A rice planting method for rapidly screening high-temperature-resistant rice varieties

Abstract

The application discloses a rice planting method for rapidly screening high-temperature-resistant rice varieties, and relates to the technical field of rice planting. The method effectively shortens the initial preparation period through standardized seedling raising, intensive synchronous planting and gradient high-temperature acceleration for differential phenotype appearance, omits the initialization planting link in secondary verification, finally outputs a screening report containing high-temperature-resistant grades and core advantage traits, and further shortens the whole-process cycle; the key growth period is dynamically determined and the stress parameters are adapted to ensure that the high-temperature-resistant differences among varieties are fully displayed, the determination error is greatly reduced, the multi-dimensional traits and recovery capacity are monitored, the quantitative analysis and secondary verification are combined, the high-temperature-resistant variety determination is ensured to be accurate, the process standardization degree is high, and the method is suitable for different rice varieties; the high-temperature-resistant grades are clearly divided, a detailed screening report is generated, accidental factors are excluded through data consistency calibration, the repeatability and reliability of the screening result are improved, and the demand for large-scale and multi-scenario rice high-temperature-resistant screening is met.

Description

Technical Field

[0001] This invention relates to the field of rice cultivation technology, and in particular to a method for rapidly screening heat-resistant rice varieties. Background Technology

[0002] Global warming has led to frequent high-temperature stress, which seriously affects the growth, development, and yield of rice. Traditional methods for screening rice for high-temperature tolerance have obvious defects. For example, Chinese patent CN109122304B discloses a breeding method for rapidly cultivating high-temperature tolerant and direct-seeding tolerant rice. This method improves the high-temperature tolerance of rice by using high-temperature tolerant rice mutants and selects improved rice varieties that are both high-temperature tolerant and direct-seeding tolerant by direct-seeding planting under high-temperature treatment. This patent can effectively shorten the breeding time for selecting new rice varieties (lines) that are both high-temperature tolerant and direct-seeding tolerant and improve breeding efficiency.

[0003] However, despite the aforementioned patents, the following problems still exist: Existing technologies lack a standardized dual-environment regulation system for high-temperature stress and control. Field planting is greatly affected by environmental heterogeneity, and non-temperature factors interfere with screening results. High-temperature stress parameters are single and fixed, without dynamic adjustment based on the sensitivity differences of rice during key growth stages, resulting in insufficient manifestation of differential phenotypes. Furthermore, monitoring indicators are limited to traits during the stress period, ignoring the evaluation of recovery capacity, resulting in incomplete screening dimensions. Moreover, no independent secondary high-temperature stress verification step is set up, leading to insufficient reliability and repeatability of screening results. The overall screening cycle is long and inefficient, making it difficult to meet the needs of large-scale variety screening. Summary of the Invention

[0004] The purpose of this invention is to provide a method for rapidly screening heat-resistant rice varieties. By standardizing seedling cultivation and intensive planting, and combining gradient high-temperature stress with dynamic adaptation to key growth stages, the screening cycle is significantly shortened, non-temperature factors are eliminated, and quantitative analysis and secondary verification are combined to ensure accurate and reliable screening results. This provides a scientific basis for rice breeding and production, and solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for rapidly screening heat-resistant rice varieties in rice cultivation includes the following steps: Experimental material selection and pretreatment: Rice varieties to be screened were selected, pretreated, and then sown into planting containers to form several variety planting units. The planting units were uniformly initialized and cultivated to obtain rice seedlings of each rice variety. Experimental environment construction and grouping settings: A high-temperature stress environment and a normal growth control environment were set up, and gradient high-temperature stress parameters were set. The gradient high-temperature stress parameters include three stress levels, and each stress level corresponds to a group of parallel planting units. Rice cultivation: Rice seedlings of various rice varieties were planted in a standardized manner, and the constructed gradient high temperature stress parameters were applied to the rice seedlings during the key growth period of rice, and the stress conditions were maintained until the end of the growth period. Dynamic monitoring of indicators: Target traits of rice after high temperature stress are detected, key response traits of corresponding rice varieties under high temperature stress are captured, data on the differences in high temperature tolerance among rice varieties are obtained, and multi-dimensional trait detection data of rice are integrated. Comprehensive evaluation of high temperature resistance: Screening criteria were set based on the obtained multi-dimensional trait detection data of rice, and the correlation coefficient of high temperature resistance of each indicator was obtained. Rice varieties with high temperature resistance correlation coefficients higher than the preset correlation coefficient threshold were selected for secondary high temperature stress verification. Through comprehensive quantitative analysis, high temperature resistant rice varieties were distinguished.

