Technologies and applications of "three-dimensional immunity" for controlling botrytis based on precise detection of botrytis cinerea

Abstract

The application provides a "three-dimensional immunity" prevention and control technology of gray mold based on precise detection of Botrytis cinerea and application thereof, and belongs to the field of biological disease prevention and control. In order to achieve the prevention and control effect of gray mold, a precise mathematical model of Botrytis cinerea spore release is established: S = 2366 + 58RH-96T + 2.6RHxT + 1.2RH 2 -1.4T 2 ; the S is the spore content, the unit is spore / m 3 ; T is the temperature of the environment to be measured, the unit is DEG C; RH is the relative humidity of the environment to be measured, the unit is %. And the prevention and control means such as molecular detection technology are deeply combined to form a whole "three-dimensional immunity" prevention and control network from "seed-plant-space environment". The technology provided by the application is suitable for the prevention and control of gray mold of tomatoes, cucumbers, eggplants, peppers, roses, Chinese roses, lettuces, celery, chives and the like vegetables and flowers.

Description

Technical Field

[0001] This invention belongs to the field of biological disease control, specifically involving the technology and application of "stereoimmunization" for the control of gray mold based on the accurate detection of Botrytis cinerea. Background Technology

[0002] Gray mold is a common and important disease in greenhouse vegetables, caused by *Botrytis cinerea*, a fungus belonging to the Deuteromycetes. Botrytis cinerea Caused by [unclear text - possibly a specific pathogen], this is a typical disease caused by low temperature and high humidity. Its infection sources mainly come from diseased plant debris and soil. Especially in my country, where many plant species are grown in greenhouses, the pathogen can persist in the greenhouse environment for extended periods. Under suitable conditions, the pathogen spreads rapidly via rainwater, air currents, and agricultural operations. *Botrytis cinerea* has a wide host range, affecting over 1700 plant species, including vegetables, flowers, and fruit trees such as tomatoes, cucumbers, leafy vegetables, roses, lilies, strawberries, and grapes. It primarily infects the flowers, fruits, leaves, and stems of plants, causing significant economic losses in severe cases.

[0003] In recent years, with the continuous development of vegetable cultivation facilities, the occurrence and development of gray mold have remained at a relatively high level. Yield reductions can reach 20%-30% in normal years, and nearly 60% in severe cases, making gray mold one of the most significant diseases affecting greenhouse vegetables. The pathogen overwinters as sclerotia and spreads through airflow and rainwater, easily breaking out in low-temperature, high-humidity environments. Traditional control methods mainly rely on chemical agents (such as iprodione and carbendazim), but long-term use has led to a sharp increase in pathogen resistance, significantly reducing control efficacy. Furthermore, existing monitoring methods mostly rely on symptom identification after disease onset for targeted control, or laboratory PCR testing, lacking rapid, in-situ quantitative detection technology for latent pathogens in unaffected plants, ambient air, and soil. This results in a passive "prevention only after disease onset" approach to control. Because the spores produced after gray mold occurs disperse rapidly in the environment and spread quickly, large-scale outbreaks are highly likely. "Prevention before disease onset" can avoid missing the optimal control window and minimize the probability of disease occurrence. Meanwhile, clarifying the spore release and spread patterns of Botrytis cinerea and the time point when the pathogen concentration in the environment is highest is crucial for determining the optimal period and methods for disease prevention. Summary of the Invention

[0004] The technical problem that this invention aims to solve is that, in the current process of controlling gray mold, prevention and control usually only begin when the disease breaks out.

[0005] To address the aforementioned problems, this invention provides a method for controlling gray mold. The method includes sowing seeds in a planting environment to obtain plants, and monitoring the air quality in the planting environment during the planting process using a *Botrytis cinerea* spore release prediction model to determine whether pesticide application is necessary. The model is: S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 S represents the spore content, measured in spores per m³. 3 T represents the temperature of the environment under test, in °C; RH represents the relative humidity of the environment under test, in °C.

[0006] In the above method, the application of the pesticide uses a biological agent dusting method. Specifically, the biological micro-powder is evenly sprayed into the greenhouse space and soil surface using a dusting machine to achieve three-dimensional control and reduce the concentration of pathogens in the entire environment. The dusting time is based on the spore release time predicted by the mathematical prediction model, which is 14-18 hours.

[0007] In some specific embodiments of the present invention, powder spraying is performed at 15 pm.

[0008] The active ingredients of the bio-micro powder consist of 100 billion live spores / gram of Pseudomonas fluorescens wettable powder (Shandong Haililai Chemical Technology Co., Ltd.), 100 billion CFU / gram of Bacillus subtilis wettable powder (Keno Biotechnology (Hubei) Co., Ltd.), and 55 billion CFU / gram of Bacillus vesiculosus MBI600 wettable powder (BASF Europe), in a mass ratio of 1:1:1.

[0009] The above method further includes a step of detecting Botrytis cinerea in the seeds to be planted and the environment of the planting greenhouse before sowing.

[0010] In some embodiments, in order to determine whether seed disinfection is required before sowing, the method further includes a step of detecting Botrytis cinerea in the seeds to be planted and the planting space environment before sowing.

[0011] The above method may also include a step of detecting Botrytis cinerea in the plant and the planting space environment after sowing.

[0012] The detection of Botrytis cinerea includes using qPCR to detect the spore content of Botrytis cinerea in the plant and the surrounding environment.

[0013] In the above method, the qPCR is performed using primer pair BC27f / BC27r, which includes an upstream primer BC27f and a downstream primer BC27r; BC27f is a single-stranded DNA molecule with nucleotide sequence SEQ ID NO: 1; BC27r is a single-stranded DNA molecule with nucleotide sequence SEQ ID NO: 2.

[0014] Specifically, BC27f is: 5'-CCGAAAGATTGAAAAGGAATAAAA-3'; BC27r is: 5'-GAAGTAAAGCTACCACCGAGAACA-3'.

[0015] The qPCR amplification system consisted of 20 µL tubes. Each tube contained 1 µL of the sample DNA template, 10 µL of 2× Taq Pro Universal SYBR qPCR Master Mix, 0.4 µL each of forward and reverse primers, and ddH2O to a final volume of 20 µL. The reaction conditions were: 95 °C pre-denaturation for 30 s, 95 °C denaturation for 10 s, 60 °C annealing for 32 s, and 72 °C extension for 30 s, for 40 cycles. Fluorescence signals were collected during temperature increases to establish melting curves.

[0016] The Tm values ​​of the melting curves of the test sample DNA and the positive control (Botrytis cinerea) DNA after amplification should be consistent, i.e., both should be (78.0±0.5)℃. Under the premise that the results of the three replicates are consistent, if the Ct value is ≤35, it is judged as positive; if the Ct value is >35, it is judged as negative (indicating that it does not contain Botrytis cinerea or contains very low amounts).

[0017] In this invention, the plant can be any of the following:

[0018] A1) Dicotyledonous plants; A2) Monocotyledons; A3) Magnolia class plants; A4) Plants belonging to the Solanaceae, Asteraceae, or Cucurbitaceae families; A5) Plants of the genera *Solanum*, *Lactuca*, or *Cucumis*; A6) Tomatoes, lettuce, or cucumbers.

[0019] In this invention, the seed to be planted can be any of the following: B1) Seeds of dicotyledonous plants; B2) Seeds of monocotyledonous plants; B3) Seeds of Magnolia species; B4) Seeds of plants belonging to the Solanaceae, Asteraceae, or Cucurbitaceae families; B5) Seeds of plants in the genera *Solanum*, *Lactuca*, or *Cucumis*; B6) Tomato, lettuce, or cucumber seeds.

[0020] Other plants or seeds that can be infected with Botrytis cinerea are also within the scope of protection of this invention, and are not limited to the above-mentioned plants or seeds.

