Planting method for preventing cucumber fruit blotch and green mottle virus

Abstract

The invention belongs to the technical field of crop disease prevention and control, and particularly relates to a planting method for preventing cucumber fruit blotch germs and green mottle viruses, which comprises the following steps: acquiring soil and crop historical pathogen data, environment monitoring data and cucumber seed batch information of a planting area; querying supply chain quarantine records of the seeds, and carrying out standardization treatment on the seeds; after planting, the disease probability of each growth stage is predicted through a risk assessment algorithm, a dynamic monitoring plan is generated in combination with a fuzzy mapping algorithm, plant samples are collected, PCR detection of melon bacterial fruit blotch and RT-PCR detection of cucumber green mottle mosaic virus are carried out, and a quarantine report is issued. Based on the monitoring plan, the quarantine report and the real-time environment data, judging a comprehensive risk level, triggering a corresponding field intervention scheme, and recording an execution result to form an intervention log; after planting is finished, a prevention and control efficiency report is generated. Therefore, the problems of insufficient disease detection, lack of intervention measures and the like in the prior art are solved.

Description

Technical Field

[0001] This invention belongs to the field of crop disease control technology, specifically relating to a planting method for preventing cucumber fruit spot disease and green mottle virus. Background Technology

[0002] With the rapid development of large-scale planting and cross-regional distribution in facility agriculture, cucumbers, as a high-value-added economic crop, face the dual threat of bacterial fruit spot disease and cucumber green mottle mosaic virus to safe production. These two pathogens easily spread through multiple pathways, including seed-borne infection, soil residue, and cross-infection during agricultural operations. After infection, they can lead to a 30%-80% reduction in cucumber yield, and in severe cases, even total crop failure, causing a significant impact on the stability of the industry chain. The market urgently needs comprehensive prevention and control technologies with capabilities for source purification, full-cycle dynamic monitoring, and precise graded intervention. However, existing technologies lack deep integration of seed quarantine, field monitoring, and intervention measures, and lack a multi-dimensional collaborative prevention and control system and a disease risk quantification mechanism. This makes it difficult to support precise prevention and control in complex planting scenarios, and there is a significant gap between these technologies and the technical requirements for building a full-process pathogen blocking system.

[0003] However, traditional cucumber disease control has key shortcomings: seed quarantine and pretreatment rely heavily on experience, lacking standardized procedures and complete supply chain quarantine record traceability, making it difficult to effectively block pathogen transmission at the source; disease monitoring uses a fixed-frequency sampling model, failing to dynamically adjust monitoring strategies based on historical pathogen data and environmental characteristics, resulting in insufficient accuracy in responding to disease risks at different growth stages; detection methods are mostly targeted at single pathogens, failing to achieve parallel detection of two high-risk pathogens, easily leading to missed detections and misjudgments; intervention programs mostly adopt a uniform pesticide spraying model, lacking a differentiated control mechanism based on risk levels, resulting in pesticide overuse and difficulty in accurately addressing different levels of disease threats; data at each stage is limited to single-scenario recording, lacking systematic integration and correlation analysis, failing to form an effective control efficacy evaluation and strategy optimization mechanism, leading to a fragmented state of pathogen dynamic information and control measures. With the deepening of green agriculture and precision planting concepts, the market demand for highly precise and low-pollution cucumber disease control technologies is increasingly urgent, but existing technologies, due to the disconnect between control stages and limited accuracy, are unable to support the safe production needs of large-scale cucumber cultivation. Summary of the Invention

[0004] This application provides a planting method for preventing cucumber fruit spot disease and green mottle virus, in order to solve the problems of insufficient disease detection and lack of intervention measures in the prior art.

[0005] The first aspect of this application provides a planting method for preventing cucumber fruit spot disease and green mottle virus, comprising the following steps: obtaining historical pathogen screening data of soil and surrounding crops in the target planting area, historical environmental monitoring data, and batch information of the selected cucumber seeds; querying quarantine records of the seed supply chain based on the batch information, and standardizing the seeds using a hot water soaking procedure; planting the standardized seeds, and based on the historical pathogen screening data and historical environmental monitoring data, predicting the probability of disease occurrence at each key growth stage during the planting cycle using a risk assessment algorithm, while combining a fuzzy mapping algorithm... A dynamic monitoring plan is generated, and based on the plan, plant samples are collected at each growth stage. PCR testing for bacterial fruit spot disease of cucurbits and RT-PCR testing for cucumber green mottle mosaic virus are performed simultaneously, and a quarantine report is issued. Based on the dynamic monitoring plan and the quarantine report, combined with real-time environmental monitoring data, a decision tree classification algorithm is used to determine the comprehensive risk level and trigger the corresponding field intervention plan. The execution results of the field intervention plan are recorded to form an intervention log. After the planting cycle ends, the final yield data is obtained and summarized with the quarantine report and intervention log to generate a control effectiveness report.

[0006] Preferably, a warm water soaking procedure is used to standardize the seed treatment, including: soaking the seeds in a sodium hypochlorite solution with an effective chlorine concentration of 0.5%-1% (w / v) at room temperature for 10-15 minutes; rinsing the chemically disinfected seeds thoroughly with clean water, and then soaking them in constant temperature water at 48-52℃ for 15-20 minutes; quickly transferring the seeds to warm water at 22-28℃ for 3-5 minutes to cool them down, and recording the batch number, treatment time, and operator information of the soaking operation to complete the standardized treatment.

[0007] Preferably, the risk assessment algorithm is used to predict the probability of disease occurrence at each key growth stage during the planting cycle, including: constructing a risk assessment algorithm; inputting historical pathogen screening data and historical environmental monitoring data into the risk assessment algorithm, calculating the potential occurrence probability of fruit spot pathogens during the seedling stage, vine extension stage, and fruit setting stage using a logistic regression formula, and predicting the seasonal outbreak risk probability of green mottle virus using a time series formula; and weighting and fusing the potential occurrence probability of fruit spot pathogens and the seasonal outbreak risk probability of green mottle virus to generate a comprehensive disease occurrence probability value for each stage.

[0008] Preferably, generating a dynamic monitoring plan includes: constructing a fuzzy mapping algorithm; inputting the disease occurrence probability value into the fuzzy mapping algorithm, calculating and outputting the monitoring level corresponding to each growth stage; and matching the monitoring frequency and sampling quantity for each growth stage based on the monitoring level, wherein the higher the monitoring level, the higher the matching monitoring frequency and the more sampling quantity. By integrating the monitoring level, monitoring frequency, and sampling quantity, a structured list of monitoring tasks is output, generating a dynamic monitoring plan.

[0009] Preferably, plant samples are collected and tested in parallel using PCR with specific primers for cucurbit bacterial fruit spot pathogens and RT-PCR with specific primers for cucumber green mottle mosaic virus. A quarantine report is then issued, including: collecting representative plant tissue samples from each growth stage of cucumber and recording the sample information in association with seed batch and field location; performing PCR detection using specific primer BX-S for cucurbit bacterial fruit spot pathogens, and further verifying pathogen isolation using BIO-PCR based on semi-selective culture medium TWZ for samples with positive results; and performing RT-PCR detection using specific primers CGMMV1 and CGMMV2 for cucumber green mottle mosaic virus. Each test must include a positive control and a negative control, and the experiment must be repeated twice to obtain the test results. Based on the test results, a standardized quarantine report containing sample information, test methods, and clear conclusions is generated.

[0010] Preferably, the field intervention plan includes: triggering a basic intervention plan when the risk is low, including increasing field patrols, removing scattered diseased plants and recording their locations; triggering an enhanced intervention plan when the risk is medium, adding targeted spraying of biological pesticides or low-toxicity chemical fungicides to the basic intervention plan, and disinfecting the soil in the pathogen-detected area; triggering an emergency intervention plan when the risk is high, on the basis of the enhanced intervention plan, implementing regional isolation, destroying severely diseased plants, applying therapeutic agents, and adjusting the irrigation mode.

