CN117958131A - Prevention and control method for bakanae disease of rice - Google Patents
Prevention and control method for bakanae disease of rice Download PDFInfo
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Abstract
The invention discloses a method for preventing and treating bakanae disease of rice, which comprises the following steps: big data and artificial intelligence, ecological system management, precise pesticide application and fertilization, genetic improvement, variety screening, farmer training and technical popularization are utilized. The invention can reduce the incidence rate of bakanae disease of rice and reduce damage of diseases to crops by an ecological system management and intelligent decision support system method, thereby improving the yield of rice, reducing the use amount of pesticides by the application of accurate application, biological control and disease-resistant varieties, being beneficial to reducing environmental pollution, protecting the health of an ecological system, reducing the pesticide cost of farmers, reducing the infection of diseases to rice, improving the quality of agricultural products, reducing the disease spots and disease-related toxin residues, and improving the food safety of grains.
Description
Technical Field
The invention relates to the field of planting industry, in particular to a method for preventing and treating bakanae disease of rice.
Background
The disease sources mainly comprise pathogens and seeds, seedlings and residues carried by the pathogens, and the disease sources such as infected seedlings, diseased rice stems, diseased seedling soil and weeds in the occurrence range of the diseases are removed and destroyed in time, so that the transmission of the pathogens can be effectively reduced;
Healthy seeds without diseases and insect pests are selected for sowing, so that the transmission and occurrence of pathogens can be effectively reduced, before sowing, seed disinfection can be carried out, for example, 75% formalin is used for soaking the seeds for 30 minutes, then airing is carried out, and sowing is carried out again, so that the pathogens on the surfaces of the seeds can be killed;
the rice is prevented from being planted in the areas with serious disease occurrence, and meanwhile, a reasonable rotation system is implemented, rotation of rice and non-rice crops is reasonably arranged, so that accumulation of pathogens in cultivated lands can be reduced, and the occurrence probability of diseases is reduced;
Conventional agriculture relies on chemical pesticides to control pests and diseases, is often widely sprayed with pesticides to kill or inhibit pests and pathogens, and the use of large amounts of fertilizers and pesticides can cause pollution to the soil ecosystem and water bodies, and conventional agriculture often breaks ecological balances because they often weed and kill pests, possibly killing organisms other than just pests, including natural enemies and beneficial microorganisms.
Disclosure of Invention
The invention aims to provide a method for preventing and treating bakanae disease of rice, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for preventing and treating bakanae disease of rice comprises the following steps:
step S1, big data and artificial intelligence are utilized:
Establishing a rice disease database: collecting data related to rice diseases in the global scope, including disease types, geographic positions and cultivation management measures, and identifying risk factors and disease occurrence rules related to rice bakanae disease by integrating and analyzing the data;
disease prediction model: using machine learning and deep learning algorithms to establish a disease prediction model, and predicting the occurrence probability and degree of bakanae disease of rice by inputting environmental data, meteorological data and soil conditions;
Intelligent disease recognition system: an intelligent disease recognition system based on image recognition and deep learning is developed, a farmer shoots images of leaves or plants by using a smart phone or other equipment, and the system rapidly recognizes the type and the severity of bakanae disease of rice by comparing the disease images in an image database, so that an accurate basis is provided for prevention and control.
Step S2, ecological system management:
Ecological agriculture practice: the method for promoting ecological agriculture comprises organic agriculture, biological diversity protection and ecological balance regulation, increases the biological diversity of farmlands, improves the stability and disease resistance of an ecological system, and reduces the occurrence of bakanae disease of rice;
rotation and interplanting: the rotation and interplanting of the rice are reasonably arranged to destroy the continuous host relation of the diseases, reduce the spread of the diseases, and timely plant disease non-host plants or other crops, so that the occurrence risk of the diseases can be reduced;
and (3) cultivation management optimization: the planting density, the fertilization level and the irrigation management cultivation measures are reasonably adjusted, the health condition and the disease resistance of rice are improved, field weeds are strictly managed, and the survival and the transmission of pathogenic bacteria are reduced.
