A method for producing high-quality corn hybrid seeds based on efficiency

Abstract

The application discloses a method based on high-quality corn hybrid seed production efficiency, and relates to the technical field of seed cultivation, which comprises the following steps: performing callus induction and leaf sheath color marking on seeds of a nuclear sterile line and a maintainer line in MS culture medium, obtaining a marked seedling population, classifying and removing impurities from the marked seedling population, forming a purified nuclear sterile line population data, inputting the purified nuclear sterile line population data into a seedling cultivation digital twin model, simulating seedling development processes under different paternal sowing periods by a paternal decision layer, performing pollinable window analysis and filament elongation rate calculation by a maternal perception layer, and outputting an efficient pollination cultivation scheme. Through the seedling cultivation digital twin model and obtained variety-yield coupling parameters, the application realizes quantitative evaluation and trend prediction of production efficiency, and improves controllability and optimization capability of the whole process of corn hybrid seed production.

Description

Technical Field

[0001] This invention relates to the field of seed breeding technology, and in particular to a method for improving the production efficiency of high-quality maize hybrid seeds. Background Technology

[0002] In modern agricultural production, maize, as one of the most important food crops, directly impacts food security through its yield. Traditional methods for cultivating hybrid maize seeds primarily rely on field trials and manual intervention, which to some extent addressed the issues of parental purity control and large-scale production. In recent years, the widespread application of technologies such as callus induction and gene markers has enabled a shift from experience-based breeding to precision and digital breeding. This has improved the accuracy of seedling identification and weed control, while simultaneously shortening the breeding cycle and enhancing overall production efficiency.

[0003] Existing methods for producing hybrid maize seeds generally suffer from two main shortcomings: First, traditional pollination methods are inefficient, failing to accurately match the optimal timing of pollen shedding from the male parent and silk receptivity from the female parent, resulting in low pollination success rates. Second, there is a lack of effective means to systematically analyze the dynamic fluctuations in grain yield data, making it difficult to quantify the coupling relationship between variety and yield, thus limiting the accurate assessment and optimization of production efficiency. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method based on the production efficiency of high-quality maize hybrid seeds to solve the problems of low efficiency and insufficient production efficiency assessment capabilities of traditional pollination methods.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for improving the production efficiency of high-quality maize hybrid seeds, which includes inducing callus and marking leaf sheath color in seeds of nuclear male sterile lines and maintainer lines in MS medium to obtain marked seedling populations, and classifying and removing impurities from the marked seedling populations to form purified nuclear male sterile line population data.

[0008] The purified nuclear male sterile line population data is input into the seedling cultivation digital twin model. The male parent decision layer simulates the seedling development process under different male parent sowing dates, and the female parent perception layer performs pollination window analysis and filament elongation rate calculation to output an efficient pollination cultivation scheme.

[0009] According to the efficient pollination and cultivation program, the marked seedling population was pollinated in vitro to obtain hybrid hypocotyls; the hybrid hypocotyls were then subjected to BAP differentiation culture to generate regenerated seedlings.

[0010] Greenhouse transplanting and seed setting rate monitoring of regenerated seedlings were conducted to obtain grain yield data; dynamic fluctuation analysis of grain yield data was performed to output a production efficiency report.

[0011] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the step of obtaining the marked seedling population specifically includes the following steps.

[0012] Seeds of the nuclear male sterile line and the maintainer line were inoculated into MS medium, and callus was induced by growth regulators to obtain embryogenic callus.

[0013] Anthocyanins were introduced into embryogenic callus tissue using Agrobacterium-mediated transformation to obtain a labeled seedling population.

[0014] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds described in this invention, the step of forming purified nuclear male sterile line population data specifically refers to performing light culture and classification to remove impurities from the marked seedling population, outputting a colorimetric seedling population, and performing PCR genotyping verification on the colorimetric seedling population to form purified nuclear male sterile line population data.

[0015] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the specific construction process of the digital twin model for seedling cultivation is as follows.

[0016] The parent decision layer is built using the Transformer architecture, and the mother perception layer is built using the LSTM network.

[0017] By applying jump connections to perform channel splicing and residual stacking on the parent decision layer and the mother perception layer, a digital twin model for seedling cultivation is constructed.

[0018] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the paternal parent decision-making layer simulates the seedling development process under different paternal parent sowing dates, specifically including the following steps.

[0019] The purified nuclear male sterile line population data is input into the seedling cultivation digital twin model. The paternal decision layer simulates the seedling development process under different paternal sowing dates through the APSIM engine and generates a developmental accumulated temperature response matrix.

[0020] Time series clustering was performed on the developmental accumulated temperature response matrix to obtain the distribution of the peak pollen shedding period of the paternal parent.