[0011] A second aspect of this application provides a planting system for preventing cucumber fruit spot disease and green mottle virus, comprising: an acquisition module for acquiring historical pathogen screening data of soil and surrounding crops in the target planting area, historical environmental monitoring data, and batch information of the selected cucumber seeds; a processing module for querying quarantine records of the seed supply chain based on the batch information and standardizing the seeds using a hot water soaking procedure; and a detection module for predicting the probability of disease occurrence at each key growth stage during the planting cycle based on the historical pathogen screening data and historical environmental monitoring data after planting the standardized seeds, using a risk assessment algorithm, and simultaneously combining a fuzzy mapping algorithm. The system generates a dynamic monitoring plan and, based on this plan, collects plant samples at each growth stage, simultaneously performing PCR testing for cucurbit bacterial fruit spot disease and RT-PCR testing for cucumber green mottle mosaic virus, and issues a quarantine report. An intervention module, based on the dynamic monitoring plan and the quarantine report, combined with real-time environmental monitoring data, uses a decision tree classification algorithm to determine the comprehensive risk level and trigger corresponding field intervention plans, while recording the execution results of the field intervention plans to form an intervention log. A summary module, after the planting cycle ends, obtains the final yield data and summarizes it with the quarantine report and intervention log to generate a control effectiveness report.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a planting method for preventing cucumber fruit spot disease and green mottle virus as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a planting method for preventing cucumber fruit spot disease and green mottle virus as described in the above embodiments.

[0014] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a planting method for preventing cucumber fruit spot disease and green mottle virus as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application integrates historical pathogen screening data from the soil and surrounding crops in the target planting area, historical environmental monitoring data, seed batch information, and supply chain quarantine records to provide comprehensive data support for subsequent prevention and control, avoiding the omission of key information. A standardized hot water soaking procedure is used to purify pathogens at the seed source, clearly defining operating parameters and pesticide ratios to reduce the risk of pathogen transmission from the source. Risk assessment algorithms are used to accurately predict the probability of disease occurrence at each key growth stage, providing forward-looking guidance for prevention and control actions. A dynamic monitoring plan is formed by combining fuzzy mapping algorithms, ensuring that monitoring frequency and sampling volume are matched to risk levels, improving monitoring targeting. Parallel PCR and RT-PCR detection technologies are used to accurately and efficiently detect two high-risk pathogens, reducing missed detections and misjudgments caused by single detection. A decision tree classification algorithm is used to trigger tiered field intervention plans corresponding to low, medium, and high risks, avoiding pesticide overuse caused by uniform prevention and control and reducing environmental pressure. A control effectiveness report is used to summarize and analyze yield, quarantine results, and intervention logs. Therefore, this solves the problems of insufficient disease detection and lack of intervention measures in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a planting method for preventing cucumber fruit spot disease and green mottle virus according to an embodiment of this application; Figure 2 This is a schematic diagram of a planting method for preventing cucumber fruit spot disease and green mottle virus according to an embodiment of this application; Figure 3 A detailed view of a cucumber tissue sample provided according to an embodiment of this application; Figure 4 The PCR detection results of cucumber tissue samples using the fruit spot pathogen-specific primer BX-S provided according to an embodiment of this application; Figure 5 This image shows the PCR detection results of cucumber tissue samples using the green mottle mosaic virus-specific primers CGMMV1 / 2 provided according to an embodiment of this application. Figure 6 This is a schematic diagram of a planting system for preventing cucumber fruit spot disease and green mottle virus according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0019] The following describes a planting method for preventing cucumber fruit spot disease and green mottle virus, according to an embodiment of this application, with reference to the accompanying drawings. To address the issue of insufficient disease detection mentioned in the background section, this application provides a planting method for preventing cucumber fruit spot pathogen and green mottle virus. This method integrates historical pathogen screening data from the soil and surrounding crops in the target planting area, historical environmental monitoring data, seed batch information, and supply chain quarantine records to provide comprehensive data support for subsequent control and avoid missing key information. A standardized hot water soaking procedure is used to purify the seeds at the source, clearly defining operating parameters and pesticide ratios to reduce the risk of pathogen transmission from the source. Risk assessment algorithms are used to accurately predict the probability of disease occurrence at each key growth stage, predicting risk points in advance and providing forward-looking guidance for control actions. A dynamic monitoring plan is formed using a fuzzy mapping algorithm, ensuring that the monitoring frequency and sampling volume are matched to the risk level, improving the targeting of monitoring. Parallel PCR and RT-PCR detection technologies are used to accurately and efficiently detect the two high-risk pathogens, reducing missed detections and misjudgments caused by single detection. A decision tree classification algorithm is used to trigger tiered field intervention plans corresponding to low, medium, and high risks, avoiding pesticide overuse caused by uniform control and reducing environmental pressure. A control effectiveness report is used to summarize and analyze yield, quarantine results, and intervention logs. This solves the problems of insufficient disease detection and lack of intervention measures in existing technologies.

[0020] Specifically, Figure 1 This is a schematic flowchart illustrating a planting method for preventing cucumber fruit spot disease and green mottle virus, provided in an embodiment of this application.

[0021] like Figure 1 As shown, this cultivation method for preventing cucumber fruit spot disease and green mottle virus includes the following steps: In step S101, historical pathogen screening data of soil and surrounding crops in the target planting area, historical environmental monitoring data, and batch information of the selected cucumber seeds are obtained.

[0022] Among them, historical pathogen screening data refers to the records of past infection status, high incidence periods, residual levels and distribution characteristics of fruit spot pathogens and green mottle virus in the target planting area and surrounding crops, providing historical reference data for disease risk prediction and prevention and control strategy formulation.

[0023] It is understood that the embodiments of this application obtain historical pathogen screening data to clarify the infection trajectory, high incidence period and residual status of fruit spot pathogens and green mottle virus in the target planting area in the past, avoid blind judgment on the regional pathogen background, provide core historical basis for risk assessment algorithm, help accurately predict the probability of disease occurrence at each growth stage, make dynamic monitoring plan and intervention plan more in line with regional pathogen characteristics, and improve the targeting and effectiveness of prevention and control measures.

[0024] In step S102, the quarantine records of the seed supply chain are queried according to the batch information, and the seeds are standardized by using a hot water soaking procedure.

[0025] Among them, the quarantine records of the seed supply chain refer to the formal certificates issued by authoritative quarantine agencies after conducting special tests on target pathogens such as cucumber fruit spot pathogen and green mottle virus in various stages of seed production, processing and circulation. These certificates specify the quarantine methods, test results, seed batches and compliance conclusions. They are the core basis for assessing the initial risk of seed contamination.

[0026] It is understood that, by querying the quarantine records of the seed supply chain, this application embodiment can clearly trace the pathogen detection results, qualification status and treatment traces of seeds from production, processing to circulation, and exclude seeds with the risk of infection from entering the planting stage. This provides a preliminary verification basis for seed safety and forms a source control linkage with the hot water soaking procedure. This avoids the omission of pathogens caused by relying on a single treatment method, making seed pretreatment more targeted, reducing the initial risk for subsequent disease control, building a solid source safety defense line for cucumber planting, and ensuring the basic effectiveness and stability of disease control during the planting cycle.

[0027] In this embodiment, a warm water soaking procedure is used to standardize the seed treatment, including: soaking the seeds in a sodium hypochlorite solution with an effective chlorine concentration of 0.5%-1% (w / v) at room temperature for 10-15 minutes; rinsing the chemically disinfected seeds thoroughly with clean water, and then soaking them in constant temperature water at 48-52℃ for 15-20 minutes; quickly transferring the seeds to warm water at 22-28℃ for 3-5 minutes to cool them down, and recording the batch number, processing time, and operator information of the soaking operation to complete the standardization treatment.

[0028] It is understood that the embodiments of this application, through a step-by-step operation process of soaking in sodium hypochlorite solution at room temperature, rinsing with clean water, high-temperature constant temperature treatment, warm water cooling, and information recording, fully leverage the synergistic bactericidal effect of chemical disinfection and physical high temperature, effectively killing fruit spot pathogens and green mottle virus attached to the seed surface and shallow layers. The clean water rinsing step effectively reduces the damage of sodium hypochlorite residue to the seed embryo. The precise temperature control and cooling after high-temperature treatment alleviates heat stress and ensures seed germination vitality. Standardized information recording enables traceability of the treatment process, avoids operational deviations, reduces the initial pathogen risk for subsequent planting stages, and builds a solid foundation for the healthy growth of cucumber seedlings.

[0029] For example, a large-scale cucumber planting base conducts standardized seed pretreatment before spring sowing. Technicians first verify the batch number of the seeds to be treated, then prepare a sodium hypochlorite solution with an effective chlorine concentration of 0.8% (w / v). Maintaining a room temperature of 23°C, 50 kg of cucumber seeds are divided into breathable gauze bags and completely immersed in the solution for 12 minutes. During this time, the bags are gently turned every 3 minutes to ensure even contact between the seeds and the solution. After soaking, the seeds are transferred to a running water tank and rinsed continuously with clean water for 5 minutes until no obvious solution residue is visible on the seed surface. Then... The constant temperature water bath was set to 50℃, and the rinsed seeds were placed in it to soak for 18 minutes, maintaining a stable water temperature throughout. After soaking, the seeds were quickly transferred to 25℃ warm water and soaked for 4 minutes to cool. Finally, the seeds were removed and drained. The batch number, start and end times of the treatment, operator's name, and other information were filled in completely on the record sheet to complete the entire standardized treatment process. Subsequent sampling and testing showed that the fruit spot disease pathogen kill rate of this batch of seeds reached 99%, the green mottle virus infection rate was reduced to below 0.3%, and the seed germination rate was increased by 8% compared with conventional treatment.

