Method for assisting hawthorn breeding based on phenotypic omics
Hawthorn data were collected using a high-throughput imaging system and a phenotypic analysis system. A multidimensional phenotypic database was established, and a phenotypic-genotype association model was constructed to enable early prediction and parent selection in hawthorn breeding. This solved the problems of long breeding cycles and high costs in hawthorn breeding, and improved breeding efficiency and success rate.
Patent Information
- Application Number
- CN202511656870.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-12
- Publication Date
- 2026-02-10
AI Technical Summary
Hawthorn breeding has a long cycle and high costs, and traditional phenotypic analysis methods have limited data collection capabilities, which has become a bottleneck in modern fruit tree breeding.
Hawthorn phenotypic data were collected using a high-throughput imaging system and a phenotypic analysis system. A multidimensional phenotypic database was established, a phenotypic-genotype association model was constructed, new functional genes were discovered, and a phenotypic index weighted algorithm was created for parental selection.
It improved the efficiency of hawthorn breeding, shortened the breeding cycle, enhanced the scientific nature and precision of parent selection, and increased the breeding success rate.
Smart Images
Figure CN121506237A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of phenomics-assisted hawthorn breeding technology, and more specifically to a method for phenomics-assisted hawthorn breeding. Background Technology
[0002] Phenonomics, proposed by Steven A. Garan in 1996, aims to systematically study the complete phenotypes of organisms or cells under different environmental conditions. Its research object, the phenome, encompasses the entire set of traits throughout an organism's life cycle, from molecular to macroscopic characteristics, emphasizing the mechanisms of gene-environment interaction and is considered a key area of life sciences after genomics.
[0003] Plant phenomics, by integrating automated platforms and information technology, acquires massive amounts of multi-scale, multi-habitat, and multi-source heterogeneous plant phenotypic data, forming plant phenomics big data. Through phenotypic analysis, it describes key traits and systematically and deeply explores the intrinsic relationships between genotype, phenotype, and environment from an omics perspective, comprehensively revealing the formation mechanisms of specific biological traits. This will greatly promote the progress of functional genomics, crop molecular breeding, and efficient cultivation. Compared to single-trait analysis, plant phenomics provides comprehensive scientific evidence for plant research. With the rapid development of emerging technologies such as artificial intelligence, big data, and image recognition, the combination of phenomics technology with corresponding genomic and environmental data is expected to significantly accelerate the breeding process and cultivate groundbreaking new varieties that are high-quality, high-yielding, and highly resistant.
[0004] Hawthorn belongs to the subfamily Maloideae of the family Rosaceae and is widely distributed in Asia, Europe, Central and North America, and northern South America. my country is one of the centers of origin for plants in the genus *Crataegus*, which contains 20 species, 7 varieties, and 1 form, including the large-fruited hawthorn (*Crataegus pinnatifida*). Crataegus pinnatifida *Begonia var. major* is a unique variety native to my country and a major cultivated species in the northern hawthorn-producing areas. Most hawthorn fruits have low kernel percentage, long seed stratification time, and slow seedling growth, resulting in low breeding efficiency, long breeding cycles, and high costs. Currently, hawthorn cultivars are mainly obtained through seed selection or bud mutation selection of local or farm-grown varieties; truly hybrid varieties are very rare.
[0005] Breeders need to accurately measure large amounts of phenotypic data to screen for superior traits and select new varieties that are high-quality, high-yielding, and highly resistant. However, traditional phenotypic analysis methods have become a bottleneck in modern fruit tree breeding due to their limited data acquisition capabilities. Phenomics analysis technology can integrate multi-dimensional data from high-resolution images, spectral analysis, environmental sensors, etc., including morphological characteristics of hawthorn (plant type, leaf and fruit size, color, and shape), physiological and biochemical indicators (photosynthetic efficiency and water use efficiency), and environmental parameters (temperature, humidity, and light intensity), aiming to comprehensively capture the phenotypic characteristics of hawthorn at different growth stages. Therefore, how to use phenomics technology to assist hawthorn breeding and improve breeding efficiency is a problem that still needs to be solved in this field. Summary of the Invention
[0006] In view of this, the present invention provides a method for hawthorn breeding based on phenomics, which solves the problems of long breeding cycle and high cost of hawthorn and effectively improves the efficiency of hawthorn breeding.