[0021] This invention also provides a method for predicting the spore content of *Botrytis cinerea* in a test environment. The method includes using a model to predict the spore content in the test environment, wherein the model is: S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 S represents the spore content, measured in spores per m³. 3 T represents the temperature of the environment under test, in degrees Celsius; RH represents the relative humidity of the environment under test, in %. In some embodiments of the present invention, the environment to be tested is air.

[0022] This invention also provides the application of the above method in the prevention and control of gray mold.

[0023] This invention provides qualitative and quantitative analysis of *Botrytis cinerea* in the environment and plants, identifying the optimal time for *Botrytis cinerea* production and release based on different application scenarios, thus providing a basis for key control points. Based on this detection data, a "three-dimensional immunization" prevention program for gray mold throughout the entire growth cycle is developed. The established "three-dimensional immunization technology for gray mold control based on precise detection of *Botrytis cinerea*" is applicable to the control of gray mold in greenhouse vegetables and flowers such as tomatoes, cucumbers, eggplants, peppers, roses, lilies, lettuce, celery, and leeks. Through mathematical model prediction, seed immunization, monitoring and treatment of bacterial contamination in the planting environment, detection and immunization treatment of bacterial contamination in plants, and monitoring and treatment of bacterial contamination in the soil, the "three-dimensional immunization" technology is applied throughout the entire growth cycle to reduce the incidence of disease. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the "stereoimmune" prevention and control of gray mold disease according to the present invention.

[0025] Figure 2 This is a visualization of the spore release simulation model before optimization.

[0026] Figure 3 This is the visualization effect after optimizing the spore release simulation model.

[0027] Figure 4 This is the optimal release point for monitoring Botrytis cinerea after the model was optimized.

[0028] Figure 5This is an electrophoresis image of products from different pathogens amplified by synchronous PCR. Lanes 1-10 represent the genomes of *Cercospora multiflora*, *Peronospora*, *Pseudomonas stolonifera*, *Fusarium oxysporum*, *Rhizoctonia solani*, *Pythium*, *Anthracis*, *Agrostis sclerotiorum*, *Sclerotinia sclerotiorum*, and *Botrytis cinerea*, respectively. M: 100-2000 bp Marker; N: Negative control (ddH2O).

[0029] Figure 6 The results show the sensitivity analysis of the qPCR detection system using Botrytis cinerea primers BC27f / BC27r, where A: qPCR melting curves at different template concentrations; B: qPCR amplification curves at different template concentrations; and C: standard curve.

[0030] Figure 7 It describes the temperature and humidity changes in the greenhouse and the spore release patterns.

[0031] Figure 8 It represents the amount of spatial spores released from different distances from the disease center at different stages of the disease outbreak. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0033] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0034] In the following examples, the disease index was determined according to the grading standard for gray mold (GB / T 17980.28-2000), and the specific criteria for classifying leaf disease levels are as follows: Grade 1: The area of ​​diseased leaves is less than 5%; Grade 3: The area of ​​diseased leaves is between 6% and 10%; Level 5: The area of ​​diseased leaves is between 11% and 20%; Level 7: The area of ​​diseased leaves is between 21% and 50%; Level 9: The area of ​​diseased leaves is greater than 50%.

[0035] DI = [∑(s×n) / (N×S)]×100 In the formula: DI—Disease Index s — Representative numerical value for each disease level n — Number of leaves at each disease level N – Total number of leaves surveyed S – Representative number of the highest disease level In the following embodiments, the formula for calculating the prevention and control effect is as follows: Prevention and control effect (%) = 100 × (disease index of control group - disease index of treatment group) / disease index of control group.

[0036] In the following examples, the active ingredients of the bio-micro powder consist of 100 billion live spores / gram of Pseudomonas fluorescens wettable powder (Shandong Haililai Chemical Technology Co., Ltd.), 100 billion CFU / gram of Bacillus subtilis wettable powder (Keno Biotechnology (Hubei) Co., Ltd.), and 55 billion CFU / gram of Bacillus vesiculosus MBI600 wettable powder (BASF Europe), in a mass ratio of 1:1:1.

[0037] Example 1: Establishment and Application of "Stereoimmunization" Prevention and Control I. Overall Framework of "Three-Dimensional Immunization" Prevention and Control To address the problems of "lack of early warning and monitoring, poor targeted disease prevention, and a lack of 'three-dimensional' and 'multi-dimensional' prevention plans for gray mold control in major facility vegetables, leading to unstable prevention effects," this invention deeply integrates a precise mathematical model of Botrytis cinerea spore release, molecular detection technology, and three-dimensional immunization and multi-dimensional control methods. It establishes a comprehensive "three-dimensional immunization" control network from "seed-plant-space environment," achieving the goals of "early detection, early intervention, less pesticide use, and controlled losses." The schematic diagram of the "three-dimensional immunization" control model for gray mold is shown below. Figure 1 As shown. The core content includes: 1. Seed stage health management technology: With "source reduction of bacteria" as the core, it integrates early warning and seed immune coating technology. Precise pathogen detection: Using early warning technologies for diseases such as qPCR, the pathogens carried by seeds are quantitatively detected, and seed batches with excessive pathogen rates are eliminated, reducing the risk of disease occurrence from the source.

[0038] Disease-resistant and immune-boosting seed coating: Functional seeds are prepared using Bacillus cereus YK87, which has high biocontrol activity and stress resistance, as the active ingredient, combined with functional adjuvants, through seed coating technology. The coating layer not only slowly releases probiotics and forms a rhizosphere protective zone during seed germination, but also provides the nutrients needed for germination, improves seedling survival rate, and induces disease resistance.

[0039] 2. Planting environment pretreatment technology Environmental pathogen monitoring and comprehensive disinfection should be carried out before planting to reduce the initial pathogen population. 2.1 Functional model of the relationship between planting environment and Botrytis cinerea spore release concentration: S = β 0 + β 1 T + β 2 T² + β 3 RH + β 4 RH² + β 5 (T × RH) + ε 2.2 Three-dimensional monitoring of pathogens in the spatial environment: Addressing the high humidity and poor air circulation characteristics of greenhouses, a "spore abundance prediction mathematical model" is used to continuously monitor spores of pathogens such as Botrytis cinerea within the greenhouse, allowing for real-time tracking of spore dispersal dynamics and prediction of disease trends. Through spatial dusting of pesticides, the powder not only adheres to leaf surfaces but also settles in the air and on soil surfaces, forming a durable pesticide barrier. This effectively blocks the infection pathway of Botrytis cinerea, achieving a shift from "end-of-pipe treatment" to "source control," providing a safe spatial guarantee for "three-dimensional immunization."

[0040] 2.3 Three-dimensional immunomodulatory control using bio-based powdering: For the detected pathogens, a bio-based powdering method was employed. The bio-based micro-powder was evenly sprayed onto the greenhouse space and soil surface using a powdering machine, achieving three-dimensional control and reducing the concentration of pathogens throughout the environment. The powdering time was based on the spore release time predicted by the mathematical prediction model as 14-18 hours; this study used 15 hours for powdering.

[0041] 2.4 Soil Environmental Pathogen Monitoring: To address soil-borne diseases caused by continuous cropping obstacles, qPCR was used to quantitatively detect the residual amount of Botrytis cinerea in the soil and assess the potential risk of disease in the plot. During the summer fallow period, the soil in the greenhouse was treated with high-temperature mulching (the greenhouse was sealed after covering with mulch film, and the soil temperature was raised to above 55℃ and maintained for 7-10 days) to eliminate or reduce pathogens in the soil. At the same time, the entire greenhouse was subjected to high-temperature fumigation to reduce the base number of pathogens in the environment such as the greenhouse frame and walls.

[0042] 3. Plant bacterial infection detection and immune activation enhance disease resistance. Conduct bacterial carrier testing on disease-free plants. Once Botrytis cinerea DNA is detected on the surface of healthy leaves or fruits, an early warning mechanism is immediately triggered, and "immunization" induction technology is used to prevent the disease from spreading explosively in the field.