[0030] In step S103, after the standardized seeds are planted, based on historical pathogen screening data and historical environmental monitoring data, a risk assessment algorithm is used to predict the probability of disease occurrence at each key growth stage during the planting cycle. At the same time, a dynamic monitoring plan is generated by combining a fuzzy mapping algorithm. Based on the dynamic monitoring plan, plant samples are collected at each growth stage, and PCR testing for bacterial fruit spot disease of cucurbits and RT-PCR testing for cucumber green mottle mosaic virus are carried out simultaneously, and a quarantine report is issued.

[0031] Among them, plant samples at each growth stage refer to representative plant tissue or organ samples selected from the planting area at different growth stages of cucumber seed germination, seedling, mature plant, flowering and fruiting.

[0032] It is understood that the embodiments of this application, by collecting plant samples at each growth stage, fully cover the disease monitoring nodes of cucumber from germination to fruiting, accurately capture the infection characteristics of fruit spot pathogens and green mottle virus at different stages, avoid missed detection of diseases caused by sampling at a single period, provide sufficient and representative detection materials for PCR and RT-PCR detection, support the implementation of dynamic monitoring plans, ensure the accuracy of disease occurrence probability prediction results, provide a scientific basis for timely adjustment of intervention plans, and improve the timeliness and accuracy of disease prevention and control throughout the entire cycle.

[0033] For example, after standardized seed treatment, a large-scale cucumber planting base strictly followed a dynamic monitoring plan to collect plant samples at each growth stage: 7 days after seed germination, 20 seedlings were randomly selected from the seedling shed, and cotyledon samples were collected; at 20 days of seedling stage, 30 seedlings were selected from a 30-acre planting area using a five-point sampling method, and true leaf samples were collected; at 35 days of mature plant stage, samples were collected focusing on the lower and middle functional leaves and stems of the plant; at the early flowering and fruiting stage, leaf, flower, and young fruit samples were collected simultaneously, with three sets of parallel samples set up for each growth stage. After collection, samples were immediately numbered and sent to the testing laboratory for simultaneous PCR and RT-PCR testing, and the predicted disease incidence rate was adjusted based on the test results. Through targeted sampling throughout the entire growth cycle, the base detected sporadic fruit spot infection in the seedling stage 12 days earlier, promptly initiating mild intervention to prevent the spread of the disease, ultimately reducing the disease incidence rate by 15% and improving detection accuracy by 20% compared to conventional sampling.

[0034] In this embodiment of the application, a risk assessment algorithm is used to predict the probability of disease occurrence at each key growth stage during the planting cycle. This includes: constructing a risk assessment algorithm; inputting historical pathogen screening data and historical environmental monitoring data into the risk assessment algorithm; calculating the potential occurrence probability of fruit spot pathogens during the seedling, vine extension, and fruit setting stages using a logistic regression formula; predicting the seasonal outbreak risk probability of green mottle virus using a time series formula; and weighting and fusing the potential occurrence probability of fruit spot pathogens and the seasonal outbreak risk probability of green mottle virus to generate a comprehensive disease occurrence probability value for each stage.

[0035] Among them, the disease occurrence probability value refers to the comprehensive risk assessment value used to quantitatively predict the possibility of the co-occurrence of cucumber fruit spot pathogen and green mottle virus during a specific growth stage.

[0036] It is understood that the embodiments of this application quantify the infection risk of fruit spot pathogens and green mottle virus at different growth stages of cucumber by generating comprehensive disease occurrence probability values ​​at each stage, identify high-risk prevention and control nodes, avoid the blindness and lag in disease prevention and control, provide accurate data support for the formulation of dynamic monitoring plans, guide the implementation of differentiated sampling and detection strategies, help to deploy targeted intervention measures in advance, improve the scientificity and effectiveness of disease prevention and control throughout the entire cycle, and reduce the impact of diseases on cucumber yield and quality.

[0037] It should be noted that the risk assessment algorithm formula refers to a mathematical prediction model built based on logistic regression and time series analysis. Its core function is to quantify historical pathogen and environmental data into quantitative risk probabilities of combined infection by fruit spot pathogen and green mottle virus at each growth stage of cucumber. The formula is as follows: ; ; ; ; ; ; in, For the first Probability of fruit spot disease occurrence during the growth stage; For the Sigmoid function; For the intercept term; For the first The regression coefficients of the influencing factors; For the first Phase 1 One influencing factor; This is the cucumber growth stage; For the first Probability of green mottle virus occurrence during the growth stage; For the first Autoregressive coefficient of order; For the first Before the growth stage The probability of green mottle virus occurrence per cycle; For the first Moving average coefficient; For the first Before the growth stage The random error term for each period; For the first Stage random error term; The order of autoregression; The moving average order; The weight of fruit spot pathogens; Weighting for green mottled virus; The average yield loss rate caused by fruit spot disease in the target planting area over the past 3-5 years; The average yield loss rate caused by green mottle virus in the target planting area over the past 3-5 years; For the first The probability of occurrence of diseases in a given stage.

[0038] For example, a cucumber planting base developed a risk assessment algorithm before the new season's planting. It then retrieved historical pathogen screening data and environmental monitoring data from the region over the past three years and input them into the algorithm. Using logistic regression, the algorithm calculated the potential occurrence probability of fruit spot disease during the seedling, vine extension, and fruit setting stages. Time series analysis was then used to predict the seasonal outbreak risk probability of green mottle virus in these three stages. Finally, the yield loss rates of the two diseases over the past three years were combined to calculate weights, and the two probabilities were weighted and merged to generate a comprehensive disease occurrence probability value for each stage. The final results showed that the comprehensive probability during the seedling stage was in the low-risk range, the vine extension stage was medium-risk, and the fruit setting stage reached the high-risk threshold. Based on this, the base adjusted its monitoring plan accordingly: sampling and testing were conducted every 7 days during the seedling stage, shortened to once every 5 days during the vine extension stage, and increased to once every 3 days during the fruit setting stage. Special disease control agents for the fruit setting stage were also prepared in advance. During subsequent planting, disease outbreaks during the fruit setting stage were promptly detected and controlled, ultimately reducing the cucumber disease loss rate by 18% compared to the previous year.

[0039] In this embodiment of the application, generating a dynamic monitoring plan includes: constructing a fuzzy mapping algorithm; inputting the disease occurrence probability value into the fuzzy mapping algorithm, calculating and outputting the monitoring level corresponding to each growth stage; based on the monitoring level, matching the corresponding monitoring frequency and sampling quantity for each growth stage, wherein the higher the monitoring level, the higher the matching monitoring frequency and the more sampling quantity; integrating the monitoring level, monitoring frequency, and sampling quantity, outputting a structured monitoring task list, and generating a dynamic monitoring plan.

[0040] The dynamic monitoring plan is a list of monitoring tasks that is dynamically adjusted according to the growth stage of cucumbers, based on the disease occurrence probability value output by the risk assessment algorithm and generated by the fuzzy mapping algorithm. The core content is to match differentiated field inspection frequency and plant sample collection quantity according to the risk level, so as to provide an execution basis for subsequent accurate detection and graded intervention.

[0041] It is understood that the embodiments of this application generate dynamic monitoring plans, use fuzzy mapping algorithms to convert disease occurrence probability values ​​into corresponding monitoring levels, and then match differentiated monitoring frequencies and sampling quantities to achieve risk adaptation for disease monitoring: high-risk stages involve intensive monitoring and increased sampling, while low-risk stages involve reasonably reducing the frequency. This avoids resource waste caused by over-monitoring and prevents missed disease detection due to low-frequency monitoring. At the same time, the structured monitoring task list clarifies the operational requirements for each stage, making monitoring execution more standardized and feasible. This helps growers to capture disease dynamics in a timely manner, provides timely data support for precise prevention and control, and improves the efficiency and accuracy of full-cycle disease management.

