Breeding method and system of new high-oil and high-yield peanut variety and medium
By screening and hybridizing excellent peanut germplasm resources, designing appropriate hybridization combinations, and through continuous screening and self-brokening, the problem of lack of high oil content peanut varieties in the existing technology is solved, and new varieties of high oil and high yield peanuts are efficiently selected and bred.
Patent Information
- Application Number
- CN202510354041.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-25
- Publication Date
- 2025-05-09
AI Technical Summary
The existing technology lacks peanut varieties with high oil content, which leads to blind selection of high oil traits and makes it difficult to cultivate new peanut varieties with high oil and high yield.
By collecting peanut germplasm resources of excellent traits, screening parent varieties with excellent traits, stable genetics and strong adaptability, phenotype and genotype evaluation, designing appropriate hybridization combinations, conducting artificial pollination and field planting, recording various indicators, and rapidly stabilizing ideal traits through continuous screening and self-brokenness.
The scientificity, speed and efficiency of breeding new varieties of high-oil and high-yield peanuts has been improved. The selected parent varieties have strong adaptability, and the selected new varieties have high yield and high oil content and are highly resistant to disease.
Smart Images
Figure CN119949239A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of peanut breeding, and in particular relates to a breeding method, system and medium for a new high-oil and high-yield peanut variety. Background Art
[0002] Peanut is an important oil crop and cash crop in my country. At present, the peanut planting area in my country is about 80 million mu, and the total peanut output is about 18 million tons, of which about 50% is used for oil extraction. Therefore, the oil content of peanuts is the most important quality indicator of peanuts. However, the widely planted peanut varieties have a low oil content and cannot meet the needs of the market. Therefore, breeding new peanut varieties with high oil content and high yield has become one of the main goals of peanut breeding, and it plays an important role in increasing the income of peanut growers and improving corporate benefits.
[0003] However, due to the lack of high-oil varieties in peanut production and the lack of technology to breed and screen high-oil materials and varieties, the selection of high-oil traits has been blind, and the high-oil peanut variety breeding technology has become a difficult problem in the world peanut breeding industry.
[0004] Therefore, it is an urgent problem to provide a breeding method, system and medium for a new high-oil and high-yield peanut variety to improve the scientificity, speed and efficiency of cultivating a new high-oil and high-yield peanut variety. Summary of the invention
[0005] In view of the above-mentioned technical problems, the present invention provides a breeding method, system and medium for a new high-oil and high-yield peanut variety.
[0006] In a first aspect, the present invention provides a method for breeding a new high-oil and high-yield peanut variety, the method comprising the following steps:
[0007] Step 1: Collect peanut germplasm resources with excellent traits;
[0008] Step 2: Based on the ideal traits, select parent varieties with excellent traits, genetic stability and strong adaptability from peanut germplasm resources;
[0009] Step 3: Conduct phenotypic and genotypic evaluation on the parental varieties and design appropriate hybrid combinations based on the evaluation results;
[0010] Step 4: artificially pollinate the parent varieties in any hybrid combination, then record various indicators of the growth process of all hybrid varieties through field planting observation to obtain first monitoring data, and after the fruit matures, measure the oleic acid content and yield of all hybrid varieties to obtain second monitoring data and third monitoring data;
[0011] Step 5: Based on the first monitoring data, the second monitoring data and the third monitoring data, new parent varieties are selected from all hybrid varieties in the manner of step 2, and then steps 3 to 4 are repeated;
[0012] Step 6: After the N1 generation hybridization is completed, based on the first monitoring data, the second monitoring data and the third monitoring data, plants with excellent performance and stable traits are selected;
[0013] Step 7: Self-pollinate the plants for one generation, and further select strains with complete ideal traits to obtain high-yield, high-oil strains.
[0014] Specifically, ideal traits include short, upright plant height, dark leaf color, large fruit size, high number of full fruits per plant, strong disease resistance, high yield and high oil content.