[0043] Seedbed safety management technology: Focusing on "strong seedlings and disease prevention" during the seedling stage, constructing a three-dimensional protective network for the seedbed: 3.1 Root health cultivation: Add Zhongshugenbao® compound microbial fertilizer to the seedling substrate and irrigate once before transplanting to enhance root vitality and improve seedling resistance.

[0044] 3.2 Seedling disease prevention: Add Trichoderma harzianum and Bacillus subtilis to the substrate in proportion to prevent gray mold in seedlings through microbial competition and antagonism.

[0045] Precision disease prevention technology during the growing season: A comprehensive strategy of "pathogen monitoring + immune induction + biological control" is adopted during the growing season to achieve joint prevention and control of diseases in time and space. 3.3 Immune induction: During the growing season, regularly spray the disease resistance activator Junjiang® (Bacillus belyssus) and plant immune protein preparation to activate the salicylic acid and jasmonic acid signaling pathways in lettuce, thereby enhancing the plant's systemic resistance and stress resistance to gray mold.

[0046] 3.4 Three-dimensional preventive measures using biological agents: During the pathogen warning period, the biological micro-powder is evenly sprayed into the greenhouse space using a powder spraying technique. The spraying dosage for the greenhouse space is 200g / 667m². 3 This enables three-dimensional prevention and control, inhibiting disease occurrence through microbial occupation and antagonistic effects, and reducing the concentration of pathogens in the entire environment.

[0047] II. Establishment of a Mathematical Model for Predicting Environmental Botrytis cinerea Spore Count To quantify the synergistic effects of ambient temperature and humidity on space spore release, the average temperature (T, °C) and average relative humidity (RH, %) over a 24-hour period were monitored in the field, along with the space spore release (S, spores·m³) collected at the corresponding time points. - ³), and a mathematical prediction model for the spatial spore release of Botrytis cinerea in response to temperature and humidity was constructed.

[0048] First, the linear and nonlinear relationships among T (temperature), RH (humidity), and S (spore release) were preliminarily assessed using scatter plots and correlation analysis. Given the complexity of biological processes, a data modeling method combining multinomial regression analysis and response surface methodology (RSM) was employed to optimize the fit of multiple parameters, including temperature and humidity. By comparing the Akaike information criterion and the coefficient of determination, the optimal model form was ultimately determined to be a function containing quadratic terms of temperature and humidity, as well as their interaction term. S = β 0 + β 1 T + β 2 T² + β 3 RH + β 4 RH² + β 5 (T × RH) + ε In the formula, β0 is the intercept, β1 to β5 are the regression coefficients to be estimated, and ε is the random error term. The estimation of model parameters and significance testing were performed using Python in the VSCode environment, and the scikit-learn library was used for fitting.

[0049] 2. Modeling Approach and Technical Implementation Based on Spore Release Data Overall Modeling Approach The core objective of this study is to construct a mathematical model capable of accurately predicting spore release by systematically analyzing the relationship between temperature, humidity, and spore release. The modeling approach follows the principles of "data-driven, model optimization, and biological validation," forming a complete technical loop from raw data processing to final model application. Data flow: Raw Excel data → Cleaning and integration → Feature engineering → Model training → Validation and optimization → Deployment and application; Methodological flow: Statistical analysis → Machine learning regression → Visual validation → Biological validation; Language and environment configuration: The language used is Python 3.7+, and the environment configuration is Visual Studio Code (VSCode).

[0050] 3. Technological Implementation Methods for Establishing a Spore Release Rate Model (1) Data import and preprocessing Objective: To extract structured data from raw Excel files, understand the basic characteristics of the data, transform the raw data into a clean dataset that can be used for modeling, handle missing values ​​and outliers, and standardize the data format.

[0051] Using functions / methods: pandas.read_excel() - Reads raw data in Excel format. pandas.DataFrame.info() / .describe() - Exploratory Data Analysis pandas.fillna() + .interpolate() - handling missing values Interquartile Range (IQR) Method - Outlier Detection and Handling pandas.to_datetime() - Time format standardization Specific steps: First, the `read_excel` function from the pandas library was used to import two Excel data files, obtaining structured data containing fields such as temperature, humidity, and spore release. Then, the `.info()` and `.describe()` methods were used to quickly understand the basic data information, including data volume, variable types, and numerical ranges. For any missing values, linear interpolation was used to fill in the gaps, ensuring data continuity. The IQR method was used to identify and remove extreme outliers to prevent them from interfering with model training. Finally, timestamps were uniformly converted to a standard date and time format to prepare for subsequent time series analysis.

[0052] (2) Feature Engineering Objective: To create more predictive features based on domain knowledge and capture the complex interactions between temperature and humidity.

[0053] Using functions / methods: Exponentiation ( 2) - Create quadratic term features Dot product operation - Creating interactive item features pandas.Series.dt.hour - Time Feature Extraction pandas.get_dummies() - One-hot encoding of categorical variables Conditional function - Creating derived features Specific steps: Based on domain knowledge and statistical analysis, the original features are expanded and transformed. Quadratic features of temperature and humidity are created using exponentiation to capture the nonlinear relationship between spore release and temperature and humidity. A dot product operation is used to create an interaction term for temperature and humidity, quantifying their synergistic effect on spore release. Temporal features such as hour and daytime / nighttime characteristics are extracted from timestamps to analyze the diurnal rhythm of spore release. Categorical variables such as environmental conditions are one-hot encoded to make them processable by the model. Derived features, such as "duration of sustained high humidity," are created through conditional judgments to enrich the model's expressive power.

[0054] (3) Exploratory data analysis Objective: To visualize data distribution and relationships, identify potential patterns, and guide model selection and parameter settings.

[0055] Using functions / methods: seaborn.scatterplot() - scatter plot drawing pandas.DataFrame.corr() - Correlation coefficient calculation seaborn.heatmap() - Correlation heatmap visualization Polynomial trend line fitting - Relationship pattern recognition Specific methods: By plotting scatter plots of temperature, humidity, and spore release, the distribution relationship and trends among variables can be visually observed. Pearson correlation coefficients are calculated for each variable to quantify the degree of linear correlation. The correlation coefficient matrix is ​​visualized as a heatmap to quickly identify key influencing factors. A polynomial trend line is overlaid on the scatter plot to preliminarily determine whether a non-linear relationship exists between temperature, humidity, and spore release, providing a basis for subsequent model selection.

[0056] (4) Model training and evaluation Objective: To select the most suitable model based on data characteristics, train it to obtain a prediction function, and quantify the effects of temperature and humidity on spore release.

[0057] Using functions / methods: sklearn.preprocessing.PolynomialFeatures() - Polynomial Feature Generation sklearn.linear_model.LinearRegression() - Least Squares Regression sklearn.metrics.r2_score() - Calculates the coefficient of determination sklearn.metrics.mean_squared_error() - Calculates mean squared error Ordinary Least Squares (OLS) - Coefficient Estimation The specific approach is as follows: A multinomial regression model is used to fit the data, and the quadratic and interaction term feature matrices are automatically generated using PolynomialFeatures. LinearRegression is used to estimate the model coefficients based on ordinary least squares, obtaining a quantitative relationship between temperature, humidity, and spore release. The coefficient of determination (R²) is calculated using r²_score to evaluate the model's ability to explain data variation. The root mean square error (RMSE) is calculated using mean_squared_error to measure the model's predictive accuracy. The final output is a complete function expression containing all coefficients.

[0058] (5) Model validation and optimization Objective: To ensure the model's generalization ability on new data, prevent overfitting, and improve stability through cross-validation and hyperparameter tuning.