[0021] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the maternal parent sensing layer performs pollination window analysis and silk elongation rate calculation, specifically including the following steps.

[0022] The maternal sensing layer uses bidirectional LSTM to perform pollination window analysis on purified nuclear male sterile line population data to obtain pollination activity temporal characteristics.

[0023] Based on the temporal characteristics of pollination vigor, the filament elongation rate is calculated to form the acceptance rate of the parent filament.

[0024] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the step of outputting an efficient pollination and breeding scheme specifically includes the following steps.

[0025] The distribution of the peak pollen shedding period of the male parent and the filament receptivity of the female parent are fused using topological structure to generate a spatiotemporally optimized pollination map.

[0026] Spatiotemporal matching analysis and dynamic partitioning are performed on the pollination spatiotemporal optimization map to output an efficient pollination and cultivation scheme.

[0027] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the generation of regenerated seedlings specifically includes the following steps.

[0028] According to the efficient pollination and cultivation program, the marked seedling population was pollinated in vitro using the aseptic in vitro ovule pollination method, while placenta retention was performed simultaneously to obtain hybrid hypocotyls;

[0029] The hybrid hypocotyls were transplanted into NAA differentiation medium for cytokinin labeling and BAP differentiation culture to generate regenerated seedlings.

[0030] In a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the step of obtaining grain yield data specifically includes the following steps.

[0031] The regenerated seedlings were transplanted to a greenhouse for cultivation, and images of unthreshed corn ears were collected.

[0032] Identify the outlines of clump-like kernels in images of unthreshed corn ears, perform effective kernel screening, and obtain the ear filling rate;

[0033] The ear grain number conversion and thousand-grain weight correlation analysis were performed on the ear grain filling rate to obtain grain yield data.

[0034] As a preferred embodiment of the method for improving the production efficiency of high-quality maize hybrid seeds according to the present invention, the output of the production efficiency report specifically refers to performing dynamic volatility analysis on the grain yield data to obtain the variety-yield coupling parameters; integrating the variety-yield coupling parameters and the grain yield data to output the production efficiency report.

[0035] The beneficial effects of this invention are as follows: It constructs a digital twin model for seedling cultivation, utilizing the paternal decision-making layer of the Transformer architecture and the maternal perception layer of the LSTM network for collaborative analysis. This achieves precise matching between the peak pollination period of the paternal parent and the pollination window of the maternal parent, thereby improving the timeliness and success rate of pollination. Furthermore, by performing dynamic volatility analysis on grain yield data and constructing variety-yield coupling parameters, it enables quantitative assessment and trend prediction of production efficiency, enhancing the controllability and optimization capabilities of the entire maize hybrid seed production process. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a method for improving the production efficiency of high-quality maize hybrid seeds.

[0038] Figure 2 A schematic diagram of the construction of a digital twin model for seedling cultivation.

[0039] Figure 3 A flowchart generated for a production efficiency report.

[0040] Figure 4 A flowchart for obtaining labeled seedling populations. Detailed Implementation

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0044] Reference Figures 1-4This is one embodiment of the present invention, which provides a method for improving the production efficiency of high-quality maize hybrid seeds, comprising the following steps:

[0045] S1. Callus induction and leaf sheath color marking were performed on seeds of the nuclear male sterile line and maintainer line in MS medium to obtain marked seedling populations. The marked seedling populations were then classified and impurities were removed to form purified nuclear male sterile line population data.

[0046] Specifically, the operations include the following:

[0047] S1.1 Seeds of the nuclear male sterile line and the maintainer line were inoculated in MS medium and callus was induced by growth regulators to obtain embryogenic callus.

[0048] In specific operations,

[0049] Genotyping of maize hybrid seeds was performed using a PCR instrument to obtain nuclear male sterile (NMS) and maintainer line seeds. The NMS and maintainer line seeds were then soaked in ethanol and HgCl2 solution to remove surface lipids and kill attached microorganisms, resulting in pretreated NMS and maintainer line seeds.

[0050] Pretreated nuclear male sterile line seeds and maintainer line seeds were inoculated in MS medium. The pretreated nuclear male sterile line seeds and maintainer line seeds were dedifferentiated by the growth regulator in MS medium to obtain callus tissue masses. Cell proliferation of the callus tissue masses was regulated to obtain primary callus tissue.

[0051] It should be noted that cell proliferation regulation refers to the process of monitoring the mitotic rate of callus masses and regulating the mitotic rate through exogenous hormones (such as 2,4-D hormone).

[0052] Primary callus tissues were subcultured and screened. Further, tissue texture images of primary callus tissues were acquired using a hyperspectral imager, and the spectral features of the tissue texture images were quantified to obtain spectral intensity parameters. When the spectral intensity parameters were greater than the intensity threshold, the primary callus tissues were identified as effective callus tissues. Effective callus tissues were subjected to antioxidant treatment to reduce oxidative damage and maintain differentiation potential to obtain embryogenic callus tissues.