[0042] It should be noted that the fuzzy mapping algorithm transforms the continuous comprehensive disease occurrence probability value calculated by the risk assessment algorithm into a discrete monitoring level based on preset fuzzy rules and membership functions. This completes the key transformation algorithm from quantitative risk prediction to qualitative management decision-making. The formula is as follows: ; ; ; ; ; ; ; ; ; in, For input values Fuzzy membership degree for low-risk levels, For input values Fuzzy membership degree for medium-risk levels, For input values Fuzzy membership degree for high-risk levels; This represents the overall probability of disease occurrence at each growth stage, and ,in Calculated by a risk assessment algorithm; To establish a low-risk, fuzzy threshold, historical data from the target planting area was collected to determine the probability values ​​corresponding to growth stages without significant disease occurrence (yield loss rate <5%), and the 90th percentile was used as the threshold. ; To determine a high-risk, fuzzy threshold, historical data from the target planting area was collected: the probability values ​​corresponding to the growth stages where severe diseases occurred (yield loss rate > 30%) were statistically analyzed, and the 10th percentile was used as the threshold. ; This is the upper limit of the probability value, fixed at 1.0; For the first The decision weights for risk levels, among which These correspond to low, medium, and high risks, respectively. For the first The direct cost of prevention and control per unit area under different risk levels; For the first The direct cost of prevention and control per unit area under the risk level, among which , which is used as a loop variable in the summation formula; For the first The expected yield loss due to the failure of prevention and control measures at a risk level of Class II is determined by collecting data on the normal yield per unit area of ​​the target region. Average yield of plots where prevention and control measures failed under this risk Calculate the production loss rate Combined with the average price of cucumbers and the corresponding risk of disease spread coefficient (Low / Medium / High Risk) (Take values ​​of 1.0 / 1.5 / 2.0 respectively), through It can be concluded that; For the first The expected production loss due to failure to control measures at a risk level of [class name], among which... In the summation formula, it is used as a loop variable to determine the logical AND. Consistent; The loss cost conversion factor is determined based on the ratio of the average yield value of the target planting area over the past three years to the total cost of prevention and control, with a value ranging from 2.0 to 5.0. For the first The quantified value of the risk level; To pass the continuous monitoring level value; To be The final monitoring level after discretization; This is the maximum monitoring level, which is 3. To monitor the frequency matching coefficient; The optimal response period for the disease is set at 3 days. The sampling quantity matching coefficient; To meet the minimum sampling requirement of 95% confidence level, it is set to 10 samples; The sample uniformity coefficient is determined based on the assessment of the regularity of field shape and the spatial clustering of diseases, and its value ranges from 0.8 to 1.2. For monitoring frequency; This represents the number of samples taken in a single sampling.

[0043] For example, when a cucumber planting base was planting for the new season, it first constructed a fuzzy mapping algorithm, and then input the comprehensive disease occurrence probability values ​​for each growth stage into the algorithm: the probability value for the seedling stage was 0.28, and after fuzzification, the output monitoring level was 1; the probability value for the vine extension stage was 0.39, and the output monitoring level was 2; the probability value for the fruit setting stage was 0.52, and the output monitoring level was 3. Next, resources were matched based on the monitoring level: level 1 corresponds to a monitoring frequency of 7 days / time, with 10 samples per time; level 2 corresponds to 5 days / time, with 20 samples; and level 3 corresponds to 3 days / time, with 30 samples. Finally, this information was integrated to generate a structured monitoring task list: 10 samples were collected every Wednesday during the seedling stage, 20 samples every 5 days during the vine extension stage, and 30 samples every 3 days during the fruit setting stage. During the execution, the high-frequency monitoring during the fruit setting stage promptly detected sporadic fruit spot pathogen infections, and the base immediately initiated control measures. Ultimately, the disease spread range was reduced by 60% compared to the previous year, and the monitoring cost was reduced by 40% compared to high-frequency monitoring throughout the entire cycle.

[0044] In this embodiment, plant samples were collected and tested in parallel using specific primers for PCR against cucurbitacinth and specific primers for RT-PCR against cucumber green mottle mosaic virus. A quarantine report was then issued, including: collecting representative plant tissue samples from each growth stage of cucumber and recording the sample information in association with seed batch and field location; performing PCR detection using specific primer BX-S against cucurbitacinth; for samples with positive results, further pathogen isolation and verification were performed using BIO-PCR based on semi-selective culture medium TWZ; and performing RT-PCR detection using specific primers CGMMV1 and CGMMV2 against cucumber green mottle mosaic virus. Each test required a positive control and a negative control, and the experiment was repeated twice to obtain the test results. Based on the test results, a standardized quarantine report containing sample information, test methods, and clear conclusions was generated.

[0045] Among them, specific primers are oligonucleotide fragments designed for specific gene sequences of cucurbitaceous bacterial fruit spot pathogens and cucumber green mottle mosaic virus. They can accurately bind to the nucleic acid sequence of the target pathogen in PCR / RT-PCR reactions, complete the specific amplification of the target pathogen, and avoid interference from non-target microorganisms.

[0046] It is understood that the embodiments of this application use specific primers for cucurbitaceous bacterial fruit spot pathogens and cucumber green mottle mosaic virus to accurately identify the characteristic nucleic acid sequences of the target pathogens, thereby achieving targeted selection of PCR / RT-PCR amplification templates. This effectively avoids non-specific amplification of nucleic acid fragments from non-target microorganisms, improves the specificity and accuracy of the detection results, shortens the effective time of the detection reaction, and reduces the probability of false positives and false negatives during the experiment. This provides reliable detection data support for subsequent pathogen isolation verification and standardized quarantine report generation.

[0047] It should be noted that semi-selective culture medium TWZ refers to a culture medium specifically used for isolating bacterial fruit spot pathogens of cucurbits. By adding specific inhibitors to suppress the growth of other bacteria, only the target pathogen is allowed to reproduce, thus completing the purification and isolation of the target pathogen.

[0048] PCR, or Polymerase Chain Reaction, is a molecular biology technique used to amplify specific DNA fragments. It can rapidly amplify the DNA fragments of target pathogens millions of times in vitro, facilitating subsequent detection and analysis. The specific procedure is as follows: First, sample pretreatment is performed, with corresponding operations based on the sample type. For cucumber plant tissue samples, representative parts such as true leaves or cotyledons of seedlings are selected, and the tissue to be tested is obtained by punching holes. For cucumber seed samples, approximately 400 seeds are selected, split open, and soaked in sterile water for 4 hours. The leachate after soaking is concentrated to obtain the sample solution to be tested. Subsequently, template preparation is performed. Total DNA is extracted directly from the pretreated tissue samples and from the concentrated seed leachate. At the same time, leachate prepared from healthy cucumber plant tissue or healthy seeds is used as a negative control, and a standard strain of a known bacterial fruit spot pathogen is selected as a positive control. To ensure the reliability of the test results, the entire experimental process is repeated twice. Next, a 25 μL standard PCR reaction system was prepared, containing 2.5 μL of 10× PCR reaction buffer, 2 mM Mg²⁺, 50 μM each of four dNTPs, 1.25 units of Taq enzyme, 0.5 μM upstream and downstream specific primers designed for the characteristic gene sequence of cucurbitaceous bacterial fruit spot pathogens, and 100 U Taq DNA polymerase. All components were mixed thoroughly in the specified proportions before PCR amplification. The amplification program was set as follows: 95℃ pre-denaturation for 5 minutes, followed by 30 cycles. Each cycle included 95℃ denaturation for 30 seconds, 68℃ annealing for 30 seconds, and 72℃ extension for 30 seconds, with a final extension at 72℃ for 5 minutes after each cycle to complete the amplification reaction. If the initial PCR test result was positive, further pathogen isolation and verification were performed using the BIO-PCR method. This involved inoculating the corresponding sample solution into a semi-selective medium for enrichment, isolating the suspected pathogen strain, and then performing a second PCR verification using the same PCR reaction system and amplification program to ensure the accuracy of the test results and avoid false positives.

