Crop disease prevention and control method and system for improving pesticide use efficiency

Abstract

The invention relates to the technical field of crop disease prevention and control, in particular to a crop disease prevention and control method and system for improving pesticide use efficiency, and the method comprises the steps that soil samples and plant samples are collected, and the soil samples and the plant samples come from different growth areas and environments and different growth stages; extracting microbiome in the soil sample and the plant sample, and extracting DNA of microorganisms; performing high-throughput metagenome sequencing on the total DNA of the microorganisms to obtain sequencing data; according to the sequencing data, identifying pathogenic bacteria in the microbial community and drug-resistant genes thereof by using a bioinformatics analysis method; constructing a phylogenetic tree of the microflora, and analyzing succession relationship of pathogenic bacteria; analyzing pesticide resistance characteristics and variation trend of pathogens; according to analysis results of the microbial development trees and the drug-resistant genes, disease prevention and control suggestions are generated, selection and use of pesticides in the disease prevention and control process are guided, the pesticide use amount is greatly reduced, and the disease prevention and control efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crop disease prevention and control, and more specifically, to a crop disease prevention and control method and a system thereof for improving the efficiency of pesticide use. Background Art

[0002] In recent years, with the intensification and scale of agricultural production, the importance of crop disease prevention and control in ensuring food security and sustainable agricultural development has become increasingly prominent. Traditional crop disease prevention and control methods mainly rely on fixed formulas and regular application of pesticides. Although this method can control the spread of diseases to a certain extent, it also has many problems.

[0003] First, traditional methods lack precision. Because they do not take into account the actual drug resistance of pathogens, they often lead to excessive or insufficient use of pesticides. Excessive use not only increases production costs, but also causes serious environmental pollution; while insufficient use may lead to poor prevention and control effects and make the disease out of control.

[0004] Secondly, this one-size-fits-all approach to pesticide application ignores the actual needs of crops in different plots and at different growth stages, and fails to provide personalized prevention and control measures for specific situations. This not only reduces the efficiency of pesticide use, but may also accelerate the development of pathogen resistance due to improper use.

[0005] Furthermore, traditional methods lack dynamic monitoring of changes in pathogen resistance. Over time, pathogens may develop new resistance mechanisms, and fixed medication regimens cannot respond to such changes in a timely manner, resulting in a gradual decline in prevention and control effects.

[0006] In addition, some existing improved methods, such as molecular detection technology based on a single or a few genes, have improved the accuracy of detection to a certain extent, but they still cannot fully reflect the complexity and dynamic changes of microbial communities. These methods can often only detect known drug-resistant genes, and it is difficult to detect and respond to emerging drug-resistant mechanisms in a timely manner.

[0007] Finally, current crop disease prevention and control generally lacks an in-depth understanding of the overall structure and function of the microbial community. The detection and prevention of a single pathogen cannot reflect the complexity of the ecosystem and ignores the impact of interactions between microorganisms on the occurrence and development of diseases.

[0008] In view of the above problems, there is an urgent need for a crop disease prevention and control method and system that can comprehensively, dynamically and accurately monitor the drug resistance of pathogens and guide the rational use of pesticides accordingly. The present invention is an innovative solution to this demand. Summary of the invention

[0009] The present invention proposes a crop disease prevention and control method and system for improving the efficiency of pesticide use, aiming to solve the technical problems of traditional prevention and control methods, such as insufficient accuracy, inability to dynamically monitor changes in drug resistance, and difficulty in dealing with emerging drug resistance mechanisms. By introducing microbial metagenomic analysis technology, the present invention achieves comprehensive detection and accurate prediction of drug resistance of crop pathogens, thereby guiding the rational use of pesticides.

[0010] The present invention provides a crop disease prevention and control method for improving the efficiency of pesticide use, comprising:

[0011] Sample acquisition steps include:

[0012] Collecting soil samples and plant samples, wherein the soil samples and plant samples come from different environments, different growth stages and different growth areas;

[0013] Extracting total microbial DNA from the soil samples and plant samples;

[0014] Sequencing analysis steps include:

[0015] Based on the total DNA, high-throughput sequencing is performed to obtain sequencing data;

[0016] Based on the sequencing data, using bioinformatics analysis methods to identify pathogens and their drug resistance genes;

[0017] Based on microbial metagenomic data, construct a microbial phylogenetic tree and analyze the succession relationship of pathogens;

[0018] Analyze the pesticide resistance characteristics and changing trends of pathogens based on the identified resistance genes;

[0019] Output steps include:

[0020] According to the analysis results of the microbial development tree and the drug-resistant gene, a disease prevention and control suggestion is generated, wherein the disease prevention and control suggestion includes the selection of pesticide types, dosage and application frequency.

[0021] Preferably, the sample acquisition step specifically includes:

[0022] Multiple sampling points were randomly selected within the predetermined plot;

[0023] At each sampling point, surface soil and deep soil were collected;

[0024] Collect leaves, stems and roots of healthy plants and plants suspected of being diseased;

[0025] Use sterile tools to collect samples and store them in sterile sampling bags.

[0026] Preferably, the high-throughput sequencing in the sequencing analysis specifically includes:

[0027] Construct a sequencing library using a DNA library construction kit;

[0028] Sequencing was performed using paired-end sequencing technology;

[0029] Generate raw sequencing data.

[0030] Preferably, the bioinformatics analysis method in the sequencing analysis specifically includes:

[0031] FASTQC software was used to perform quality control on raw sequencing data;

[0032] Use cutadapt software to remove adapter sequences;

[0033] Use LEGO software to classify microorganisms at the species level and calculate the abundance of drug-resistant genes;

[0034] GO analysis was used to annotate gene functions.