[0053] It should be noted that spectral feature quantization refers to the process of extracting feature bands and matching spectral features from tissue texture images; the intensity threshold is defined based on the distribution of spectral intensity parameters of historical primary callus tissue, with an exemplary value range of 0.70-0.85.

[0054] S1.2. The anthocyanin synthesis gene was introduced into embryogenic callus tissue via Agrobacterium-mediated transformation to obtain a marker seedling population.

[0055] In specific operations,

[0056] Anthocyanins were introduced into embryogenic callus via Agrobacterium-mediated transformation. Further, Agrobacterium suspension was injected into MS liquid medium containing acetylsyringone anthocyanins to obtain Agrobacterium infection solution. Embryogenic callus was placed in the Agrobacterium infection solution and subjected to thorough shaking and dark culture to obtain resistant callus. The resistant callus was then subjected to tissue disruption and centrifugation to obtain crude anthocyanin extract. The actual anthocyanin expression level in the crude anthocyanin extract was determined using HCl-methanol reagent. The actual anthocyanin expression level was compared with the standard anthocyanin reference value. When the actual anthocyanin expression level was within the range of the standard anthocyanin reference value, the current resistant callus was identified as a transgenic positive callus. The transgenic positive callus was then subjected to shoot induction and rooting culture to obtain a regenerated seedling population.

[0057] It should be noted that the standard anthocyanin reference values ​​are defined based on the anthocyanin expression rate in historical transgenic positive callus tissue.

[0058] Leaf sheath images of the regenerated seedling population were collected, and Gaussian filtering and HSV color space conversion were performed on the leaf sheath images to obtain the H channel feature vector. The mean-standard deviation of the H channel feature vector was then calculated to obtain the standard color value. The specific mathematical format is as follows.

[0059] ;

[0060] in, Indicates standard color values, Represents the H-channel feature vector. This represents the mean of the eigenvectors of the H channel. The standard deviation of the eigenvectors of the H channel;

[0061] It should be noted that HSV color space conversion refers to the process of hue separation and saturation normalization of leaf sheath images.

[0062] Based on the standard color value, the leaf sheaths of the regenerated seedling population are marked with color. For example, when the standard color value is higher than the color development threshold, the leaf sheaths of the regenerated seedling population are marked as dark purple; when the standard color value is lower than the color development threshold, the leaf sheaths of the regenerated seedling population are marked as light purple, thus obtaining a marked seedling population.

[0063] It should be noted that the color rendering threshold is defined based on the normal distribution of historical standard color values, with an exemplary range of 1.2-1.8.

[0064] S1.3. Mark the seedling population, conduct light culture and classify and remove impurities, output the chromogenic seedling population, and perform PCR genotyping verification on the chromogenic seedling population to form purified nuclear male sterile line population data.

[0065] In specific operations,

[0066] The marked seedling population was transplanted into a light culture chamber, and environmental parameters were set for the light culture chamber, such as setting the light intensity to 2000-2500 lux, the daily light duration to 16 hours, the temperature to 25℃, and the humidity to 60-70%. In the light culture chamber, the marked seedling population was cultivated in a standardized manner, and the colorimetric characteristic data were monitored simultaneously using image analysis software (such as OpenCV-Python).

[0067] Based on the color development feature data, the marked seedling population is classified and impurities are removed using the DBSCAN clustering algorithm. Further, density clustering is performed on the color development feature data to form color development clusters. Eigenvalue decomposition is performed on the color development clusters, and the top k eigenvalues ​​are extracted as color development principal component feature vectors. Euclidean distance is used to measure the centroid distance between clusters. Nonlinear projection and numerical normalization are applied to the centroid distance between clusters to generate a color development quality score. When the color development quality score fails to meet the color development qualification standard, the corresponding marked seedling population is removed, and the retained marked seedling population is used as the color development seedling population.

[0068] It should be noted that density clustering refers to the process of performing neighborhood scanning and weighted aggregation on colorimetric feature data; Euclidean distance metric refers to the process of quantifying the spatial distance of the colorimetric principal component feature vectors through square root operation of the square difference; and the colorimetric qualification standard is based on the colorimetric stability definition of historical colorimetric seedling populations.

[0069] Subsequently, PCR (polymerase chain reaction) genotyping was performed on the chromogenic seedling population. Further, leaf tissues were randomly selected from the chromogenic seedling population, and genomic DNA was extracted to obtain DNA samples. Specific primers were used to amplify the DNA samples, outputting PCR products. The PCR products were then quantitatively detected using a fluorescent dye (such as SYBR Green I) to generate validation Ct values. Weighted fusion of the validation Ct values ​​was performed to output purified nuclear male-sterile line population data.