[0049] RT-PCR, or reverse transcription polymerase chain reaction, is a method that first reverse transcribes RNA into complementary DNA, and then uses cDNA as a template for PCR amplification. It is specifically used for detecting RNA viruses. The specific procedure is as follows: First, sample pretreatment is performed, with the treatment method optimized according to the sample type. For cucumber plant tissue samples, cotyledonary samples are taken directly from the seedlings; approximately 400 cucumber seeds are selected, split open, and soaked in sterile water for 4 hours, with the extract concentrated for later use; fresh cucumber samples are pretreated according to standard sample processing procedures to ensure they meet RNA extraction requirements. Positive and negative controls are also set up. The positive control uses samples known to be infected with cucumber green mottle mosaic virus, and the negative control uses healthy cucumber samples not infected with the virus. Subsequently, RNA extraction is performed. An RNA extraction kit is used to extract total RNA from all types of pretreated samples. The extraction process strictly follows RNase-free operating procedures to avoid RNA degradation affecting the detection results. Next, cDNA synthesis was performed. A 10 μL reverse transcription reaction system was prepared, which contained 2 μL 5×RT Buffer, 0.5 μL LTE nzyme, 0.5 μL L loligodTPrimer 50 μM, 2 μL L random 6mers 100 μM, 2 μL extracted sample RNA, 0.5 μL specific downstream primers for cucumber green mottle mosaic virus, and 2.5 μL RNase-free ddH2O. After mixing the components, the reverse transcription reaction was carried out under the set conditions: incubation at 37°C for 15 minutes, heating at 85°C for 5 seconds, and finally storage at 4°C to complete the conversion of RNA to cDNA. PCR amplification was performed using the synthesized cDNA as a template. The amplification system was consistent with the 25 μL standard system for PCR detection described above, containing 10× PCR reaction buffer, Mg²⁺, dNTPs, Taq polymerase, specific upstream and downstream primers designed for the characteristic gene sequence of cucumber green mottle mosaic virus, and Taq DNA polymerase. The amplification program was set to 95℃ pre-denaturation for 5 minutes, followed by 30 cycles (95℃ denaturation for 30 seconds, 60℃ annealing for 30 seconds, and 72℃ extension for 30 seconds), and a final extension at 72℃ for 5 minutes after the cycles. To ensure the reliability of the detection results, the entire RT-PCR detection procedure was repeated twice. The results of positive and negative controls were used to eliminate experimental interference and ensure the accuracy and rigor of the final detection conclusion.

[0050] For example, in order to accurately control bacterial fruit spot disease and cucumber green mottle mosaic virus in cucurbits, a large-scale cucumber planting base collected 25 representative plant tissue samples at the seedling stage, vine extension stage, and fruit setting stage, covering cotyledons, true leaves, and stem segments. The seed batch (three batches, A, B, and C) and field location (planting beds 1-8) of each sample were recorded simultaneously to establish a complete traceability file. To target the bacterial fruit spot pathogen of cucurbits, samples were pretreated, and total DNA was extracted from representative sites after perforation. A positive control containing the target pathogen and a negative control of healthy tissue were set up. The experiment was repeated twice. Subsequently, a 25 μL standard PCR reaction system was prepared (containing 2.5 μL 10× PCR buffer, 2 mM Mg²⁺, 50 μM each of the four dNTPs, 1.25 units of Taq enzyme, 0.5 μM specific primer BX-S, and 100 U Taq DNA polymerase). The amplification was performed according to the program of 95℃ pre-denaturation for 5 minutes, 30 cycles (95℃ denaturation for 30 seconds, 68℃ annealing for 30 seconds, 72℃ extension for 30 seconds), and 72℃ final extension for 5 minutes. One sample from the vine extension stage initially tested positive, and the suspected strain was then enriched by BIO-PCR using the semi-selective medium TWZ. The same system and program were then used for secondary verification. To target cucumber green mottle mosaic virus, total RNA was extracted from samples using an RNA extraction kit (following RNase-free procedures throughout). A 10 μL reverse transcription system (containing 2 μL 5×RT Buffer, 0.5 μL LTE nzyme, etc.) was prepared, and cDNA was synthesized under the following conditions: incubation at 37°C for 15 minutes, heating at 85°C for 5 seconds, and storage at 4°C. The same PCR amplification system was then prepared using the cDNA as a template, and 30 cycles of amplification were performed using specific primers CGMMV1 and CGMMV2 at annealing temperature of 60°C. Controls were also included, and the experiment was repeated. Finally, all detection data were integrated to generate a standardized quarantine report containing sample information, detailed detection procedures, and clear detection results, providing a scientific basis for targeted prevention and control at the base.

[0051] In step S104, based on the dynamic monitoring plan and quarantine report, combined with real-time environmental monitoring data, the comprehensive risk level is determined by the decision tree classification algorithm, and the corresponding field intervention plan is triggered. At the same time, the execution results of the field intervention plan are recorded to form an intervention log.

[0052] Among them, the comprehensive risk level is a risk quantification and classification result that is dynamically divided by decision tree algorithm based on real-time quarantine results, environmental monitoring data and historical risk predictions, and is used to accurately match field intervention measures of different intensities.

[0053] It is understood that the embodiments of this application, by determining the comprehensive risk level, integrate dynamic monitoring, quarantine reports, and real-time environmental data into a concrete risk classification result, clarifying the actual threat level of current field diseases, avoiding risk misjudgment caused by a single data dimension, and matching corresponding field intervention plans for different levels to achieve the linkage between risk classification and precise intervention. This not only prevents the waste of resources caused by excessive intervention in low-risk stages, but also avoids the spread of diseases caused by delayed intervention in high-risk stages. Combined with the recording of intervention logs, it can also provide practical basis for risk assessment and plan optimization in subsequent planting cycles, and improve the accuracy of field disease management.

[0054] It should be noted that the decision tree classification algorithm is a supervised learning algorithm that constructs a tree-like decision model based on dynamic monitoring data, quarantine reports, and real-time environmental data. It determines the comprehensive risk level of field diseases at each growth stage by progressively dividing feature attribute nodes. The formula is: ; ; ; ; ; ; in, For dataset The impurity of the gin; Number of risk level categories; For dataset The Middle Percentage of samples at each risk level; Features Split the dataset The Gini coefficient after; For dataset The total number of samples; Features The number of samples that meet the criteria; Features The number of samples that do not meet the criteria; for The impurity of the gin; for The impurity of the gin; The optimal partitioning feature; For the set of all candidate features; This is an operation to retrieve the minimum value; Features The weights; Features Information gain; The sum of the information gains of all candidate features; Features Weighted information gain; For dataset Information entropy; Features The number of value categories; Features Take the first A subset of samples with values; for Information entropy; The overall risk level is output. To iterate through the calculation results corresponding to all risk levels from 1 to n, take the maximum value among them; For the decision leaf node sample set; The leaf node Number of samples for each risk level; The total number of leaf node samples.

[0055] For example, when a cucumber planting base is implementing field management during the vine extension stage, it first retrieves the phased data from the dynamic monitoring plan (monitoring level 2, sampling every 5 days, 3 out of 20 samples showing traces of fruit spot pathogens). This data is combined with a quarantine report issued by a third-party quarantine agency (confirming that the base has not shown any green mottle virus, but there is a potential risk of fruit spot pathogen transmission), and simultaneously accesses real-time environmental monitoring data (average temperature of 26℃ and relative humidity of 78% over the past 3 days, meeting the conditions for fruit spot pathogen reproduction). The above three types of data are then input into a pre-set decision tree classification algorithm. The algorithm first calculates the impurity of each feature (pathogen detection, humidity, monitoring level) using the Gini coefficient, then selects "pathogen detection rate" and "environmental humidity" as core dividing nodes based on the feature condition Gini coefficient. Combining feature weights, it completes layer-by-layer decision-making, ultimately determining the overall risk level of the current vine extension stage as medium risk.

[0056] In this application embodiment, the field intervention plan includes: when the risk is low, triggering the basic intervention plan, including increasing field patrols, removing scattered diseased plants and recording their locations; when the risk is medium, triggering the enhanced intervention plan, adding targeted spraying of biological pesticides or low-toxicity chemical fungicides to the basic intervention plan, and disinfecting the soil in the areas where pathogens were detected; when the risk is high, triggering the emergency intervention plan, on the basis of the enhanced intervention plan, carrying out regional isolation, destroying severely diseased plants, applying therapeutic agents, and adjusting the irrigation mode.

[0057] It is understood that the embodiments of this application, by setting up a graded field intervention plan, match differentiated prevention and control measures according to low, medium and high risk levels, and clarify the operational standards under different risk levels. In the low-risk stage, the focus is on basic prevention and control to avoid excessive intervention. In the medium-risk stage, targeted agents and soil disinfection are added to block the spread of diseases. In the high-risk stage, isolation and destruction and therapeutic agents are used to quickly curb the spread of diseases. At the same time, the measures at each stage are progressive and well-connected, which not only reduces the ineffective consumption of prevention and control resources, but also improves the pertinence and timeliness of disease management, and ensures the healthy growth of crops during the planting cycle.