[0059] Using functions / methods: sklearn.model_selection.train_test_split() - Dataset splitting sklearn.model_selection.cross_val_score() - Cross-validation sklearn.model_selection.GridSearchCV() - Grid search parameter tuning sklearn.pipeline.Pipeline() - Pipeline the modeling process Response surface analysis - optimal condition search Specific methods: The dataset is proportionally divided into training and test sets to ensure the objectivity of model evaluation. Five-fold cross-validation is used to evaluate the model's stability and generalization ability. Grid search is used to find the optimal hyperparameters, such as the polynomial order, in a predefined parameter space. A pipeline is used to encapsulate feature engineering and model training steps into a unified process to avoid data leakage. Finally, response surface methodology is applied to search for the maximum spore release point within the feasible temperature and humidity domain to determine the optimal environmental conditions.

[0060] 4. Optimization process of spore release rate model (1) Initial model (based on the first set of data) Construction Method: The basic model was constructed using the first dataset (spore production data.xlsx). An initial function was obtained through multinomial regression fitting. Model evaluation showed R²=0.86 and RMSE=64.2, indicating that the model has certain predictive ability, but its accuracy and generalization need to be improved.

[0061] Limitations analysis: 1. Limited data volume and insufficient model stability. 2. The prediction error is relatively large for extreme temperature and humidity conditions. 3. It does not include sufficient variation under different environmental conditions. (2) Optimize the model (merge two sets of data) Optimization method: The two datasets were merged to increase sample size and data diversity. After retraining the model, all coefficients were adjusted appropriately, resulting in a significant improvement in model performance.

[0062] 5. Final results of the spore release prediction model (1) Spore release prediction model function Simulation test model (based on initial data): S = 2512 + 56RH - 104T + 2.4R × T + RH 2 - 1.6T 2 ; Field optimization model (after incorporating new data): S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 .

[0063] (2) Performance comparison of spore release prediction models Key optimization points for the model: ① Enhanced influence of humidity: The humidity coefficient increased from 56 to 58, more accurately reflecting the promoting effect of low humidity environment on spore release. ② Temperature suppression weakened: The temperature coefficient was adjusted from -104 to -96, correcting the original model's overestimation of the high-temperature suppression effect. ③ Enhanced interaction effect: The coefficient of the temperature-humidity interaction term increased from 2.4 to 2.6, better capturing the synergistic effect between the two. ④ Nonlinear correction: Optimization of quadratic term coefficients makes the model's predictions more reasonable under extreme conditions. ⑤ Expanded scope of application: The effective prediction range of the model has been expanded from 20-28℃ to 15-32℃, and the accuracy has been expanded from 75-98% to 55-98%.

[0064] Table 1. Comparison of parameters before and after model performance optimization

[0065] (3) Visualization effect of spore release prediction model This model characterizes the surface and function of spore release response to changes in temperature and humidity. Figure 2 Visualization of the spore release simulation model before optimization. Figure 3 The visualization effect after optimization of the spore release simulation model. Under conditions of 24±0.5℃ and 90±2.5% humidity, the spore release rate can reach 19811 spores / m³. 3 ,like Figure 4 As shown, there is a distinct peak region in spore release. This indicates that these environmental conditions are most favorable for spore release and accumulation.

[0066] The functional model constructed in this study successfully quantified the synergistic effects of temperature and humidity on spore abundance in the field. The significant negative interaction term in the model indicates that the promoting effects of low temperature and high humidity on spore release are not simply additive; when humidity exceeds a certain threshold, the effect of temperature weakens, which may be related to limitations imposed by spore viability or other environmental factors. This function can be directly integrated into disease early warning systems as a core algorithm for predicting spore dispersal potential.

[0067] 6. Analysis of the application effect of the spore release prediction model To verify the effectiveness of the established spore release model, we analyzed actual field observation data and model prediction data.

[0068] The model's predictive performance was validated in a greenhouse in Shouguang, Shandong. Winter greenhouse monitoring results are shown in Table 2. The relative errors at 16:00, 4:00, and 7:00 were all below 1%, indicating that the model can accurately capture the dynamics of spore release in a typical greenhouse environment from night to early morning. Throughout the entire monitoring period, the model's prediction accuracy was above 90%, ranging from 92.96% to 99.43%.

[0069] Table 2. Monitoring results and model validation of Botrytis cinerea in Shouguang winter greenhouse environment.

[0070] The results of the spring greenhouse environment monitoring are shown in Table 3. During the entire monitoring period, the model prediction accuracy was higher than 85%, ranging from 86.35% to 97.74%.

[0071] Table 3. Monitoring results and model validation of Botrytis cinerea in Shouguang greenhouse environment during spring.

[0072] The model was validated in a solar greenhouse in Changping District, Beijing during the spring. The results are shown in Table 4. The model performed excellently during relatively stable daytime environmental conditions (such as 10:00 and 13:00), with a relative accuracy between 94.74% and 96.39%. Even though the error increased during periods of significant temperature and humidity fluctuations at night, the overall prediction accuracy remained acceptable, indicating that the model has good adaptability to greenhouse environments under different regions and management conditions.

[0073] Table 4. Monitoring results and model validation of Botrytis cinerea in greenhouse environment in Changping District during spring.

[0074] The above verification results show that the established spore release prediction model can systematically reflect the overall trend of spore release with temperature and humidity changes. In most actual observation scenarios, the prediction error is small, and the predicted values ​​are highly consistent with the actual observed values. It shows good applicability, reliability and prediction stability under different seasons and environmental conditions. The model can not only adapt to climate change in different seasons, but also maintain the consistency of prediction over a wide range of temperature and humidity, demonstrating good practicality and suitability for the construction of greenhouse disease monitoring and early warning systems.

[0075] III. Establishment and Validation of the qPCR Detection System Experimental objective: To detect the development dynamics of Botrytis cinerea in the field, and to rapidly, specifically, sensitively, and quantitatively detect the occurrence frequency of gray mold in the field, providing a theoretical basis for efficient and rational control.

[0076] 1. Design of Botrytis cinerea-specific primers Based on the NCBI database B.cinerea Reference genome, selection B.cinerea The conserved gene B.05 was used as the target DNA sequence. Primers were designed using Primer Premier 5.0 software, and the optimal qPCR primers were determined according to primer design principles. The optimal qPCR primer pair was determined to be BC27f / BC27r by comparing the primers found in the literature with the designed primers.

[0077] Table 5 Information on primers for screening

[0078] 2. Establishment of qPCR fluorescence quantitative reaction system The qPCR amplification system used in this study was 20 µL. Each reaction tube contained 1 µL of the sample DNA template, 10 µL of 2 × Taq Pro Universal SYBR qPCR Master Mix, 0.4 µL each of forward and reverse primers, and ddH2O to a final volume of 20 µL. The reaction conditions were: 95 °C pre-denaturation for 30 s, 95 °C denaturation for 10 s, 60 °C annealing for 32 s, and 72 °C extension for 30 s, for 40 cycles. Fluorescence signals were collected during temperature increases to establish melting curves.

[0079] 3. Primer specificity verification All extracted DNA from the tested strains was used as templates in a qPCR system for detection, with ddH2O as a control. Five replicates were performed. The Ct value was used to determine the specificity of the system and to verify whether the detection system could be interfered with by other non-target DNA.

[0080] Using genomic DNA from seven common pathogenic fungi causing vegetable diseases—including *Botrytis cinerea*, *Corynebacterium multiflorum*, *Pythium*, and *Fusarium oxysporum*—as templates, and with enzyme-free sterile water as a negative control, qPCR amplification was performed using five pairs of designed candidate primers. The results are as follows: Figure 5 As shown, the BC27f / BC27r primers specifically amplified *Botrytis cinerea*, and their Ct values ​​were significantly lower than those of other pathogens, indicating that the primers have high specificity for *Botrytis cinerea*. BC27f is: 5'-CCGAAAGATTGAAAAGGAATAAAA-3' (SEQ ID NO: 1) and BC27r is: 5'-GAAGTAAAGCTACCACCGAGAACA-3' (SEQ ID NO: 2).