[0070] It should be noted that specific primer amplification refers to the process of denaturing and annealing DNA samples at high temperatures using a thermal cycler.

[0071] S2. Input the purified nuclear male sterile line population data into the seedling cultivation digital twin model. The male parent decision layer simulates the seedling development process under different male parent sowing periods, and the female parent perception layer performs pollination window analysis and filament elongation rate calculation to output an efficient pollination cultivation scheme.

[0072] Specifically, the operations include the following:

[0073] S2.1 Construct and train a digital twin model for seedling cultivation.

[0074] In specific operations,

[0075] In the PyTorch framework, the Transformer architecture is called via the `nn.Transformer` parameter or function call and initialized. For example, the input dimension is set to 512, the number of attention heads is set to 8, and the number of encoder layers is set to 6. The APSIM engine is then connected after the Transformer architecture to simulate seedling development and predict the dynamics of the parent pollen activity. Feature dimensions are compressed using fully connected layers to complete the construction of the parent decision layer. The LSTM network is then called via the `nn.keras` function and initialized. For example, the number of hidden units is set to 128, returning the complete sequence is set to True, and the dropout rate is set to 0.2. An attention mechanism is then connected after the LSTM network to weight temporal features to capture the elongation features of the parent filaments, completing the construction of the parent perception layer.

[0076] Skip connections are used to perform cross-layer feature interaction between the parent decision layer and the mother perception layer to obtain the parent feature matrix. Channel concatenation of the parent feature matrix is ​​then performed to generate the parent-mother fusion feature. The sigmoid function is applied to the parent-mother fusion feature for non-linear activation to generate the parent-mother fusion weight. Based on the parent-mother fusion weight, the parent decision layer and the mother perception layer are stacked with residuals to complete the construction of the seedling cultivation digital twin model.

[0077] Next, the seedling cultivation digital twin model is trained. Further, the historical purified nuclear male-sterile line population data is divided into a sample set, a training set, and a validation set. On the sample set, MinMaxScaler is used for data normalization, and synthetic sampling (ADASYN) is used for sample balancing to form an enhanced training set. On the training set, the AdamW optimizer is used for gradient backpropagation, and a gradient pruning function is applied simultaneously for norm constraint to obtain updated seedling cultivation digital twin model parameters. On the validation set, early stopping monitoring is performed on the updated seedling cultivation digital twin model parameters, and a smooth L1 loss function is applied for loss quantization to obtain the validation loss. When the validation loss exceeds the convergence threshold for several consecutive rounds (e.g., 5 times), training terminates, and the trained seedling cultivation digital twin model is output simultaneously.

[0078] It should be noted that the convergence threshold is defined based on the mean and standard deviation of historical validation loss, with an exemplary range of 0.001-0.005.

[0079] S2.2 The paternal parent decision-making layer simulates the seedling development process under different paternal parent sowing dates to obtain the distribution of the peak pollen shedding period of the paternal parent.

[0080] In specific operations,

[0081] The purified nuclear male sterile line population data was input into the seedling cultivation digital twin model via a RESTful API interface. The paternal parent decision layer simulated the seedling development process under different paternal parent sowing dates (e.g., sowing a batch every 5 days, simulating a total of 6 sowing dates) using the APSIM engine. Furthermore, paternal parent genotype parameters were set for the APSIM engine (e.g., photoperiod sensitivity parameter set to 200℃·d, grain-filling period parameter set to 800℃·d). Based on the paternal parent genotype parameters, dynamic simulation of the growth period was performed on the purified nuclear male sterile line population data to obtain developmental stage time-series data. Effective accumulated temperature (GDD) was calculated on the developmental stage time-series data, and environmental impact factors were applied for weighted correction to generate a developmental accumulated temperature response matrix. The specific mathematical format is as follows.

[0082] ;

[0083] in, This represents the developmental accumulated temperature response matrix. Represents the basic accumulated temperature matrix. Indicates environmental impact factors, Represents a random disturbance term;

[0084] It should be noted that the basic accumulated temperature matrix is ​​obtained by accumulating the effective accumulated temperature on a daily basis using time-series data of the development stage; the environmental impact factor is defined based on a linear weighted combination of environmental parameters (collected by temperature and humidity sensors), with an exemplary value range of 0.5-1.5; the random disturbance term refers to the unquantized environmental noise value in the simulation process, with an exemplary value range of ±5.