[0007] To achieve the above objectives, the present invention provides the following technical solution: A phenomics-based method for hawthorn breeding includes the following steps: S1. Use a high-throughput imaging system and a phenotypic analysis system to collect phenotypic data of normally growing hawthorns, and detect and analyze various traits of hawthorns; S2. Establish a multidimensional phenotypic database for hawthorn and obtain quantitative indicators of key traits for hawthorn breeding; S3. Based on the obtained quantitative indicators of key traits in hawthorn breeding, construct a phenotypic-genotypic association model, discover new functional genes, and achieve early prediction in breeding. S4. Create a phenotypic index weighted algorithm for parent selection.
[0008] Optionally, in S1, a high-throughput imaging system and a phenotypic analysis system are used to collect phenotypic data at three scales: canopy, plant, and organs of normally growing hawthorn, to detect and analyze the plant morphology, biomass, yield, and resistance traits of hawthorn. High-throughput imaging drones equipped with multispectral cameras were used to acquire canopy images; RGB cameras are used to acquire color images of the overall morphology of the plant and the fruit, and laser scanners are used to acquire three-dimensional structural data of the plant. The microscopic features of leaves were observed using a microscope imaging system, and the internal structural information of the fruit was obtained using a micro-CT scanning system. The internal structural information of the fruit included the number, size, and distribution of seeds, as well as the cavities inside the fruit.
[0009] Optionally, in S2, a multidimensional phenotypic database of hawthorn is established to obtain the specific content of the quantitative indicators of key traits in hawthorn breeding: The establishment of the hawthorn multidimensional phenotypic database is based on phenotypic data at three scales: canopy, plant, and organ. Various types of raw data collected by the high-throughput imaging system are standardized. A structured database architecture was built, using the relational database management system MySQL. Tag sub-tables were set up by variety number and sampling time, and multiple tag sub-tables were linked to perform data query and retrieval. The hawthorn multidimensional phenotypic database is stored in layers according to the attributes of phenotypic data, namely, morphological structure layer, physiological function layer and environmental response layer; Based on multidimensional data from the hawthorn multidimensional phenotypic database, key traits for breeding objectives were screened and quantified.
[0010] Optional quantitative indicators for key traits include yield, quality, resistance, and growth potential.
[0011] Optionally, in S3, based on the obtained quantitative indicators of key traits in hawthorn breeding, a phenotypic-genotypic association model is constructed to discover new functional genes and achieve early prediction in breeding. A phenotype-genotype association model was constructed using genome-wide association analysis. First, the phenotypic data is tested for normality and standardized to remove outliers; Then, using a mixed linear model, the population structure Q matrix and kinship K matrix were added as covariates to the phenotype-genotype association model; The association degree between each molecular marker and each key phenotypic trait was calculated using a phenotypic-genotype association model to obtain the significantly associated SNP sites; Based on the obtained significantly associated SNP sites, new functional genes are discovered through gene annotation and functional prediction. Based on the constructed phenotype-genotype association model and the newly discovered functional genes, early prediction of breeding can be achieved.
[0012] Optionally, based on the obtained significantly associated SNP sites, the specific details of mining new functional genes through gene annotation and functional prediction are as follows: First, the genomic regions where significantly associated SNP sites are located are identified, and candidate genes within these regions are screened using hawthorn reference genome annotation information. Sequence analysis of candidate genes is performed to predict the structure and function of the encoded proteins; Gene expression analysis was used to verify the correlation between the expression patterns of candidate genes and phenotypic traits. If a candidate gene is highly expressed during the fruit development stage and its expression level is positively correlated with the weight of a single fruit, then the candidate gene is a new functional gene that regulates fruit size. If a candidate gene is highly expressed during the fruit coloring stage, then the candidate gene is a new functional gene that regulates fruit coloring.