[0015] Specifically, the ideal traits are: plant height of 40cm to 45cm, the angle between the first pair of side branches and the main stem is less than 45°, high chlorophyll content, strong photosynthesis, 100-fruit weight of more than 250g, harvest of more than 20 mature and full fruits per plant, low disease level, peel fruit yield of more than 500kg and oil content of more than 55%.
[0016] Specifically, when screening parent varieties, the parent varieties must possess at least 4 of the ideal traits, which must include the upright trait.
[0017] Specifically, when designing hybrid combinations in step 3, the traits of the two parental varieties in any hybrid combination must be complementary, and inbreeding must be avoided.
[0018] Specifically, step 3 includes:
[0019] Step 31, all parent varieties are combined in pairs to generate N2 initial hybrid combinations;
[0020] Step 32, extract any initial hybrid combination, and obtain phenotypic data, genotypic data and environmental data of each parent variety in any initial hybrid combination;
[0021] Step 33, inputting the phenotypic data, genotypic data and environmental data of each parent variety into a preset model to predict the phenotype of any initial hybrid combination;
[0022] Step 34, repeating steps 32 and 33, after traversing all the initial hybrid combinations, taking the initial hybrid combinations that meet the ideal traits in all the prediction results as hybrid combinations.
[0023] Specifically, the preset model is a random forest model, a neural network model or a support vector machine model.
[0024] In a second aspect, the present invention also provides a breeding system for a new high-oil and high-yield peanut variety, the system comprising: a resource collection module, a parent screening module, a parent combination module, a planting monitoring module, a plant selection module and a strain determination module;
[0025] Resource collection module, used to collect peanut germplasm resources with excellent traits;
[0026] The parent screening module is used to select parent varieties with excellent traits, genetic stability and strong adaptability from peanut germplasm resources based on ideal traits;
[0027] The parent combination module is used to evaluate the phenotype and genotype of parent varieties and design appropriate hybrid combinations based on the evaluation results;
[0028] The planting monitoring module is used to obtain various indicators of the growth process of all hybrid varieties after artificial pollination of the parent varieties in any hybrid combination, which are recorded as the first monitoring data. After the fruit is ripe, the oleic acid content and yield of all hybrid varieties are measured to obtain the second monitoring data and the third monitoring data;
[0029] The parent screening module is further used to screen new parent varieties from all hybrid varieties according to the first monitoring data, the second monitoring data and the third monitoring data;
[0030] A plant selection module, for selecting plants with excellent performance and stable traits based on the first monitoring data, the second monitoring data and the third monitoring data after the N1 generation hybridization is completed;
[0031] The strain determination module is used to select strains with complete ideal traits after the plant is self-pollinated for one generation, so as to obtain high-yield and high-oil strains.
[0032] In a third aspect, the present invention provides a computer storage medium storing program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute any one of the above-mentioned methods for breeding new high-oil and high-yield peanut varieties.
[0033] In a fourth aspect, the present invention provides a processor for running a program, wherein when the program is running, any one of the above-mentioned methods for breeding a new high-oil and high-yield peanut variety is executed.
[0034] Compared with the prior art, the beneficial effects of the present invention are at least as follows:
[0035] 1. The selected parent varieties have strong adaptability, which helps the new varieties grow under different environmental conditions;
[0036] 2. Use preset models to predict the phenotype of hybrid combinations, improving the scientificity and accuracy of the breeding process;
[0037] 3. Ensure that the traits of the parent varieties in the hybrid combination are complementary, avoid inbreeding, help maintain genetic diversity and reduce the risk of genetic diseases;
[0038] 4. Through continuous screening and self-pollination, ideal traits can be quickly stabilized, the breeding cycle can be accelerated, and the breeding efficiency can be improved;
[0039] 5. Disease resistance is taken into consideration during the breeding process, which helps to breed new peanut varieties with stronger resistance to diseases and pests;
[0040] 6. Through a systematic breeding process, new peanut varieties with high yield and high oil content can be quickly cultivated. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0042] Figure 1 This is a flow chart of a method for breeding a new high-oil and high-yield peanut variety of the present invention;
[0043] Figure 2 The modular schematic diagram of a breeding system for a new high-oil and high-yield peanut variety of the present invention. DETAILED DESCRIPTION
[0044] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.