[0035] Preferably, the sequencing analysis further comprises:

[0036] Calculating a drug resistance index based on the analysis results of the drug resistance gene;

[0037] The sensitivity of the pathogen to different types of pesticides is determined based on the drug resistance index.

[0038] Preferably, the process of constructing a microbial phylogenetic tree in the sequencing analysis comprises:

[0039] Select genes related to the microorganism from the obtained gene abundances using the RAST program;

[0040] Gene homology analysis was performed using the protein orthologs mapper program;

[0041] The phylogenetic tree was constructed based on homologous genes using the maximum likelihood method.

[0042] Preferably, the output step further comprises:

[0043] Generate a heat map of drug resistance gene distribution to intuitively display the distribution of drug resistance genes of different pathogens;

[0044] Generate resistance trend reports to predict possible future resistance issues.

[0045] Preferably, a verification step is also included:

[0046] Based on the pesticide use recommendations, pesticide application experiments were conducted in the test fields;

[0047] Regularly collecting soil and plant samples from the test field, and repeating the sample acquisition steps, sequencing analysis and output steps;

[0048] Compare the changes in the distribution of resistance genes before and after the experiment to evaluate the effectiveness of pesticide use recommendations.

[0049] As a preferred embodiment, the step of updating the database is also included:

[0050] storing the drug resistance gene analysis results, microbial development tree and pesticide use recommendations in a database;

[0051] Update the database regularly and establish a long-term monitoring system;

[0052] Optimize the pesticide use recommendation algorithm based on the updated database.

[0053] As a preference, it includes:

[0054] A sample collection module, used for collecting soil samples and plant samples;

[0055] A DNA extraction module, used to extract total DNA from the soil sample and the plant sample;

[0056] A sequencing module, used to perform high-throughput sequencing on the total DNA to obtain sequencing data;

[0057] Data analysis module for:

[0058] Based on the sequencing data, identifying the drug resistance gene of the pathogen;

[0059] Construct a microbial phylogenetic tree to analyze the evolutionary relationships and drug resistance trends of pathogens;

[0060] A decision support module, for generating pesticide use recommendations based on the analysis results of the drug-resistant genes and the microbial development tree;

[0061] A validation module, which is used to conduct pesticide application experiments in test fields and evaluate the effectiveness of pesticide application recommendations;

[0062] Database module, used to store and update the results of drug-resistant gene analysis, microbial development trees and pesticide use recommendations.

[0063] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0064] First, the present invention significantly improves the accuracy and efficiency of crop disease prevention and control. Through comprehensive microbial metagenomic analysis of soil and plant samples, this method can accurately identify and quantify various drug resistance genes, providing a scientific basis for the formulation of personalized medication plans. This not only improves the effect of disease prevention and control, but also significantly reduces the amount of pesticides used, reduces environmental pollution and production costs.

[0065] Secondly, the present invention realizes dynamic monitoring and prediction of pathogen resistance. Through regular sampling and analysis, combined with the constructed microbial development tree, this method can track the evolution trend of drug-resistant genes and promptly discover new drug-resistant mechanisms. This forward-looking monitoring enables pesticide use strategies to be adjusted at any time, effectively delaying the development of drug resistance.

[0066] Furthermore, the method of the present invention has wide applicability and scalability. Although rice bacterial blight is used as an example in the embodiment, the method is applicable to various crop diseases in principle. At the same time, with the continuous advancement of sequencing technology and bioinformatics analysis methods, the accuracy and efficiency of the method can be further improved.

[0067] In addition, the present invention promotes a deeper understanding of crop-pathogen-microbial community interactions. By comprehensively analyzing the structure and function of microbial communities, this method provides new perspectives and data support for studying disease occurrence mechanisms and ecological prevention and control strategies.

[0068] Finally, the implementation of the present invention helps to promote the development of agricultural production towards precision, intelligence and sustainability. By establishing a long-term monitoring system and a continuously optimized database, this method provides a scientific basis for formulating regional and long-term disease prevention and control strategies, which is of great significance to improving agricultural production efficiency and ensuring food security.

[0069] In summary, the present invention innovatively solves many technical problems faced by traditional crop disease prevention and control methods by integrating multidisciplinary technologies, and shows significant advantages in improving prevention and control effects, reducing environmental impacts, and delaying the development of drug resistance, providing a new, efficient, precise, and sustainable way of disease prevention and control for modern agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 The present invention is a flow chart of the method.

[0071] Figure 2 It is a logic block diagram of the data analysis module of the present invention.

[0072] Figure 3 It is a logic block diagram of the decision support module of the present invention. DETAILED DESCRIPTION

[0073] Please refer to Figure 1-3The present invention provides a crop disease prevention and control method and system for improving the efficiency of pesticide use. The method uses microbial metagenomic analysis technology to achieve accurate detection and prediction of crop pathogen resistance, thereby guiding the rational use of pesticides, improving prevention and control effects, and reducing environmental pollution risks.

[0074] Specifically, the method of the present invention comprises the following steps:

[0075] First, in the sample acquisition step, the method collects soil samples and plant samples. These samples come from different environments, different growth stages, and different growth areas to ensure the representativeness and diversity of the samples. For example, samples can be collected before sowing, in the seedling stage, in the flowering stage, and in the fruiting stage, respectively, while taking into account the differences in different locations in the field and the depth of the soil. Total microbial DNA is extracted from the soil samples and plant samples; based on the total DNA, high-throughput sequencing is performed to obtain sequencing data; based on the sequencing data, the pathogens and their drug-resistant genes are identified using bioinformatics analysis methods;

[0076] In sequencing analysis, this method first performs high-throughput sequencing on the extracted total microbial DNA. High-throughput sequencing technology can quickly and massively obtain genetic information in samples. Preferably, the Illumina sequencing platform can be used for double-end sequencing, the read length can be set to 150bp, and the sequencing depth is recommended to reach more than 10Gb to ensure the quality and coverage of the data.