[0081] Table 6 Results of qPCR specificity validation test

[0082] Note: "-" indicates that no valid Ct value was detected.

[0083] 4. Sensitivity detection and establishment of standard curve The concentration of *Botrytis cinerea* genomic DNA was measured using an ultra-micro spectrophotometer. Known concentrations of *Botrytis cinerea* genomic DNA were diluted using ddH2O in 10-fold gradients. During dilution, the DNA solution was thoroughly mixed by pipetting 30 times, vortexing for 10 seconds, and centrifuging. qPCR amplification was performed using DNA at different concentrations as templates, and the sensitivity of specific primers was evaluated by the fluorescence intensity. A standard curve was constructed with Ct value on the ordinate and the logarithm of each DNA concentration gradient on the abscissa, and the correlation coefficient R was calculated. 2 Evaluation, R 2 The closer the value is to 1, the better the linear relationship. Based on the obtained linear regression equation, the DNA concentration corresponding to a Ct value of 35 is calculated, which is taken as the lowest detection limit (sensitivity) of this detection system.

[0084] The initial template DNA concentration was 1.41 × 10⁻⁶. 2 After serial dilution of ng / μL to 10-fold concentrations, qPCR amplification was performed using specific primers BC27r / BC27f, and the results are as follows. Figure 6 As shown, A represents the qPCR melting curves at different template concentrations; B represents the qPCR amplification curves at different template concentrations; and C represents the standard curve. Melting and amplification curves were obtained after each qPCR reaction, thus determining the specificity peak Tm of this detection system to be (78.0±0.5)℃. When the template concentration was as low as 1 pg / μL, detection was achieved in all three replicates, with an average Ct value of 34.88; when the template concentration was as low as 10 fg·μL… -1 At that time, one replicate well was not detected, and the average Ct value of the remaining two replicate wells was 36.56. Therefore, a standard curve was plotted based on the amplification results of each concentration template. The results showed that the standard curve exhibited good linearity across seven concentration gradients from 100 ng / μL to 10 fg / μL. The linear relationship between the logarithm (X) of Botrytis cinerea DNA concentration and the Ct value (Y) was: Y = -3.1157X + 43.217, and the coefficient of determination R of the linear regression equation was [value missing]. 2 =0.9983. Based on this linear equation, when the Ct value is 35 (the conventional detection threshold), the corresponding DNA concentration is 4.211 pg / μL, which is the lowest detectable concentration of this system.

[0085] Based on the above analysis, the following criteria are established: the Tm values ​​of the melting curves of the test sample DNA and the positive control (Botrytis cinerea) DNA after amplification should be consistent, i.e., both should be (78.0±0.5)℃, and the results of the three replicates should be consistent. If the Ct value is ≤35, it is judged as positive; if the Ct value is >35, it is judged as negative (indicating that it does not contain Botrytis cinerea or contains very low amounts).

[0086] 5. Detection of pathogens using qPCR detection system in greenhouse environment 5.1 Effects of temperature and humidity on Botrytis cinerea spore concentration Different humidity conditions were set up for the experiment: continuous high humidity (relative humidity >90%, 24h), continuous dryness (relative humidity <70%, 24h), and initial dryness followed by high humidity (relative humidity <70%, 12h; relative humidity >90%, 12h). Humidity control was mainly achieved by humidifying the airflow generated by a humidifier into the glass chamber, while drying was controlled by placing color-changing silica gel as an adsorbent desiccant inside the glass chamber. A temperature and humidity recorder was placed inside the glass chamber for each experiment, recording the temperature and humidity data every 5 minutes.

[0087] Preparation of bacterial suspension: After culturing the tested *Botrytis cinerea* on PDA medium at 28°C in the dark for 10 days, 5 mL of 0.05% Tween 20 was added to a petri dish. The colonies were gently brushed with a sterile brush, and the mixture of hyphae and conidia was filtered through four layers of gauze. The suspension concentration was adjusted to 1×10⁻⁶ using a hemocytometer. 5 1 spore / mL, to obtain a bacterial suspension for later use.

[0088] When cucumber seedlings are at the 2-3 leaf stage, inoculate them using a spray method. Evenly suspend the above-mentioned bacterial suspension and spray it onto both sides of the cucumber leaves until the droplets are evenly distributed and do not run off the leaf surface. Once the cucumber plants show obvious symptoms of gray mold, they are ready for use.

[0089] After 3 days of treatment under different humidity conditions, conidia of *Botrytis cinerea* were collected from the greenhouse at 1:00, 4:00, 7:00, 10:00, 13:00, 16:00, 19:00, and 22:00. Sampling was performed using a pathogen spore collector, collecting the pathogen onto aluminum foil coated with 600 μL of paraffin oil and onto selective culture medium. Collection time was 5 min for each treatment, with a flow rate of 20 L / min.

[0090] After collecting the oil film, place the foil-coated oil film in a 50 mL centrifuge tube for DNA extraction.

[0091] Place the aluminum foil oil film in a 50 mL centrifuge tube, press the oil film down slightly with the tube cap, and centrifuge at 8000 r / min for 5 min. Transfer the paraffin oil at the bottom of the centrifuge tube to a 1.5 mL centrifuge tube, add sterile water to 1 mL, and centrifuge at 12000 r / min for 2 min. Discard the upper paraffin oil layer, and use the bottom precipitate for DNA extraction. DNA extraction was performed using a fungal genomic DNA extraction kit. qPCR amplification was performed using the previously screened Botrytis cinerea-specific primers BC27f (5'-CCGAAAGATTGAAAAGGAATAAAA-3') and BC27r (5'-GAAGTAAAGCTACCACCGAGAACA-3'), which were synthesized by Beijing Bomaide Technology Co., Ltd. The qPCR reaction system (20 μL) consisted of: 10 μL SYBR qPCR Master Mix (Bomaide, Beijing), 0.4 μL each of forward and reverse primers, and 1 µL DNA template. Reaction conditions: 95℃ pre-denaturation for 15 min, 95℃ denaturation for 10 s, 60℃ annealing for 32 s, for a total of 40 cycles. Amplification and melting curves were analyzed after the reaction. Each qPCR reaction was performed in triplicate.

[0092] Based on qPCR results and temperature and humidity monitoring data, the diurnal dynamics of *Botrytis cinerea* spore concentration collected in the greenhouse clearly reflected its prevalence characteristics under low temperature and high humidity. During the experiment, the spore concentration in the greenhouse exhibited a diurnal variation pattern of higher concentrations during the day and lower concentrations at night. Temperature and humidity conditions were as follows... Figure 7 As shown in Figure A, the spore concentration is as follows: Figure 7 As shown in Figure B, the peak spore concentration occurred at 16:00, when the temperature had begun to decrease from its midday high (10:00-13:00), while the humidity had not yet reached its nighttime peak. This transitional phase of decreasing temperature and increasing humidity provided the optimal window for spore release and diffusion. The spore release rate collected in the space was 10874±389 spores / m². 3 Subsequently, during the night to early morning (1:00-4:00), in a typical low-temperature, high-humidity environment where the temperature drops to its lowest point and humidity rises to its highest point (>85% RH), the spore concentration actually drops to its lowest point, with the amount of spores released in the space collected being 1268±14. 3 spores / m 3 This indicates that extremely high humidity environments may promote spore settling, attachment, or germination and infection, thereby reducing the number of free spores in the air. This is consistent with the ecological characteristic of Botrytis cinerea spore release being driven by temperature and humidity, further confirming the biological rationale behind the detection results.

[0093] The qPCR detection system established in this study has successfully achieved the detection of *Botrytis cinerea* (Glaucus spp.) within a greenhouse space for 24 hours. Botrytis cinereaDynamic monitoring of spore concentration verified the applicability and reliability of this system in detecting the spatial diffusion of pathogens. As a representative of airborne diseases, *Botrytis cinerea* conidia are mainly diffused within greenhouse spaces via airflow; therefore, the spatiotemporal dynamic changes in spore concentration directly affect the risk of disease outbreaks.