[0058] For example, a cucumber planting base, determined by a decision tree algorithm, had a low overall risk level during the seedling stage. A basic intervention plan was immediately triggered, with personnel increasing daily field inspections. Three sporadic diseased plants were promptly removed, and their locations were marked on a field map. As the vines extended, the risk level rose to medium, prompting the base to implement an enhanced intervention plan. In addition to continuing basic inspections and diseased plant removal, the base sprayed the biological pesticide kasugamycin on the detected fruit spot pathogen and disinfected the soil within a 3-meter radius of the diseased plants. During the fruit-setting period, continuous high temperature and humidity caused the risk level to reach high. The base then triggered an emergency intervention plan, further enhancing the intervention measures. High-risk areas were isolated with protective netting, 20 severely diseased plants were destroyed, and the therapeutic fungicide tebuconazole was used. Simultaneously, flood irrigation was switched to drip irrigation to reduce field humidity, ultimately effectively controlling the spread of the disease and reducing disease losses by 25% compared to the same period last year.

[0059] In step S105, after the planting cycle ends, the final yield data is obtained and summarized with the quarantine report and intervention log to generate a prevention and control effectiveness report.

[0060] It is understood that the embodiments of this application generate a prevention and control effectiveness report by summarizing the final yield data, quarantine report and intervention log after the planting cycle ends. The yield results are correlated with the whole cycle of disease monitoring and intervention behavior, quantifying the actual effect of intervention measures under different risk levels, clarifying the advantageous links and nodes to be optimized in the prevention and control process, providing data support for the adjustment of dynamic monitoring plans, optimization of decision tree algorithm parameters and improvement of hierarchical intervention schemes in subsequent planting cycles. At the same time, a traceable closed-loop management system for disease prevention and control is formed, improving the scientific nature and continuity of disease prevention and control work.

[0061] For example, after the new season's planting cycle ended, a cucumber planting base first obtained the final yield data of 4200 kg per mu, an 8% increase compared to the previous year. Then, it compiled six quarantine reports from the entire cycle (recording the detection rate of fruit spot pathogens during the seedling stage (3%), vine extension stage (5%), and fruit setting stage (2%)) and detailed intervention logs (recording the execution time and pesticide dosage for low-risk inspection and clearing, medium-risk spraying and disinfection, and high-risk isolation and irrigation conversion). Subsequently, a correlation analysis was conducted: comparing the yield data with the quarantine reports, it was found that the 2% decrease in the pathogen detection rate during the fruit setting stage corresponded to the most significant increase in yield per mu. This study demonstrates the crucial role of emergency intervention in ensuring crop yield. By correlating intervention logs with disease detection data, it was found that spraying kasugamycin during the medium-risk phase reduced the spread of pathogens by 60%, and the amount of biopesticide used was only 50% of that used in traditional methods, verifying that the enhanced intervention program is both effective and economical. Furthermore, by comparing the monitoring levels at each stage with the actual control effects, it was found that the risk level determined by the decision tree algorithm matched the disease occurrence trend by 92%, demonstrating the scientific validity of the dynamic monitoring plan. Finally, all data and analysis conclusions were integrated to generate a complete control effectiveness report.

[0062] According to the embodiments of this application, a planting method for preventing cucumber fruit spot pathogen and green mottle virus is proposed. This method integrates historical pathogen screening data from the soil and surrounding crops in the target planting area, historical environmental monitoring data, seed batch information, and supply chain quarantine records to provide comprehensive data support for subsequent prevention and control, avoiding the omission of key information. A standardized hot water soaking procedure is used to purify pathogens at the seed source, clarifying operating parameters and pesticide ratios to reduce the risk of pathogen transmission from the source. A risk assessment algorithm is used to accurately predict the probability of disease occurrence at each key growth stage, predicting risk points in advance and providing forward-looking guidance for prevention and control actions. A dynamic monitoring plan is formed by combining fuzzy mapping algorithms, ensuring that the monitoring frequency and sampling volume are adapted to the risk level, improving the targeting of monitoring. Parallel PCR and RT-PCR detection technologies are used to accurately and efficiently detect the two high-risk pathogens, reducing missed detections and misjudgments caused by single detection. A decision tree classification algorithm is used to trigger tiered field intervention plans corresponding to low, medium, and high risks, avoiding pesticide overuse caused by unified prevention and control and reducing environmental pressure. A summary analysis of yield, quarantine results, and intervention logs is completed through a prevention and control effectiveness report. This solves the problems of insufficient disease detection and lack of intervention measures in existing technologies.

[0063] The following will illustrate a planting method for preventing cucumber fruit spot disease and green mottle virus through a specific embodiment, such as... Figure 2 As shown, it includes: A large-scale cucumber planting base plans to launch a new round of planting, and the primary task is to comprehensively collect relevant basic data on the target planting area. Technicians retrieved soil and surrounding crop historical pathogen screening data for the past three years, confirming that bacterial fruit spot pathogens had sporadically infected cucumbers during the fruit-setting period, and that cucumber green mottle mosaic virus showed slight outbreaks during hot and humid seasons. The data also recorded the residual levels and distribution range of pathogens at different times. Historical environmental monitoring data showed that the average temperature in the area during the growing season was between 22-30℃, with the rainy season concentrated from the cucumber vine extension stage to the fruit-setting stage, and relative humidity often exceeding 75%. This data provided crucial information for subsequent risk prediction. Furthermore, technicians confirmed the batch information of the selected cucumber seeds, tracing the complete quarantine records of the supply chain through the seed supplier. This verified that the batch of seeds underwent specialized testing during production and processing, and no target pathogens were detected, meeting planting safety standards.

[0064] During the seed pretreatment stage, technicians strictly followed the standardized warm water soaking procedure. They first prepared a sodium hypochlorite solution with an effective chlorine concentration of 0.8% (w / v). At room temperature (24℃), 500 kg of cucumber seeds were divided into breathable gauze bags and completely immersed in the solution for 13 minutes. During this time, the bags were gently turned every 3 minutes to ensure each seed was evenly contacted with the solution. After soaking, the seeds were transferred to a running water tank and rinsed continuously with clean water for 6 minutes until no trace of the solution remained on the surface. Subsequently, a constant temperature water bath was set to 50℃, and the rinsed seeds were immersed in it for 18 minutes. Throughout the process, a dedicated person monitored the water temperature to ensure fluctuations did not exceed ±0.5℃. After the high-temperature treatment, the seeds were quickly transferred to 25℃ warm water for 4 minutes to cool, and finally drained. Technicians meticulously recorded the batch number of the seeds, the start and end times of the soaking treatment, the concentration of the solution used, and the operator's name on a dedicated record sheet, completing the source purification treatment of the seeds.

[0065] After seed sowing, technicians input historical pathogen screening data and historical environmental monitoring data into a pre-set risk assessment algorithm. Logistic regression calculations showed that the potential occurrence probabilities of fruit spot pathogens during the seedling, vine extension, and fruit setting stages were 0.25, 0.38, and 0.46, respectively. Time series analysis predicted the seasonal outbreak risk probabilities of green mottle virus during the seedling, vine extension, and fruit setting stages as 0.18, 0.32, and 0.41, respectively. Combining this with the average yield loss rate caused by the two diseases in the region over the past three years, the weights for fruit spot pathogens (0.55) and green mottle virus (0.45) were calculated. Weighted fusion was then used to generate comprehensive disease occurrence probabilities for each stage: 0.22 for the seedling stage, 0.35 for the vine extension stage, and 0.44 for the fruit setting stage. These probability values ​​were then input into a fuzzy mapping algorithm to calculate and output the monitoring level for each growth stage: Level 1 for the seedling stage, Level 2 for the vine extension stage, and Level 3 for the fruit setting stage. Based on the monitoring levels, technicians matched corresponding monitoring frequencies and sampling quantities for each stage: Level 1 monitoring corresponds to sampling once every 7 days, with 20 samples collected each time; Level 2 corresponds to sampling once every 5 days, with 30 samples collected each time; and Level 3 corresponds to sampling once every 3 days, with 40 samples collected each time. This was ultimately integrated into a structured monitoring task list, specifying sampling every Tuesday during the seedling stage, every 5 days at 9:00 AM during the vine-spreading stage, and every 3 days in the early morning during the fruit-setting stage. The sampling area covered different plots throughout the entire planting area to ensure sample representativeness. During the sampling process, technicians meticulously recorded the seed batch association information and specific field location coordinates for each sample, and collected samples of different tissues according to growth stages, including cotyledons, true leaves, stem segments, flowers, and young fruits. All samples were packaged according to specifications and labeled with numbers. Figure 3 Detailed images of cucumber tissue samples are provided to establish a complete traceability record for subsequent testing.