[0013] Optionally, genetic transformation experiments can be conducted on candidate genes using transgenic technology to observe the effects on phenotypic traits, thereby verifying gene function.
[0014] Optionally, the specific details of creating a phenotypic index weighted algorithm in S4 for parental mating are as follows: From the established quantitative indicators of key breeding traits, the core phenotypic indicators that have the greatest impact on hawthorn breeding objectives were selected, and each selected core phenotypic indicator was standardized. The weights of each core phenotypic indicator were determined by combining the analytic hierarchy process (AHP) with the breeding objectives. Based on the standardized values and weights of each core phenotypic indicator, a weighted summation formula is used to calculate the comprehensive phenotypic index for each parent. ; Candidate parents are ranked according to their phenotypic composite index. Parents with high composite indices are selected as alternative materials. At the same time, parents that perform well in different phenotypic indicators are selected for hybridization to obtain the best parent combination.
[0015] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method for hawthorn breeding based on phenomics, which has the following beneficial effects: (1) This invention solves the problems of long breeding cycle and high cost of hawthorn, realizes phenomics-assisted breeding, and effectively improves the efficiency of hawthorn breeding; (2) This invention improves the scientific nature and accuracy of parent selection, helps to scientifically and efficiently screen out the best parent combination, improves the breeding success rate, and shortens the breeding cycle. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0017] Figure 1 The flowchart illustrates a method for phenomics-assisted hawthorn breeding provided by this invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] See Figure 1 As shown, this invention discloses a method for hawthorn breeding based on phenomics, comprising the following steps: S1. Use a high-throughput imaging system and a phenotypic analysis system to collect phenotypic data of normally growing hawthorns, and detect and analyze various traits of hawthorns; S2. Establish a multidimensional phenotypic database for hawthorn and obtain quantitative indicators of key traits for hawthorn breeding; S3. Based on the obtained quantitative indicators of key traits in hawthorn breeding, construct a phenotypic-genotypic association model, discover new functional genes, and achieve early prediction in breeding. S4. Create a phenotypic index weighted algorithm for parent selection.
[0020] Furthermore, in S1, a high-throughput imaging system and a phenotypic analysis system were used to collect phenotypic data at three scales: canopy, plant, and organs of normally growing hawthorn, to detect and analyze the plant morphology, biomass, yield, and resistance traits of hawthorn. High-throughput imaging drones equipped with multispectral cameras were used to acquire canopy images; RGB cameras are used to acquire color images of the overall morphology of the plant and the fruit, and laser scanners are used to acquire three-dimensional structural data of the plant. The microscopic features of leaves were observed using a microscope imaging system, and the internal structural information of the fruit was obtained using a micro-CT scanning system. The internal structural information of the fruit included the number, size, and distribution of seeds, as well as the cavities inside the fruit.
[0021] Furthermore, in S2, a multidimensional phenotypic database of hawthorn was established to obtain the specific content of quantitative indicators of key traits in hawthorn breeding: The establishment of the hawthorn multidimensional phenotypic database is based on phenotypic data at three scales: canopy, plant, and organ. Various types of raw data collected by the high-throughput imaging system are standardized. A structured database architecture was built, using the relational database management system MySQL. Tag sub-tables were set up by variety number and sampling time, and multiple tag sub-tables were linked to perform data query and retrieval. The hawthorn multidimensional phenotypic database is stored in layers according to the attributes of phenotypic data, namely, morphological structure layer, physiological function layer and environmental response layer; Based on multidimensional data from the hawthorn multidimensional phenotypic database, key traits for breeding objectives were screened and quantified.
[0022] Furthermore, key trait quantitative indicators include yield indicators, quality indicators, resistance indicators, and growth potential indicators.