[0085] Subsequently, time-series cluster analysis was performed on the developmental accumulated temperature response matrix. Further, a time-series decay factor was used to dynamically weight the developmental accumulated temperature response matrix to obtain a time-series decay correction matrix. Hierarchical clustering was then performed on the time-series decay correction matrix to obtain early-sowing, suitable-sowing, and late-sowing clusters. The median accumulated temperature in the early-sowing, suitable-sowing, and late-sowing clusters was extracted as the corresponding accumulated temperature threshold for the peak pollen shedding period of the parent plant. Gaussian smoothing filtering and curve fitting were applied to the accumulated temperature threshold for the peak pollen shedding period of the parent plant to obtain a smoothed accumulated temperature threshold curve. The first derivative of the smoothed accumulated temperature threshold curve was then taken to obtain the critical point for the peak pollen shedding period. Spline interpolation was then performed on the critical point for the peak pollen shedding period to output the distribution of the peak pollen shedding period of the parent plant.

[0086] It should be noted that the time decay factor is defined based on the time decay trend of the historical development accumulated temperature response matrix, and the exemplary value range is 0.02-0.1; hierarchical clustering refers to the process of merging minimum variance clusters on the time decay correction matrix.

[0087] S2.3 The maternal parent sensing layer performs pollination window analysis and filament elongation rate calculation to form the maternal parent filament acceptability.

[0088] In specific operations,

[0089] The purified nuclear male sterile line population data was input into a bidirectional LSTM network in time series for pollination window analysis. Further, a forward LSTM was used to extract forward temporal features from the purified nuclear male sterile line population data to obtain a forward hidden state sequence; pollen activity features were encoded from the forward hidden state sequence to obtain pollinable pollen activity features; a backward LSTM was used to extract backward temporal features from the purified nuclear male sterile line population data to generate a backward hidden state sequence; filament dynamic response encoding was performed from the backward hidden state sequence to obtain a pollinable filament vitality vector; the pollinable pollen activity features and the pollinable filament vitality vector were concatenated along the channel dimension to obtain the pollination window features.

[0090] It should be noted that pollen activity feature encoding refers to the process of applying temporal attention weighting and feature dimension compression to the forward hidden state sequence; filament dynamic response encoding refers to the process of applying time-step moving average and dynamic normalization to the backward hidden state sequence.

[0091] The pollination window features are randomly discarded by the dropout layer to prevent overfitting and generate anti-interference window features. Gaussian noise is injected into the anti-interference window features to enhance robustness and output pollination vitality time-series features.

[0092] A smoothed vitality feature vector was obtained by applying a sliding window mean filter to the pollination vitality time-series characteristics. The central difference method was then used to calculate the filament elongation rate from the smoothed vitality feature vector to generate the parent filament acceptability. The specific mathematical formula is as follows.

[0093] ;

[0094] in, Indicates a point-in-time index. Indicates a point in time Acceptance of the parent plant's filaments express The length of the filament at a given time point express The length of the filament at a given time point Indicates the time step. Indicates a point in time Vitality value;

[0095] It should be noted that the filament length is obtained by performing a least-squares fit on the smoothed vitality feature vector; the vitality value is obtained by performing a numerical normalization operation on the smoothed vitality feature vector.

[0096] S2.4. Perform topological fusion of the peak pollen shedding period distribution of the male parent and the filament receptivity of the female parent to generate a spatiotemporal optimization map of pollination. Perform spatiotemporal matching analysis and dynamic partitioning on the spatiotemporal optimization map of pollination to output an efficient pollination and cultivation scheme.

[0097] In specific operations,

[0098] The pollen intensity parameters of the male parent during the peak pollen shedding period are extracted. The pollen intensity parameters are used as male parent nodes, and the filament acceptability of the female parent is used as female parent nodes to construct a spatiotemporal pollination topology network. Then, spatial interpolation is performed on the spatiotemporal pollination topology network to generate a preliminary pollination optimization map. Gaussian smoothing filtering is performed on the preliminary pollination optimization map to output the spatiotemporal pollination optimization map.

[0099] Pollination efficiency is calculated by using a 3D convolution kernel to optimize the spatiotemporal pollination map, resulting in a pollination matching score. The specific mathematical formula is as follows.

[0100] ;

[0101] in, This indicates the pollination matching score. Indicates activator. Represents the weights of the 3D convolution kernel. This represents the local spatiotemporal feature value of the pollination spatiotemporal optimization map. Indicates the bias term;

[0102] It should be noted that the activation factor is defined based on the Sigmoid function; the weight of the three-dimensional convolution kernel is defined based on the pollination efficiency conversion rate of the pollination spatiotemporal optimization map, with an exemplary value range of (-1.5, 1.5); the local spatiotemporal feature value is obtained by performing a sliding window truncation operation on the pollination spatiotemporal optimization map; the bias term is defined based on the mean offset of the historical pollination matching score, with an exemplary value range of [0.1, 0.3].