Step S3, accurately applying and fertilizing:
The pesticide dosage is precisely controlled: the intelligent spraying equipment and the sensing technology are introduced to realize the accurate control of the pesticide dosage, the spraying dosage and the frequency are dynamically adjusted according to the development condition and the severity of diseases, and the use amount of the pesticide and the environmental pollution are reduced;
organic agriculture and biological control: encourages popularization of organic agriculture, reduces dependence on chemical pesticides, and simultaneously pays attention to application of biological control methods, including control of spread and infection of diseases by natural enemies, parasitic bacteria and disease-preventing beneficial microorganisms.
Step S4, genetic improvement and variety screening:
Genetic improvement: through gene editing and transgenic technology, disease-resistant genes are introduced into rice varieties, so that the bakanae disease resistance of the rice varieties is improved, meanwhile, disease-resistant gene research is developed, the resistance mechanism of the bakanae disease of the rice is deeply known, and the varieties with long-acting resistance are promoted and bred;
Variety screening: the polyploid breeding and molecular marker assisted breeding technology is utilized to accelerate the breeding process of the disease-resistant rice varieties, and the genes related to the disease resistance are identified through high-throughput sequencing and genetic map analysis, so that the basis is provided for breeding the excellent varieties with the disease resistance.
Step S5, farmer training and technical popularization:
Farmer training program: the training courses aiming at the bakanae disease of the rice are organized, so that the knowledge and prevention and control skills of farmers on the disease are improved, and the training contents comprise disease identification, preventive measures, application technology and knowledge and practice skills in reasonable cultivation management;
Agricultural technology popularization service: an agricultural technology popularization service platform is established, real-time disease early warning information, prevention and control guidance and technical support are provided for farmers through Internet and mobile phone application programs, disease and pest prevention and control training activities are regularly held, communication interaction between agricultural technology personnel and farmers is enhanced, and prevention and control effects are improved.
Further, in the step S1, the establishment of the rice disease database specifically includes the following aspects:
And (3) data collection: acquiring data related to rice diseases by using an automation technology and a sensor;
Data integration and cleaning: integrating and cleaning the collected various data;
data storage and management: establishing a special database system to store and manage rice disease data;
data analysis and mining: analyzing and excavating rice disease data;
data visualization and interaction: displaying the analysis result in a form of a chart and a map by utilizing a data visualization technology;
data sharing and application: the disease database is shared open and is widely used by farmers, agricultural technical staff and research institutions.
Further, in the step S1, the disease prediction model specifically includes the following aspects:
And (3) data collection: collecting data relating to disease;
feature extraction: extracting features related to disease occurrence from the collected data;
data preprocessing: preprocessing the extracted characteristic data;
And (3) establishing a model: establishing a deep learning model according to the predicted target and the data characteristics;
Model training and verification: dividing the collected data into a training set and a verification set;
disease prediction: the new data is predicted using the trained model.
Further, in the step S1, the intelligent disease recognition system specifically includes the following aspects:
and (3) data collection: collecting data related to disease identification;
data preprocessing: preprocessing the collected image data;
feature extraction: extracting features related to disease identification from the preprocessed image;
And (3) establishing a model: selecting a proper model to build according to the extracted characteristic data;
Model training and verification: dividing the collected image data into a training set and a verification set;
disease identification: the new image is identified using the trained model.
Further, in the step S3, the precise pesticide dosage control specifically includes the following aspects:
pesticide dose measurement: the accurate control of the pesticide dosage first requires the measurement of the pesticide used;
An automation control system: precise control of pesticide dosage is often achieved by means of automated control systems;
data analysis and algorithm: for accurate dose control, data analysis and algorithms play an important role in the pesticide spraying process;
real-time monitoring and feedback: in the pesticide spraying process, real-time monitoring and feedback are key to achieving accurate control.
Further, in the step S3, the organic agriculture specifically includes the following aspects:
Soil management: organic agriculture focuses on optimizing the soil ecosystem;
Selecting a crop variety: organic agriculture is more prone to select crop varieties that are naturally resistant or adaptable;
biodiversity protection: organic agriculture encourages protection of the natural ecosystem around farmlands.