[0006] Furthermore, the specific procedures for obtaining rice seedlings for various rice varieties include: Select mature seeds of each rice variety to be screened, remove empty and shriveled grains and impurities, soak the remaining seeds in clean water at 25℃-30℃ for 30 minutes, retain the plump seeds that sink to the bottom, and retain no less than 300 plump seeds for each variety. After cleaning, soak the plump seeds in a sodium hypochlorite solution with a mass concentration of 0.1%-0.3% for 15-20 minutes to disinfect the surface. After disinfection, rinse with sterile water 3-5 times. After rinsing, the seeds are placed in a constant temperature environment and a gradient humidity is set. Gradual water absorption is carried out by combining the constant temperature environment and the gradient humidity. The gradient humidity includes three stages: the first stage humidity is 50-60% and is maintained for 12 hours; the second stage humidity is 70-80% and is maintained for 12 hours; and the third stage humidity is 90-95% and is maintained for 24 hours. After the seeds have absorbed water, they are evenly sown into standardized planting containers. The containers are filled with a homogenized seedling substrate. Each variety is planted in 3-5 repeat planting units. The planting containers are then placed in an initial growth environment to cultivate rice seedlings to the 3-leaf-1-heart stage, thus obtaining rice seedlings with consistent growth.

[0007] Furthermore, a high-temperature stress environment and a normal growth control environment were set up, specifically including: A dual-environment system was constructed, consisting of an independent high-temperature stress environment control system and a normal growth control environment control system. The parameters of the light, humidity, and ventilation modules of each environment control system were set to be consistent, while the temperature control module operated independently. Gradient parameter setting determines the specific range and duration of the gradient high temperature stress parameters, and calibrates the temperature control accuracy of the high temperature stress environment. At the same time, a real-time temperature feedback mechanism is set up. When the temperature deviates from the set temperature range, the temperature control module is triggered to correct the temperature. The rice varieties were divided into control, mild high temperature, moderate high temperature and severe high temperature groups based on repeated planting units of each rice variety. Each group had 3 parallel planting units, and the differences in seedling height and number of leaves between the planting units of each group did not exceed the corresponding preset parameter thresholds.

[0008] Furthermore, the high-temperature stress environment control system also includes high-temperature stress process optimization, specifically including: The layout positions of each parallel planting unit in the high temperature stress environment control system are obtained, and temperature sensing elements are installed at the layout positions of each parallel planting unit. Based on the air temperature data and substrate temperature data collected by the temperature sensing element, the temperature distribution status in each parallel planting unit is determined. Based on the temperature distribution, if the obtained air temperature data and matrix temperature data deviate from the preset temperature range of the gradient high temperature stress parameters, the high temperature stress optimization mechanism of the high temperature stress environment control system will be triggered. Temperature within the planting unit is regulated by a local airflow regulation module and a substrate temperature control auxiliary module. Monitoring data after temperature regulation is obtained, and microenvironment uniformity monitoring records are generated. Acquire growth data recorded during the initial growth and cultivation of rice seedlings, and determine the key growth period nodes for each rice variety to be screened based on the growth data; The corresponding gradient high temperature stress parameters are retrieved based on the key reproductive period nodes, and the duration of the gradient high temperature stress parameters is adjusted based on the differences in high temperature sensitivity at the key reproductive period nodes.

[0009] Furthermore, after adjusting the duration of the gradient high-temperature stress parameters, the following also applies: Based on the end signal of the gradient high temperature stress cycle, parallel planting units of mild high temperature group, moderate high temperature group and severe high temperature group were obtained and transferred to the normal growth control environment control system. The environmental parameters during the recovery cultivation period are controlled according to the environmental parameters of the normal growth control environment, and the environmental parameters are kept consistent with the parameters of the normal growth control environment. During the recovery cultivation period, the recovery traits of rice were continuously monitored. Based on the monitoring results, recovery trait data were generated. The recovery trait data and microenvironment homogeneity monitoring records were integrated with the multidimensional trait detection data of rice to form a complete detection dataset.