[0103] Based on the pollination matching score, the spatiotemporal optimization map of pollination is dynamically partitioned. Further, the range of the first-level threshold is set to [0.8, 1.0], the range of the second-level threshold to [0.5, 0.8), and the range of the third-level threshold to [0, 0.5]. When the pollination matching score is within the range of the first-level threshold, it is divided into a priority pollination area (circular area); when the pollination matching score is within the range of the second-level threshold, it is divided into a secondary pollination area (strip-shaped area); and when the pollination matching score is within the range of the third-level threshold, it is divided into an observation area (rectangular area). The information from the divided pollination areas is integrated to output an efficient pollination and cultivation scheme.

[0104] It should be noted that the three-level threshold is based on the definition of historical pollination success rate.

[0105] S3. According to the efficient pollination and cultivation program, the marked seedling population is subjected to in vitro pollination to obtain hybrid hypocotyls; the hybrid hypocotyls are then subjected to BAP differentiation culture to generate regenerated seedlings.

[0106] Specifically, the operations include the following:

[0107] S3.1 According to the efficient pollination and cultivation program, the marked seedling population is pollinated in vitro using the sterile in vitro ovule pollination method, and the placenta is preserved simultaneously to obtain the hybrid hypocotyl.

[0108] In specific operations,

[0109] The pistils of the marked seedling population were placed in a sterile environment and surface disinfected with ethanol and mercuric chloride solution to completely inactivate surface microorganisms, thus obtaining sterile pistil samples.

[0110] Information on the pollination zone in the efficient pollination culture program was extracted, and sterile pistil samples were pollinated in vitro using the sterile in vitro ovule pollination method. Further, the ovules of the sterile pistil samples were separated using a microsurgical scalpel to obtain sterile in vitro ovules. In the priority pollination zone, sterile in vitro ovules were subjected to high-frequency precise pollination (e.g., 3 times / hour). In the secondary pollination zone, sterile in vitro ovules were subjected to medium-frequency pollination (e.g., 2 times / hour). In the observation zone, sterile in vitro ovules were subjected to low-frequency pollination (e.g., 1 time / hour) to obtain a population of pollinated ovules.

[0111] After completing the three-dimensional pollination, the placenta was preserved in the pollinated ovule population. Further, the placenta tissue of the pollinated ovule population was cut to generate ovules with placentas. The ovules with placentas were cultured in liquid suspension using MS liquid medium to obtain enlarged hypocotyls. The enlarged hypocotyls were induced to differentiate by GA3 to obtain hybrid hypocotyls.

[0112] It should be noted that GA3-induced differentiation refers to the process of cell elongation activation and meristematic tissue differentiation through exogenous gibberellin on the enlarged embryonic axis.

[0113] S3.2. The hybrid hypocotyl is transplanted into NAA differentiation medium for cytokinin labeling and BAP differentiation culture to generate regenerated seedlings.

[0114] In specific operations,

[0115] Using sterile forceps, the hybrid hypocotyl is implanted into the NAA differentiation medium in the polar direction (radicle end downward); then the NAA differentiation medium is placed in the dark under constant temperature and light conditions (e.g., temperature of 25℃ and light intensity of 2000 lux), and the development degree of callus tissue (e.g., diameter and tissue compactness) is monitored in real time.

[0116] When the development of the callus tissue reaches the qualified standard (e.g., diameter reaches 1.0±0.2mm, tissue compactness reaches grade II), cytokinin labeling is performed. Further, callus tissue blocks of hybrid hypocotyls are excised, transferred to shoot induction medium, and cytokinin fluorescent labeling agent (e.g., 6-BA-FITC) is added to the shoot induction medium to label the active division area. Simultaneously, the fluorescent labeling signal is monitored in real time using a fluorescence microscope. When the fluorescent labeling signal reaches a stable intensity range, the corresponding callus tissue block is determined to be a labeled positive shoot.

[0117] It should be noted that the qualification criteria are defined based on the morphological characteristics of the developmental degree of historical callus tissue; the stable strength range is defined based on the effective labeling success rate of historical marker-positive buds, with an exemplary range of 800-2000 a.u.

[0118] Next, the labeled positive buds were cultured in BAP for differentiation. Then, the labeled positive buds were transferred to BAP rooting medium for rooting induction to obtain rooted seedlings. The rooted seedlings were subjected to low-temperature adaptation acclimatization to form seedlings with complete root systems. The seedlings with complete root systems were hardened off and transplanted to obtain regenerated seedlings.

[0119] It should be noted that low-temperature acclimatization refers to the process of acclimatizing rooted seedlings to cold resistance through gradient cooling (e.g., 15℃→10℃).

[0120] S4. Conduct greenhouse transplanting and seed setting rate monitoring of regenerated seedlings to obtain grain yield data; analyze the proportion of embryogenic callus in the grain yield data and output a two-dimensional report on effect and efficiency.