Further, in the step S3, the biological control method specifically includes the following aspects:
natural enemies are utilized: natural enemies refer to other organisms that predate or parasitize crop pests;
Parasitic and pathogenic bacteria utilization: parasitic bacteria and pathogenic bacteria have potential for controlling crop diseases;
Disease-preventing beneficial microorganism utilization: probiotics and fungi can be symbiotic with plants to promote plant growth and health.
Further, in the step S4, the variety screening specifically includes the following aspects:
Polyploid breeding: polyploid breeding is to multiply the number of plant cells or tissue chromosomes through artificial induction;
Molecular marker assisted breeding technology: molecular markers refer to DNA sequences located at a specific site that can be used to identify and track a gene or region of a gene of interest;
high throughput sequencing: the high-throughput sequencing technology can rapidly and efficiently determine the DNA sequence of the whole genome;
Genetic map analysis: by analyzing the genetic linkage relationship between different varieties or different individuals, a genetic map is established.
Further, in the step S4, the gene editing and transgenic technology specifically includes the following aspects:
Gene editing technology: utilizing a CRISPR-Cas9 gene editing tool to carry out fixed-point modification on genes related to disease resistance in rice;
Transgenic technology: by introducing a foreign gene, a gene having disease resistance is introduced into rice from another species.
Further, in the step S4, the disease resistance gene study specifically includes the following aspects:
identification of disease resistance genes: by comparing genetic differences or expression differences of disease-resistant and susceptible varieties;
functional analysis of disease resistance genes: performing functional analysis on the identified disease resistance genes;
disease resistance gene transfer and optimization: based on the deep knowledge of the disease resistance genes, these disease resistance genes are introduced into rice by transgenic techniques.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention can reduce the incidence rate of bakanae disease of rice and reduce damage of diseases to crops by an ecological system management and intelligent decision support system method, thereby improving the yield of rice, reducing the use amount of pesticides by the application of accurate application, biological control and disease-resistant varieties, being beneficial to reducing environmental pollution, protecting the health of an ecological system, reducing the pesticide cost of farmers, reducing the infection of diseases to rice, improving the quality of agricultural products, reducing the disease spots and disease-related toxin residues, and improving the food safety of grains;
2. The invention adopts organic agriculture, ecological agriculture and reasonable rotation method to help realize sustainable development of agriculture, which is not only beneficial to environmental protection, but also helps to maintain soil health and ecological balance, helps farmers obtain more prevention and control knowledge and skills through training and technical popularization, can improve agricultural output and income of farmers, cultivates rice varieties with long-term disease resistance through genetic improvement and variety screening, reduces investment of farmers in disease prevention and control, and improves production stability of rice;
3. The invention provides disease prediction, decision support and technical guidance for farmers by utilizing big data and artificial intelligence technology, helps the farmers to manage farmlands and cope with disease threats more effectively, and adopts an eco-friendly method such as biological control and organic agriculture, thereby being beneficial to reducing adverse effects of agriculture on water resources and ecological environment and reducing ecological risks.
Drawings
FIG. 1 is a schematic flow chart of a method for controlling bakanae disease of rice.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution:
a method for preventing and treating bakanae disease of rice comprises the following steps:
step S1, big data and artificial intelligence are utilized:
Establishing a rice disease database: collecting data related to rice diseases in the global scope, including disease types, geographic positions and cultivation management measures, and identifying risk factors and disease occurrence rules related to rice bakanae disease by integrating and analyzing the data;
in this embodiment, the establishment of the rice disease database specifically includes the following aspects:
And (3) data collection: the method comprises the steps of acquiring data related to rice diseases by using an automation technology and a sensor, wherein the data comprise disease types, geographical positions, occurrence seasons, meteorological conditions and cultivation management measures, and can be obtained from farmland observation, experimental fields, monitoring data of agricultural departments, research results of scientific research institutions and field records of farmers;
Data integration and cleaning: integrating and cleaning various collected data, including unifying data formats, removing repeated data, repairing error data and filling missing data, and screening according to the credibility and the integrity of the data to ensure the quality and the usability of the data;
Data storage and management: establishing a special database system for storing and managing rice disease data, wherein a relational database or a non-relational database can be adopted, selection is carried out according to the characteristics and the requirements of the data, and the database system needs to have efficient query and search functions so as to facilitate subsequent analysis and utilization;
data analysis and mining: analyzing and mining rice disease data to find rules, influencing factors and trends of occurrence of the diseases, wherein the data analysis method comprises statistical analysis, spatial analysis, time sequence analysis and machine learning, and the factors related to occurrence of bakanae disease of the rice can be identified through the data analysis, including climate conditions, soil characteristics and agricultural measures;
Data visualization and interaction: the analysis result is displayed in the form of a chart and a map by utilizing a data visualization technology, so that the data is easier to understand and use, the relationship among disease distribution, trend and influence factors can be visually displayed by visualization, and meanwhile, an interactive interface is provided, so that a user can freely perform data query, screening and analysis according to own requirements;
Data sharing and application: the disease database is shared in an open way and is widely used by farmers, farm technicians and research institutions, data is published and spread through the Internet or mobile application programs, the disease database can provide real-time disease early warning and prevention suggestions for farmers, research data is provided for scientific researchers, and a reference basis for making agricultural policies is provided for decision makers.