[0077] After obtaining the sequencing data, the method uses bioinformatics analysis methods to identify the drug resistance genes of pathogens. This step is one of the cores of the present invention. Specifically, ARIBA (Antimicrobial Resistance Identification By Assembly) software can be used to identify and classify drug resistance genes. ARIBA software can simultaneously perform sequence assembly and drug resistance gene annotation, improving the accuracy and efficiency of the analysis.

[0078] After identifying the drug-resistant genes, this method constructs a microbial phylogenetic tree based on these data to analyze the evolutionary relationship and drug resistance trend of pathogens. In the process of constructing the phylogenetic tree, RAxML (Randomized Axelerated Maximum Likelihood) software can be used to construct a phylogenetic tree using the maximum likelihood method. This step helps to understand the structure and dynamic changes of pathogen communities and provides an important basis for formulating prevention and control strategies.

[0079] In the output step, the method generates pesticide use recommendations based on the analysis results of resistance genes and the microbial development tree. These recommendations include the type of pesticide, dosage, and application time. For example, if a significant increase in the abundance of β-lactam antibiotic resistance genes is detected, it may be necessary to reduce the use of related pesticides and choose pesticides with other mechanisms of action.

[0080] In one embodiment of the method of the present invention, the sample acquisition step can be further refined. Specifically, multiple sampling points can be randomly selected within a predetermined plot, and the number of each sampling point can be determined according to the size of the plot. It is usually more appropriate to select 3-5 sampling points per hectare. At each sampling point, the surface soil (0-20cm) and deep soil (20-40cm) are collected respectively. At the same time, leaves, stems and roots of healthy plants and suspected diseased plants are collected. During the sampling process, sterile tools such as sterilized shovels and scissors are used for operation, and the samples are immediately placed in sterilized ziplock bags for storage and transportation at 4°C.

[0081] In sequencing analysis, the specific process of high-throughput sequencing is also worth describing in detail. First, a DNA library construction kit (such as the Illumina TruSeq DNA PCR-Free Library Preparation Kit) is used to construct a sequencing library. The library construction process includes steps such as DNA fragmentation, end repair, A-tail addition, adapter ligation, and PCR amplification. Then, sequencing is performed using double-end sequencing technology, usually using the Illumina NovaSeq 6000 system, with a read length set to 2×150bp. After sequencing is completed, raw sequencing data is generated, usually in fastq format.

[0082] The method of the present invention achieves accurate analysis and prediction of the drug resistance of crop pathogens by comprehensively applying multidisciplinary technologies. This method not only improves the pertinence and effectiveness of pesticide use, but also helps to reduce unnecessary pesticide use, thereby reducing environmental pollution risks and promoting sustainable agricultural development. At the same time, through long-term monitoring and data accumulation, this method can also provide valuable data support for studying the evolution of pathogen resistance and promote scientific research progress in related fields.

[0083] In a preferred embodiment of the present invention, the bioinformatics analysis method in sequencing analysis is further refined to improve the accuracy and efficiency of data processing. Specifically, the method first uses FASTQC software to perform quality control on the original sequencing data. FASTQC can generate a series of charts and statistical data to help researchers quickly evaluate the quality of sequencing data. For example, it can check the parameters such as sequencing quality score, GC content distribution, sequence repetition level of each base. Generally, it is expected that the average quality score of each base is not less than 30 (i.e. Q30), which means that the sequencing error rate is less than 0.1%. The impact of low-quality data on subsequent analysis can be effectively reduced, and the reliability of overall data is improved. FASTQC can quickly identify potential problems, such as GC content deviation, sequence repetition, etc., to help researchers adjust experimental design or sequencing parameters in time to avoid wasting time and resources.

[0084] After quality control is completed, the method uses cutadapt software to remove the adapter sequence. During the sequencing process, the adapter sequence may be read by the sequencer, and these sequences need to be removed to avoid affecting subsequent analysis. Cutadapt software can efficiently identify and remove these adapter sequences. Preferably, the minimum overlap length can be set to 3bp and the allowed mismatch rate can be 10% to balance the removal efficiency and data retention rate.

[0085] cutadapt identifies and removes these sequences by aligning sequencing reads with known adapter sequences. It uses a dynamic programming algorithm to find the best match and cuts according to the set minimum overlap length and maximum mismatch rate. This algorithm is able to retain valid data to the maximum extent while maintaining high removal efficiency. By setting a reasonable minimum overlap length (3bp) and mismatch rate (10%), it is possible to avoid mistakenly cutting valid data while removing adapter sequences, ensuring the accuracy of subsequent analysis. The presence of adapter sequences may interfere with subsequent gene annotation and functional analysis. By efficiently removing adapter sequences, the quality of downstream analysis can be significantly improved and false positive results can be reduced.

[0086] Next, the method of the present invention uses LEGO (LEt's GO) software to perform microbial species-level classification and resistance gene abundance calculation. LEGO software is a tool specifically for metagenomic data analysis. It uses an improved nearest neighbor classification algorithm to quickly and accurately classify microorganisms. When classifying species, the similarity threshold can be set to 97%, which is a common standard for distinguishing bacterial species. For the calculation of resistance gene abundance, LEGO uses a k-mer-based method that can effectively process large-scale data.