[0023] Furthermore, in S3, based on the obtained quantitative indicators of key traits in hawthorn breeding, a phenotypic-genotypic association model is constructed to discover new functional genes and achieve early prediction in breeding. A phenotype-genotype association model was constructed using genome-wide association analysis. First, the phenotypic data is tested for normality and standardized to remove outliers; Then, using a mixed linear model, the population structure Q matrix and kinship K matrix were added as covariates to the phenotype-genotype association model; The association degree between each molecular marker and each key phenotypic trait was calculated using a phenotypic-genotype association model to obtain the significantly associated SNP sites; Based on the obtained significantly associated SNP sites, new functional genes are discovered through gene annotation and functional prediction. Based on the constructed phenotype-genotype association model and the newly discovered functional genes, early prediction of breeding can be achieved.
[0024] Furthermore, based on the obtained significantly associated SNP sites, the specific details of mining new functional genes through gene annotation and functional prediction are as follows: First, the genomic regions where significantly associated SNP sites are located are identified, and candidate genes within these regions are screened using hawthorn reference genome annotation information. Sequence analysis of candidate genes is performed to predict the structure and function of the encoded proteins; Gene expression analysis was used to verify the correlation between the expression patterns of candidate genes and phenotypic traits. If a candidate gene is highly expressed during the fruit development stage and its expression level is positively correlated with the weight of a single fruit, then the candidate gene is a new functional gene that regulates fruit size. If a candidate gene is highly expressed during the fruit coloring stage, then the candidate gene is a new functional gene that regulates fruit coloring.
[0025] Genetic transformation experiments are conducted on candidate genes using transgenic technology to observe their effects on phenotypic traits, thereby verifying gene function.
[0026] Furthermore, the specific details of creating a phenotypic index weighted algorithm in S4 for parental mating are as follows: From the established quantitative indicators of key breeding traits, the core phenotypic indicators that have the greatest impact on hawthorn breeding objectives were selected, and each selected core phenotypic indicator was standardized. The weights of each core phenotypic indicator were determined by combining the analytic hierarchy process (AHP) with the breeding objectives. Based on the standardized values and weights of each core phenotypic indicator, a weighted summation formula is used to calculate the comprehensive phenotypic index for each parent. ; Candidate parents are ranked according to their phenotypic composite index. Parents with high composite indices are selected as alternative materials. At the same time, parents that perform well in different phenotypic indicators are selected for hybridization to obtain the best parent combination.
[0027] In one specific embodiment, three hawthorn varieties, 'Da Jin Xing', 'Jin Ru Yi', and 'Yu Gan Hong', were selected as research subjects. Breeding work was carried out according to the process of this invention, specifically including the following: Step 1: Phenotypic Data Collection and Trait Detection and Analysis Thirty normally growing plants of each of the three varieties were selected, and multi-scale phenotypic data were collected using a high-throughput imaging system and a phenotypic analysis system. At the canopy scale, canopy images were acquired during the peak fruiting period using a multispectral UAV. The NDVI values of 'Da Jin Xing', 'Jin Ru Yi', and 'Yu Gan Hong' were calculated to be 0.72, 0.68, and 0.70, respectively, with canopy coverage of 85%, 80%, and 82%, respectively. The net photosynthetic rates were 7.99 μmol / (m²). 2 ·s), 13.56μmol / (m 2 ·s), 11.71 μmol / (m 2 •s). At the plant scale, the tree shape was determined to be open-center using a phenotypic analysis platform, with plant heights of 2.5m, 2.3m, and 2.4m respectively. The Fv / Fm values, detected by a chlorophyll fluorescence spectrometer, were 0.83, 0.81, and 0.82, respectively. At the organ scale, microscopic observation revealed stomatal densities of 10⁵ stomata / mm². 2 269 pieces / mm 2 128 pieces / mm 2 The average weight of a single fruit was measured using calipers and an electronic balance. The average weights were 15.6g, 12.4g, and 10.2g, respectively, and the fruit shape indices were 1.2, 1.1, and 1.15, respectively.
[0028] Analysis of the collected data showed that 'Da Jin Xing' performed well in yield-related traits such as single fruit weight and canopy NDVI value; and its low leaf stomatal density may give it an advantage in water retention; 'Yu Gan Hong' had moderate plant height and canopy coverage, with relatively balanced traits; and 'Jin Ru Yi' had the highest net photosynthetic rate and higher biomass accumulation efficiency.