Disease prediction model: using machine learning and deep learning algorithms to establish a disease prediction model, and predicting the occurrence probability and degree of bakanae disease of rice by inputting environmental data, meteorological data and soil conditions;
in this embodiment, the disease prediction model specifically includes the following aspects:
And (3) data collection: collecting data related to diseases, wherein the data comprise the occurrence time, the place, the disease type, meteorological data, soil data and the crop growth stage of the diseases, and the data can be derived from farmland observation, meteorological observation sites, sensor equipment, satellite remote sensing data and farmer field records;
Feature extraction: extracting characteristics related to disease occurrence from the collected data, wherein the characteristics comprise temperature, humidity, precipitation, sunshine hours, soil humidity and plant growth indexes, and the characteristics are selected to be considered in balance according to specific diseases and crop types;
Data preprocessing: preprocessing the extracted characteristic data, including data cleaning, data smoothing and data normalization, wherein the data cleaning comprises abnormal value removal and missing value filling processing, the data smoothing can remove noise, so that the data is smoother and more stable, the data normalization can unify the numerical ranges among different characteristics, and the excessive influence of certain characteristics on model training is avoided;
and (3) establishing a model: according to the predicted target and the data characteristics, a deep learning model is established, the deep learning model is trained by utilizing the association relation between historical data and disease occurrence, and a model for predicting the future disease occurrence probability is learned;
Model training and verification: dividing the collected data into a training set and a verification set, training and parameter optimization are carried out on the model by utilizing the training set, then the performance and accuracy of the model are evaluated by utilizing the verification set, and the parameters of the model are repeatedly adjusted until a satisfactory prediction effect is achieved;
Disease prediction: the new data are predicted by using the trained model, the probability or classification result of disease occurrence can be given by the model according to the input characteristic data, and corresponding disease control measures can be timely adopted according to the prediction result, so that the yield and quality of crops are improved.
Intelligent disease recognition system: developing an intelligent disease recognition system based on image recognition and deep learning, shooting blade or plant images by farmers by using smart phones or other equipment, and rapidly recognizing the type and severity of bakanae disease of rice by comparing the disease images in an image database by the system, so as to provide accurate basis for prevention and control;
In this embodiment, the intelligent disease recognition system specifically includes the following aspects:
and (3) data collection: collecting data related to disease identification, including image data of plant leaves or fruits, the images from field photography, farm observation and farmer field recording;
data preprocessing: preprocessing the collected image data, including image enhancement, noise reduction and size unification, wherein the preprocessing operation aims at improving the image quality and reducing the interference of noise on subsequent processing;
Feature extraction: extracting features related to disease identification from the preprocessed image, wherein the features comprise textures, shapes, colors and edges, and the feature extraction method comprises gray level co-occurrence matrix, local binary pattern and principal component analysis;
And (3) establishing a model: according to the extracted characteristic data, a proper model is selected for establishment, and common disease identification models are a machine learning-based method, including a support vector machine, a random forest, an artificial neural network and a deep learning-based method, including a convolutional neural network, and the model can learn the characteristic mode of the disease through the image data of a training set, so that the images can be classified and identified;
Model training and verification: dividing the collected image data into a training set and a verification set, training and optimizing parameters of the model by using the training set, then evaluating the performance and accuracy of the model by using the verification set, and repeatedly adjusting the parameters and the optimization strategy of the model until a satisfactory disease identification effect is achieved;
Disease identification: the new image is identified by using the trained model, the model outputs the corresponding disease category or the probability of existence of the disease according to the input image data, and corresponding disease control measures can be timely adopted according to the identification result, so that farmers can be helped to timely adjust agricultural production measures.
