer, and pesticide control, mark them as target tobacco planting areas, and divide the target tobacco planting areas into grids to obtain different target tobacco planting sub-regions;

[0058] Tobacco samples were collected and processed from all the target tobacco planting sub-regions to obtain tobacco samples;

[0059] Image acquisition and analysis were performed on all tobacco samples to extract the surface color features of the tobacco samples. At the same time, the standard color features of the tobacco samples were determined, and the Mahalanobis distance between the standard color features and the surface color features of the tobacco samples was calculated.

[0060] If the Mahalanobis distance between the standard color characteristics and the surface color characteristics of a tobacco sample is greater than a predetermined value, the corresponding tobacco sample will be classified into the disease category, labeled as a diseased tobacco sample, and the growth period of the diseased tobacco sample will be determined.

[0061] By combining the growth stage of diseased tobacco samples, the disease type and disease severity of the tobacco samples are determined, and the real-time disease index of the tobacco is calculated.

[0062] It should be noted that, due to the potentially large size of tobacco planting areas, categorized analysis is necessary to improve efficiency. Therefore, the tobacco planting areas are divided into grids to obtain different sub-regions for analysis. Tobacco samples are collected from different sub-regions for image acquisition and analysis to obtain the surface color characteristics of the tobacco samples. If the tobacco color differs from the standard color characteristics, it indicates the presence of pests or diseases; otherwise, the color should be normal. Furthermore, different colors at different growth stages correspond to different diseases. Growth stages include seedling stage, seedling stage, vegetative stage, and maturity stage. Disease types include, but are not limited to, black shank, bacterial wilt, and mosaic virus. The Mahalanobis distance between the standard color characteristics and the surface color characteristics of the tobacco samples is calculated to determine the similarity between the tobacco sample color characteristics and the standard value; a larger Mahalanobis distance indicates lower similarity.

[0063] Furthermore, in a preferred embodiment of the present invention, the step of determining the disease type and severity of the diseased tobacco sample by combining the growth stage of the diseased tobacco sample, and calculating the real-time disease index of the tobacco, specifically includes:

[0064] By introducing a big data network, the disease type of the diseased tobacco sample is determined based on its growth stage and surface color characteristics, and then labeled as the target tobacco disease type.

[0065] Identify the abnormal color locations on the surface of diseased tobacco samples, mark them as the diseased areas, and calculate the lesion area and the number of diseased areas in the samples using image analysis.

[0066] Construct a tobacco disease grade knowledge comparison map, wherein the tobacco disease grade knowledge comparison map is used to determine the disease grade of a tobacco sample within the target tobacco disease type based on the area and number of lesions at the site of disease, and at the same time determine the highest disease grade of the tobacco sample;

[0067] Determine the number of tobacco plants within the target tobacco planting area, and based on the number of tobacco plants within the target tobacco planting area, determine the number of tobacco plants with diseases.

[0068] Based on the disease severity of the tobacco samples and the number of tobacco plants with disease, the number of tobacco plants with different disease severity is determined. Combined with the number of tobacco plants in the target tobacco planting area, the real-time disease index of tobacco plants with different disease severity is calculated.

[0069] It should be noted that the big data network stores various historical data. Within the big data network, it is possible to query the types of tobacco diseases corresponding to different tobacco color characteristics at different growth stages. Secondly, it is necessary to determine the tobacco disease index, which represents the severity of disease in the tobacco. The calculation formula is: Disease Index = (∑(Disease Grade × Number of Plants at That Grade) / (Highest Disease Grade × Total Number of Plants) × 100). The tobacco disease grade knowledge map is used to determine the disease grade of the diseased tobacco sample, as well as the highest disease grade of the diseased tobacco sample. Combined with the number of plants, the real-time disease index of tobacco at different disease grades is obtained.