[0066] The detection process employed a parallel PCR and RT-PCR method. For the bacterial fruit spot pathogen affecting cucurbits, technicians used specific primers BX-S for PCR detection. Total DNA was extracted from each plant tissue sample. A positive control (PC) containing the known fruit spot pathogen strain xjl12 and a negative control (NC) from healthy cucumber tissue were also included. The experiment was repeated twice. The PCR reaction system consisted of 25 μL of 10× PCR buffer, 2 mM Mg²⁺, 50 μM each of the four dNTPs, 1.25 units of Taq polymerase (TaKaRa), 0.5 μM specific primers, and 100 U Taq DNA polymerase. The amplification program was set to 95℃ pre-denaturation for 5 minutes, followed by 30 cycles (95℃ denaturation for 30 seconds, 68℃ annealing for 30 seconds, 72℃ extension for 30 seconds), and a final extension at 72℃ for 5 minutes. The detection results are shown in the figure. Figure 4This figure shows the PCR detection results of cucumber tissue samples using the specific primer BX-S for the bacterial fruit spot disease of cucurbits. In the figure, M represents the DL2000 molecular weight standard, and 1 represents cucumber tissue extract. No specific amplification bands corresponding to the positive control were observed in the sample, and no interference was observed in the negative control. Two samples initially showed suspected positive results during the vine extension stage. Technicians immediately used BIO-PCR based on the semi-selective TWZ medium for pathogen isolation and verification. After inoculating the sample solution into the medium for enrichment, a second PCR test was performed on the isolated suspected strain, ultimately confirming one sample as a true positive. To detect cucumber green mottle mosaic virus (CMV), specific primers CGMMV1 and CGMMV2 were used for RT-PCR. Total RNA was first extracted from the samples using an RNA extraction kit from Tiangen Biotech Co., Ltd., following RNase-free procedures throughout. A 10 μL reverse transcription reaction system was then prepared (containing 2 μL 5×RT Buffer, 0.5 μL LTE nzyme, 0.5 μL LligodTPrimer (50 μM), 2 μL Lrandom6mers (100 μM), 2 μL sample RNA, 0.5 μL CGMMV2 (2 μM), and 2.5 μL RNase-free ddH2O). cDNA was synthesized under the following conditions: incubation at 37℃ for 15 minutes, heating at 85℃ for 5 seconds, and storage at 4℃. PCR amplification was then performed using the cDNA as a template. The amplification system was consistent with that used for detecting the green mottle mosaic virus, with the annealing temperature adjusted to 60℃. The detection results are shown in the figure. Figure 5 The results of PCR detection of cucumber tissue samples using the cucumber green mottle mosaic virus-specific primers CGMMV1 / 2 are shown in the figure. M is the DL2000 molecular weight standard, PC is the green mottle positive control, NC is the extract from healthy seeds, and 1 is the cucumber tissue extract. The sample showed no specific amplification bands, and the detection result was negative. Based on the implementation of the dynamic monitoring plan, quarantine report results, and real-time environmental monitoring data, technicians determined the overall risk level using a decision tree classification algorithm. During the seedling stage, the quarantine report detected no pathogens, and real-time environmental data showed an air temperature of 23℃ and relative humidity of 65%, indicating a low overall risk level. The base immediately triggered a basic intervention plan, assigning dedicated personnel to conduct an additional field inspection daily, focusing on observing whether abnormal spots appeared on the seedling leaves. During this period, four sporadic diseased plants with abnormal growth were discovered and promptly removed, their specific locations marked on the field map, and detailed records of the symptoms and treatment time were kept. During the vine extension stage, one sample tested positive for fruit spot pathogens, and real-time monitoring showed a relative humidity of 78% for three consecutive days, raising the overall risk level to medium. The base initiated an enhanced intervention plan. In addition to continuing inspections and diseased plant removal, the biological pesticide kasugamycin was sprayed on the pathogen-detected area and surrounding plots. Simultaneously, a low-toxicity soil disinfectant was used to spray the soil in the area, recording the dosage, spraying time, and coverage area. After the fruit-setting period began, the base was affected by the sustained high temperature and humidity. Real-time monitoring data showed that the temperature reached 29℃ and the relative humidity was 82%. Three new samples tested positive for fruit spot pathogens, and the overall risk level was determined to be high. The base immediately triggered an emergency intervention plan, isolating the high-risk area with protective netting, destroying 25 severely infected plants, and switching to the therapeutic fungicide tebuconazole for spraying. Simultaneously, the original flood irrigation method was changed to drip irrigation to reduce field humidity and minimize pathogen transmission. Throughout the intervention process, technicians meticulously recorded the results, creating a complete intervention log covering information such as the number of inspections, the number of diseased plants removed, the type and dosage of pesticides used, the scope of soil disinfection, and details of irrigation mode adjustments.

[0067] After the planting cycle ended, the base's statistics showed a final yield of 4,500 kg per mu, a 10% increase compared to the previous planting round. Technicians summarized and analyzed the yield data along with quarantine reports and intervention logs from the entire cycle to generate a prevention and control effectiveness report. The report showed that the low-risk intervention program during the seedling stage effectively curbed the early spread of pathogens, achieving a 100% eradication rate of diseased plants; after spraying kasugamycin during the medium-risk stage of vine extension, the pathogen spread rate decreased by 65%, and soil disinfection ensured that no positive samples were subsequently detected in the area; isolation measures and irrigation pattern adjustments during the high-risk stage of fruit setting kept the disease spread within 5%, and the use of therapeutic agents effectively reduced yield losses. Correlation analysis revealed that the risk level determined by the decision tree algorithm matched the actual disease occurrence trend by 93%, the targeted sampling of the dynamic monitoring plan improved detection accuracy by 22%, and the rational use of biological pesticides and low-toxicity fungicides reduced pesticide usage by 40% compared to the traditional uniform spraying method, reducing environmental pressure while ensuring cucumber quality. This report on the effectiveness of disease control clearly defines the actual effects of intervention measures at each stage, providing detailed data support for optimizing risk assessment algorithm parameters, adjusting dynamic monitoring plans, and improving intervention programs in subsequent planting, thus forming a closed-loop disease control management system.

[0068] In summary, this invention achieves source control of disease by integrating historical pathogen and environmental data of the target area, tracing quarantine records of the seed supply chain, and combining standardized hot water seed soaking. It generates dynamic monitoring plans using risk assessment and fuzzy mapping algorithms, accurately identifies pathogens through parallel PCR and RT-PCR detection and BIO-PCR verification, triggers low, medium, and high-risk-level intervention programs based on risk levels, and blocks disease transmission at each level. Finally, it summarizes yield, quarantine reports, and intervention logs to form a closed loop of control effectiveness. The entire process achieves precise control throughout the entire cycle from seed pretreatment to field intervention, effectively improving detection accuracy and intervention targeting, achieving increased yield, reduced pesticide use, and a significant decrease in disease incidence, providing a scientific and reliable disease control solution for large-scale cucumber cultivation.

[0069] Next, referring to the accompanying drawings, a planting system for preventing cucumber fruit spot disease and green mottle virus is described according to an embodiment of this application.

[0070] Figure 6 This is a schematic diagram of a planting system for preventing cucumber fruit spot disease and green mottle virus according to an embodiment of this application.

[0071] like Figure 6 As shown, the planting system 10 for preventing cucumber fruit spot disease and green mottle virus includes: an acquisition module 100, a processing module 200, a detection module 300, an intervention module 400, and a summarization module 500.

[0072] The system comprises several modules: an acquisition module 100, which acquires historical pathogen screening data and environmental monitoring data of the soil and surrounding crops in the target planting area, as well as batch information of the selected cucumber seeds; a processing module 200, which queries the quarantine records of the seed supply chain based on the batch information and performs standardized seed treatment using a hot water soaking procedure; and a detection module 300, which, after planting the standardized seeds, predicts the probability of disease occurrence at each key growth stage during the planting cycle based on historical pathogen screening data and environmental monitoring data using a risk assessment algorithm, and simultaneously generates a dynamic monitoring plan based on a fuzzy mapping algorithm. The monitoring plan involves collecting plant samples at each growth stage and simultaneously conducting PCR testing for bacterial fruit spot disease of cucurbits and RT-PCR testing for cucumber green mottle mosaic virus, generating a quarantine report. The intervention module 400 is used to determine the comprehensive risk level based on the dynamic monitoring plan and quarantine report, combined with real-time environmental monitoring data, using a decision tree classification algorithm, and triggering the corresponding field intervention plan. At the same time, it records the execution results of the field intervention plan and forms an intervention log. The summary module 500 is used to obtain the final yield data after the planting cycle ends, and summarize it with the quarantine report and intervention log to generate a control effectiveness report.

