1146 results about "Disease prevention" patented technology

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention belongs to the technical field of aquaculture automation control, and particularly discloses an aquaculture environment intelligent disease early warning system and a storage medium, the system comprises a data acquisition layer, a data transmission layer, a processing platform layer and an application service layer; the data acquisition layer comprises an environment monitoring unit, a water quality sensor group, portable detection equipment and a data acquisition module; the data transmission layer comprises a wireless transmission module and an edge computing node; the processing platform layer comprises a machine learning prediction model and an environment factor correlation analysis model; the application service layer comprises an early warning information push module and a prevention and control scheme generation module. According to the intelligent disease early warning system for the aquaculture environment and the storage medium, a correlation model of environmental factors and disease occurrence is innovatively established, the early warning accuracy is 85% or above by adopting a machine learning technology, comprehensive monitoring and real-time early warning in the aquaculture process can be achieved, and accurate and efficient disease prevention and control are supported.

The invention discloses a crop growth prediction system and method based on an agricultural unmanned aerial vehicle remote sensing technology, and relates to the technical field of agricultural information and intelligent monitoring, the system uses an unmanned aerial vehicle remote sensing module and a ground Internet of Things sensor module to collect multi-source data of banana planting, fish pond breeding and hog house breeding, and the multi-source data is used as a data expression recording carrier. The edge computing node module preprocesses data, and the cloud intelligent platform module is internally provided with a multi-modal data fusion model, a growth prediction model and the like as processing record carriers to realize data depth analysis and prediction. And realizing automatic control of the production process according to the data through block chain evidence storage and the intelligent contract. And constructing a disease prevention and control model by using the hyperspectral image of the unmanned aerial vehicle and related data. According to the invention, the collection, processing and application capabilities of agricultural production data are improved, accurate prediction and intelligent regulation are realized, the resource utilization efficiency is improved, the disease loss is reduced, and the agricultural intelligent development is promoted.

The invention discloses an intelligent pest and disease damage identification and targeted spraying method and system based on machine vision, and the method comprises the steps: inputting a multispectral image sequence and environment sensing data into a processing system, and obtaining a high-quality fusion image through spectrum-space joint correction and environment adaptive compensation; constructing a region growing algorithm based on a topological flow theory, and realizing accurate extraction of a target region in combination with curvature flow boundary optimization; integrating the spatial features, the time sequence features and the context information by adopting a multi-modal feature fusion method, and establishing a pest state evaluation and behavior prediction model; and according to an evaluation result, a spraying track is optimized through an environment constraint field, a self-adaptive parameter control system is adopted to adjust spraying parameters, and closed-loop optimization is realized through real-time monitoring. The technical problems of low pest and disease identification precision, poor spraying effect and the like in a complex environment are solved, and the accuracy and efficiency of prevention and treatment are improved.

The invention is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention relates to the technical field of Internet of Things and artificial intelligence, in particular to a livestock disease prevention, control, monitoring and early warning system based on Internet of Things, which comprises a data acquisition module, a data preprocessing module, a self-adaptive data analysis module, an early warning generation module and a user interface module, wherein the data acquisition module is used for acquiring physiological data, behavior data and environment data of livestock; the data preprocessing module is used for cleaning and processing the data to eliminate noise and interference; the self-adaptive data analysis module is used for dynamically adjusting monitoring parameters and early warning threshold values and identifying potential disease risks; and the early warning generation module is used for generating early warning information of different levels and sending early warning. According to the invention, by combining the Internet of Things, machine learning and deep learning technologies, real-time monitoring of livestock health, accurate identification of disease risks and dynamic adjustment of monitoring parameters and early warning thresholds are realized, and the accuracy and timeliness of disease prevention and control are significantly improved.

A method, belonging to the field of road disease prevention technologies, for constructing a simulation model for roadbed diseases, a test method, and a system, where a simulation model for roadbed diseases is first constructed, and based on the model, an experimental method is performed to simulate diseases such as roadbed settlement, cracks, loosening, and pipeline breakage. Data of multi-sensing devices is associated with different diseases in different control groups, data statuses of various roadbed diseases in complex scenarios are acquired, and data patterns are summarized through control tests, to obtain sensor thresholds when the diseases occur in actual roadbed conditions, thereby providing a basis for road disease prevention.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention relates to the field of microorganisms, in particular to a mortierella alpina strain MA with a plant growth promoting function and application of the mortierella alpina strain MA. The strain has remarkable nitrogen fixation, siderophore production, auxin (IAA) production and cellulase activity, can inhibit various soil-borne pathogenic bacteria such as phytophthora capsici and fusarium, and remarkably improves the disease resistance and growth performance of crops such as pepper. The strain has various functions and broad-spectrum action, is suitable for replacing part of chemical fertilizers and pesticides in intensive agriculture, and realizes green and efficient planting of crops.