[0070] Furthermore, in a preferred embodiment of the present invention, the step of calculating the concentration of pathogens in diseased tobacco based on the real-time disease index of the tobacco, and simultaneously designing a target microbial fertilizer-pesticide composition, specifically involves:

[0071] The harmfulness index of tobacco is preset for different disease levels. If the real-time disease index of tobacco is less than the harmfulness index in all disease levels, the corresponding tobacco is labeled as harmless tobacco.

[0072] If the real-time disease index of tobacco in any disease level is not less than the harmful disease index, then the corresponding tobacco will be labeled as diseased tobacco.

[0073] Based on big data networks, all pathogens present under the current surface color characteristics of diseased tobacco are retrieved and identified as potential pathogens. At the same time, based on the potential pathogens, pathogenic microorganisms are identified in the diseased tobacco and the soil corresponding to the diseased tobacco to determine the real-time concentration of different potential pathogens on the diseased tobacco.

[0074] Preset the concentration of dangerous pathogens, and mark the pathogens to be selected with a real-time concentration not less than the concentration of dangerous pathogens as key pathogens;

[0075] Based on the key pathogens and their corresponding real-time concentrations, search within the big data network for all microbial agents, fertilizers, and pesticides used to control key pathogens at known real-time concentrations.

[0076] All the retrieved microbial agents, fertilizers, and pesticides were classified and combined to obtain different types of microbial-fertilizer-pesticide combinations. Key pathogen samples with known real-time concentrations were obtained, and different types of microbial-fertilizer-pesticide combinations were applied to the key pathogen samples with known real-time concentrations.

[0077] Calculate the concentration decrease rate of key pathogen samples with known real-time concentrations, select the microbial fertilizer-pesticide composition with the lowest concentration decrease rate, and label it as the target microbial fertilizer-pesticide composition.

[0078] It should be noted that analyzing the tobacco disease index and the damage index can identify tobacco plants with diseases. Tobacco diseases are usually caused by pathogens. Killing the pathogens and conditioning the tobacco can achieve the goal of tobacco disease control. First, the pathogens need to be killed. This requires determining the pathogen concentration. Identifying the pathogenic microorganisms on the diseased tobacco and the corresponding soil can determine the real-time concentration of different selected pathogens on the diseased tobacco. Different pathogen concentrations require different microbial-fertilizer-pesticide combinations. Different combinations of microbial agents, fertilizers, and pesticides are freely combined to obtain different microbial-fertilizer-pesticide combinations. Experiments are then conducted on each combination to kill the pathogens, and the rate of decrease in pathogen concentration is calculated. The faster the rate of decrease, the faster the selected microbial-fertilizer-pesticide combination takes effect, and it can be selected as the target microbial-fertilizer-pesticide combination for tobacco disease control.

[0079] Figure 2 A flowchart illustrating a method for integrated control of tobacco diseases using a target microbial fertilizer-pesticide combination is shown, including the following steps:

[0080] S202: Integrated control of tobacco diseases using a target microbial fertilizer-pesticide combination, and evaluation of the control effect after integrated control.

[0081] S204: Construct a quantitative scoring model to score the control of tobacco diseases.

[0082] Furthermore, in a preferred embodiment of the present invention, the integrated control of tobacco diseases using the target microorganism-fertilizer-pesticide combination, and the evaluation of the control effect after integrated control, specifically includes:

[0083] Based on the real-time concentration of key pathogens, the optimal concentration of the target bacteria fertilizer-pesticide combination is determined, and the organic fertilizer concentration that matches the optimal concentration of the target bacteria fertilizer-pesticide combination is retrieved from the big data network and calibrated as the target organic fertilizer concentration.

[0084] Obtain organic fertilizer, wherein the concentration of organic fertilizer is equal to the target organic fertilizer concentration, and mix the organic fertilizer with the target microbial fertilizer-pesticide composition at the optimal concentration to obtain an organic fertilizer mixture;

[0085] The organic fertilizer mixture is introduced into the diseased tobacco areas of the target tobacco planting area. The introduction method is to use a drone to spray the leaves of the diseased tobacco areas in the target tobacco planting area and mix the organic fertilizer mixture into the soil of the diseased tobacco areas in the target tobacco planting area.

