89 results about "Phenotypic trait" patented technology

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

The invention provides a crop whole genome prediction method and system based on machine learning, and the method comprises the steps: firstly obtaining a gene expression data set of a target crop in a plurality of growth cycles, the gene expression data set containing a plurality of gene sequence samples composed of genetic markers and phenotypic character data; performing feature extraction on the gene expression data set to obtain gene association features and growth character features, performing fusion prediction on the gene association features and the growth character features by using a preset machine learning model to generate fusion prediction features, and determining a whole genome prediction result according to the fusion prediction features. The whole genome prediction result can indicate character expression trends of crops in different environments, and finally, an adaptability optimization strategy is generated based on the whole genome prediction result and fed back to a crop cultivation system, cultivation parameter adjustment is triggered, crop cultivation precision and intelligence are achieved, and the cultivation efficiency and crop adaptability are improved.

The invention provides a crop population three-dimensional reconstruction and organ phenotypic character analysis method, and relates to the field of three-dimensional reconstruction, and the method comprises the steps: collecting cotton envelope image data, carrying out the data preprocessing, and carrying out the motion recovery processing and Gaussian sputtering of the cotton envelope image data, the method comprises the following steps of: preprocessing a group cotton point cloud, removing a ground point cloud, obtaining a complete cotton group point cloud model, segmenting a cotton individual point cloud model and a corresponding organ point cloud model from the complete cotton group point cloud model, automatically analyzing cotton key phenotypic characters, and performing correlation analysis. According to the method, a cotton original habitat three-dimensional point cloud model is constructed based on mobile visual equipment, and key characters such as cotyledon node height, plant height and leaf area can be automatically analyzed, so that the problems that phenotypic character extraction in the prior art is high in cost, low in flux, easy to make mistakes, inconsistent in standard, generally destructive, labor-consuming, time-consuming and the like are solved.

The invention discloses a rice drought resistance phenotype detection and genetic analysis method based on a phenotypic robot, which is characterized in that rice hyperspectral phenotypic character data is acquired based on the phenotypic robot, and the relative water content RWC of rice is predicted. Through high-frequency data acquisition, RWC (t) based on a time sequence is obtained, RWC (t) curve change characteristic indexes are calculated, the dynamic change process of a rice experiment material in the whole drought experiment period is quantified, the drought resistance index DTI of the rice experiment material is obtained, and the drought resistance of the rice variety is comprehensively evaluated. DTI as a user-defined phenotypic character can be used for whole genome association analysis (GWAS), and a key gene for controlling the drought resistance of rice is excavated. According to the invention, the problems of low efficiency and low flux of traditional rice phenotype detection are solved, phenotype-gene mining based on the mobile robot is realized, and a new way is expanded for improvement of rice genetic breeding.

The invention discloses a corn genetic performance prediction method fusing multiple environmental factors. The corn genetic performance prediction method comprises the following steps: acquiring a parent genotype data matrix, a hybrid phenotype data vector and a multi-environmental time sequence climate data tensor; a climate hysteresis effect matrix is constructed, and hysteresis influence degrees of different climate factors on corn growth and development are quantified; constructing an environment time-space relation matrix and an enhanced genome relation matrix considering non-equal contributions of sections; constructing a linear hybrid prediction model fusing the space-time environment information; estimating a variance component through a constraint maximum likelihood method, and solving model parameters based on a Henderson hybrid model equation; and predicting the phenotypic character of the to-be-predicted corn hybrid in the target environment by using the solving parameters. According to the method, the genotype data and the multi-environment time sequence climate data are fully utilized, the genotype effect, the environment space-time effect and the interaction effect of the genotype effect and the environment space-time effect are effectively integrated, and the accuracy of corn hybrid phenotype prediction is remarkably improved.

The invention relates to haplotypes of cobia fatness degree related SNP sites, a primer group and application of the haplotypes and the primer group, and belongs to the technical field of molecular markers, seven SNP sites related to cobia fatness degree are screened, primers for amplifying the SNP sites are provided, breeding of new varieties with fatness degree characters can be assisted by utilizing the SNP sites or the primers, and breeding of cobia fatness degree characters is facilitated. The method can greatly shorten the breeding period, reduce the dependence on phenotypic characters, improve the selective breeding efficiency, and have good application prospects.