The invention discloses a citrus orchard interplanting pest and disease prevention and control method, which comprises the following steps: installing a drip irrigation facility in a citrus orchard, and uniformly distributing drippers of the drip irrigation facility between each citrus tree and green manure planting rows; soil humidity sensors are buried in multiple positions of the citrus orchard to monitor soil humidity data in real time through the soil humidity sensors; and determining whether to start the drip irrigation facility based on the soil humidity data acquired by the plurality of soil humidity sensors and the position relationship among the plurality of soil humidity sensors. Therefore, whether the irrigation facility is started or not can be automatically decided by monitoring the soil humidity of different positions in the citrus garden in real time, and the intelligent control mode is beneficial to timely discovering and processing the soil water shortage condition; the problems that in a traditional mode, whether irrigation is carried out or not cannot be judged according to the actual humidity condition of soil, and irrigation hysteresis exists are solved, and then a basis is provided for citrus orchard interplanting pest and disease prevention and control management.

The invention discloses a shrimp health state early warning method and system based on water quality monitoring, and aims to improve the disease prevention and control efficiency in a shrimp culture process. The method comprises the following steps: collecting historical breeding disease data and water quality change data of a target shrimp breeding water area, and constructing a water quality pathopoiesis knowledge graph; then water quality change data in a preset monitoring time period is obtained, space-time tracing is carried out by using the water quality change prediction model, and a water quality anomaly propagation path diagram is generated; formulating a water quality monitoring strategy based on the propagation path diagram, and optimizing the layout and frequency of monitoring points; and finally, monitoring the water quality in real time according to the established monitoring strategy, and realizing dynamic early warning of the health state of the prawns by combining real-time monitoring data and a water quality pathopoiesis knowledge graph. Water quality related diseases can be effectively prevented and controlled, and the stability and yield of shrimp culture are improved.

The invention discloses an intelligent pest and disease damage monitoring system based on multi-modal data fusion of an unmanned aerial vehicle, and the system carries a multi-source heterogeneous data collection module through the unmanned aerial vehicle, obtains optical, multispectral, thermal infrared and other multi-modal data in parallel, and stores the data in a ground end after wireless link coding transmission and format adaptation. According to the system, an improved YOLO algorithm is used to construct a multi-level feature pyramid and an adaptive convolution kernel architecture, and feature extraction and disease and pest candidate region generation are carried out on multi-modal data; time sequence dynamic analysis and depth feature mining are realized by means of an optimized long-short-term memory network in combination with parameters such as a pest and disease damage growth cycle; and through a weight adaptive adjustment fusion strategy, decision-level fusion is carried out on results of the two algorithms, and final monitoring data is output. Besides, various correlation formulas are designed in each link of the system, so that parameters in multiple aspects of data acquisition, algorithm processing and the like are mutually coordinated, efficient and accurate monitoring of diseases and insect pests is realized, and technical support is provided for prevention and control of agricultural diseases and insect pests.

The invention relates to a layout image generation method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the element analysis of original image data, and obtaining image element information; performing feature extraction on the image element information to obtain image features and text features; performing hypergraph structure construction according to a preset design rule, the image features, the text features and the image element information to obtain hypergraph structure information; performing style parameter adjustment and coordinate adjustment on the hypergraph structure information according to the matching similarity corresponding to the element embedding vector to obtain to-be-rendered information; and performing image layout rendering according to the to-be-rendered information to obtain an effective layout image. The method can be applied to application scenes of financial science and technology and digital medical systems, and insurance product propaganda images or disease prevention propaganda images which effectively express image elements and features can be effectively, accurately and quickly generated according to original insurance product images or health education images in the system.

The invention belongs to the field of prevention and treatment of tilletia controversa Kuhn, and particularly relates to a tilletia controversa Kuhn effect protein g16561 and application thereof. An amino acid sequence of the Tilletia controversa Kuhn effect protein is shown as SEQ ID NO: 2. The effect protein plays an important role in the process of inhibiting plant defense reaction, and has an interaction relationship with a wheat protein in the process of infecting wheat by Tilletia controversa Kuhn. According to the invention, the pathogenesis of Tilletia controversa Kuhn can be deeply researched, and the excavation of wheat disease-resistant genes and the cultivation of wheat disease-resistant varieties can be promoted.