[0086] A pre-set control standard time is set. After the control standard time, the real-time disease index of different disease levels of tobacco is calculated in the target tobacco planting area, and it is determined whether there are still any disease levels whose real-time disease index is not less than the damage disease index.

[0087] If so, continue to use the target microbial fertilizer and pesticide combination to carry out integrated prevention and control of diseased tobacco in the target tobacco planting area until the real-time disease index of all disease levels of all tobacco is less than the disease damage index, and obtain disease-controlled tobacco.

[0088] A quantitative scoring model was constructed to score the tobacco disease control.

[0089] It should be noted that mixing organic fertilizer with soil and spraying the organic fertilizer mixture onto tobacco leaves via drones are methods for applying the microbial-fertilizer-pesticide combination to diseased tobacco. After the microbial-fertilizer-pesticide combination is applied to the diseased tobacco, it will automatically control the disease. After a period of time, the disease index of the tobacco after the application of the microbial-fertilizer-pesticide combination needs to be calculated. If the disease index is still high, integrated control needs to continue until the real-time disease index of all disease levels of all tobacco is lower than the harmful disease index, thus obtaining disease-controlled tobacco.

[0090] Furthermore, in a preferred embodiment of the present invention, the construction of a quantitative scoring model to score the control of tobacco diseases specifically includes:

[0091] The tobacco plants under disease control and those without harmful effects within the target tobacco planting area are collectively referred to as tobacco to be evaluated. The ratio of beneficial bacteria to pathogens is calculated for all tobacco plants to be evaluated. At the same time, the residual concentration of the target bacteria fertilizer and pesticide combination in the soil within the target tobacco planting area is also calculated.

[0092] A quantitative scoring model was established based on different beneficial bacteria / pathogen ratios and different residual concentrations of the target bacteria fertilizer-pesticide combination. Different combinations of beneficial bacteria / pathogen ratios and residual concentrations of the target bacteria fertilizer-pesticide combination resulted in different scores for the tobacco being evaluated in the quantitative scoring model.

[0093] Based on the quantitative scoring model, all tobacco products to be evaluated are scored and output with different prevention and control scores. A non-compliance score is preset, and tobacco products with a prevention and control score not greater than the non-compliance score are marked as waste tobacco. Meanwhile, tobacco products with a prevention and control score greater than the non-compliance score are marked as qualified tobacco.

[0094] It should be noted that after controlling tobacco diseases, a control score needs to be calculated. A quantitative scoring model is constructed for this purpose, based on different beneficial bacteria / pathogen ratios and different residual concentrations of the target bacteria fertilizer-pesticide combination. The scoring indicators of the quantitative scoring model include positive and negative indicators. The positive indicator is the beneficial bacteria / pathogen ratio; a higher ratio indicates a higher control score. The negative indicator is the residual concentration of the target bacteria fertilizer-pesticide combination; a higher concentration indicates a lower control score. The score of the tobacco to be evaluated is output by combining different beneficial bacteria / pathogen ratios and residual concentrations of the target bacteria fertilizer-pesticide combination. The beneficial bacteria / pathogen ratio and the residual concentration of the target bacteria fertilizer-pesticide combination have different weights. Once the weights are determined, the score can be output, and the tobacco should be discarded based on the score.

[0095] like Figure 3 As shown, the second aspect of the present invention also provides an integrated microbial-fertilizer-pesticide control evaluation system based on tobacco disease status analysis. The integrated microbial-fertilizer-pesticide control evaluation system includes a memory 31 and a processor 32. The memory 31 stores an integrated microbial-fertilizer-pesticide control evaluation method. When the integrated microbial-fertilizer-pesticide control evaluation method is executed by the processor 32, the following steps are implemented:

[0096] Identify tobacco plants with diseases, record the disease type and severity for each plant, and calculate the real-time disease index of the tobacco.