[0010] Furthermore, the dynamic determination process for the key growth period of rice includes: Acquire growth data recorded during the initial growth and cultivation of rice seedlings, analyze the growth consistency of seedlings of different varieties, determine the starting time node for pre-monitoring of key growth periods, and conduct pre-monitoring of key growth periods. During the pre-monitoring process, plant morphology indicators of each variety planting unit are obtained, and the monitoring frequency is adjusted according to the morphology indicator detection results. Based on the monitoring results, feature variables associated with the key growth stages of rice were extracted, and a key growth stage feature dataset was generated based on the extracted feature variables. Based on the key growth period characteristics of various types of rice varieties retrieved from historical planting databases, and combined with the key growth period characteristic dataset, a key growth period characteristic threshold model is constructed. Real-time collected rice seedling growth data is input into the key growth period feature threshold model for comparison, and the matching degree between the feature variables and the preset threshold of the key growth period feature threshold model is obtained. Based on the matching results, it is determined whether each rice variety has entered the target critical growth period. If the threshold condition is met, the critical growth period start signal and start time node of the rice variety are generated.

[0011] Furthermore, the process of applying the constructed gradient high-temperature stress parameters to rice seedlings includes: Data on recovery traits during the recovery period were obtained, the baseline duration of high temperature stress parameters at each gradient was retrieved, and a correlation matrix between growth period, stress intensity, and recovery capacity was established. Based on the correlation matrix, the sensitivity coefficients of each rice variety to each gradient of high temperature stress at different critical growth stages are calculated. The basic duration of the gradient high temperature stress parameters is weighted and corrected according to the sensitivity coefficients to generate the appropriate stress parameters for each rice variety at different critical growth stages. Based on the key growth period start signals and start time nodes of the rice variety, the corresponding adaptive stress parameters are retrieved, and combined with the temperature gradient warming rate, a gradient high temperature stress timing application scheme for each variety is generated. Key response trait data were collected in real time during the implementation of the gradient high temperature stress time series application scheme and compared with the preset normal fluctuation range of the variety under the corresponding stress intensity. If the key response trait data are detected to deviate from the preset normal fluctuation range of the variety under the corresponding stress intensity, the high temperature stress optimization mechanism is triggered to correct the stress temperature or duration. After the gradient high temperature stress cycle ends, record complete stress application process data, and integrate the stress application process data with the key growth period characteristic dataset of the variety to form a stress growth period related archive.

[0012] Furthermore, the process of obtaining the high-temperature resistance difference data includes: The starting time of the critical reproductive period and the timing of gradient high temperature stress were obtained. Based on the starting time of the critical reproductive period and the timing of stress, the monitoring sequence was planned, and a standardized monitoring indicator system was generated according to the indicator categories. Parallel planting units were obtained for the control group and each high-temperature stress group. The indicators of each variety planting unit were tested according to the planned monitoring sequence. 3-5 representative plants were selected for each variety planting unit, and the mean value of the test results was calculated as the indicator data of the variety planting unit to generate the original test dataset of each variety planting unit. Obtain the original test datasets for each variety planting unit, retrieve the index data of the normal growth control environment, perform data preprocessing, and generate a standardized dataset. Obtain data related to the stress-induced growth period, extract trend data of index changes for each rice variety under different gradients of high temperature stress, and retrieve standardized data of known high-temperature resistant control varieties and sensitive control varieties. Calculate the similarity of indicators between each variety to be screened and two control varieties, screen indicators whose similarity difference exceeds the preset similarity threshold, and use the screened indicators as the core features of high temperature resistance difference to generate a dataset of high temperature resistance difference for each rice variety. We acquired microenvironment homogeneity monitoring records, key growth period characteristic datasets, and stress application process data, and correlated them with high temperature tolerance difference datasets of various rice varieties to generate multidimensional trait detection data for rice.