[0087] The LEGO software uses an improved nearest neighbor classification algorithm to calculate the similarity between sequencing reads and known sequences in a reference database. The similarity threshold is usually set to 97%, which is a common standard for distinguishing bacterial species. For the calculation of resistance gene abundance, LEGO uses a k-mer-based method to estimate the relative abundance of resistance genes by counting the frequency of occurrence of each k-mer in different samples. LEGO's improved algorithm can quickly and accurately classify microorganisms on large-scale data sets, especially when dealing with complex metagenomic data, which can significantly improve classification efficiency. The k-mer-based method can effectively process large-scale data, provide accurate estimates of resistance gene abundance, and help researchers understand the distribution of resistance genes in different samples.

[0088] Finally, this method uses GO (Gene Ontology) analysis to annotate gene functions. GO analysis can describe gene functions from three aspects: molecular function, biological process, and cellular component. When performing GO analysis, commonly used tools such as Blast2GO or DAVID can be used. Preferably, the E-value threshold can be set to 1e -5 GO analysis can describe gene functions from three aspects: molecular function, biological process and cellular component, helping researchers to understand the mechanism of action of genes more comprehensively and guiding subsequent experimental design and data analysis.

[0089] In another embodiment of the present invention, the sequencing analysis further includes calculating a drug resistance index based on the analysis results of the drug resistance gene. This step provides a quantitative indicator for evaluating the overall drug resistance level of the pathogen. The drug resistance index (RI) can be calculated by the following formula:

[0090]

[0091] Where n is the number of drug-resistant gene types detected, is the weight of the ith resistance gene (which can be determined based on the importance of the gene). For example, some resistance genes may have stronger resistance to specific pesticides and should be given higher weights. By standardizing the abundance of each resistance gene to a ratio relative to the highest abundance, the impact of absolute abundance differences between different samples can be eliminated, making the RI more comparable. i is the abundance of the ith drug resistance gene, A max It is the most abundant value among all drug resistance genes.

[0092] After calculating the resistance index, this method determines the sensitivity of pathogens to different types of pesticides based on this index. For example, the RI value can be divided into the following intervals: RI < 0.3 is low resistance, 0.3 ≤ RI < 0.6 is medium resistance, and RI ≥ 0.6 is high resistance. This classification can intuitively guide the selection and use of pesticides.

[0093] In the process of constructing the microbial phylogenetic tree, the method of the present invention uses a series of bioinformatics tools and algorithms. First, the RAST (Rapid Annotation using Subsystem Technology) program is used to select genes related to microorganisms from the obtained gene abundance. RAST is an automated genome annotation system that can quickly identify and annotate genes, especially genes related to metabolism.

[0094] Subsequently, this method uses the protein orthologs mapper program to perform gene homology analysis. This step aims to identify homologous genes between different species and lay the foundation for constructing a phylogenetic tree. When performing homology analysis, the E-value threshold can be set to 1e -10 , to ensure highly reliable homology relationships.

[0095] Finally, based on homologous genes, this method uses the maximum likelihood method to construct a phylogenetic tree. The maximum likelihood method is a statistical method that can infer the most likely evolutionary history based on observed data. In practice, this process can be implemented using RAxML software. RAxML uses a fast algorithm that can handle large-scale data sets. Preferably, the GTR+Γ model can be selected as the nucleotide substitution model, which is a widely used complex model that can better describe the evolution of DNA sequences.

[0096] Maximum likelihood is a statistical method that seeks the evolutionary history that best explains the observed data. RAxML software uses a fast algorithm to calculate the maximum likelihood value and select the most likely evolutionary tree. The GTR+Γ model is a widely used complex model that can better describe the evolutionary process of DNA sequences, especially taking into account the differences in evolutionary rates at different sites (modeled by Gamma distribution). By constructing a phylogenetic tree, the evolutionary relationship between different pathogens can be revealed, helping researchers understand the transmission path of drug resistance among different species. Based on the results of the developmental tree, more targeted prevention and control strategies can be formulated, such as selecting pesticides that are effective against specific pathogens, or developing new disease-resistant varieties.

[0097] The output step of the method of the present invention not only generates pesticide use recommendations, but also includes generating a heat map of the distribution of drug-resistant genes and a resistance trend report. The heat map of the distribution of drug-resistant genes intuitively displays the distribution of drug-resistant genes of different pathogens, which helps researchers quickly identify the main types and distribution patterns of drug-resistant genes. When generating a heat map, the pheatmap package of the R language can be used, an appropriate color scheme can be selected (such as from blue to red to indicate abundance from low to high), and hierarchical clustering can be performed to reveal the relationship between samples and genes.

[0098] The drug resistance trend report predicts drug resistance problems that may arise in the future. This report uses time series analysis methods to make predictions based on historical data and current analysis results. For example, the ARIMA (Autoregressive Integrated Moving Average) model can be used for trend analysis. When selecting model parameters, the AIC (Akaike Information Criterion) can be used to balance the complexity and goodness of fit of the model.

[0099] Through these detailed analysis and visualization steps, the method of the present invention not only provides a comprehensive picture of the current resistance status, but also predicts future trends, providing a strong scientific basis for the formulation and adjustment of pesticide use strategies. This data-driven approach greatly improves the accuracy and foresight of crop disease prevention and control, and is expected to significantly improve the efficiency of pesticide use, reduce unnecessary pesticide use, thereby reducing environmental pressure and promoting sustainable agricultural development.

[0100] The method of the present invention also includes a key verification step in practical application to ensure the validity and reliability of the pesticide use recommendation. In this verification step, firstly, based on the pesticide use recommendation obtained from the above analysis, a pesticide application experiment is carried out in the test field. The setting of the test field usually adopts a randomized block design to eliminate the influence of field environmental differences. Preferably, 3-5 repetitions can be set, and the area of ​​each plot is not less than 20 square meters to ensure the statistical significance of the test results.