[0073] It should be noted that the foregoing explanation of an embodiment of a planting method for preventing cucumber fruit spot fungus and green mottle virus also applies to an embodiment of a planting system for preventing cucumber fruit spot fungus and green mottle virus, and will not be repeated here.

[0074] According to the embodiments of this application, a planting system for preventing cucumber fruit spot pathogen and green mottle virus is proposed. This system integrates historical pathogen screening data from the soil and surrounding crops in the target planting area, historical environmental monitoring data, seed batch information, and supply chain quarantine records to provide comprehensive data support for subsequent prevention and control, avoiding the omission of key information. A standardized hot water soaking procedure is used to purify pathogens at the seed source, clearly defining operating parameters and pesticide ratios to reduce the risk of pathogen transmission from the source. A risk assessment algorithm is used to accurately predict the probability of disease occurrence at each key growth stage, providing forward-looking guidance for prevention and control actions. A dynamic monitoring plan is formed by combining a fuzzy mapping algorithm, ensuring that the monitoring frequency and sampling volume are adapted to the risk level, improving the targeting of monitoring. Parallel PCR and RT-PCR detection technologies are used to accurately and efficiently detect the two high-risk pathogens, reducing missed detections and misjudgments caused by single detection. A decision tree classification algorithm is used to trigger tiered field intervention plans corresponding to low, medium, and high risks, avoiding pesticide overuse caused by uniform prevention and control and reducing environmental pressure. A summary analysis of yield, quarantine results, and intervention logs is completed through a prevention and control effectiveness report. This solves the problems of insufficient disease detection and lack of intervention measures in existing technologies.

[0075] Figure 7A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0076] When the processor 702 executes the program, it implements a planting method for preventing cucumber fruit spot disease and green mottle virus provided in the above embodiments.

[0077] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0078] The memory 701 is used to store computer programs that can run on the processor 702.

[0079] The memory 701 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0080] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0081] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0082] The processor 702 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0083] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described planting method for preventing cucumber fruit spot disease and green mottle virus.

[0084] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described planting method for preventing cucumber fruit spot disease and green mottle virus.

[0085] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0088] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0090] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A planting method for preventing cucumber fruit spot disease and green mottle virus, characterized in that, include: Obtain historical pathogen screening data of soil and surrounding crops in the target planting area, historical environmental monitoring data, and batch information of the selected cucumber seeds; Based on the batch information, the quarantine records of the seed supply chain were queried, and the seeds were standardized using a hot water soaking process. After the standardized seeds are planted, based on the historical pathogen screening data and historical environmental monitoring data, the probability of disease occurrence at each key growth stage during the planting cycle is predicted using a risk assessment algorithm. At the same time, a dynamic monitoring plan is generated by combining a fuzzy mapping algorithm. Based on the dynamic monitoring plan, plant samples are collected at each growth stage, and PCR detection for bacterial fruit spot disease of cucurbits and RT-PCR detection for cucumber green mottle mosaic virus are carried out simultaneously, and a quarantine report is issued. Based on the dynamic monitoring plan and the quarantine report, combined with real-time environmental monitoring data, the comprehensive risk level is determined by a decision tree classification algorithm, and the corresponding field intervention plan is triggered. At the same time, the execution results of the field intervention plan are recorded to form an intervention log. After the planting cycle is completed, the final yield data is obtained and summarized with the quarantine report and intervention log to generate a prevention and control effectiveness report.

2. The planting method for preventing cucumber fruit spot disease and green mottle virus according to claim 1, characterized in that, Standardized seed treatment using a warm water soaking process includes: Soak the seeds in a sodium hypochlorite solution with an effective chlorine concentration of 0.5%-1% (w / v) at room temperature for 10-15 minutes. After chemical disinfection, rinse the seeds thoroughly with clean water, and then soak them in constant temperature water at 48-52℃ for 15-20 minutes. Quickly transfer the seeds to warm water at 22-28℃ and soak for 3-5 minutes to cool them down. Record the batch number, processing time, and operator information of the soaking operation to complete the standardized treatment.

3. The planting method for preventing cucumber fruit spot disease and green mottle virus according to claim 1, characterized in that, Using risk assessment algorithms, the probability of disease occurrence at each key growth stage during the planting cycle is predicted, including: Develop risk assessment algorithms; Historical pathogen screening data and historical environmental monitoring data are input into the risk assessment algorithm. The potential occurrence probability of fruit spot pathogens during the seedling, vine extension, and fruit setting stages is calculated using a logistic regression formula. The seasonal outbreak risk probability of green mottle virus is predicted using a time series formula. The potential occurrence probability of the fruit spot pathogen and the seasonal outbreak risk probability of the green mottle virus are weighted and fused to generate a comprehensive disease occurrence probability value for each stage.

4. A planting method for preventing cucumber fruit spot disease and green mottle virus according to claim 1, characterized in that, The generation of the dynamic monitoring plan includes: Construct a fuzzy mapping algorithm; The probability value of disease occurrence is input into the fuzzy mapping algorithm to calculate and output the corresponding monitoring level for each growth stage; Based on the monitoring level, a corresponding monitoring frequency and sampling number are matched for each growth stage. The higher the monitoring level, the higher the matched monitoring frequency and the more sampling numbers. By integrating the monitoring level, monitoring frequency, and sampling quantity, a structured list of monitoring tasks is output, generating a dynamic monitoring plan.

5. A planting method for preventing cucumber fruit spot disease and green mottle virus according to claim 1, characterized in that, Plant samples were collected and tested in parallel using PCR with specific primers for cucurbit bacterial fruit spot disease and RT-PCR with specific primers for cucumber green mottle mosaic virus. A quarantine report was issued, including: Collect representative plant tissue samples from cucumbers at each growth stage and associate the sample information with seed batch and field location. For bacterial fruit spot pathogens of cucurbits, PCR detection was performed using specific primers BX-S. For samples with positive results, the pathogen was further isolated and verified using BIO-PCR based on semi-selective culture medium TWZ. RT-PCR was performed to detect cucumber green mottle mosaic virus using specific primers CGMMV1 and CGMMV2. Each test must include a positive control and a negative control, and the experiment must be repeated twice to obtain the test results. Based on the test results, a standardized quarantine report containing sample information, testing methods, and clear conclusions must be generated.

6. A planting method for preventing cucumber fruit spot disease and green mottle virus according to claim 1, characterized in that, The field intervention program includes: When the risk is low, trigger basic intervention programs, including increasing field patrols, removing scattered diseased plants and recording their locations; When the risk level is medium, an enhanced intervention plan is triggered, which involves adding targeted spraying of biological pesticides or low-toxicity chemical fungicides to the basic intervention plan, and disinfecting the soil in the areas where pathogens are detected. In cases of high risk, an emergency intervention plan is triggered. Based on the enhanced intervention plan, regional isolation is implemented, severely diseased plants are destroyed, therapeutic agents are applied, and irrigation patterns are adjusted.

7. A planting system for preventing cucumber fruit spot pathogen and green mottle virus, which can implement the planting method for preventing cucumber fruit spot pathogen and green mottle virus as described in any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire historical pathogen screening data of soil and surrounding crops in the target planting area, historical environmental monitoring data, and batch information of the selected cucumber seeds; The processing module is used to query the quarantine records of the seed supply chain based on the batch information and to standardize the seeds using a hot water soaking process. The detection module is used to predict the probability of disease occurrence at each key growth stage during the planting cycle based on historical pathogen screening data and historical environmental monitoring data after planting the standardized seeds, using a risk assessment algorithm. At the same time, it generates a dynamic monitoring plan by combining a fuzzy mapping algorithm. Based on the dynamic monitoring plan, plant samples are collected at each growth stage, and PCR detection for bacterial fruit spot disease of cucurbits and RT-PCR detection for cucumber green mottle mosaic virus are carried out simultaneously, and a quarantine report is issued. The intervention module is used to determine the comprehensive risk level based on the dynamic monitoring plan and the quarantine report, combined with real-time environmental monitoring data, through a decision tree classification algorithm, and to trigger the corresponding field intervention plan. At the same time, it records the execution results of the field intervention plan and forms an intervention log. The summary module is used to obtain the final yield data after the planting cycle ends, and summarize it with the quarantine report and intervention log to generate a prevention and control effectiveness report.

8. An electronic device, characterized in that, The invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the planting method for preventing cucumber fruit spot disease and green mottle virus as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When a computer program or instruction is executed, it implements a planting method for preventing cucumber fruit spot disease and green mottle virus as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When a computer program or instruction is executed, it implements a planting method for preventing cucumber fruit spot disease and green mottle virus as described in any one of claims 1-6.

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