The present application is directed to a method for identifying at least one candidate (Loc), namely a gene location and / or a sequence variant, indicative for at least one selected (phenotypic) trait of an organism, in particular of a plant, comprising the steps of:a. receiving a plurality of candidate lists (Can1, Can2, Can3) of candidates (Loc), the candidate lists being ordered;b. receiving a reference set (RefDB) with gene locations and / or sequence variants;c. matching at least a subset of candidates (Loc) from the candidate lists (Can1, Can2, Can3) with the reference list (RefDB) to determine an evaluation value (EV) for at least the subset;d. assigning each evaluation value (EV) to the respective candidate (Loc) in the respective candidate lists (Can1, Can2, Can3);e. calculating for each candidate list a performance value based on the evaluation value (EV), in particular by using the evaluation values (EV);f. selecting at least one candidate (Loc) as (preferred) candidate (Loc) from one of the candidate lists (Can1, Can2, Can3) using the performance values.

The invention discloses a fig germplasm resource diversity evaluation system and a fig germplasm resource diversity evaluation method, and relates to the field of comprehensive evaluation.The fig germplasm resource diversity evaluation method comprises the steps that after multi-climate zone fig germplasm resources are collected and samples are processed in a standardized mode, 32 phenotypic characters are obtained in combination with hyperspectral imaging, 3D morphological scanning and a physiological sensor; a UPLC-QTOF-MS technology is utilized to construct metabolite fingerprints of leaves and fruits, and an SNP / InDel molecular marker is developed through whole genome re-sequencing for genotyping. Integrating stress resistance physiological data in a stress environment, and calculating a comprehensive score by adopting an analytic hierarchy process. And screening core evaluation indexes through weighted principal component analysis, constructing a similarity matrix in combination with an improved Shannon-Wiener index and a Neii's genetic distance algorithm, and finally forming a germplasm clustering map and a genetic diversity thermodynamic diagram. The method has the advantages that germplasm representativeness is guaranteed through multi-climate-zone sampling, multi-dimensional data are integrated, core indexes are accurately analyzed and screened, a visual atlas is finally generated, and scientific support is provided for germplasm research and breeding.

The invention belongs to the technical field of algae phenotypic character measurement, and particularly relates to a kelp phenotypic character measurement method and device, and the method comprises the steps: extracting feature points of kelp from each local image of the kelp; for each feature point, calculating a descriptor of a local area where the feature point is located; calculating the similarity of descriptors among the continuous kelp local images, and finding matched feature point pairs among the continuous kelp local images; after the matched feature point pairs are found, calculating a homography matrix between the two continuous local kelp images, and performing perspective transformation to obtain a preliminarily spliced complete kelp image; performing image fusion on the preliminarily spliced complete kelp image; and real kelp phenotype data is obtained by taking the calibration plate as a calculation template through a segmentation algorithm. According to the method, the phenotypic characters of the kelp can be rapidly and accurately measured.

The invention relates to an SNP (Single Nucleotide Polymorphism) molecular marker associated with the fatness of turbots fed with fermented soybean meal and application of the SNP molecular marker, and belongs to the technical field of molecular markers. The SNP molecular marker obviously associated with the fatness character is obtained by analyzing the fatness character of the turbots through genome-wide association analysis and comprises at least one of six SNP loci; the scophthalmus maximus molecular marker can be applied to scophthalmus maximus molecular assisted breeding, screening is conducted on the genome level through the breeding method, the breeding period can be greatly shortened, dependence on phenotypic characters is reduced, the selective breeding efficiency is improved, and the scophthalmus maximus molecular marker has good application prospects.

The present invention discloses a method for predicting and simulating the genotypes of advanced generations of hybrid combinations of self-pollinated crops, comprising the following steps: constructing a parental genotype data set and screening effective marker loci; calculating the linkage disequilibrium coefficient between marker loci, constructing a genomic linkage map, and determining the recombination rate between loci; using a random simulation algorithm to construct a gamete formation model and generate the genotypes of F1 generation individuals; based on the genotypes of the F1 generation, randomly simulating the selfing process to generate genotype data from F2 to Fn generations; establishing a mapping relationship between genotypes and phenotypes using a machine learning model to predict the phenotypic traits of advanced-generation selfing lines; the method of the present invention takes into account chromosomal exchange and linkage disequilibrium, can accurately simulate the gene recombination and segregation phenomena of the hybrid offspring of self-pollinated crops, and combines the gradient boosting decision tree model to achieve high-precision phenotypic prediction, providing an efficient auxiliary tool for the breeding of self-pollinated crops, which can significantly shorten the breeding cycle and improve the breeding efficiency.