[0094] 5.2 Correlation between the disease severity index of gray mold and the concentration of Botrytis cinerea spores in the environment Tomato seedlings were sprayed with the bacterial suspension prepared in section 5.1. The upper and lower surfaces of the tomato leaves were sprayed separately until droplets appeared to slide off the leaves. After inoculation, the top and side vents of the greenhouse were closed to maintain humidity. Weather forecasts predicted continuous cloudy or rainy weather for 7 days after inoculation, with the ambient temperature maintained between 18-23℃ and the relative humidity above 98% RH for at least 8 hours. Leaf disease was observed daily after inoculation. Five monitoring points were evenly distributed throughout the facility to continuously monitor temperature, humidity, and the number of spores released into the air.

[0095] Starting 14 days post-inoculation, samples were collected from monitoring points every 7 days inside the greenhouse. A liquid aerosol sampler was used for regional aerosol collection at a flow rate of 400 L / min for 2 minutes, collecting the aerosols into collection tubes containing 2 mL of sterile water. qPCR amplification using primers BC27f / BC27r was performed to determine the amount of pathogens released within the space.

[0096] Table 11 shows the monitoring data of Botrytis cinerea release collected in the facility environment. As the monitoring time increased, the release of Botrytis cinerea in the greenhouse increased from 1579.72 spores / m³ 14 days after inoculation. 3 By day 21, the number of spores in the greenhouse was 2223.16 spores / m². 3 The disease index rose from 11.07 to 21.93, indicating that the disease had entered its outbreak phase. This suggests that the high concentration of spores released in the space led to faster disease spread and diffusion. By day 35, the concentration of *Botrytis cinerea* collected in the space reached as high as 6058.32 spores / m³. 3 The disease index rose to 37.38, at which point the disease was difficult to control.

[0097] Table 7. Changes in gray mold disease severity and spore count in greenhouse-grown tomato plants (variety: Zhongza 201)

[0098] 5.3 Spatiotemporal Relationship between Botrytis cinerea spore release concentration and its subsequent dispersal distance The spatiotemporal relationship between spore release concentration and its subsequent dispersal distance is as follows: Figure 8As shown. After the disease occurs, the pathogen spreads rapidly from the center of infection to the surrounding areas. Fourteen days after inoculation, the spore concentration in the central disease area was 1381 spores / m². 3 The spore concentration decreased with increasing distance from the central lesion area; at a distance of 8 meters from the central lesion area, the spore concentration was only 308.4 spores / m². 3 At this point, the disease index (Table 8) decreased from 20.81 in the central area to 7.41, indicating that disease control was still effective at this stage. As the disease progressed, the spore concentration and the disease index of the plants increased exponentially, both in the central disease area and at different locations away from it. By 35 days after inoculation, the spore concentration in the environment of the central disease area (Table 9) reached 9489 spores / m². 3 The spore concentration at a distance of 8 meters from the center has reached 2284.8 spores / m². 3 The infection rate was 7.4 times that of 14 days after inoculation. The disease index in the central disease area and 8 meters away from it rose to 64.89 and 23.96 respectively. At this point, gray mold had spread widely in the field and the control effect was very poor.

[0099] Table 8. Statistics on disease index of communities at different times and distances from the outbreak center.

[0100] Table 9. Statistics on spore release collection from plots at different inoculation times and distances from the disease center.

[0101] Note: Spore concentration unit: spores / m³ 3 This system utilizes primers and probes designed specifically for the gene sequence of *Botrytis cinerea*, and employs real-time quantitative PCR technology to achieve precise quantitative detection of the target DNA fragment during amplification. The system exhibits high sensitivity and can effectively capture the presence of low concentrations of spores, providing a quantitative molecular detection tool for early warning and spatial transmission dynamics research of gray mold in greenhouses.

[0102] IV. Performance of "Three-Dimensional Immunization" Technology for Controlling Botrytis cinerea Based on Precise Detection of Botrytis cinerea in Yield Increase In January 2025, Jingcai 8 tomatoes (from Beijing Modern Farmer Seedling Technology Co., Ltd.) were planted in two greenhouses (70m long, 8m wide, and 3m high) at Tianan Agriculture Changping Organic Farm. The two greenhouses were randomly divided into a control group (traditional pest control) greenhouse and a demonstration group (three-dimensional immunization) greenhouse. The same batch of Jingcai 8 tomato seeds was randomly divided into control and demonstration group seeds and planted in the corresponding greenhouses for parallel experiments, as detailed below: 1. Control group treatment The control group seeds were planted in the control group greenhouse, with each plant spaced 30 cm apart. The temperature and light duration were set at 25℃ and 200 µmol / m² light intensity. 2 s 1 16h, 16℃ in the dark for 8h.

[0103] Simultaneously, the qPCR method established in step three was used to detect the space-collected samples. The collection method was as follows: at the collection point, a liquid aerosol sampler was used to collect aerosols from the facility space at different morbidity days after inoculation. The flow rate of the aerosol sampler was 400 L / min, the collection time was 2 min, and the aerosols were collected into a collection tube containing 2 mL of sterile water.

[0104] Thirty days after tomato transplanting, disease symptoms began to appear in the field. At this time, the Ct value of qPCR was 32.31, <35, indicating the need for pesticide application. The control method is conventional spraying. Use 45% iprodione·cyprodinil water-dispersible granules (Shandong Yilan Technology Co., Ltd.), at a dosage of 70 grams per acre, once every 7 days, for 3-5 consecutive applications.

[0105] 2. Demonstration Group Processing 2.1 Seed Treatment One hundred seeds were randomly selected from the demonstration group as test samples. After mixing, the seeds were crushed, and DNA was extracted from the test samples. The following qPCR detection was performed: Total DNA was extracted from the test samples, and qPCR was performed using primers BC27f: 5'-CCGAAAGATTGAAAAGGAATAAAA-3' (SEQ ID NO: 1) and BC27r: 5'-GAAGTAAAGCTACCACCGAGAACA-3' (SEQ ID NO: 2). The qPCR amplification system was 20 µL. Each reaction tube contained 1 µL of total DNA from the test sample, 10 µL of 2 × Taq Pro Universal SYBR qPCR Master Mix, 0.4 µL each of forward and reverse primers, and ddH2O to a final volume of 20 µL. The reaction conditions were: 95℃ pre-denaturation for 30 s, 95℃ denaturation for 10 s, 60℃ annealing for 32 s, and 72℃ extension for 30 s, for 40 cycles. Fluorescence signals were collected during temperature increases to establish melting curves.

[0106] Standard curve: The linear relationship between the base-10 logarithm (X) of *Botrytis cinerea* DNA concentration and the Ct value (Y) is: Y = -3.1157X + 43.217, and the coefficient of determination R of the linear regression equation is... 2=0.9983. Based on this linear equation, a Ct value of 35 (the conventional detection threshold) corresponds to a DNA concentration of 4.211 pg / μL, which is the lowest detectable concentration of this system. Therefore, the following criteria are used to determine whether a test sample contains *Botrytis cinerea*: the Tm values ​​of the melting curves after amplification of the test sample DNA and the positive control (*Botrytis cinerea* strain) DNA should be consistent, i.e., both should be (78.0±0.5)℃, and the results of the three replicates should be consistent. If the Ct value > 35, the sample is considered negative (not containing *Botrytis cinerea* or containing very low concentrations); if the Ct value ≤ 35, the sample is considered positive.