[0013] Furthermore, the secondary high-temperature stress verification specifically includes: Obtain a list of rice varieties to be verified whose high temperature resistance correlation coefficient is higher than a preset correlation coefficient threshold. Based on the sensitivity stress level of each rice variety in the list of varieties to be verified, retrieve the corresponding gradient high temperature stress parameter range and determine the secondary verification stress parameters. The planting units of the varieties to be verified were transferred to the high temperature stress environment control system. Based on the secondary verification stress parameters, a second high temperature stress was performed. During the stress period, the core characteristic indicators of high temperature tolerance differences were monitored repeatedly according to the planned monitoring sequence to generate monitoring data for the secondary verification process. The monitoring data of the secondary verification process is preprocessed to obtain the core feature dataset of the secondary verification. The core feature dataset of the secondary verification is compared with the high temperature resistance difference dataset to calculate the data consistency coefficient and generate the secondary verification calibration dataset.

[0014] Further, comprehensive quantitative analysis, specifically including: We acquired and classified rice multidimensional trait detection data and secondary verification calibration datasets, and calculated and generated a comprehensive evaluation dataset based on a fixed weight system. The comprehensive evaluation data of known heat-resistant control varieties and sensitive control varieties were retrieved as reference sequences, and the comprehensive evaluation datasets of each variety to be screened were used as comparison sequences to generate the comprehensive heat resistance correlation coefficient of each rice variety. The preset correlation coefficient threshold is obtained, and the high-temperature resistance level is divided by combining the comprehensive high-temperature resistance correlation coefficient distribution. The level determination results of each rice variety, the comprehensive evaluation dataset and the stress growth period correlation file are integrated to generate a high-temperature resistance variety screening report.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention effectively shortens the initial preparation cycle by standardizing seedling cultivation, intensive synchronous planting, and accelerating the emergence of differential phenotypes through gradient high temperatures. Secondary verification eliminates the initial planting step, and the final output includes a screening report containing heat tolerance levels and core advantageous traits, further shortening the overall process cycle. By constructing a dual-environment regulation system and a microenvironment homogenization mechanism, interference from non-temperature factors is eliminated. Dynamic determination of key growth stages and adaptation to stress parameters ensure that heat tolerance differences among varieties are fully manifested, significantly reducing judgment errors. Based on multi-dimensional trait and recovery capacity monitoring, combined with quantitative analysis and secondary verification, accurate identification of heat-tolerant varieties is ensured. The process is highly standardized and adaptable to different genotypes and ecotypes of rice. Clearly defining heat tolerance levels and generating detailed screening reports, data consistency calibration eliminates accidental factors, significantly improving the repeatability and reliability of screening results, meeting the needs of large-scale, multi-scenario rice heat tolerance screening. Attached Figure Description

[0016] Figure 1 This is a flowchart of the rice cultivation method for rapidly screening heat-resistant rice varieties according to the present invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1 A method for rapidly screening heat-resistant rice varieties in rice cultivation includes the following steps: Experimental material selection and pretreatment: Rice varieties to be screened were selected, pretreated, and then sown into planting containers to form several variety planting units. The planting units were uniformly initialized and cultivated to obtain rice seedlings of each rice variety. Experimental environment construction and grouping settings: A high-temperature stress environment and a normal growth control environment were set up, and gradient high-temperature stress parameters were set. The gradient high-temperature stress parameters include three stress levels, namely mild high temperature, moderate high temperature, and severe high temperature. Each stress level corresponds to a set of parallel planting units. Rice cultivation: Rice seedlings of various rice varieties were standardized and planted to ensure that the seedlings completed their seedling growth under uniform environmental conditions. During the critical growth period of rice, the constructed gradient high temperature stress parameters were applied to the rice seedlings and the stress conditions were maintained until the end of the growth period. Dynamic monitoring of indicators: Target traits of rice after high temperature stress are detected, key response traits of corresponding rice varieties under high temperature stress are captured, data on the differences in high temperature tolerance among rice varieties are obtained, and multi-dimensional trait detection data of rice are integrated. Comprehensive evaluation of high temperature resistance: Screening criteria were set based on the obtained multi-dimensional trait detection data of rice, and the correlation coefficient of high temperature resistance of each indicator was obtained. Rice varieties with high temperature resistance correlation coefficients higher than the preset correlation coefficient threshold were selected for secondary high temperature stress verification. Through comprehensive quantitative analysis, high temperature resistant rice varieties were distinguished.