[0045] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.
[0046] Figure 1 The flowchart of an embodiment of a method for breeding a new high-oil and high-yield peanut variety provided by the present invention is shown, and the flowchart specifically includes the following steps:
[0047] Step 1: Collect peanut germplasm resources with excellent traits.
[0048] The collected peanut planting resources include high-oil and high-yield peanut varieties from home and abroad, wild species, local varieties and breeding intermediate materials, etc. These planting resources are the basis for breeding new varieties.
[0049] Step 2: Based on the ideal traits, select parent varieties with excellent traits, genetic stability and strong adaptability from the peanut germplasm resources.
[0050] Specifically, ideal traits include short, upright plant height, dark leaf color, large fruit size, high number of full fruits per plant, strong disease resistance, high yield and high oil content.
[0051] The breeding goal of the technical solution of the present invention is to breed new peanut varieties with high oil content and high yield, but other important traits, such as adaptability, disease resistance and agronomic traits, should also be considered. Considering disease resistance will help breed new peanut varieties with stronger resistance to diseases and pests, help improve the yield stability of new varieties, reduce the use of pesticides, and enhance crop adaptability.
[0052] Specifically, the ideal traits are: plant height of 40cm to 45cm, the angle between the first pair of side branches and the main stem is less than 45°, high chlorophyll content, strong photosynthesis, 100-fruit weight of more than 250g, harvest of more than 20 mature and full fruits per plant, low disease level, peel fruit yield of more than 500kg and oil content of more than 55%.
[0053] When evaluating disease resistance, disease levels are usually graded based on the description of disease symptoms. For example, in the Technical Regulations for Identification of Peanut White Blight, disease levels are divided into 0 to 4, where 0 means the plant has no disease symptoms and 4 means more than 50% of the plant shows systemic symptoms.
[0054] Specifically, when screening parent varieties, the parent varieties must possess at least 4 of the ideal traits, which must include the upright trait.
[0055] Step 3: Conduct phenotypic and genotypic evaluations on the parental varieties and design appropriate hybrid combinations based on the evaluation results.
[0056] Specifically, when designing hybrid combinations in step 3, the traits of the two parental varieties in any hybrid combination must be complementary, and inbreeding must be avoided.
[0057] Avoiding or reducing inbreeding as much as possible helps maintain genetic diversity while reducing the risk of inbreeding depression.
[0058] Preferably, full use is made of the advantages of distant hybrids and parent varieties with greater differences are selected to formulate hybrid combinations.
[0059] As a preferred technical solution of the present invention, step 3 comprises:
[0060] Step 31: combine all parental varieties in pairs to generate N2 initial hybrid combinations.
[0061] Step 32: extract any initial hybrid combination, and obtain the phenotypic data, genotypic data and environmental data of each parent variety in any initial hybrid combination.
[0062] Step 33: Input the phenotypic data, genotypic data and environmental data of each parent variety into a preset model to predict the phenotype of any initial hybrid combination.
[0063] Step 34, repeating steps 32 and 33, after traversing all the initial hybrid combinations, taking the initial hybrid combinations that meet the ideal traits in all the prediction results as hybrid combinations.
[0064] For example, phenotypic data include growth rate, plant morphology, fruit size, oil content, yield, quality, disease resistance, etc., genotypic data include NP markers, gene sequences, etc., and environmental data include climate, soil, water, nutrients, etc. Because environmental factors have an important influence on phenotypes, environmental data need to be considered when making phenotypic predictions.
[0065] The use of preset models to predict the phenotypes of hybrid combinations improves the scientificity and accuracy of the breeding process.
[0066] Specifically, the preset model is a random forest model, a neural network model or a support vector machine model.
[0067] When building a preset model, first clarify the phenotypic characteristics that need to be predicted, such as crop yield, quality, disease resistance, etc. Different prediction targets may require different prediction models and methods. Commonly used methods include prediction based on genetic distance, prediction based on whole genome selection (GS), prediction based on machine learning (such as random forest, support vector machine), etc.