[0107] The results showed that the Tm values ​​of the melting curves of the DNA from the demonstration group seeds and the greenhouse soil in the demonstration group were consistent with those of the positive control (Botrytis cinerea), i.e., (78.0±0.5)℃. Furthermore, the results from the three replicates were consistent. The Ct value of the demonstration group seeds was 36.8, which is greater than 35, indicating that the seeds in the demonstration group did not contain Botrytis cinerea or contained very low levels of it, and therefore did not require disinfection. The Ct value of the greenhouse soil in the demonstration group was 37.2, which is greater than 35, indicating that the greenhouse soil in the demonstration group did not contain Botrytis cinerea or contained very low levels of it, and therefore did not require disinfection.

[0108] 2.2 Using a mathematical prediction model for Botrytis cinerea spore release to predict Botrytis cinerea spore content in greenhouse air to guide the control of gray mold. The seeds of the demonstration group were planted in the demonstration group greenhouse, with each plant spaced 30m apart. The temperature and light duration were set at 25℃ and 200 µmol / m² light intensity. 2 s 1 16 hours, 16℃, 8 hours in darkness. A monitoring point was set up in the central area of ​​the greenhouse, and a temperature and humidity recorder (model: [model number missing]) was used to monitor the actual temperature (T) and relative humidity (RH). Seven days after planting (7 dpi), based on the monitoring of various parameter values ​​in the mathematical model, the following mathematical prediction model for Botrytis cinerea spore release was used to predict the Botrytis cinerea spore content in the greenhouse air: S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 S represents spore content, measured in spores / m³. 3 T represents the greenhouse temperature in °C; RH represents the greenhouse relative humidity in %. The air temperature and relative humidity of the greenhouse were measured, and the Botrytis cinerea spore content in the greenhouse air was obtained based on the above model. An analysis of the probability of gray mold occurrence in the greenhouse was conducted based on the predicted results. The results showed that on the 7th day after planting, the predicted spore count in the greenhouse air was 292 spores / m³. 3 The actual measured value was 273 spores / m³. 3On day 15 post-planting (15 dpi), the measured concentration of *Botrytis cinerea* spores in the greenhouse air was 1240 spores / m³. 3 (More than 1000 spores / m) 3 When the disease threshold is reached, apply the following biological agent using the powdering method at 3 PM: Evenly spray the biological micro-powder into the greenhouse space using a powdering machine. The application rate for the greenhouse space is 200g / 667m². 3 This enables comprehensive prevention and control, reducing the concentration of pathogens throughout the environment.

[0109] The predicted spore release rate is 1200 spores / m³. 3 (More than 1000 spores / m) 3 When applying pesticides, use the biological agent powdering method.

[0110] Table 10. Monitoring results and model validation of *Botrytis cinerea* in greenhouse environment of Changping District.

[0111] 2.3. After planting, the space was tested for Botrytis cinerea using qPCR. After accurate prediction using mathematical prediction models after planting, environmental pathogen monitoring is still needed during the post-planting growth period to provide parameters for disease control. Additionally, qPCR testing is conducted on the environmental space at 15, 30, and 45 days after planting, as described in section 2.1. The qPCR results are as follows: On day 15 post-planting, the Ct value of the qPCR test in the environment was 36.97, which is greater than 35, indicating that the content of *Botrytis cinerea* in the space was low on day 15 post-planting, and spraying with bio-agents via pollination was not required. However, because the model predicted that the spore quantity exceeded the standard value, bio-agent pollination was still carried out. On day 30, the Ct value of the qPCR test in the environment was 35.67, which is greater than 35, and no spraying was required. On day 45, the qPCR test results showed that the Ct value was 33.27, which, compared with the control group's 23.77, indicates that the use of the mathematical prediction model and the application of bio-agents via pollination on day 15 effectively inhibited the spread of *Botrytis cinerea* in the space.

[0112] 3. Results Sixty days after planting, the tomatoes entered the ripening period. The ripe fruits were harvested and the yield was statistically analyzed. The demonstration group greenhouse achieved a control effect of more than 60% on gray mold, with an average yield of 4790 kg / mu, which is 6.4% higher than the average yield of 4500 kg / mu of the control group.

[0113] Table 11 Statistics on the collection of space spore release

[0114] Example 2: The effect of precise detection of Botrytis cinerea on the "three-dimensional immunity" of tomatoes in controlling gray mold (Shangyi) In July 2025, Jingcai No. 8 (variety) tomatoes were planted in two greenhouses (80m long, 10m wide, and 3.5m high) at Tianan Agriculture Shangyi Organic Farm. The experiment was divided into a control group (traditional control) and a demonstration group (stereoimmunization), as detailed below: Control group: Seeds of Jingcai No. 8 tomatoes were planted in the greenhouse of Tianan Agriculture Shangyi Organic Farm, with each plant spaced 28cm apart. The remaining plants were planted and tested according to the method described in section IV of Example 1, the control group. When disease symptoms appeared in the field, control measures were implemented according to the treatment method described in section III of Example 1, the control group.

[0115] Demonstration group: Seeds were treated in accordance with step 2.1 of section IV of Example 1. The Ct value of the seeds in the demonstration group was 36.15. Since the Ct value is greater than 35, it indicates that the seeds in the demonstration group do not contain Botrytis cinerea or contain very low amounts of it, and therefore do not require disinfection treatment.

[0116] Following the method described in step 2.2 of section IV of Example 1, the mathematical prediction model for Botrytis cinerea spore release was applied. On the 7th day after planting, the predicted spore count in the greenhouse air was 308.5 spores / m³. 3 The actual measured value was 430 spores / m³. 3 On day 15 post-planting (15 dpi), the measured concentration of *Botrytis cinerea* spores in the greenhouse air was 1331 spores / m³. 3 (More than 1000 spores / m) 3 When the disease threshold is reached, the biological agent powdering method is applied at 3 pm according to section 2.2 of Example 1.

[0117] The spatially collected samples, collected according to the method described in Section IV of Example 1, were tested using the qPCR method in Example 1. On day 15, the Ct value was 35.25; on day 30, the Ct value of the demonstration group was 34.19, which is <35, indicating that the biological agent pollination method described in Example 1 should be applied. The agent can be applied again after 15 days, for a total of 3 applications throughout the entire planting cycle.

[0118] After the third application of three-dimensional pest control, Tianan Shangyi Organic Farm achieved a control effect of over 70% against gray mold. The average yield of the demonstration group was 4,800 kg / mu, which was 6.7% higher than the 4,500 kg / mu yield of the control group. After deducting the input cost of 55 yuan / mu, the farm achieved an increase in income of 2,945 yuan.

[0119] Table 12 Statistics on the collection of space spore release

[0120] Example 3: The effect of "stereoimmunization" technology for controlling gray mold based on precise detection of Botrytis cinerea on lettuce. In January 2025, head lettuce (variety: Shooter 101) was planted in three greenhouses (50m long, 8m wide, and 3.5m high) of the Lv'ao Vegetable Professional Cooperative. The experiment was divided into a no-treatment group (greenhouse without any treatment), a control group (greenhouse with traditional treatment), and a demonstration group (greenhouse with "three-dimensional immunity" treatment), as detailed below: Untreated group: Seeds of Shooter 101 lettuce were planted in the greenhouse of Lv'ao Vegetable Professional Cooperative, with a plant spacing of 40cm. The temperature, light and duration were set at 25℃ and 200 µmol / m² light. 2 s 1 16 hours, 16℃ in darkness. Disease index was measured on the plants. The disease index was 19.2 on the 15th day after planting (February 27, 2025) and 25.6 on the 30th day after planting (March 6, 2025).

[0121] Control group: Seeds of Shooter 101 lettuce were planted in the greenhouse of the Lv'ao Vegetable Professional Cooperative, with a plant spacing of 40cm. The remaining plants were planted and tested according to the control group method in Section IV of Example 1. When disease symptoms appeared in the field on the 15th day after planting, control measures were taken according to the control group treatment method in Section III of Example 1. On the 15th day after planting (February 27, 2025), the disease index was 8.2, with a control efficacy of 57.3% compared to the untreated group; on the 30th day after planting (March 6, 2025), the disease index was 13.1, with a control efficacy of 48.8% compared to the untreated group. Demonstration group: Seeds were treated in accordance with step 2.1 of section IV of Example 1. The seeds in the demonstration group had no Ct value, indicating that the seeds in the demonstration group did not contain Botrytis cinerea or contained very low levels of it, and therefore did not require disinfection.