The invention relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

The invention discloses a Burkholderia sp. Strain and an application thereof in prevention and treatment of eucalyptus blight. The Burkholderia sp. AS-SF-1 disclosed by the invention has the preservation number of GDMCC (Graphical December Microorganism Center) No. 66223; the culture and volatile matter of the strain can inhibit growth of various forest pathogenic bacteria, the eucalyptus has a remarkable control effect on branch blight, siderophore can be generated, absorption and utilization of iron can be promoted, various types of carbon sources or nitrogen sources can be utilized, and the strain can grow within the pH range of 4-9. The burkholderia sp. AS-SF-1 disclosed by the invention has an important application value on planting of eucalyptus and prevention of diseases.

The invention relates to the field of prevention and treatment of respiratory diseases, and discloses a preparation method of a human respiratory syncytial virus preventive mRNA vaccine. The main components of the mRNA vaccine comprise mRNA for coding an antigen and lipid nanoparticles. According to the mRNA sequence provided by the invention, an independently researched and developed 5 'UTR / 3' UTR sequence and a codon optimization mode are adopted, and efficient translation of target protein can be realized. According to the mRNA vaccine provided by the invention, RSV F protein is selected as an antigen, and the RSV F protein sequence is optimally designed, so that stable pre-fusion conformation RSV F protein can be expressed, and after a mouse is immunized, the mouse can be induced to generate high-level RSV F protein specific binding antibody titer and neutralizing antibody titer.

The invention relates to a microbial strain and application thereof, in particular to bacillus brevis, a microbial agent prepared from the bacillus brevis and application of the microbial agent. The invention provides the cold-resistant bacillus subtilis which has multiple functions of generating capsular polysaccharide, being high in salt resistance and cold resistance, promoting growth and disease resistance of plants in saline-alkali soil and the like. The bacterial strain can generate capsular polysaccharide, experiments prove that the bacterial strain has high capabilities of dissolving phosphorus, fixing nitrogen and producing siderophore, IAA, ACCD and cytokinin, and has strong antagonistic action on fusarium, curvularia and microplasmodium, and the generated capsular polysaccharide can promote salt tolerance of plants, so that the yield of the plants is increased, and the yield of the plants is increased. The bio-fertilizer is suitable for production of multifunctional bio-fertilizers with disease prevention, growth promotion, salt and alkali resistance, cold resistance and the like, is applied to production of saline-alkali soil grain crops, pasture and greenhouse commercial crops, has remarkable effects of promoting cold damage resistance, root development, prevention and control of root diseases, improvement of crop yield and the like, can reduce the use of chemical fertilizers and pesticides, and has a wide application prospect.

The invention discloses a toxoplasma gondii attenuated vaccine strain RH delta rop67 as well as a construction method and application thereof, and belongs to the technical field of parasitic disease prevention and control and biological product preparation. The attenuated vaccine strain is constructed by performing targeted knockout on the ROP67 gene in a toxoplasma gondii strain RH delta ku80 through a CRISPR / Cas9 mediated gene editing technology. Compared with a wild type strain, the attenuated vaccine strain shows remarkable attenuation characteristic and good immunogenicity. A test result shows that the attenuated vaccine strain can induce a host to generate specific immune response mainly based on cellular immunity, maintains a protection effect on toxoplasma gondii infection in a relatively long immune period, and has a protection effect on tachyzoite infection and a chronic infection stage of toxoplasma gondii strains with different virulence; the survival ability of a host to tachyzoite infection can be improved, and the formation level of cysts in tissues is reduced. The invention provides a technical scheme with long-term immune potential for research and development of toxoplasma gondii attenuated vaccines.

The invention discloses a microbial agent and application thereof in preventing and treating rice blast and promoting rice growth, active ingredients of the microbial agent are formed by compounding cold bacillus with the preservation number of CGMCC (China General Microbiological Culture Collection Center) NO: 33492 and bacillus velezensis with the preservation number of CGMCC NO: 33493, and the microbial agent aims at solving the synergistic problem of preventing and treating rice blast and promoting rice growth. According to the complex microbial inoculant, through the synergistic interaction between the two strains, the metabolite of the complex microbial inoculant can completely inhibit the spore germination of magnaporthe oryzae; meanwhile, the fungicide can degrade cellulose, obviously promote the growth of plant height, fresh weight and dry weight of rice, and realize the integration of disease prevention and growth promotion. The invention further discloses a preparation method of the fungicide and an application method of the fungicide for preventing and treating rice blast and promoting growth in a root irrigation mode in the three-leaf stage of rice. The microbial agent disclosed by the invention has dual functions of efficiently preventing diseases and stably promoting growth, and an efficient and safe biological solution is provided for green and sustainable development of agriculture.