[0121] Specifically, the operations include the following:

[0122] S4.1 Transplant the regenerated seedlings to a greenhouse for cultivation and collect images of unthreshed corn ears; identify the outlines of sticky kernels in the images of unthreshed corn ears and screen for effective kernels to obtain the ear filling rate.

[0123] In specific operations,

[0124] After transplanting the regenerated seedlings to the greenhouse, monitor the ear development progress in real time. When the ears begin to swell naturally (e.g., diameter ≥ 3cm), use a high-resolution industrial camera to collect images of unthreshed corn ears at a fixed frequency (e.g., once every 3 days).

[0125] The OpenCV image processing library was used to identify the outline of the stuck kernels in the image of the unthreshed corn cob. Then, Gaussian filtering was applied to the image of the unthreshed corn cob to eliminate ambient light interference and obtain a clear kernel image. The clear kernel image was then converted to HSV color space to obtain color feature regions. The Canny operator was used to enhance the edges and connect the outlines of the color feature regions to generate the outline data of the stuck kernels.

[0126] It should be noted that the Canny operator is called directly using the cv2.Canny function in the OpenCV image processing library.

[0127] Effective kernels are screened based on the outline data of the adhered kernels. Further, the gray-scale mean of the adhered kernel outline data is statistically analyzed to obtain the gray-scale value inside the kernel. When the gray-scale value inside the kernel reaches the gray-scale threshold, the kernel is determined to be an effective kernel. At the same time, the corresponding image of the unthreshed corn ear is covered with a red mark, and the image of the ear marked with effective kernels is output.

[0128] It should be noted that grayscale mean statistics refer to the process of extracting region pixels and averaging the outline data of adhered grains using the cv2.mean() function of the OpenCV image processing library; the grayscale threshold is defined based on the grayscale distribution characteristics of historical adhered grain outline data, and the exemplary value range is 180-220.

[0129] The number of effective kernels in the ear images marked with effective kernels is counted to obtain the total number of effective kernels. The percentage of the total number of effective kernels is then converted to generate the ear grain filling rate.

[0130] S4.2 Perform ear grain number conversion and thousand-grain weight correlation analysis on ear grain filling rate to obtain grain yield data.

[0131] In specific operations,

[0132] The ear-filling rate is converted to the number of grains per ear. Furthermore, the ear-filling rate is dynamically calibrated using a benchmark grain number factor to obtain a standardized ear-filling rate. The standardized ear-filling rate and the actual field area are then weighted and multiplied to obtain the total number of grains per ear per unit area.

[0133] It should be noted that the baseline grain number factor is defined based on the average yield of the maize variety, with an exemplary range of 450-520 grains / 100% seed setting rate; the actual field area was collected using an RTK mapping instrument.

[0134] Next, a thousand-grain weight correlation analysis was performed on the total number of grains per ear per unit area. Further, samples of the total number of grains per ear per unit area were taken and weighed to obtain the measured thousand-grain weight. The measured thousand-grain weight was corrected for moisture content to obtain the basic value of grain yield. The basic value of grain yield was converted to a unit (e.g., the basic value of grain yield was converted to kg / mu) to generate grain yield data.

[0135] It should be noted that moisture content correction refers to the process of evaporating moisture and maintaining a constant dry matter content in the measured thousand-grain weight using a constant-temperature drying method.

[0136] S4.3 Perform dynamic volatility analysis on grain yield data to obtain variety-yield coupling parameters; integrate the variety-yield coupling parameters and grain yield data to output a production efficiency report.

[0137] In specific operations,

[0138] Dynamic volatility analysis of grain yield data is performed using a time-series sliding window algorithm. Further, the grain yield data is subjected to window sliding to calculate the mean and standard deviation of the yield. Trend fitting is performed on the mean and standard deviation of the yield to generate a yield volatility sequence. The variety response factor and the yield volatility sequence are weighted and aggregated to output the variety-yield coupling parameters.

[0139] It should be noted that trend fitting refers to the process of sampling and modeling the linear relationship between the mean and standard deviation of yield using the least squares method; the variety response factor is defined based on the regression slope of environmental test data (obtained through standard weather stations), with an exemplary value range of 0.5-1.5.

[0140] The system performs weighted fusion and report structure transformation on variety-yield coupling parameters and grain yield data to output a production efficiency report.

[0141] This embodiment also provides a computer device applicable to the method based on the production efficiency of high-quality maize hybrid seeds, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method based on the production efficiency of high-quality maize hybrid seeds as proposed in the above embodiment.