[0019] In this embodiment, the rice varieties cover different genotypes, different ecotypes, and known heat-resistant control varieties and sensitive control varieties. For each variety, 500 seeds from the same batch and the same storage environment are directly selected. The seeds must be from the same batch and harvested in the same planting environment to ensure that the initial genetic background is consistent with the storage conditions after harvest. In this embodiment, scientific grouping ensures the uniqueness of experimental variables, avoids interference from non-temperature factors such as light, humidity, and fertility, targets gradient high-temperature stress, accelerates the emergence of differentiated phenotypes, shortens the stress and difference manifestation cycle, observes core related traits, avoids redundant trait detection, reduces observation workload and data processing time, establishes a fixed weight system and clear scoring thresholds, and directly determines results through quantitative calculation without complex comparative analysis; and the secondary verification only repeats the core stress and observation process without re-initializing planting, further shortening the confirmation cycle and achieving efficient connection of the entire process from planting to screening, thereby achieving rapid screening.

[0020] In this embodiment, the specific process for obtaining rice seedlings of various rice varieties includes: A combination of wind and water selection was used to select mature seeds of each rice variety to be screened. Empty and shriveled grains and impurities were removed by wind screening. The remaining seeds were soaked in clean water at 25℃-30℃ for 30 minutes to remove floating and unripe grains and retain plump seeds that sink to the bottom. The number of plump seeds retained for each variety was no less than 300, so as to eliminate the interference of seed quality differences on germination consistency. After cleaning, plump seeds are soaked in a sodium hypochlorite solution with a mass concentration of 0.1%-0.3% for 15-20 minutes for surface disinfection. After disinfection, rinse with sterile water 3-5 times until there is no chlorine smell to remove microorganisms attached to the seed surface and avoid microorganisms affecting seed germination and seedling growth stability. After rinsing, the seeds are placed in a constant temperature environment with a gradient humidity setting. The constant temperature environment and the gradient humidity are combined to carry out gradient water absorption. The gradient humidity includes three stages: the first stage humidity is 50-60% and maintained for 12 hours; the second stage humidity is 70-80% and maintained for 12 hours; and the third stage humidity is 90-95% and maintained for 24 hours. This allows the seeds to gradually reach a saturated water absorption state, avoids rapid water absorption that could damage the embryo, and ensures that the seeds start germination synchronously. After the seeds have absorbed water, they are evenly sown into standardized planting containers. The containers are filled with a homogenized seedling substrate. Each variety is planted in 3-5 replicate planting units to create a uniform initial growth microenvironment. The planting containers are then placed in the initial growth environment to cultivate rice seedlings to the 3-leaf-1-heart stage. This process ensures that rice seedlings with consistent growth status are obtained, eliminating initial differences that could interfere with subsequent screening.

[0021] In this embodiment, a high-temperature stress environment and a normal growth control environment are set up, specifically including: A dual-environment system was constructed, consisting of an independent high-temperature stress environment control system and a normal growth control environment control system. The parameters of the light, humidity, and ventilation modules of each environment control system were set to be consistent, while the temperature control module operated independently. This ensured that all environmental factors except temperature were consistent, eliminating the interference of non-target environmental factors on the screening results. The gradient parameters are set to determine the specific range and duration of the gradient high-temperature stress parameters. A high-precision temperature sensor is used to calibrate the temperature control accuracy of the high-temperature stress environment, ensuring that the actual temperature fluctuation does not exceed ±0.5℃, thus guaranteeing the accurate implementation of the gradient high-temperature stress parameters. Simultaneously, a real-time temperature feedback mechanism is set up. When a temperature deviation from the set temperature range is detected, the temperature control module is triggered to correct the temperature, ensuring the stability of the gradient high-temperature stress conditions in real time and preventing abnormal stress intensity caused by temperature fluctuations. The rice varieties were divided into control, mild high temperature, moderate high temperature and severe high temperature groups based on repeated planting units of each rice variety. Each group had three parallel planting units, and the differences in seedling height and number of leaves among the planting units of each group did not exceed the corresponding preset parameter thresholds to ensure that the initial growth status of each group was consistent and to ensure the effectiveness of the stress effect comparison.