The invention discloses a method for predicting and judging the diarrhea risk grade of suckling piglets, and belongs to the technical field of piglet epidemic disease prevention and control. Aiming at the problems that traditional piglet diarrhea prediction depends on manual observation, multi-dimensional data relevance is insufficient, early warning lags and the like, a multi-parameter monitoring device is arranged in a piglet breeding environment to collect environment data in real time, and an excretion behavior detection module is used for triggering an excrement image collection device to obtain a high-quality image; and inputting multi-source data into a random forest model for risk level prediction by combining regular weight measurement and blood detection in a specific time period after environment adjustment. The system dynamically adjusts pig environment parameters according to a prediction result, after spraying, heating compensation is started to maintain humidity and control ammonia concentration, and closed-loop feedback regulation is formed. According to the method, through multi-modal data fusion and model iterative optimization, early-stage accurate early warning of diarrhea risks is realized, the method is suitable for intelligent management of a large-scale pig farm, the diarrhea occurrence rate can be effectively reduced, and the breeding benefits are improved.

The invention discloses a specific target sequence, a primer pair, a detection method and a kit for identifying alternaria species based on a temporal method. According to the method, the specific target sequence obtained through whole genome analysis and screening can realize accurate identification of alternaria related species. The kit disclosed by the invention has the characteristics of rapid detection, high sensitivity, high specificity, convenience in carrying and the like, and is suitable for rapid detection and monitoring in the fields of agriculture, phytopathology and disease prevention and control.

The application belongs to the technical field of biological detection, and particularly relates to a monoclonal antibody pair for detecting feline immunodeficiency virus p24 protein and application thereof. The monoclonal antibody pair is monoclonal antibody 1D8 and monoclonal antibody 1H10, which can specifically recognize feline immunodeficiency virus p24 protein, and the heavy chain and light chain variable region complementarity determining region sequences of 1D8 and 1H10 are clear, and are respectively shown as SEQ ID NO. 1-12. The antibody pair can effectively avoid cross reaction, and significantly improve the accuracy and sensitivity of detection; the colloidal gold detection test strip constructed based on the antibody pair is simple and fast in operation, is suitable for early antigen detection of FIV infection, solves the problem that existing antibody detection cannot distinguish natural infection and vaccine immunization, and provides a reliable technical means for on-site screening and disease prevention and control of FIV.

The invention discloses an animal quarantine method, and aims to solve the problems of high omission ratio, early warning lagging and lack of environment optimization of the traditional quarantine means. According to the method, multi-dimensional biological signals such as the heart rate, the respiratory rate, the body temperature and the acceleration of an animal are collected in real time through an ear tag type wearable device, and the anomaly detection precision is improved through a self-adaptive sampling mechanism; meanwhile, the environment monitoring module collects the temperature, humidity and ammonia gas concentration of the farm, and dynamically regulates and controls the temperature, humidity and ammonia gas concentration in case of abnormality. And the edge computing node adopts a long-short-term memory network to fuse and analyze data, outputs an epidemic disease risk probability, and optimizes the model through self-learning. The embodiment shows that the method can perform early warning and environment optimization in the incubation period, the quarantine time is shortened to 5-10 minutes per head, the omission ratio is lower than 5%, and an efficient and accurate epidemic disease prevention and control solution is provided for the breeding industry.

The present invention discloses a system and method for preventing and controlling frost heave damage in railway subgrades adapted to severe cold climates. This system belongs to the field of seasonal frozen soil engineering technology. The prevention and control system includes several ground-source heat pump devices consisting of heating units, heating pipes, heat collecting pipes, and a detection assembly. Several heating units are arranged at the foot of the subgrade slope along the length of the railway. The heating pipes are laid in the frost heave development layer of the subgrade, and the heat collecting pipes are installed in the stable layers of the foundation on both sides of the subgrade. A heat carrier circulates through the pipeline consisting of the heating pipes, heating units, and heat collecting pipes. Temperature sensors, pressure sensors, and displacement sensors are used to monitor the subgrade freezing temperature, heat carrier temperature, compressor intake and exhaust pressures, and track deformation, respectively. A control assembly receives signals from the detection assembly and controls the operating mode of the heating units. The present invention ensures rapid compressor superheat establishment during startup in severe cold environments and rationally controls the compressor exhaust temperature range and operating mode during normal operation.