The invention discloses a medicinal plant phenotype prediction method based on a gene action mode, which comprises the following steps of: firstly, constructing a medicinal plant phenotype prediction model based on the gene action mode according to an influence mode of a gene on phenotype, and introducing Gaussian noise and sparse regularization loss to extract key gene characteristics aiming at the influence of the key gene on the phenotype; meanwhile, a hidden layer is used for carrying out data dimension reduction to ensure the model efficiency, gene information of different scales is used for enriching gene feature representation, the inhibition effect between genes is extracted through self-attention, the nonlinear synergistic effect of the genes and the genes is extracted through polynomial features, and finally accurate prediction of phenotypic characters is achieved. According to the method disclosed by the invention, phenotype prediction is carried out from interaction between key genes according to an action mode of a genome on phenotypes, and compared with an existing phenotype prediction technology, the method disclosed by the invention has a better effect in phenotype prediction of medicinal plants, realizes rapid and accurate prediction of crop phenotypes, and accelerates breeding and seed production processes.

The invention provides a compositions and methods for controlling phenotypic traits in plants. Genes of interest are placed under the control of a gene switch to allow inducible control or expression of a gene of interest “on-demand” by treatment of the plant with a chemical ligand.

The application relates to a haplotype of a SNP site related to the fullness degree of Larimichthys crocea, a primer set and application thereof, and belongs to the technical field of molecular markers. Seven SNP sites related to the fullness degree of Larimichthys crocea are screened, and primers for amplifying the SNP sites are provided. The SNP sites or the primers can assist the breeding of new varieties with the fullness degree, can greatly shorten the breeding period, reduce the dependence on phenotypic traits, improve the selection and breeding efficiency, and have a good application prospect.

A phenotyping device, method and system for measuring phenotypic traits of one or more plants in a target canopy, wherein the comprises a canopy spreader for spreading the target canopy, a camera unit, a means for controlling the angle and the distance of the camera unit in relation to the canopy spreader, an electronic control unit for controlling the camera unit. The phenotyping device, method and system enables recording the raw data from the target canopy during a data collection session comprising information of the plants that are not visible to a camera unit without spreading the canopy.

The invention relates to an SNP molecular marker related to the weight of scophthalmus maximus fed with fermented soybean meal and application of the SNP molecular marker, and belongs to the technical field of molecular markers, the weight character of the scophthalmus maximus is analyzed through genome-wide association analysis, the SNP molecular marker remarkably related to the weight character of the scophthalmus maximus fed with the fermented soybean meal is obtained, and the SNP molecular marker comprises at least one of six SNP sites, the scophthalmus maximus molecular marker can be applied to scophthalmus maximus molecular assisted breeding, screening is conducted on the genome level through the breeding method, the breeding period can be greatly shortened, dependence on phenotypic characters is reduced, the selective breeding efficiency is improved, and the scophthalmus maximus molecular marker has good application prospects.

The invention belongs to the technical field of molecular markers, and particularly discloses a primer of a molecular marker of an ACBD6 gene for detecting chicken lateral crown branch defects and application of the primer of the molecular marker of the ACBD6 gene for detecting the chicken lateral crown branch defects. Phenotype determination and whole genome re-sequencing related to 300-day-old chicken lateral crown branches are carried out on 1496 pure-line Qingyuan partridge chicken individuals, whole genome association analysis is carried out, and the molecular marker of the ACBD6 gene for detecting the chicken lateral crown branch defects is obtained. The excavated Chr8: 5934516 (GRCg7b) (rs737527050) site has extremely high genetic correlation (p = 3.49207e-10) with the lateral crown branch character phenotype, so that the excavated Chr8: 5934516 (GRCg7b) (rs737527050) site can be effectively used for molecular marker-assisted selective breeding of the lateral crown branch defect of the chicken; individuals with the molecular marker Chr8: 5934516 site as the GG genotype are selected as breeding hens, the cockscomb lateral branch defect alleles in breeding populations and the crown lateral branch occurrence rate of subsequent generations of populations can be rapidly reduced, and meanwhile the carcass uniformity of breeding core populations can be indirectly improved.