[0029] Step 2: Establishment of Hawthorn Multidimensional Phenotypic Database and Quantification of Key Traits
[0030] Integrating all phenotypic data obtained in step 1, a multidimensional hawthorn phenotypic database was established, encompassing three scales: canopy, plant, and organs. In the database, each variety's data was labeled with metadata such as collection time (peak fruiting period) and growing environment (temperature 25℃, humidity 60%). Through data analysis and filtering, quantitative indicators for key breeding traits were determined, including canopy NDVI value, canopy coverage, net photosynthetic rate, plant height, Fv / Fm value, average single fruit weight, leaf stomatal density, and fruit shape index. Among these, average single fruit weight and canopy NDVI value served as core yield indicators; Fv / Fm value reflected photosynthetic capacity; leaf stomatal density was related to drought resistance; and the fruit shape index affected fruit appearance quality.
[0031] Step 3: Phenotype-Genome Association Model Construction and Early Prediction
[0032] Genotypic data for three varieties were obtained, and GWAS analysis revealed a significant SNP locus in a genomic region associated with single fruit weight. 'Da Jin Xing' showed a dominant allele at this locus, while 'Jin Ru Yi' and 'Yu Gan Hong' showed non-dominant alleles, consistent with the phenotypic finding of 'Da Jin Xing' having a larger single fruit weight in step 1. Further investigation revealed a candidate gene in the region containing this SNP locus. qRT-PCR validation showed that this gene's expression level during the fruit development stage of 'Da Jin Xing' was significantly higher than that of the other two varieties, suggesting it may be a novel functional gene regulating fruit size.
[0033] Based on the constructed phenotypic-genotypic association model, early predictions were made for the hybrid offspring of the three varieties. If 'Da Jin Xing' is crossed with 'Jin Ru Yi', the offspring are highly likely to carry the dominant allele, and the predicted average single fruit weight may be between 13.5g and 16.5g; if 'Da Jin Xing' is crossed with 'Yu Gan Hong', the offspring may have good overall performance in canopy traits and fruit shape index; if 'Yu Gan Hong' is crossed with 'Jin Ru Yi', the offspring may have good overall performance in yield and biomass.
[0034] Step 4: Phenotypic index weighted algorithm for parental mating
[0035] Among them, PI is the phenotypic composite index, with a value range of [0,1]. The closer the value is to 1, the more the parent's overall performance in the selected key traits is in line with the breeding objectives. Standardized value = (Original value - Minimum value) / (Maximum value - Minimum value); The weight of the i-th key phenotypic indicator: Based on the breeding objectives, the importance coefficient of the indicator is determined by the analytic hierarchy process (AHP). The sum of the weights of all indicators is 1 to ensure the rationality of the calculation logic. The weights of NDVI value i1, canopy coverage i2, net photosynthetic rate i3, plant height i4, Fv / Fm value i5, average single fruit weight i6, and fruit shape index i7 are 0.15, 0.1, 0.2, 0.1, 0.1, 0.25, and 0.1, respectively. As shown in Table 1, the key phenotypic indicators include NDVI value i1, canopy coverage i2, net photosynthetic rate i3, plant height i4, Fv / Fm value i5, average single fruit weight i6, and fruit shape index i7. Due to the large difference in leaf stomatal density, the data for 'Jin Ruyi' is abnormally high and is not included to avoid interfering with the comprehensive evaluation. All trees are open-center shaped and have no difference, so they are not included in the calculation. Table 1. Statistics of original values and extreme values of various indicators for the three varieties.