[0101] During the trial, this method requires regular collection of soil and plant samples from the test field. The sampling frequency can be determined based on the crop growth cycle and the occurrence of diseases. It is usually more appropriate to sample during the main growth periods (such as seedling stage, flowering stage, and fruit development stage). The collected samples are then processed in the same way as the initial analysis, i.e., the aforementioned sample acquisition steps, sequencing analysis, and output steps are repeated. This periodic sampling and analysis allows researchers to dynamically monitor changes in drug-resistant genes.

[0102] The key to validation is to compare the changes in the distribution of resistance genes before and after the experiment to evaluate the effectiveness of the pesticide use recommendation. This comparison can be quantified by calculating the rate of change in the abundance of resistance genes:

[0103]

[0104] Preferably, a change rate of less than -20% can be considered as a significant decrease, between -20% and 20% as stability, and greater than 20% as a significant increase. This quantitative analysis can objectively evaluate the effect of pesticide use strategies and provide a basis for further optimization.

[0105] Another innovative point of the present invention is to establish a dynamically updated database system. This database update step is crucial to the long-term effectiveness of the method. First, the method stores key information such as drug resistance gene analysis results, microbial development trees and pesticide use recommendations in a specially designed database. This database uses a relational database management system (such as PostgreSQL) to ensure data integrity and consistency.

[0106] Regular updates of the database are key to keeping the system up to date. Preferably, a comprehensive update can be performed quarterly, and an automatic update mechanism can be set up to update relevant fields instantly when new analysis results are generated. This update mechanism ensures that the database always contains the latest drug resistance information and prevention and control recommendations.

[0107] By establishing such a long-term monitoring system, this method can not only track the trend of resistance in a single field or region, but also analyze the evolution of resistance at a larger temporal and spatial scale. For example, time series analysis methods, such as seasonal ARIMA models, can be used to predict future resistance trends. Such predictions are of great guiding significance for the formulation of long-term disease prevention and control strategies.

[0108] Based on the continuously updated database, the method of the present invention also includes continuous optimization of the pesticide use recommendation algorithm. This optimization process uses machine learning methods, such as random forests or gradient boosted decision trees, to continuously learn new data patterns and improve the accuracy of predictions. After each optimization, cross-validation is performed to ensure that the new algorithm maintains good performance in various situations.

[0109] Finally, the present invention also provides a crop disease prevention and control system corresponding to the above method. The system is composed of multiple functional modules, each module is responsible for a specific step in the method.

[0110] The sample collection module 1 is responsible for on-site sampling. The module is equipped with special sampling tools, such as soil samplers and plant tissue samplers, and has a built-in GPS positioning system to ensure accurate recording of sampling locations. Preferably, the module also includes a mobile application for recording environmental parameters (such as temperature, humidity) and plant conditions during sampling.

[0111] The DNA extraction module 2 uses automated extraction equipment, such as a magnetic bead-based nucleic acid extractor, which can efficiently extract total DNA from various samples. The design of this module takes into account the characteristics of different sample types and can automatically adjust the extraction parameters according to the properties of the sample to ensure the quality and yield of DNA.

[0112] Sequencing Module 3 uses the latest high-throughput sequencing technology, such as the Illumina NovaSeq system, which can complete the sequencing of a large number of samples in a short period of time. The module also includes an automated library preparation system, which greatly reduces the need for manual operation.

[0113] The data analysis module 4 is the core of the system, which integrates a variety of bioinformatics analysis tools and algorithms. This module adopts a distributed computing architecture and can quickly process massive sequencing data. Preferably, this module also has machine learning capabilities and can continuously optimize the analysis algorithm as data accumulates.

[0114] In data analysis module 4, a novel dynamic resistance evaluation and microbial community structure analysis (DREMCSA) method was introduced.

[0115] First, construct a drug resistance gene abundance matrix R, where r ij Represents the relative abundance of the jth drug resistance gene in the ith sample.

[0116]

[0117] Wherein, m is the number of samples, and n is the number of drug-resistant genes detected. The LEGO software in the preferred embodiment can be used as a specific implementation tool for constructing the matrix.

[0118] The drug resistance gene abundance matrix R is an m×n matrix. The construction of this matrix is ​​based on metagenomic sequencing data, and its abundance is determined by calculating the number of sequence reads of different drug resistance genes in each sample.

[0119] By constructing a resistance gene abundance matrix, the distribution of resistance genes in different samples can be accurately identified. This helps to understand which resistance genes are more common in a specific environment, thus providing a basis for subsequent analysis and decision-making.

[0120] Each row in the matrix represents a sample, and each column represents a resistance gene. By performing statistical analysis on the matrix (such as principal component analysis and cluster analysis), the differences in microbial community structure between different samples can be evaluated and potential trends in drug resistance can be revealed.

[0121] The matrix can be displayed intuitively in the form of heat maps, etc., to help researchers quickly identify the main types and distribution patterns of drug-resistant genes and guide subsequent experimental design and data analysis.

[0122] Based on the drug resistance gene abundance matrix, a topological drug resistance network is constructed. The association strength s between drug resistance genes is defined ij for:

[0123]

[0124] in, and are the average abundances of the i-th and j-th drug-resistant genes, respectively.

[0125] The strength of association between drug resistance genes ij , which is the Pearson correlation coefficient. It measures the co-expression relationship between two drug-resistant genes in different samples. The numerator represents the covariance of the two genes in all samples, and the denominator is the product of their respective standard deviations, which ensures the standardization of the results. The Pearson correlation coefficient can be used to evaluate the co-expression relationship between genes, while the phylogenetic tree can reveal the evolutionary relationship between genes. The combination of the two can provide a more comprehensive drug-resistant gene network analysis.