This invention discloses a method for wheat grain quality detection and functional gene analysis based on hyperspectral imaging technology, belonging to the field of agricultural testing technology. The method includes: extracting hyperspectral indices; measuring wheat grain quality indicators as manual measurements; assigning weight coefficients to each quality indicator to digitize the wheat quality phenotype; extracting characteristic wavelengths to establish predictive models for each indicator and wheat quality values; performing GWAS on hyperspectral indices and chemical indicators as phenotypic traits with genotypes to screen SNP loci, conduct co-location analysis, obtain candidate genes, and perform functional verification. This invention, through hyperspectral technology combined with chemometric modeling, is suitable for real-time monitoring during wheat quality breeding and processing; by combining spectral phenotype with GWAS gene mining, it overcomes the limitations of traditional quality detection that only serves phenotypic screening, forming a closed loop of "non-destructive detection - gene localization," providing targeted genes for wheat quality improvement.

The invention relates to a wheat low-phosphorus response transcription factor TaERF112 and application thereof, and belongs to the technical field of plant bioengineering. The low-phosphorus response transcription factor TaERF112 gene is cloned from wheat, the CDS region sequence of the gene is 882bp, 294 amino acids are encoded, the molecular weight is 31 and 572.92 Da, and the gene is hydrophilic protein. By analyzing the TaERF112 gene structure, protein physicochemical properties, subcellular localization and species evolution characteristics, the characteristics and function prediction of the gene are known. Furthermore, a wheat taerf112 mutant is created by using a CRISPR / Cas9 technology, phenotypic characters, biomass and phosphorus utilization characteristics of a transformed strain under different phosphorus conditions are evaluated, and the important effect of the wheat taerf112 mutant in regulation and control of a wheat root system and phosphorus homeostasis is preliminarily revealed. Theoretical basis and gene resources are provided for efficient genetic improvement of wheat phosphorus.

This application provides a method for screening hybrid parent combinations based on genomic functional site information, relating to the field of hybridization breeding technology, including the following steps: obtaining a functional site library and recording the dominant genotypes of each functional site and their contribution to phenotypic traits; obtaining the actual genotypes of the first and second parents at each functional site; for each functional site, simulating the possible candidate genotypes and their probabilities of occurrence in offspring according to Mendelian laws based on the parent genotypes; for each candidate genotype, adding a positive contribution if it is the same as the dominant genotype of a certain trait, and deducting a negative contribution if it is different, and accumulating them to obtain a comprehensive score; determining the contribution value of each site based on the comprehensive score and occurrence probability of each candidate genotype; accumulating the contribution values ​​of all sites to obtain the matching score of the parent combination, which is used to characterize its potential to produce superior offspring; finally, screening recommended combinations based on the matching scores, realizing the assessment of the breeding potential of parent combinations without actual hybridization.

The invention discloses a fish body size measuring method based on computer vision, and belongs to the technical field of computer vision. The method comprises the following steps: establishing and training an image segmentation model; obtaining a fish body image by using a binocular camera and calibrating the fish body image; performing variety identification and key region segmentation on the image by using the trained model to generate a three-dimensional point cloud; denoising and coordinate transformation are carried out on the point cloud, and a fish body feature point set is extracted; and calculating phenotypic characters such as body length, body width, head length and body length. The device has the advantages of being non-contact, efficient, high in precision, high in adaptability and the like, is suitable for the fields of aquaculture, fishery resource investigation and the like, and can achieve rapid and automatic detection of the size and weight of the fish.

The invention belongs to the technical field of molecular biology, and particularly relates to a universal SNP molecular marker for predicting multiple phenotypic characters of corn, and a screening method and a prediction system thereof. According to the method, through vectorization conversion of genotype data, mutual information screening, LD filtering, LASSO and random forest fusion screening three-time dimensionality reduction is carried out on the converted genotype data, invalid and redundant markers are removed layer by layer, a universal marker suitable for predicting multiple phenotypic characters of corn is developed, and compared with feature screening of traditional whole genome selection, the method has the advantages that the method is simple and convenient to operate, and the efficiency is high. While the cost is reduced, the breadth of screening traits is improved. According to the method, an integrated model of the self-attention neural network, the LASSO and the random forest is constructed, the advantages of the three models are fused through advantage complementation and weight optimization of different models, the limitation of a single model is made up, accurate and efficient prediction of multiple corn phenotypic characters is finally achieved, and the breeding period is shortened.