The invention belongs to the field of corn disease prevention, relates to a novel small peptide, provides a small peptide NCRP246 and application thereof in improvement of corn southern leaf blight resistance, and provides feasibility for plant disease resistance by using non-classical peptides. A corn southern leaf blight resistance identification experiment proves that NCRP246 can significantly improve the resistance of corn to southern leaf blight, and further researches that the NCRP246 reduces oxidative damage of pathogenic bacteria infection of the southern leaf blight to the corn by reducing H2O2 accumulation so as to mediate the resistance of the corn to the southern leaf blight; the analysis from the molecular perspective proves that the NCRP246 can induce the expression of defense related genes.

The invention discloses a railway roadbed frost heaving disease prevention and control system and method adapting to severe cold climate, and belongs to the technical field of seasonal frozen soil engineering.The prevention and control system comprises a plurality of ground source heat pump devices composed of heating units, heat supply pipes, heat collection pipes and detection assemblies, and the heating units are arranged at the slope toe position of a roadbed in the length direction of a railway; the heat supply pipe is laid on a frost heaving development layer of a roadbed, the heat collecting pipes are arranged on stable layers of the roadbed on the two sides of the roadbed, and the heat-carrying agent circularly flows in a pipeline composed of the heat supply pipe, the heating unit and the heat collecting pipes. A temperature sensor, a pressure sensor and a displacement sensor are used for monitoring the roadbed freezing temperature, the heat-carrying agent temperature, the suction and exhaust pressure of a compressor and the deformation amount of a track; and the control assembly receives a signal of the detection assembly and controls the operation mode of the heating unit. According to the method, rapid establishment of the superheat degree of the compressor in the starting stage in the severe cold environment and reasonable control over the exhaust temperature range and the operation mode of the compressor in the normal operation stage can be guaranteed.

The invention provides a control method for apple alternaria leaf spot, and belongs to the technical field of crop disease control. The prevention and treatment method comprises the following steps: spraying a foliar biological pesticide fertilizer to apple trees; the foliar biological medical fertilizer comprises a bacillus fermentation product and a trichoderma fermentation product, the bacillus is at least one of bacillus amyloliquefaciens TS-1203, bacillus subtilis B1 and bacillus subtilis BS-1208, and the bacillus is at least one of bacillus amyloliquefaciens TS-1203, bacillus subtilis B1 and bacillus subtilis BS-1208; the trichoderma is at least one of T6 and Ym. The combined action of bacillus and trichoderma is utilized, the morbidity of apple alternaria leaf spot can be effectively reduced, the growth of apple trees can be promoted, the photosynthetic capacity is improved, the combination of disease prevention and yield increase is realized, and the method is environment-friendly and suitable for green agricultural development.

A disease prevention and management system based on image recognition and big data, specifically relates to the technical field of disease prevention and management of image recognition. The scheme includes an image acquisition module for acquiring image information of the current patient and converting the acquired analog signal into a digital signal. The big data processing module is used to adopt the distributed storage and parallel processing technology of big data to extract data information of the same gender and age group, and to perform disease prediction evaluation according to the normal interval and severe interval of the disease prediction severity score PF of the same gender and age group, and collect and manage after the evaluation. The feature extraction and prediction module calculates and outputs the preliminary evaluation value QT of the image feature, the abnormal area feature value YT, and the disease prediction severity score PF in sequence. The feedback display module is used to display the severity of the disease prediction. The present invention can more accurately evaluate the health status of the patient and provide a scientific basis for the prevention and management of the disease.

The invention discloses a soybean phospholipid disease-preventing and root-promoting microbial agent as well as a preparation method and application thereof, and belongs to the technical field of microorganisms. The microbial agent comprises a microbial composition, modified enzymolysis soybean phospholipid, a carrier and auxiliary components. According to the invention, the modified enzymolysis soybean phospholipid and microorganisms are combined to achieve a synergistic effect. According to the method, soybean phospholipid is modified and subjected to enzymolysis, and the steps of pretreatment, stepped low-temperature treatment, enzymolysis treatment, enzyme inactivation and purification and the like are carried out, so that the quality and performance of the soybean phospholipid are improved, the rhizosphere colonization rate of strains can be increased, the disease prevention and root promotion effects can be improved, the soil micro-ecological environment can be improved, and good growth conditions are created for plant root systems; and the disease-preventing and root-promoting effects of the microbial agent are further enhanced. The microbial agent disclosed by the invention can be used for remarkably reducing the morbidity of wheat sharp eyespot and reducing the use amount of chemical pesticides, can also be applied to planting of other crops, and can play a role in preventing and controlling various plant diseases and promoting root growth.