[0097] Based on the real-time disease index of tobacco, the concentration of pathogens in tobacco with diseases is calculated, and a fertilizer-pesticide combination for the target bacteria is designed.

[0098] The control of tobacco diseases was carried out in an integrated manner by using a combination of target bacteria, fertilizer and pesticide, and the control effect was evaluated after the integrated control was carried out.

[0099] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An evaluation method for integrated microbial-fertilizer-pesticide control based on tobacco disease status analysis, characterized in that, Includes the following steps: Identify tobacco plants with diseases, record the disease type and severity, and calculate the real-time disease index. Specifically: Identify the tobacco planting areas that require integrated microbial, fertilizer, and pesticide control, mark them as target tobacco planting areas, and divide the target tobacco planting areas into grids to obtain different target tobacco planting sub-regions; Tobacco samples were collected and processed from all the target tobacco planting sub-regions to obtain tobacco samples; Image acquisition and analysis were performed on all tobacco samples to extract the surface color features of the tobacco samples. At the same time, the standard color features of the tobacco samples were determined, and the Mahalanobis distance between the standard color features and the surface color features of the tobacco samples was calculated. If the Mahalanobis distance between the standard color characteristics and the surface color characteristics of a tobacco sample is greater than a predetermined value, the corresponding tobacco sample will be classified into the disease category, labeled as a diseased tobacco sample, and the growth period of the diseased tobacco sample will be determined. Based on the growth stage of the diseased tobacco samples, the disease type and severity were determined, and the real-time disease index of the tobacco was calculated. By introducing a big data network, the disease type of the diseased tobacco sample is determined based on its growth stage and surface color characteristics, and then labeled as the target tobacco disease type. Identify the abnormal color locations on the surface of diseased tobacco samples, mark them as the diseased areas, and calculate the lesion area and the number of diseased areas in the samples using image analysis. Construct a tobacco disease grade knowledge comparison map, wherein the tobacco disease grade knowledge comparison map is used to determine the disease grade of a tobacco sample within the target tobacco disease type based on the area and number of lesions at the site of disease, and at the same time determine the highest disease grade of the tobacco sample; Determine the number of tobacco plants within the target tobacco planting area, and based on the number of tobacco plants within the target tobacco planting area, determine the number of tobacco plants with diseases. Based on the disease severity of the tobacco samples and the number of tobacco plants with disease, the number of tobacco plants with different disease severity is determined. Combined with the number of tobacco plants in the target tobacco planting area, the real-time disease index of tobacco plants with different disease severity is calculated. Based on the real-time disease index of tobacco, the concentration of pathogens in diseased tobacco was calculated, and a target microbial fertilizer-pesticide combination was designed, specifically: The harmfulness index of tobacco is preset for different disease levels. If the real-time disease index of tobacco is less than the harmfulness index in all disease levels, the corresponding tobacco is labeled as harmless tobacco. If the real-time disease index of tobacco in any disease level is not less than the harmful disease index, then the corresponding tobacco will be labeled as diseased tobacco. Based on big data networks, all pathogens present under the current surface color characteristics of diseased tobacco are retrieved and identified as potential pathogens. At the same time, based on the potential pathogens, pathogenic microorganisms are identified in the diseased tobacco and the soil corresponding to the diseased tobacco to determine the real-time concentration of different potential pathogens on the diseased tobacco. Preset the concentration of dangerous pathogens, and mark the pathogens to be selected with a real-time concentration not less than the concentration of dangerous pathogens as key pathogens; Based on the key pathogens and their corresponding real-time concentrations, search within the big data network for all microbial agents, fertilizers, and pesticides used to control key pathogens at known real-time concentrations. All the retrieved microbial agents, fertilizers, and pesticides were classified and combined to obtain different types of microbial-fertilizer-pesticide combinations. Key pathogen samples with known real-time concentrations were obtained, and different types of microbial-fertilizer-pesticide combinations were applied to the key pathogen samples with known real-time concentrations. Calculate the concentration decrease rate of key pathogen samples with known real-time concentrations, select the microbial fertilizer-pesticide composition with the lowest concentration decrease rate, and label it as the target microbial fertilizer-pesticide composition. The control of tobacco diseases was carried out in an integrated manner by using a combination of target bacteria, fertilizer and pesticide, and the control effect was evaluated after the integrated control was carried out.