The invention discloses a wheat flag picking and heading uniformity monitoring method and system based on deep learning, and further discloses a wheat growth period consistency monitoring method and system based on deep learning. According to the wheat flag picking and heading uniformity monitoring method and system based on deep learning, high-resolution time sequence image analysis and a deep learning algorithm are fused, the wheat flag picking and heading uniformity monitoring method and system based on deep learning are established, and the wheat flag picking and heading uniformity can be dynamically monitored; the invention also provides a wheat growth period consistency monitoring method and system based on deep learning, which can dynamically monitor the flag picking-heading consistency phenotypic character of wheat, accurately quantify the individual development heterogeneity of the population, reveal the association between the population and the yield, and in a word, improve the yield of wheat. The technical scheme provided by the invention provides a theoretical reference and a practical basis for improving the yield stability of wheat populations.

The present application relates to the field of breeding trait analysis, and provides a breeding trait analysis method, device, equipment, medium and computer program product.The method comprises the following steps: constructing a target data set based on genotype data and phenotype trait data of a breeding sample; training a trait prediction model through the target data set; analyzing the effect of a genetic variation site on a phenotype trait through the trait prediction model to obtain an effect matrix; the genetic variation site is determined based on the genotype data; and performing aggregation processing on the effect matrix to obtain a key variation site corresponding to a target trait; the target trait is selected from the phenotype trait data according to requirements.The present application mines the effect of a genetic site on a trait through a trait prediction model, fully utilizes genotype data and phenotype data, and improves the accuracy of breeding trait analysis.

The present disclosure provides yeasts, which can be recombinant yeast host cells, exhibiting prolonged persistence when submitted to a plurality of fermentation cycles. The yeasts exhibit at least one of the following phenotypic trait: a fast settling phenotype, a rugose phenotype, an improved invertase activity, triploidy, increased signaling in a RAS / cAMP / PKA pathway or combinations thereof.

The invention discloses a wheat grain quality detection and functional gene analysis method based on a hyperspectral imaging technology, and belongs to the technical field of agricultural detection. The method comprises the following steps: extracting a hyperspectral index; determining a wheat grain quality index as an artificial measurement value; a weight coefficient is given to each quality index, and the wheat quality phenotype is digitalized; extracting a characteristic wavelength, and establishing a prediction model of each index and a wheat quality value; gWAS, SNP site screening and co-localization analysis are performed on the hyper-spectral index and the chemical index as phenotypic characters and genotypes respectively, and candidate genes are obtained and subjected to functional verification. The method combines a hyperspectral technology with chemometrics modeling, and is suitable for real-time monitoring in wheat quality breeding and processing processes; the spectrum phenotype is combined with GWAS gene mining, the limitation that traditional quality detection only serves phenotype screening is broken through, a'nondestructive testing-gene positioning 'closed loop is formed, and a target gene is provided for wheat quality improvement.

The invention discloses a mesosulfuron-resistant SNP site of a wheat TaASADH-A1 gene, a KASP marker and application, and relates to the technical field of molecular marker-assisted breeding and application of the SNP site, the SNP site takes a common wheat Chinese spring genome Chine Spring RefSeq v1.1 as a reference genome, the SNP is the SNP site of a TraesCS5A02G398300 gene promoter region on a wheat 5A chromosome, and the KASP marker is the KASP marker of the mesosulfuron-resistant SNP site of the wheat TaASADH-A1 gene, the KASP marker is the KASP marker of the mesosulfuron-resistant SNP site of the wheat TaASADH-A1 gene, and the KASP marker is the KASP marker of the wheat TaASADH-A1 gene. Corresponding to the 101st basic group from the 5'tail end of the sequence shown in SEQ ID NO: 1, when the site is GG homozygosis, the corresponding genotype is A; when the site is CC homozygous, the corresponding genotype is B; according to the application, mesosulfuron-resistant wheat is screened or screened in an auxiliary mode, and the mesosulfuron-resistant capacity of wheat is that wheat with the homozygous genotype A is higher than wheat with the homozygous genotype A or wheat with the homozygous genotype B or wheat with the homozygous genotype B in a candidate mode. The developed KASP marker is related to the wheat herbicide-resistant mesosulfuron-methyl, and can be used for screening and identifying the phenotypic character of the wheat herbicide-resistant mesosulfuron-methyl.

The application relates to a haplotype of a SNP site related to body weight of Larimichthys crocea, a primer set and application thereof, and belongs to the technical field of molecular markers. Seven SNP sites related to the body weight of the Larimichthys crocea are screened, and primers for amplifying the SNP sites are provided. The SNP sites or the primers can assist the breeding of new varieties with the body weight, can greatly shorten a breeding period, reduce the dependence on phenotypic traits, improve the selection and breeding efficiency, and have a good application prospect.