[0022] In this embodiment, the parameters of the lighting module are: light intensity 3000-4000 lux, lighting duration 12-14 h / d; the parameters of the humidity module are: relative humidity 65-75%; and the parameters of the ventilation module are: ventilation frequency 1 h / time. In this embodiment, the temperature for mild high-temperature stress was 35-37℃ for 5-6 days, the temperature for moderate high-temperature stress was 38-40℃ for 5-7 days, and the temperature for severe high-temperature stress was 41-43℃ for 4-6 days, thus constructing a gradient of high-temperature stress intensity to accurately induce the heat tolerance differences in different rice varieties, as shown in Table 1 below:

[0023] In this embodiment, the high-temperature stress environment control system further includes high-temperature stress process optimization, specifically including: The layout positions of each parallel planting unit in the high temperature stress environment control system are obtained, and temperature sensing elements are installed at the layout positions of each parallel planting unit. Based on the air temperature data and substrate temperature data collected by the temperature sensing element, the temperature distribution status in each parallel planting unit is determined. Based on the temperature distribution, if the obtained air temperature data and matrix temperature data deviate from the preset temperature range of the gradient high temperature stress parameters, the high temperature stress optimization mechanism of the high temperature stress environment control system will be triggered. Temperature within the planting unit is regulated by a local airflow regulation module and a substrate temperature control auxiliary module to maintain the consistency of microenvironment temperature in each parallel planting unit. Monitoring data after temperature regulation is obtained, and microenvironment uniformity monitoring records are generated to ensure the stability of the microenvironment throughout the entire stress cycle. Acquire growth data recorded during the initial growth and cultivation of rice seedlings, and determine the key growth period nodes for each rice variety to be screened based on the growth data; The corresponding gradient high temperature stress parameters are retrieved based on the key reproductive period nodes, and the duration of the gradient high temperature stress parameters is adjusted based on the differences in high temperature sensitivity at the key reproductive period nodes.

[0024] In this embodiment, within the high-temperature stress environment control system, temperature sensors are installed at the four corners and center of each parallel planting unit. Simultaneously, temperature sensors are sequentially installed on the surface, middle, and bottom layers of the seedling substrate in each planting unit to collect air temperature and substrate temperature data in real time. In this embodiment, before the adjusted gradient high temperature stress parameters are activated, the temperature control module is controlled to perform a gradual temperature increase operation, and after the gradient high temperature stress parameters are deactivated, the temperature control module is controlled to perform a gradual temperature decrease operation. In this embodiment, by regulating the uniformity of the microenvironment under high-temperature stress and adapting and adjusting the stress parameters during key reproductive periods, the uniformity of the microenvironment and the accuracy of the parameters within the stress cycle are achieved, ensuring the reliability of the high-temperature stress experiment.

[0025] In this embodiment, after adjusting the duration of the gradient high-temperature stress parameters, the method further includes: Based on the end signal of the gradient high temperature stress cycle, parallel planting units of mild high temperature group, moderate high temperature group and severe high temperature group were obtained and transferred to the normal growth control environment control system. The environmental parameters during the recovery cultivation period are controlled according to the environmental parameters of the normal growth control environment, and the environmental parameters are kept consistent with the parameters of the normal growth control environment. During the recovery cultivation period, the recovery traits of rice were continuously monitored. Based on the monitoring results, recovery trait data were generated. The recovery trait data and microenvironment homogeneity monitoring records were integrated with the multidimensional trait detection data of rice to form a complete detection dataset.

[0026] In this embodiment, a complete detection dataset is formed by standardizing environmental adaptation, monitoring traits throughout the entire cycle, and fusing multi-dimensional data. This provides a more comprehensive evaluation basis for screening high-temperature resistant rice varieties, facilitates the reproducibility of subsequent experimental results, and significantly improves the reliability, completeness, and application value of the experimental data.