[0126] By constructing a topological resistance network, the complex interactions between resistance genes can be captured. For example, some resistance genes may work synergistically to lead to stronger resistance; while other genes may inhibit each other. This network structure helps to understand the propagation mechanism of resistance. In the network, some nodes (i.e., resistance genes) may have high centrality or betweenness, indicating that they play a key role in the spread of resistance. Identifying these key nodes can help researchers develop targeted prevention and control strategies to reduce the spread of resistance. The structure of the resistance network will change with time and environment. By regularly updating the network, the interactions between resistance genes can be monitored in real time, and prevention and control measures can be adjusted in time to prevent the further development of resistance.

[0127] Then, using the concept of group theory, a dynamic drug resistance index DRI is defined. First, the topological network is regarded as a group G, and the group operation is defined as the synergy between drug resistance genes. Let φ be the homomorphic mapping from group G to real number set R, representing the drug resistance strength. Then DRI is defined as:

[0128] DRI=∑ g∈G φ(g)·∏ h∈N(g) (1+s gh ),

[0129] Among them, N(g) is the set of adjacent nodes of drug-resistant gene g in the topological network, s gh is the strength of the association between g and h.

[0130] This formula defines a dynamic drug resistance index DRI, which is constructed based on the concept of group theory. Group G represents the set of nodes in the drug resistance gene network, φ(g) is a homomorphic mapping, which represents the resistance strength of each drug resistance gene. N(g) is the set of adjacent nodes of node g, s gh is the strength of the association between nodes g and h. The product term ∏ in the formula h∈N(g) (1+s gh ) indicates the synergistic effect among drug-resistant genes.

[0131] By introducing the idea of ​​group theory, DRI not only considers the abundance of individual resistance genes, but also the interactions between them. This enables DRI to more comprehensively reflect the resistance level in the entire system and provides a scientific basis for the formulation of comprehensive prevention and control strategies. Over time, the structure of the resistance gene network may change, resulting in fluctuations in DRI. By monitoring the changing trend of DRI over a long period of time, it is possible to predict possible resistance problems in the future and take countermeasures in advance. DRI can be used as an indicator to assess the resistance risk of different fields or regions. According to the level of DRI, pesticides and other prevention and control resources are reasonably allocated to avoid unnecessary waste and improve the efficiency of prevention and control. DRI pays more attention to the synergistic effect between resistance genes, while RI focuses on the abundance and importance of individual resistance genes. The combination of these two indices can provide a more comprehensive assessment of resistance.

[0132] Next, the characteristic function χ(t) of the microbial community structure is defined as:

[0133]

[0134] Where F(x) is the abundance distribution function of microbial species and t is a complex parameter. By analyzing the properties of χ(t), the diversity and uniformity of the microbial community can be evaluated.

[0135] This formula defines the characteristic function χ(t) of the microbial community structure, which is the Fourier transform of the microbial species abundance distribution function F(x). The characteristic function is a commonly used probability theory tool that can describe the distribution characteristics of random variables from the perspective of frequency domain. By analyzing the properties of χ(t), the diversity and uniformity of the microbial community can be evaluated.

[0136] The characteristic function χ(t) can be used to assess the diversity of microbial communities. For example, if χ(t) shows a large amplitude in the high frequency band, it means that there are more rare species in the community; if it shows a large amplitude in the low frequency band, it means that the dominant species are more concentrated in the community. This assessment method can help researchers understand the health of farmland ecosystems. By comparing the characteristic functions at different time points, the dynamic changes of microbial communities can be analyzed. For example, if the characteristic function remains stable over a period of time, it means that the community structure is relatively stable; conversely, if the characteristic function changes significantly, it may indicate that the ecosystem has been disturbed by the outside world and corresponding protective measures need to be taken. The structure of a microbial community is closely related to its function. By analyzing the characteristic function, the functional roles of different microorganisms in the community can be inferred, and then their effects on crop growth and disease control can be predicted. For example, some microorganisms may have the effect of promoting plant growth, while others may enhance the disease resistance of crops. The characteristic function can be used to assess the diversity and evenness of the community, while heat maps and cluster analysis can intuitively display the changes in community structure. The combination of the two can provide a more comprehensive assessment of microbial communities.

[0137] Finally, a drug resistance evolution prediction model based on number theory is introduced. The drug resistance evolution function E(n) is defined as:

[0138] E(n)=∑ d|n μ(d)·DRI n / d ,

[0139] Where n is the time step, μ(d) is the Möbius function, d|n means d divides n, and DRI is the dynamic resistance index calculated previously.

[0140] This model utilizes the Möbius inversion formula in number theory and can capture the periodicity and mutation characteristics of drug resistance evolution.

[0141] This formula defines the drug resistance evolution function E(n), which is constructed based on the Möbius inversion formula in number theory. The Möbius function μ(d) is an important number theory function used to deal with integer divisibility. The summation term ∑ d|n μ(d)·DRI n / d It represents the weighted average value of drug resistance index DRI at time step n. The Möbius inversion formula can capture the periodicity and mutation characteristics of drug resistance evolution.

[0142] By introducing the Mobius function in number theory, E(n) can capture the long-term trend of resistance evolution. For example, some resistance genes may fluctuate periodically at specific time intervals, while other genes may undergo mutations. This predictive ability helps to formulate long-term disease control strategies and prevent potential risks in advance. The Mobius inversion formula is particularly suitable for dealing with discrete events (such as gene mutations). When the resistance index DRI mutates, the value of E(n) will change significantly. By monitoring the changes in E(n), resistance mutation events can be discovered in time and emergency response measures can be taken. According to the prediction results of E(n), the future amount and time of pesticide use can be reasonably planned. For example, if it is predicted that resistance will increase significantly within a certain period of time, more alternative pesticides can be reserved in advance to avoid control failures due to increased resistance. The Mobius inversion formula can capture the periodicity and mutation characteristics of resistance evolution, while the ARIMA model is more suitable for dealing with long-term trend predictions of time series data. Combining these two methods can provide more accurate resistance predictions.