Step S2, ecological system management:
Ecological agriculture practice: the method for promoting ecological agriculture comprises organic agriculture, biological diversity protection and ecological balance regulation, increases the biological diversity of farmlands, improves the stability and disease resistance of an ecological system, and reduces the occurrence of bakanae disease of rice;
rotation and interplanting: the rotation and interplanting of the rice are reasonably arranged to destroy the continuous host relation of the diseases, reduce the spread of the diseases, and timely plant disease non-host plants or other crops, so that the occurrence risk of the diseases can be reduced;
and (3) cultivation management optimization: the planting density, the fertilization level and the irrigation management cultivation measures are reasonably adjusted, the health condition and the disease resistance of rice are improved, field weeds are strictly managed, and the survival and the transmission of pathogenic bacteria are reduced.
Step S3, accurately applying and fertilizing:
The pesticide dosage is precisely controlled: the intelligent spraying equipment and the sensing technology are introduced to realize the accurate control of the pesticide dosage, the spraying dosage and the frequency are dynamically adjusted according to the development condition and the severity of diseases, and the use amount of the pesticide and the environmental pollution are reduced;
In this embodiment, the precise pesticide dosage control specifically includes the following aspects:
Pesticide dose measurement: accurate control of the pesticide dosage first requires measurement of the pesticide used, by using a specialized pesticide spraying apparatus or pesticide spraying system, the apparatus comprising a flow meter for measuring the flow of pesticide liquid through the system, a nozzle for spraying the pesticide liquid onto the crop, and a controller for monitoring and adjusting the flow to achieve accurate dosage control;
An automation control system: accurate control of pesticide dosage is often achieved by means of automated control systems, which monitor and adjust the pesticide spraying process in real time according to demand and parameter settings, using sensors for detecting environmental factors including crop growth status, wind speed, humidity, and pesticide spraying effect, and actuators for adjusting spraying devices and spraying parameters including flow, spray angle, and spray intensity;
data analysis and algorithm: for accurate dose control, data analysis and algorithms play an important role in the pesticide spraying process, collected environmental data and crop characteristics are input into an algorithm model, the optimal pesticide dose is determined through analysis and comparison, and the algorithm is developed based on a statistical, machine learning or artificial intelligence method so as to realize more accurate and efficient pesticide dose control;
Real-time monitoring and feedback: in the pesticide spraying process, real-time monitoring and feedback are key to realizing accurate control, the change of farmland environment and pesticide spraying effect can be obtained in real time through the sensor and the monitoring equipment, the data can be compared with preset parameters, the pesticide dosage is timely adjusted according to feedback information, and the pesticide is ensured to reach the expected effect and not to exceed the safe dosage.
Organic agriculture and biological control: encouraging popularization of organic agriculture, reducing dependence on chemical pesticides, and focusing on application of biological control methods, including control of disease spread and infestation by natural enemies, parasitic bacteria and disease-preventing beneficial microorganisms;
In this embodiment, the organic agriculture specifically includes the following aspects:
Soil management: organic agriculture focuses on optimizing a soil ecosystem, increasing the organic matter content of soil by adding organic fertilizer, maintaining soil coverage and reasonable rotation, improving the soil structure and the water retention capacity, and improving the resistance and the adaptability of crops to diseases;
Selecting a crop variety: organic agriculture is more prone to selecting crop varieties with natural resistance or strong adaptability, improving the overall disease resistance of crops and reducing the demand for pesticides;
Biodiversity protection: organic agriculture encourages protection of natural ecosystems around farmlands, provides habitats for beneficial insects and biological natural enemies that control diseases, improves ecological balance, and reduces the possibility of occurrence of diseases.