[0122] Following the method described in step 2.2 of section IV of Example 1, the mathematical prediction model for Botrytis cinerea spore release was applied. On the 7th day after planting, the predicted spore count in the greenhouse air was 516 spores / m³. 3 The actual measured value was 504 spores / m³. 3 On the 15th day after planting, the measured concentration of *Botrytis cinerea* spores in the greenhouse air was 1215 spores / m³. 3 (More than 1000 spores / m) 3 When the disease threshold is reached, apply the following biological agent using the powdering method at 3 PM: Evenly spray the biological micro-powder into the greenhouse space using a powdering machine. The application rate for the greenhouse space is 200g / 667m². 3This enables comprehensive prevention and control, reducing the concentration of pathogens throughout the environment.

[0123] The spatially collected samples, collected according to the method described in Section IV of Example 1, were tested using the qPCR method described in Example 1. On day 15 after planting (February 27, 2025), the disease index was 3.5, with a control efficacy of 81.8% compared to the untreated group; on day 30 after planting (March 6, 2025), the disease index was 4.1, with a control efficacy of 84.0% compared to the untreated group.

[0124] The results showed that the "stereoimmunization" technology achieved a prevention and control effect of over 80%, which is far higher than that of traditional prevention and control treatments.

[0125] Table 13. The effect of "stereoimmunization" technology in controlling gray mold in lettuce.

[0126] Example 4: The effect of "stereoimmunization" technology for controlling gray mold based on precise detection of Botrytis cinerea on cucumbers. In March 2025, cucumbers (variety: Zhongnong 6) and tomatoes were planted in three greenhouses (70m long, 10m wide, and 3.5m high) of Zhongshu Biotechnology (Shouguang) Co., Ltd. The experiment was divided into a non-control group (greenhouse without any control measures), a control group (greenhouse with traditional control treatment), and a demonstration group (greenhouse with "three-dimensional immunity" control). The details are as follows: Untreated group: Seeds of cucumber variety Zhongnong 6 were planted in the greenhouse of Zhongshu Biotechnology (Shouguang) Co., Ltd., with a plant spacing of 30cm. The temperature, light and duration were set at 25℃ and 200 µmol / m² light. 2 s 1 16h, 16℃ in darkness. Disease index was measured on the plants. The disease index was 22.9 on the 15th day after planting (March 10, 2025) and 30.0 on the 30th day after planting (March 25, 2025).

[0127] Control group: Seeds of cucumber variety Zhongnong 6 were planted in the greenhouse of Zhongshu Biotechnology (Shouguang) Co., Ltd., with a plant spacing of 30cm. Other plants were planted and tested according to the control group method in Section IV of Example 1. When disease symptoms appeared in the field on the 15th day after planting, control measures were taken according to the control group treatment method in Section III of Example 1. On the 15th day after planting (March 10, 2025), the disease index was 10.8, with a control efficacy of 52.8% compared to the untreated group; on the 30th day after planting (March 25, 2025), the disease index was 12.6, with a control efficacy of 58.3% compared to the untreated group.

[0128] Demonstration group: Seeds were treated in accordance with step 2.1 of section IV of Example 1. The seeds in the demonstration group had no Ct value, indicating that the seeds in the demonstration group did not contain Botrytis cinerea or contained very low levels of it, and therefore did not require disinfection.

[0129] Following the method described in step 2.2 of section IV of Example 1, the mathematical prediction model for Botrytis cinerea spore release was applied. On the 7th day after planting, the predicted spore count in the greenhouse air was 311 spores / m³. 3 The actual measured value was 320 spores / m³. 3 On the 15th day after planting, the measured concentration of *Botrytis cinerea* spores in the greenhouse air was 1235 spores / m³. 3 (More than 1000 spores / m) 3 When the disease threshold is reached, apply the following biological agent using the powdering method at 3 PM: Evenly spray the biological micro-powder into the greenhouse space using a powdering machine. The application rate for the greenhouse space is 200g / 667m². 3 This enables comprehensive prevention and control, reducing the concentration of pathogens throughout the environment.

[0130] The spatially collected samples, collected according to the method described in Section IV of Example 1, were tested using the qPCR method described in Example 1. On day 15 post-planting (March 10, 2025), the disease index was 5.6, with a control efficacy of 75.5% compared to the untreated group; on day 30 post-planting (March 25, 2025), the disease index was 6.4, with a control efficacy of 78.8% compared to the untreated group.

[0131] The results showed that the "stereoimmunization" technology achieved a prevention and control effect of over 75%, which is far higher than that of traditional prevention and control treatments.

[0132] Table 14. The effect of "stereoimmunization" technology on the prevention and control of cucumber gray mold.

[0133] The present invention has been described in detail above. Those skilled in the art will recognize that the invention can be practiced in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from its spirit and scope, and without requiring unnecessary experiments. While specific embodiments have been provided, it should be understood that further modifications can be made to the invention. In summary, according to the principles of the invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein.

Claims

1. A method for controlling gray mold, characterized in that, The method includes sowing seeds in a planting environment to obtain plants. During the planting process, the air in the planting environment is monitored using a Botrytis cinerea spore release prediction model to determine whether pesticide application is necessary. The model is: S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 S represents the spore content, measured in spores per m³. 3 T represents the temperature of the environment under test, in °C; RH represents the relative humidity of the environment under test, in °C.

2. The method according to claim 1, characterized in that, The method also includes a step of detecting Botrytis cinerea in the seeds to be planted and the environment of the planting greenhouse before sowing.

3. The method according to claim 1 or 2, characterized in that, The method further includes a step of detecting Botrytis cinerea in the plants and the planting environment as described in claim 1 after sowing.

4. The method according to any one of claims 1-3, characterized in that, The detection of Botrytis cinerea includes using qPCR to detect the spore content of Botrytis cinerea in the plant and the surrounding environment.

5. The method according to claim 4, characterized in that, The qPC is performed using primer pair BC27f / BC27r, including upstream primer BC27f and downstream primer BC27r; BC27f is a single-stranded DNA molecule with nucleotide sequence SEQ ID NO: 1; BC27r is a single-stranded DNA molecule with nucleotide sequence SEQ ID NO:

2.

6. The method according to any one of claims 1-5, characterized in that, The plant is any one of the following: A1) Dicotyledonous plants; A2) Monocotyledons; A3) Magnolia class plants; A4) Plants belonging to the Solanaceae, Asteraceae, or Cucurbitaceae families; A5) Plants of the genera *Solanum*, *Lactuca*, or *Cucumis*; A6) Tomatoes, lettuce, or cucumbers.

7. The method according to any one of claims 1-5, characterized in that, The seeds to be planted are any of the following: B1) Seeds of dicotyledonous plants; B2) Seeds of monocotyledonous plants; B3) Seeds of Magnolia species; B4) Seeds of plants belonging to the Solanaceae, Asteraceae, or Cucurbitaceae families; B5) Seeds of plants in the genera *Solanum*, *Lactuca*, or *Cucumis*; B6) Tomato, lettuce, or cucumber seeds.

8. A method for predicting the spore content of *Botrytis cinerea* in a test environment, characterized in that, The method includes using a model to predict the spore content in the environment under test, wherein the model is: S = 2366 + 58RH - 96T + 2.6RH × T + 1.2RH 2 -1.4T 2 S represents the spore content, measured in spores per m³. 3 T represents the temperature of the environment under test, in degrees Celsius; RH represents the relative humidity of the environment under test, in degrees Celsius.

9. The application of the method according to claim 8 in the prevention and control of gray mold.

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