[0036] Calculate the combined phenotypic index of the three varieties: 'Great Venus' Phenotypic Composite Index: The standardized value is: i1 (NDVI): (0.72-0.68) / (0.72-0.68)=1 i2 (canopy coverage): (85%-80%) / (85%-80%)=1 i3 (net photosynthetic rate): (7.99-7.99) / (13.56-7.99)=0 i4 (plant height): (2.5-2.3) / (2.5-2.3)=1 i5 (Fv / Fm): (0.83-0.81) / (0.83-0.81)=1 i6 (single fruit weight): (15.6-10.2) / (15.6-10.2)=1 i7 (Fruit Shape Index): (1.2-1.1) / (1.2-1.1)=1 Phenotypic composite index: PI=(1×0.15)+(1×0.1)+(0×0.2)+(1×0.1)+(1×0.1)+(1×0.25)+(1×0.1)=0.15+0.1+0+0.1+0.1+0.25+0.1=0.8 'Golden Ruyi' Phenotypic Composite Index: The standardized value is: i1 (NDVI): (0.68-0.68) / (0.72-0.68)=0 i2 (canopy coverage): (80%-80%) / (85%-80%)=0 i3 (net photosynthetic rate): (13.56-7.99) / (13.56-7.99)=1 i4 (plant height): (2.3-2.3) / (2.5-2.3)=0 i5 (Fv / Fm): (0.81-0.81) / (0.83-0.81)=0 i6 (single fruit weight): (12.4-10.2) / (15.6-10.2)≈0.407 i7 (Fruit Shape Index): (1.1-1.1) / (1.2-1.1)=0 Phenotypic composite index: PI=(0×0.15)+(0×0.1)+(1×0.2)+(0×0.1)+(0×0.1)+(0.407×0.25)+(0×0.1)=0+0+0.2+0+0+0.10175+0≈0.302 'Yuganhong' Phenotypic Composite Index: The standardized value is: i1 (NDVI): (0.70-0.68) / (0.72-0.68)=0.5 i2 (canopy coverage): (82%-80%) / (85%-80%)=0.4 i3 (net photosynthetic rate): (11.71-7.99) / (13.56-7.99)≈0.668 i4 (plant height): (2.4-2.3) / (2.5-2.3)=0.5 i5 (Fv / Fm): (0.82-0.81) / (0.83-0.81)=0.5 i6 (single fruit weight): (10.2-10.2) / (15.6-10.2)=0 i7 (Fruit Shape Index): (1.15-1.1) / (1.2-1.1)=0.5 Phenotypic composite index: PI=(0.5×0.15)+(0.4×0.1)+(0.668×0.2)+(0.5×0.1)+(0.5×0.1)+(0×0.25)+(0.5×0.1)=0.075+0.04+0.1336+0.05+0.05+0+0.05=0.3986 The closer the phenotypic composite index (PI) is to 1, the better. According to the composite index, 'Da Jin Xing' has the best overall performance, followed by 'Yu Gan Hong', and 'Jin Ru Yi' is slightly worse. Considering phenotypic complementarity, 'Da Jin Xing' has advantages in yield and single fruit weight, and its low leaf stomatal density may indicate better drought resistance. Selecting 'Da Jin Xing' and 'Yu Gan Hong' as parents for hybridization can yield offspring with both high yield and drought resistance.
[0037] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for phenomics-assisted hawthorn breeding, characterized in that, Includes the following steps: S1. Use a high-throughput imaging system and a phenotypic analysis system to collect phenotypic data of normally growing hawthorns, and detect and analyze various traits of hawthorns; S2. Establish a multidimensional phenotypic database for hawthorn and obtain quantitative indicators of key traits for hawthorn breeding; S3. Based on the obtained quantitative indicators of key traits in hawthorn breeding, construct a phenotypic-genotypic association model, discover new functional genes, and achieve early prediction in breeding. S4. Create a phenotypic index weighted algorithm for parent selection.
2. The method for hawthorn breeding based on phenomics according to claim 1, characterized in that, In S1, high-throughput imaging and phenotypic analysis systems were used to collect phenotypic data at three scales: canopy, plant, and organs of normally growing hawthorn, and to detect and analyze the plant morphology, biomass, yield, and resistance traits of hawthorn. High-throughput imaging drones equipped with multispectral cameras were used to acquire canopy images; RGB cameras are used to acquire color images of the overall morphology of the plant and the fruit, while laser scanners are used to acquire three-dimensional structural data of the plant. The microscopic features of leaves were observed using a microscope imaging system, and the internal structural information of the fruit was obtained using a micro-CT scanning system. The internal structural information of the fruit included the number, size, and distribution of seeds, as well as the cavities inside the fruit.