In this embodiment, the biological control method specifically includes the following aspects:
natural enemies are utilized: natural enemies refer to other organisms which prey or parasitize crop pests, including insects, birds and spiders, and biological control is realized by increasing the number and diversity of the natural enemies, so that a natural pest control system is established in a farmland, and the natural enemies can prey or parasitize the crop pests, thereby effectively limiting the increase of the number of the pests;
Parasitic and pathogenic bacteria utilization: parasitic bacteria and pathogenic bacteria have potential for controlling crop diseases, and the beneficial microorganisms can form competition situations on crops through inoculation or naturally existing modes to inhibit the growth and the transmission of the pathogenic bacteria;
Disease-preventing beneficial microorganism utilization: probiotics and fungi can symbiotic with plants, promote plant growth and health, and inhibit infection by pathogenic bacteria, and these beneficial microorganisms provide defense of crops against diseases by producing antibiotics, competitively eliminating and inducing plant immune response mechanisms.
Step S4, genetic improvement and variety screening:
Genetic improvement: through gene editing and transgenic technology, disease-resistant genes are introduced into rice varieties, so that the bakanae disease resistance of the rice varieties is improved, meanwhile, disease-resistant gene research is developed, the resistance mechanism of the bakanae disease of the rice is deeply known, and the varieties with long-acting resistance are promoted and bred;
the gene editing and transgenic technology specifically comprises the following aspects:
In this example, the gene editing technique: by utilizing a CRISPR-Cas9 gene editing tool, genes related to disease resistance in rice are subjected to fixed-point modification, and specific gene sequences can be directly inserted, deleted or modified in rice genome through gene editing, and the genes related to disease resistance are introduced or changed, so that the resistance of the rice to bakanae disease is improved;
Transgenic technology: the exogenous gene is introduced into rice from other species, and the exogenous gene is derived from plants, bacteria or other organisms except the rice and has disease resistance related characteristics, so that the transgenic technology can express resistance related protein by introducing the exogenous gene into the rice, thereby improving the resistance of the rice to bakanae disease.
Variety screening: the polyploid breeding and molecular marker assisted breeding technology is utilized to accelerate the breeding process of the disease-resistant rice varieties, and the genes related to the disease resistance are identified through high-throughput sequencing and genetic map analysis, so that the basis is provided for breeding the excellent varieties with the disease resistance.
In this embodiment, variety screening specifically includes the following aspects:
Polyploid breeding: polyploidy breeding is to increase the chromosome number of plant cells or tissues to multiple times through artificial induction, including diploid to tetraploid and hexaploid, so that the genetic variability can be obviously increased by polyploidy breeding, and plants show larger genetic diversity, which is helpful for improving the genetic potential of disease resistance and providing more material selection for breeding excellent varieties with resistance;
Molecular marker assisted breeding technology: the molecular marker is a DNA sequence positioned at a specific site, can be used for identifying and tracking a gene or a gene region of interest, and can rapidly and accurately identify and select genes related to disease resistance through a molecular marker assisted breeding technology, so that breeders can select individuals with target genes more pertinently, and the breeding efficiency is improved;
High throughput sequencing: the high-throughput sequencing technology can rapidly and efficiently determine the DNA sequence of the whole genome, and can obtain the genetic information of rice on a large scale through high-throughput sequencing, including the nucleotide sequence of the genome, the composition and the structure of genes, thereby providing a comprehensive data basis for analyzing genes related to disease resistance;
Genetic map analysis: by analyzing the genetic linkage relationship between different varieties or different individuals, a genetic map is established, the positions of genes on chromosomes and the relationship between genes can be known, and by analyzing the genetic map, the positions and genes related to disease resistance can be determined, and gene positioning and sequencing can be performed, so that the breeding process of the disease resistance variety is accelerated.
In this example, the disease resistance gene study specifically included the following aspects:
Identification of disease resistance genes: by comparing genetic differences or expression differences of disease-resistant and susceptible varieties, identifying genes related to bakanae disease resistance by utilizing a molecular marking technology, wherein the genes are involved in signal transduction of disease resistance, synthesis of disease-resistant related metabolites and expression of disease-resistant related proteins;
functional analysis of disease resistance genes: the identified disease resistance genes are functionally analyzed, the action mechanism of the identified disease resistance genes in the process of resisting bakanae disease of rice is known, and the functions and interaction network of the disease resistance genes are revealed through genetic, biochemical and cell biological means;
Disease resistance gene transfer and optimization: according to the deep knowledge of the disease resistance genes, the disease resistance genes are introduced into rice by a transgenic technology, and the expression of the disease resistance genes is further optimized and regulated so as to improve the long-acting resistance of rice varieties to bakanae disease.