[0143] The DREMCSA method achieves a comprehensive analysis from quantification of resistance gene abundance to prediction of resistance evolution. This method not only considers the abundance of individual resistance genes, but also captures the interactions between resistance genes through topological networks, quantifies the overall resistance level using group theory, and achieves in-depth analysis of microbial community structure and resistance evolution through characteristic functions and number theory methods.

[0144] The innovation of this comprehensive approach is mainly reflected in:

[0145] 1. Use topological networks to characterize the complex interactions between drug-resistant genes.

[0146] 2. Introducing the idea of ​​group theory, the drug-resistant gene network is regarded as an algebraic structure, which better describes the integrity of the system.

[0147] 3. The use of characteristic functions to analyze the structure of microbial communities provides a new method for diversity assessment.

[0148] 4. The drug resistance evolution model is constructed through the Möbius function in number theory, which can predict the long-term evolution trend of drug resistance.

[0149] The application of this method will significantly improve the accuracy and foresight of crop disease prevention and control, and provide a more reliable scientific basis for the formulation of pesticide use strategies.

[0150] Decision support module 5 generates specific recommendations on pesticide use based on data analysis results. This module adopts expert system design, combines drug resistance analysis results and agronomic knowledge base, and can provide personalized prevention and control solutions.

[0151] Verification module 6 is responsible for monitoring the situation of the test field, including the automated sampling system and data acquisition system. This module can monitor various indicators of the test field in real time and detect abnormal situations in time.

[0152] The database module 7 uses distributed storage technology to ensure the secure storage and fast access of large-scale data. The module also has data mining capabilities to discover valuable patterns and trends from historical data.

[0153] Through the collaborative work of these modules, the system of the present invention realizes the automation of the entire process from sample collection to decision support, greatly improving the efficiency and accuracy of crop disease prevention and control. The modular design of the system also makes future upgrades and expansions simple and easy, laying the foundation for the intelligent and precise agricultural production.

[0154] In order to verify the effectiveness of the crop disease prevention and control method and system proposed in the present invention, researchers conducted a field experiment lasting one growing season at an agricultural research base. The experiment selected the common rice bacterial blight as the research object. The disease is caused by the rice bacterial blight pathogen (Xanthomonas oryzae pv.oryzae) and is one of the main bacterial diseases in rice production.

[0155] Example 1 Disease prevention and control using the method of the present invention. First, soil and plant samples were collected before rice planting, in the tillering stage, and in the heading stage, respectively, for microbial metagenomic sequencing and analysis. Based on the analysis results, the researchers identified several major resistance genes, including qnrS (quinolone antibiotic resistance gene) and bla_TEM (β-lactam antibiotic resistance gene). According to the distribution and abundance of resistance genes, the system generated targeted pesticide use recommendations, including reducing the frequency of use of quinolone pesticides, increasing the proportion of biological agents, etc.

[0156] Comparative Example 1 adopts the traditional fixed formula application method, that is, spraying pesticides regularly according to the conventional recommended dosage without considering the changes in the drug resistance of pathogens.

[0157] Both experiments were repeated three times, with each plot measuring 50 square meters. During the experiment, researchers regularly observed and recorded the occurrence of diseases and measured yields during the rice maturity period. At the same time, samples were collected regularly throughout the growing season to monitor changes in drug-resistant genes.

[0158] The main test indicators and experimental methods are as follows:

[0159] 1. Disease incidence: Survey once every 7 days, randomly select 100 rice plants, record the number of diseased plants, and calculate the incidence.

[0160] 2. Disease index: The disease index is calculated on a scale of 0 to 9, with 0 being disease-free and 9 being the whole plant dead. Surveys are conducted every 7 days to calculate the disease index.

[0161] 3. Pesticide usage: Record the total amount of pesticide used throughout the growing season.

[0162] 4. Abundance of drug-resistant genes: The relative abundance of qnrS and bla_TEM genes was determined by real-time fluorescence quantitative PCR.

[0163] 5. Rice yield: Harvest at maturity and measure actual yield.

[0164] 6. Economic benefits: calculate the input-output ratio.

[0165] The experimental results are shown in the following table:

[0166] index Example 1 Comparative Example 1 Final disease incidence 12.3％ 18.7％ Final disease index 15.6 24.2 Pesticide usage (kg / ha) 2.8 4.5 Relative abundance of qnrS gene 0.85 2.37 Relative abundance of bla_TEM gene 1.12 2.85 Rice yield (t / ha) 7.8 7.2 Economic benefit (input-output ratio) 1:3.2 1:2.6

[0167] It can be seen from the experimental results that the method of the present invention shows obvious advantages in many aspects. First, in terms of disease control effect, the final disease incidence and disease index of Example 1 using the method of the present invention are significantly lower than those of Comparative Example 1, which are reduced by 34.2% and 35.5%, respectively. This shows that the precise pesticide application strategy of the present invention can more effectively inhibit the development of diseases.

[0168] Secondly, in terms of the amount of pesticide used, Example 1 reduced the amount by 37.8% compared with Comparative Example 1, which not only reduced the production cost, but also reduced the negative impact on the environment. More importantly, the relative abundance of drug-resistant genes in Example 1 was significantly lower than that in Comparative Example 1. The relative abundance of qnrS and bla_TEM genes decreased by 64.1% and 60.7%, respectively, which means that the method of the present invention can effectively inhibit the development of drug resistance and lay the foundation for long-term sustainable prevention and control.