[0068] With the development of machine learning technology, more and more researchers have begun to use machine learning models to predict hybrid combination phenotypes. Machine learning models can handle complex nonlinear relationships and perform well on large data sets. Common machine learning models include random forests, neural networks, support vector machines, etc.
[0069] Preferably, multiple preset models can be trained, and then based on the prediction effects of different models, the optimal model is selected for phenotype prediction.
[0070] Step 4: artificially pollinate the parent varieties in any hybrid combination, then record various indicators of the growth process of all hybrid varieties through field planting observation to obtain the first monitoring data. After the fruit is ripe, the oleic acid content and yield of all hybrid varieties are measured to obtain the second monitoring data and the third monitoring data.
[0071] Artificial pollination is carried out under controlled conditions to ensure successful hybridization.
[0072] Exemplarily, the first monitoring data includes growth rate, plant morphology, fruit size, disease resistance, leaf color, leaf morphology, flowering and pod setting, etc.
[0073] Step 5: Based on the first monitoring data, the second monitoring data and the third monitoring data, new parent varieties are selected from all hybrid varieties in the manner of step 2, and then steps 3 to 4 are repeated.
[0074] The best performing plants are selected continuously over multiple generations to fix and enhance the desirable traits.
[0075] Step 6: After the N1 generation hybridization is completed, plants with excellent performance and stable traits are selected based on the first monitoring data, the second monitoring data and the third monitoring data.
[0076] The value of N1 is set according to the experience of those skilled in the art or according to the actual application scenario, and is not limited in the present application embodiment.
[0077] Step 7: Self-pollinate the plants for one generation, and further select strains with complete ideal traits to obtain high-yield, high-oil strains.
[0078] Conduct yield tests to evaluate the yield levels of new varieties, and through comparative tests, select new varieties with high yield and good stability.
[0079] Figure 2 FIG. 1 is a schematic diagram of a structure of an embodiment of a breeding system for a new high-oil and high-yield peanut variety provided by the present invention. Figure 2 As shown, the system includes: a resource collection module 10, a parent screening module 20, a parent combination module 30, a planting monitoring module 40, a plant selection module 50 and a strain determination module 60.
[0080] The resource collection module 10 is used to collect peanut germplasm resources with excellent traits.
[0081] The parent screening module 20 is used to screen parent varieties with excellent traits, genetic stability and strong adaptability from peanut germplasm resources according to ideal traits.
[0082] The parent combination module 30 is used to evaluate the phenotype and genotype of the parent varieties and design a suitable hybrid combination based on the evaluation results.
[0083] The planting monitoring module 40 is used to obtain various indicators of the growth process of all hybrid varieties after artificial pollination of the parent varieties in any hybrid combination, which are recorded as the first monitoring data. After the fruit is ripe, the oleic acid content and yield of all hybrid varieties are measured to obtain the second monitoring data and the third monitoring data.
[0084] The parent screening module 20 is also used to screen new parent varieties from all hybrid varieties based on the first monitoring data, the second monitoring data and the third monitoring data.
[0085] The plant selection module 50 is used to select plants with excellent performance and stable traits based on the first monitoring data, the second monitoring data and the third monitoring data after the N1 generation hybridization is completed.
[0086] The strain determination module 60 is used to select strains with complete ideal traits after the plant is self-pollinated for one generation, so as to obtain high-yield and high-oil strains.
[0087] According to another aspect of an embodiment of the present invention, a computer storage medium is provided, wherein the computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute any one of the above-mentioned methods for breeding new high-oil and high-yield peanut varieties.
[0088] According to another aspect of an embodiment of the present invention, a processor is provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned methods for breeding a new high-oil and high-yield peanut variety is executed.
[0089] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have strict order restrictions, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.
[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0091] The above-mentioned embodiments only express the preferred implementation mode of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.