Step S5, farmer training and technical popularization:
Farmer training program: the training courses aiming at the bakanae disease of the rice are organized, so that the knowledge and prevention and control skills of farmers on the disease are improved, and the training contents comprise disease identification, preventive measures, application technology and knowledge and practice skills in reasonable cultivation management;
Agricultural technology popularization service: an agricultural technology popularization service platform is established, real-time disease early warning information, prevention and control guidance and technical support are provided for farmers through Internet and mobile phone application programs, disease and pest prevention and control training activities are regularly held, communication interaction between agricultural technology personnel and farmers is enhanced, and prevention and control effects are improved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The method for preventing and treating bakanae disease of rice is characterized by comprising the following steps:
step S1, big data and artificial intelligence are utilized:
Establishing a rice disease database: collecting data related to rice diseases in the global scope, including disease types, geographic positions and cultivation management measures, and identifying risk factors and disease occurrence rules related to rice bakanae disease by integrating and analyzing the data;
disease prediction model: using machine learning and deep learning algorithms to establish a disease prediction model, and predicting the occurrence probability and degree of bakanae disease of rice by inputting environmental data, meteorological data and soil conditions;
intelligent disease recognition system: developing an intelligent disease recognition system based on image recognition and deep learning, shooting blade or plant images by farmers by using smart phones or other equipment, and rapidly recognizing the type and severity of bakanae disease of rice by comparing the disease images in an image database by the system, so as to provide accurate basis for prevention and control;
step S2, ecological system management:
Ecological agriculture practice: the method for promoting ecological agriculture comprises organic agriculture, biological diversity protection and ecological balance regulation, increases the biological diversity of farmlands, improves the stability and disease resistance of an ecological system, and reduces the occurrence of bakanae disease of rice;
rotation and interplanting: the rotation and interplanting of the rice are reasonably arranged to destroy the continuous host relation of the diseases, reduce the spread of the diseases, and timely plant disease non-host plants or other crops, so that the occurrence risk of the diseases can be reduced;
And (3) cultivation management optimization: reasonably adjusting planting density, fertilization level and irrigation management cultivation measures, improving health condition and disease resistance of rice, strictly managing field weeds, and reducing survival and transmission of pathogenic bacteria;
Step S3, accurately applying and fertilizing:
The pesticide dosage is precisely controlled: the intelligent spraying equipment and the sensing technology are introduced to realize the accurate control of the pesticide dosage, the spraying dosage and the frequency are dynamically adjusted according to the development condition and the severity of diseases, and the use amount of the pesticide and the environmental pollution are reduced;
organic agriculture and biological control: encouraging popularization of organic agriculture, reducing dependence on chemical pesticides, and focusing on application of biological control methods, including control of disease spread and infestation by natural enemies, parasitic bacteria and disease-preventing beneficial microorganisms;
step S4, genetic improvement and variety screening:
Genetic improvement: through gene editing and transgenic technology, disease-resistant genes are introduced into rice varieties, so that the bakanae disease resistance of the rice varieties is improved, meanwhile, disease-resistant gene research is developed, the resistance mechanism of the bakanae disease of the rice is deeply known, and the varieties with long-acting resistance are promoted and bred;
Variety screening: the polyploid breeding and molecular marker assisted breeding technology is utilized to accelerate the breeding process of the disease-resistant rice varieties, and the genes related to the disease resistance are identified through high-throughput sequencing and genetic map analysis, so that a basis is provided for breeding the good varieties with the disease resistance;
step S5, farmer training and technical popularization:
Farmer training program: the training courses aiming at the bakanae disease of the rice are organized, so that the knowledge and prevention and control skills of farmers on the disease are improved, and the training contents comprise disease identification, preventive measures, application technology and knowledge and practice skills in reasonable cultivation management;
Agricultural technology popularization service: an agricultural technology popularization service platform is established, real-time disease early warning information, prevention and control guidance and technical support are provided for farmers through Internet and mobile phone application programs, disease and pest prevention and control training activities are regularly held, communication interaction between agricultural technology personnel and farmers is enhanced, and prevention and control effects are improved.
2. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S1, the establishment of the rice disease database specifically includes the following aspects:
And (3) data collection: acquiring data related to rice diseases by using an automation technology and a sensor;
Data integration and cleaning: integrating and cleaning the collected various data;
data storage and management: establishing a special database system to store and manage rice disease data;
data analysis and mining: analyzing and excavating rice disease data;
data visualization and interaction: displaying the analysis result in a form of a chart and a map by utilizing a data visualization technology;
data sharing and application: the disease database is shared open and is widely used by farmers, agricultural technical staff and research institutions.
3. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S1, the disease prediction model specifically includes the following aspects:
And (3) data collection: collecting data relating to disease;
feature extraction: extracting features related to disease occurrence from the collected data;
data preprocessing: preprocessing the extracted characteristic data;
And (3) establishing a model: establishing a deep learning model according to the predicted target and the data characteristics;
Model training and verification: dividing the collected data into a training set and a verification set;
disease prediction: the new data is predicted using the trained model.
4. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S1, the intelligent disease recognition system specifically includes the following aspects:
and (3) data collection: collecting data related to disease identification;
data preprocessing: preprocessing the collected image data;
feature extraction: extracting features related to disease identification from the preprocessed image;
And (3) establishing a model: selecting a proper model to build according to the extracted characteristic data;
Model training and verification: dividing the collected image data into a training set and a verification set;
disease identification: the new image is identified using the trained model.
5. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S3, the precise pesticide dosage control specifically includes the following aspects:
pesticide dose measurement: the accurate control of the pesticide dosage first requires the measurement of the pesticide used;
An automation control system: precise control of pesticide dosage is often achieved by means of automated control systems;
data analysis and algorithm: for accurate dose control, data analysis and algorithms play an important role in the pesticide spraying process;
real-time monitoring and feedback: in the pesticide spraying process, real-time monitoring and feedback are key to achieving accurate control.
6. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S3, the organic agriculture specifically includes the following aspects:
Soil management: organic agriculture focuses on optimizing the soil ecosystem;
Selecting a crop variety: organic agriculture is more prone to select crop varieties that are naturally resistant or adaptable;
biodiversity protection: organic agriculture encourages protection of the natural ecosystem around farmlands.
7. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S3, the biological control method specifically includes the following aspects:
natural enemies are utilized: natural enemies refer to other organisms that predate or parasitize crop pests;
Parasitic and pathogenic bacteria utilization: parasitic bacteria and pathogenic bacteria have potential for controlling crop diseases;
Disease-preventing beneficial microorganism utilization: probiotics and fungi can be symbiotic with plants to promote plant growth and health.
8. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S4, the variety screening specifically includes the following aspects:
Polyploid breeding: polyploid breeding is to multiply the number of plant cells or tissue chromosomes through artificial induction;
Molecular marker assisted breeding technology: molecular markers refer to DNA sequences located at a specific site that can be used to identify and track a gene or region of a gene of interest;
high throughput sequencing: the high-throughput sequencing technology can rapidly and efficiently determine the DNA sequence of the whole genome;
Genetic map analysis: by analyzing the genetic linkage relationship between different varieties or different individuals, a genetic map is established.
9. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S4, the gene editing and transgenic technology specifically includes the following aspects:
Gene editing technology: utilizing a CRISPR-Cas9 gene editing tool to carry out fixed-point modification on genes related to disease resistance in rice;
Transgenic technology: by introducing a foreign gene, a gene having disease resistance is introduced into rice from another species.
10. A method for controlling bakanae disease of rice according to claim 1, characterized in that: in the step S4, the disease resistance gene research specifically includes the following aspects:
identification of disease resistance genes: by comparing genetic differences or expression differences of disease-resistant and susceptible varieties;
functional analysis of disease resistance genes: performing functional analysis on the identified disease resistance genes;
disease resistance gene transfer and optimization: based on the deep knowledge of the disease resistance genes, these disease resistance genes are introduced into rice by transgenic techniques.
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