2. The integrated microbial-fertilizer-pesticide control and evaluation method based on tobacco disease status analysis as described in claim 1, characterized in that, The method of integrated control of tobacco diseases using a target microbial fertilizer-pesticide combination, followed by evaluation of the control effect after integrated control, specifically involves: Based on the real-time concentration of key pathogens, the optimal concentration of the target bacteria fertilizer-pesticide combination is determined, and the organic fertilizer concentration that matches the optimal concentration of the target bacteria fertilizer-pesticide combination is retrieved from the big data network and calibrated as the target organic fertilizer concentration. Obtain organic fertilizer, wherein the concentration of organic fertilizer is equal to the target organic fertilizer concentration, and mix the organic fertilizer with the target microbial fertilizer-pesticide composition at the optimal concentration to obtain an organic fertilizer mixture; The organic fertilizer mixture is introduced into the diseased tobacco areas of the target tobacco planting area. The introduction method is to use a drone to spray the leaves of the diseased tobacco areas in the target tobacco planting area and mix the organic fertilizer mixture into the soil of the diseased tobacco areas in the target tobacco planting area. A pre-set control standard time is set. After the control standard time, the real-time disease index of different disease levels of tobacco is calculated in the target tobacco planting area, and it is determined whether there are still any disease levels whose real-time disease index is not less than the damage disease index. If so, continue to use the target microbial fertilizer and pesticide combination to carry out integrated prevention and control of diseased tobacco in the target tobacco planting area until the real-time disease index of all disease levels of all tobacco is less than the disease damage index, and obtain disease-controlled tobacco. A quantitative scoring model was constructed to score the tobacco disease control.

3. The integrated microbial-fertilizer-pesticide control and evaluation method based on tobacco disease status analysis as described in claim 2, characterized in that, The construction of a quantitative scoring model for tobacco disease control is specifically as follows: The tobacco plants under disease control and those without harmful effects within the target tobacco planting area are collectively referred to as tobacco to be evaluated. The ratio of beneficial bacteria to pathogens is calculated for all tobacco plants to be evaluated. At the same time, the residual concentration of the target bacteria fertilizer and pesticide combination in the soil within the target tobacco planting area is also calculated. A quantitative scoring model was established based on different beneficial bacteria / pathogen ratios and different residual concentrations of the target bacteria fertilizer-pesticide combination. Different combinations of beneficial bacteria / pathogen ratios and residual concentrations of the target bacteria fertilizer-pesticide combination resulted in different scores for the tobacco being evaluated in the quantitative scoring model. Based on the quantitative scoring model, all tobacco products to be evaluated are scored and output with different prevention and control scores. A non-compliance score is preset, and tobacco products with a prevention and control score not greater than the non-compliance score are marked as waste tobacco. Meanwhile, tobacco products with a prevention and control score greater than the non-compliance score are marked as qualified tobacco.

4. An integrated microbial-fertilizer-pesticide control and evaluation system based on tobacco disease status analysis, characterized in that: The integrated microbial-fertilizer-pesticide control evaluation system includes a memory and a processor. The memory stores a program for the integrated microbial-fertilizer-pesticide control evaluation method. When the program is executed by the processor, the steps of the integrated microbial-fertilizer-pesticide control evaluation method as described in any one of claims 1-3 are implemented.

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