[0142] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0143] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for achieving high-quality maize hybrid seed production efficiency as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0144] In summary, this invention improves pollination timeliness and success rate by constructing a digital twin model for seedling cultivation and utilizing the paternal decision-making layer of the Transformer architecture and the maternal perception layer of the LSTM network for collaborative analysis. This achieves precise matching between the peak pollination period of the paternal parent and the pollination window of the maternal parent. Furthermore, by performing dynamic volatility analysis on grain yield data and constructing variety-yield coupling parameters, it enables quantitative assessment and trend prediction of production efficiency, enhancing the controllability and optimization capabilities of the entire maize hybrid seed production process.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for improving the production efficiency of high-quality maize hybrid seeds, characterized in that: include, Seeds of the nuclear male sterile line and the maintainer line were used to induce callus and mark leaf sheath color in MS medium to obtain marked seedling populations. The marked seedling populations were then classified and impurities were removed to form purified nuclear male sterile line population data. The purified nuclear male sterile line population data is input into the seedling cultivation digital twin model. The male parent decision layer simulates the seedling development process under different male parent sowing dates, and the female parent perception layer performs pollination window analysis and filament elongation rate calculation to output an efficient pollination cultivation scheme. The specific construction process of the digital twin model for seedling cultivation is as follows. A parent decision-making layer is built using a Transformer architecture, and a mother perception layer is built using an LSTM network. Jump connections are applied to perform channel splicing and residual stacking on the parent decision-making layer and the mother perception layer to construct a digital twin model for seedling cultivation. The paternal parent decision-making layer simulates the seedling development process under different paternal parent sowing dates, specifically including the following steps. The purified nuclear male sterile line population data is input into the seedling cultivation digital twin model. The paternal decision layer simulates the seedling development process under different paternal sowing dates through the APSIM engine, generates a developmental accumulated temperature response matrix, performs time series clustering on the developmental accumulated temperature response matrix, and obtains the distribution of the paternal pollen shedding peak period. The parent plant sensing layer performs pollination window analysis and filament elongation rate calculation, specifically including the following steps. The maternal parent sensing layer uses bidirectional LSTM to perform pollination window analysis on purified nuclear male sterile line population data, obtains pollination activity time-series characteristics, and calculates filament elongation rate based on pollination activity time-series characteristics to form maternal parent filament acceptability. According to the efficient pollination and cultivation program, the marked seedling population was pollinated in vitro to obtain hybrid hypocotyls; the hybrid hypocotyls were then subjected to BAP differentiation culture to generate regenerated seedlings. Greenhouse transplanting and seed setting rate monitoring of regenerated seedlings were conducted to obtain grain yield data; dynamic fluctuation analysis of grain yield data was performed to output a production efficiency report.

2. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 1, characterized in that: The acquisition of the marked seedling population specifically includes the following steps. Seeds of the nuclear male sterile line and the maintainer line were inoculated into MS medium, and callus was induced by growth regulators to obtain embryogenic callus. Anthocyanins were introduced into embryogenic callus tissue using Agrobacterium-mediated transformation to obtain a labeled seedling population.

3. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 2, characterized in that: The formation of purified nuclear male sterile line population data specifically refers to the process of culturing the marked seedling population under light and classifying and removing impurities to output a chromogenic seedling population, and then performing PCR genotyping verification on the chromogenic seedling population to form purified nuclear male sterile line population data.

4. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 1, characterized in that: The aforementioned efficient pollination and cultivation solution specifically includes the following steps. The distribution of the peak pollen shedding period of the male parent and the filament receptivity of the female parent are fused using topological structure to generate a spatiotemporally optimized pollination map. Spatiotemporal matching analysis and dynamic partitioning are performed on the pollination spatiotemporal optimization map to output an efficient pollination and cultivation scheme.

5. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 4, characterized in that: The process of generating regenerated seedlings specifically includes the following steps. According to the efficient pollination and cultivation program, the marked seedling population was pollinated in vitro using the aseptic in vitro ovule pollination method, while placenta retention was performed simultaneously to obtain hybrid hypocotyls; The hybrid hypocotyls were transplanted into NAA differentiation medium for cytokinin labeling and BAP differentiation culture to generate regenerated seedlings.

6. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 1, characterized in that: The acquisition of grain yield data specifically includes the following steps. The regenerated seedlings were transplanted to a greenhouse for cultivation, and images of unthreshed corn ears were collected. Identify the outlines of clump-like kernels in images of unthreshed corn ears, perform effective kernel screening, and obtain the ear filling rate; The ear grain number conversion and thousand-grain weight correlation analysis were performed on the ear grain filling rate to obtain grain yield data.

7. The method for improving production efficiency based on high-quality maize hybrid seeds as described in claim 1, characterized in that: The aforementioned production efficiency report specifically refers to performing dynamic volatility analysis on grain yield data to obtain variety-yield coupling parameters; integrating the variety-yield coupling parameters and grain yield data to output a production efficiency report.

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