[0027] In this embodiment, the dynamic determination process of the key growth period of rice includes: Acquire growth data recorded during the initial growth and cultivation of rice seedlings, including daily collection of leaf sheath length, tiller number, and flag leaf primordium differentiation status data of seedlings in each variety planting unit during the seedling growth stage after standardized planting. Calculate the leaf sheath length growth rate and tiller number achievement rate based on this data; analyze the growth consistency of seedlings of each variety, determine the starting time node for pre-monitoring of key growth periods, and conduct pre-monitoring of key growth periods. During the pre-monitoring process, plant morphological indicators of each variety planting unit are obtained, including flag leaf sheathing degree and panicle neck elongation. The monitoring frequency is adjusted according to the morphological indicator detection results to determine whether the plant has reached the preset late heading stage standard. If the standard is reached, the monitoring frequency of the corresponding planting unit is adjusted to high-frequency monitoring, which is used to narrow the monitoring range through intuitive morphological characteristics, focus on varieties that are about to enter the heading stage, and improve the judgment efficiency. Based on the monitoring results, characteristic variables associated with the key growth stages of rice were extracted. These characteristic variables include the growth rate of leaf sheath length, the tiller number achievement rate, the differentiation progress of flag leaf primordia, the degree of flag leaf sheathing and the length of panicle neck elongation during the pre-monitoring process, and a key growth stage characteristic dataset was generated based on the extracted characteristic variables. Based on the classification of different genotypes and ecotypes of rice varieties, the key growth period characteristics of various types of rice varieties were retrieved from the historical planting database. Combined with the key growth period characteristic dataset, a key growth period characteristic threshold model was constructed. Real-time collected rice seedling growth data is input into the key growth period feature threshold model for comparison, and the matching degree between the feature variables and the preset threshold of the key growth period feature threshold model is obtained. Based on the matching results, it is determined whether each rice variety has entered the target critical growth period. If the threshold condition is met, the critical growth period start signal and start time node of the rice variety are generated.

[0028] In this embodiment, based on the pre-monitoring scheme, regular growth period surveys are conducted on each variety planting unit, namely the control group and each high temperature stress group, in order to capture the signal of rice transitioning from vegetative growth to reproductive growth in advance and avoid missing the initial observation of the critical growth period. In this embodiment, the preset threshold of the critical growth period characteristic threshold model is determined based on the mean value of characteristic variables of the same type of variety 3-5 days before the start of the critical growth period in historical data. In this embodiment, the process of applying the constructed gradient high-temperature stress parameters to rice seedlings includes: Data on recovery traits during the recovery breeding period were obtained, the baseline duration of high temperature stress parameters at each gradient was retrieved, a correlation matrix between growth period, stress intensity and recovery capacity was established, and the corresponding relationship of recovery capacity under different combinations of growth period and stress intensity was clarified. Based on the correlation matrix, the sensitivity coefficients of each rice variety to each gradient of high temperature stress at different critical growth stages are calculated by combining different genotypes and ecotypes of rice varieties. The basic duration of gradient high temperature stress parameters is weighted and corrected according to the sensitivity coefficients to generate the appropriate stress parameters for each rice variety at different critical growth stages, including appropriate temperature level and appropriate duration. Based on the key growth period start signals and start time nodes of the rice variety, the corresponding adaptive stress parameters are retrieved, and combined with the temperature gradient warming rate, a gradient high temperature stress timing application scheme for each variety is generated. Key response trait data during the implementation of the gradient high temperature stress time sequence application scheme were collected in real time, including the relative chlorophyll content of leaves, pollen shedding rate of anthers, and grain filling rate, and compared with the preset normal fluctuation range of the variety under the corresponding stress intensity. If the key response trait data are detected to deviate from the preset normal fluctuation range of the variety under the corresponding stress intensity, the high temperature stress optimization mechanism is triggered to correct the stress temperature or duration, ensuring that the stress effect always focuses on the high temperature resistance difference induction rather than extreme environmental damage. After the gradient high temperature stress cycle ends, record complete stress application process data, including stress parameter adaptation, time-series application plan execution records, and real-time feedback adjustment records. Then, link and integrate the stress application process data with the key growth period characteristic dataset of the variety to form a stress growth period related archive.