[0169] In terms of yield, although Example 1 using the method of the present invention used less pesticides, its rice yield was 8.3% higher than that of Comparative Example 1. This may be due to more precise disease control that reduces the impact of diseases on rice growth, and may also be related to the improvement of ecosystem balance caused by reduced pesticide use.

[0170] Finally, from the perspective of economic benefits, the input-output ratio of Example 1 is 1:3.2, which is significantly higher than 1:2.6 of Comparative Example 1. This shows that the method of the present invention is not only technically advantageous, but also economically more attractive.

[0171] These results fully demonstrate the superiority of the crop disease control method based on microbial metagenomic analysis proposed in the present invention. This method achieves the precision and personalization of pesticide use by accurately identifying and monitoring drug-resistant genes, which not only improves the effect of disease control, but also significantly reduces the amount of pesticide used, inhibits the development of drug resistance, and increases crop yield and economic benefits. This method provides a new idea and effective way to solve the problems of low disease control efficiency, excessive use of pesticides, and increasingly serious drug resistance in current agricultural production.

[0172] It should be noted that the advantages of the method of the present invention may become more obvious over time. Long-term follow-up studies may find that the problem of drug resistance will become increasingly severe under traditional methods, while the method of the present invention may maintain a good control effect. Therefore, it is recommended to conduct longer-term observations in future studies to fully evaluate the long-term benefits of this method.

[0173] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preventing and controlling crop diseases for improving the efficiency of pesticide use, characterized in that: include: Sample acquisition steps include: Collecting soil samples and plant samples, wherein the soil samples and plant samples come from different environments, different growth stages and different growth areas; Extracting total microbial DNA from the soil samples and plant samples; Sequencing analysis steps include: Based on the total DNA, high-throughput sequencing is performed to obtain sequencing data; Based on the sequencing data, using bioinformatics analysis methods to identify pathogens and their drug resistance genes; Based on microbial metagenomic data, construct a microbial phylogenetic tree and analyze the succession relationship of pathogens; Analyze the pesticide resistance characteristics and changing trends of pathogens based on the identified resistance genes; Output steps include: According to the analysis results of the microbial development tree and the drug-resistant gene, a disease prevention and control suggestion is generated, wherein the disease prevention and control suggestion includes the selection of pesticide types, dosage and application frequency.

2. The method according to claim 1, characterized in that The sample acquisition step specifically includes: Multiple sampling points were randomly selected within the predetermined plot; At each sampling point, surface soil and deep soil were collected; Collect leaves, stems and roots of healthy plants and plants suspected of being diseased; Use sterile tools to collect samples and store them in sterile sampling bags.

3. The method according to claim 1, characterized in that The high-throughput sequencing in the sequencing analysis specifically includes: Construct a sequencing library using a DNA library construction kit; Sequencing was performed using paired-end sequencing technology; Generate raw sequencing data.

4. The method according to claim 1, characterized in that: The bioinformatics analysis method in the sequencing analysis specifically includes: The quality of raw sequencing data was controlled using FASTQC software; Use cutadapt software to remove adapter sequences; Use LEGO software to classify microorganisms at the species level and calculate the abundance of drug-resistant genes; GO analysis was used to annotate gene functions.

5. The method according to claim 1, characterized in that: The sequencing analysis also includes: Calculating a drug resistance index based on the analysis results of the drug resistance gene; The sensitivity of the pathogen to different types of pesticides is determined based on the drug resistance index.

6. The method according to claim 1, characterized in that The process of constructing a microbial phylogenetic tree in the sequencing analysis includes: Select genes related to the microorganism from the obtained gene abundances using the RAST program; Gene homology analysis was performed using the protein orthologs mapper program; The phylogenetic tree was constructed based on homologous genes using the maximum likelihood method.

7. The method according to claim 1, characterized in that The output step further comprises: Generate a heat map of drug resistance gene distribution to intuitively display the distribution of drug resistance genes of different pathogens; Generate resistance trend reports to predict possible future resistance issues.

8. The method according to claim 1, characterized in that Also includes a verification step: Based on the pesticide use recommendations, pesticide application experiments were conducted in the test fields; Regularly collecting soil and plant samples from the test field, and repeating the sample acquisition steps, sequencing analysis and output steps; Compare the changes in the distribution of resistance genes before and after the experiment to evaluate the effectiveness of pesticide use recommendations.

9. The method according to claim 1, characterized in that: Also includes the database update step: storing the drug resistance gene analysis results, microbial development tree and pesticide use recommendations in a database; Regularly update the database and establish a long-term monitoring system; Optimize the pesticide use recommendation algorithm based on the updated database.

10. A crop disease prevention and control system for improving the efficiency of pesticide use, characterized in that: include: A sample collection module, used for collecting soil samples and plant samples; A DNA extraction module, used to extract total DNA from the soil sample and the plant sample; A sequencing module, used to perform high-throughput sequencing on the total DNA to obtain sequencing data; Data analysis module for: Based on the sequencing data, identifying the drug resistance gene of the pathogen; Construct a microbial phylogenetic tree to analyze the evolutionary relationships and drug resistance trends of pathogens; A decision support module, for generating pesticide use recommendations based on the analysis results of the drug-resistant genes and the microbial development tree; A validation module, which is used to conduct pesticide application experiments in test fields and evaluate the effectiveness of pesticide application recommendations; Database module, used to store and update the results of drug-resistant gene analysis, microbial development trees and pesticide use recommendations.

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