Claims
1. A method for breeding a new high-oil and high-yield peanut variety, characterized in that: The steps include: Step 1: Collect peanut germplasm resources with excellent traits; Step 2: selecting parent varieties with excellent traits, genetic stability and strong adaptability from the peanut germplasm resources according to the ideal traits; Step 3: Conduct phenotypic and genotypic evaluation on the parent varieties, and design a suitable hybrid combination based on the evaluation results; Step 4: artificially pollinate the parent varieties in any hybrid combination, then record various indicators of the growth process of all hybrid varieties through field planting observation to obtain first monitoring data, and after the fruit matures, measure the oleic acid content and yield of all the hybrid varieties to obtain second monitoring data and third monitoring data; Step 5, based on the first monitoring data, the second monitoring data and the third monitoring data, new parent varieties are selected from all the hybrid varieties in the manner of step 2, and then steps 3 to 4 are repeated; Step 6: After the N1 generation hybridization is completed, based on the first monitoring data, the second monitoring data and the third monitoring data, plants with excellent performance and stable traits are selected; Step 7: self-pollinate the plants for one generation, further select strains with complete ideal traits, and obtain high-yield and high-oil strains.
2. The method according to claim 1, characterized in that The desired traits include short and upright plants, dark leaf color, large fruit, a large number of full fruits per plant, strong disease resistance, high yield and high oil content.
3. The method according to claim 2, characterized in that The ideal traits are: plant height of 40cm to 45cm, angle between the first pair of side branches and the main stem less than 45°, high chlorophyll content, strong photosynthesis, 100-fruit weight of more than 250g, harvest of more than 20 mature and full fruits per plant, low disease level, peel fruit yield of more than 500kg and oil content of more than 55%.
4. The method according to claim 2, characterized in that: When selecting parent varieties, the parent varieties must possess at least 4 of the desired traits, which must include the upright trait.
5. The method according to claim 1, characterized in that When designing a hybrid combination in step 3, the traits of the two parental varieties in any hybrid combination must be complementary, and inbreeding must be avoided.
6. The method according to claim 1, characterized in that The step 3 comprises: Step 31, all parent varieties are combined in pairs to generate N2 initial hybrid combinations; Step 32, extracting any initial hybrid combination, and respectively obtaining phenotypic data, genotypic data and environmental data of each parent variety in any of the initial hybrid combinations; Step 33, inputting the phenotypic data, the genotypic data and the environmental data of each parent variety into a preset model to predict the phenotype of any of the initial hybrid combinations; Step 34, repeating steps 32 to 33, after traversing all initial hybrid combinations, taking the initial hybrid combinations that meet the ideal traits in all prediction results as the hybrid combinations.
7. The method according to claim 6, characterized in that The preset model is a random forest model, a neural network model or a support vector machine model.
8. A breeding system for a new high-oil and high-yield peanut variety, used to implement the method according to any one of claims 1 to 7, characterized in that: include: Resource collection module, parent screening module, parent combination module, planting monitoring module, plant selection module and strain determination module; The resource collection module is used to collect peanut germplasm resources with excellent traits; The parent screening module is used to screen parent varieties with excellent traits, genetic stability and strong adaptability from the peanut germplasm resources according to ideal traits; The parent combination module is used to evaluate the phenotype and genotype of the parent varieties and design a suitable hybrid combination according to the evaluation results; The planting monitoring module is used to obtain various indicators during the growth of all hybrid varieties after artificial pollination of the parent varieties in any hybrid combination, which are recorded as first monitoring data. After the fruits are ripe, the oleic acid content and yield of all the hybrid varieties are measured to obtain second monitoring data and third monitoring data; The parent screening module is further used to screen new parent varieties from all the hybrid varieties according to the first monitoring data, the second monitoring data and the third monitoring data; The plant selection module is used to select plants with excellent performance and stable traits based on the first monitoring data, the second monitoring data and the third monitoring data after completing the N1 generation hybridization; The strain determination module is used to select strains with complete ideal traits after the plant is self-pollinated for one generation, so as to obtain high-yield and high-oil strains.
9. A computer storage medium, characterized in that The computer storage medium stores program instructions, wherein when the program instructions are executed, the device where the computer storage medium is located is controlled to execute the method according to any one of claims 1 to 7.
10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 7 when running.