3. The method for hawthorn breeding based on phenomics according to claim 1, characterized in that, In S2, a multidimensional phenotypic database of hawthorn was established, and the specific content of the quantitative indicators of key traits in hawthorn breeding was obtained as follows: The establishment of the hawthorn multidimensional phenotypic database is based on phenotypic data at three scales: canopy, plant, and organ. Various types of raw data collected by the high-throughput imaging system are standardized. A structured database architecture was built, using the relational database management system MySQL. Tag sub-tables were set up by variety number and sampling time, and multiple tag sub-tables were linked to perform data query and retrieval. The hawthorn multidimensional phenotypic database is stored in layers according to the attributes of phenotypic data, namely, morphological structure layer, physiological function layer and environmental response layer; Based on multidimensional data from the hawthorn multidimensional phenotypic database, key traits for breeding objectives were screened and quantified.
4. The method for hawthorn breeding based on phenomics according to claim 3, characterized in that, Key trait quantitative indicators include yield indicators, quality indicators, resistance indicators, and growth potential indicators.
5. The method for hawthorn breeding based on phenomics according to claim 1, characterized in that, In S3, based on the obtained quantitative indicators of key traits in hawthorn breeding, a phenotypic-genotypic association model is constructed to discover new functional genes and achieve early prediction in breeding. A phenotype-genotype association model was constructed using genome-wide association analysis. First, the phenotypic data is tested for normality and standardized to remove outliers; Then, using a mixed linear model, the population structure Q matrix and kinship K matrix were added as covariates to the phenotype-genotype association model; The association degree between each molecular marker and each key phenotypic trait was calculated using a phenotypic-genotype association model to obtain the significantly associated SNP sites; Based on the obtained significantly associated SNP sites, new functional genes are discovered through gene annotation and functional prediction. Based on the constructed phenotype-genotype association model and the newly discovered functional genes, early prediction of breeding can be achieved.
6. The method for phenomics-assisted hawthorn breeding according to claim 5, characterized in that, Based on the obtained significantly associated SNP sites, the specific content of mining new functional genes through gene annotation and functional prediction is as follows: First, the genomic regions where significantly associated SNP sites are located are identified, and candidate genes within these regions are screened using hawthorn reference genome annotation information. Sequence analysis of candidate genes is performed to predict the structure and function of the encoded proteins; Gene expression analysis was used to verify the correlation between the expression patterns of candidate genes and phenotypic traits. If a candidate gene is highly expressed during the fruit development stage and its expression level is positively correlated with the weight of a single fruit, then the candidate gene is a new functional gene that regulates fruit size. If a candidate gene is highly expressed during the fruit coloring stage, then the candidate gene is a new functional gene that regulates fruit coloring.
7. The method for hawthorn breeding based on phenomics according to claim 6, characterized in that, Genetic transformation experiments are conducted on candidate genes using transgenic technology to observe their effects on phenotypic traits, thereby verifying gene function.
8. A method for phenomics-assisted hawthorn breeding according to any one of claims 1-7, characterized in that, The specific details of creating a phenotypic index-weighted algorithm for parental mating in S4 are as follows: From the established quantitative indicators of key breeding traits, the core phenotypic indicators that have the greatest impact on hawthorn breeding objectives were selected, and each selected core phenotypic indicator was standardized. The weights of each core phenotypic indicator were determined by combining the analytic hierarchy process (AHP) with the breeding objectives. Based on the standardized values and weights of each core phenotypic indicator, a weighted summation formula is used to calculate the comprehensive phenotypic index for each parent. ; Candidate parents are ranked according to their phenotypic composite index. Parents with high composite indices are selected as alternative materials. At the same time, parents that perform well in different phenotypic indicators are selected for hybridization to obtain the best parent combination.