[0029] In this embodiment, the sensitivity coefficient is calculated by weighting the high temperature sensitivity weight during the growth period, the sensitivity benchmark value of the variety ecotype, and the reverse coefficient of recovery ability. Among them, the high temperature sensitivity weight during the growth period is: flowering period > early grain filling period > early heading period. In this embodiment, a preset normal fluctuation range under the corresponding stress intensity is determined based on data of the same type of variety from a historical planting database. In this embodiment, by dynamically collecting rice seedling growth data and constructing a key growth period characteristic threshold model based on historical planting databases, the key growth periods of each variety are accurately determined, avoiding missing key observation nodes. Based on the correlation between growth period, stress intensity, and recovery ability, sensitivity coefficients are calculated by combining variety genotype and ecotype, and the appropriate gradient high-temperature stress parameters are corrected. Then, the stress process is optimized through time-series application schemes and real-time response trait monitoring to ensure that the stress effect focuses on high-temperature tolerance differential induction rather than damage from extreme environments. At the same time, the key growth period data and stress process data are integrated to form a correlation archive, which improves the overall accuracy of the determination, parameter adaptability, and data completeness of rice high-temperature stress experiments, providing reliable support for subsequent screening of rice high-temperature tolerant varieties and research on high-temperature stress mechanisms.

[0030] In this embodiment, the process of obtaining the high-temperature resistance difference data includes: The starting time of the critical growth period and the timing of gradient high-temperature stress were obtained. Based on the starting time of the critical growth period and the stress timing scheme, the monitoring sequence was planned as follows: 1 day before stress, every 2 days during stress, 3 days after stress, and at the end of the recovery period. Combining the data categories of recovery traits, three major indicator categories were defined: agronomic traits, physiological traits, and recovery traits. A standardized monitoring indicator system was generated according to the indicator categories, as shown in Table 2 below.

[0031] Furthermore, 3-5 representative plants were selected from each planting unit, and the average value was taken. Parallel planting units were obtained for the control group and each high-temperature stress group. The indicators of each variety planting unit were tested according to the planned monitoring sequence: agronomic traits were obtained using standardized measurement tools, physiological traits were measured using corresponding detection methods, and recovery traits were recorded according to the monitoring standards. 3-5 representative plants were selected from each variety planting unit, and the mean of the test results was calculated as the indicator data of that variety planting unit to generate the original test dataset for each variety planting unit. The original test datasets of each variety planting unit were obtained, the index data of the normal growth control environment were retrieved and the data were preprocessed. The relative values ​​of the indexes of each high temperature stress group were calculated based on the index data of the control environment. The index data of different dimensions were normalized and mapped to the [0,1] interval. Abnormal data were removed and the standard deviation was calculated based on the repeated data of parallel planting units. A standardized dataset was generated. Obtain data related to the stress-induced growth period, extract trend data of index changes for each rice variety under different gradients of high temperature stress, and retrieve standardized data of known high-temperature resistant control varieties and sensitive control varieties. Calculate the similarity of indicators between each variety to be screened and two control varieties, screen indicators whose similarity difference exceeds the preset similarity threshold, and use the screened indicators as the core features of high temperature resistance differences. Integrate the core feature data with the standardized dataset; generate a dataset of high temperature resistance differences for each rice variety, and accurately focus on the key indicators of high temperature resistance differences. We acquired microenvironment homogeneity monitoring records, key growth period characteristic datasets, and stress application process data, and correlated them with high-temperature tolerance difference datasets for various rice varieties. We then categorized and integrated the data by rice variety, stress level, and key growth period, labeled the corresponding environmental parameters and stress conditions, and generated multi-dimensional trait detection data for rice. This provides comprehensive and reliable data support for the screening of high-temperature tolerant rice varieties and the study of high-temperature stress response mechanisms.

[0032] In this embodiment, the secondary high-temperature stress verification specifically includes: Obtain a list of rice varieties to be verified whose high-temperature tolerance correlation coefficient is higher than a preset correlation coefficient threshold. Based on the sensitivity stress level of each rice variety in the list of varieties to be verified, retrieve the corresponding gradient high-temperature stress parameter range and determine the secondary verification stress parameters: the stress temperature is taken as the median value of the temperature range of the level, and the duration is set as 80%-100% of the duration of the initial stress; retain the microenvironment uniformity control standard and the temperature gradual increase / decrease operation standard to generate a verification stress parameter scheme. The planting units of the varieties to be verified were transferred to the high temperature stress environment control system. Based on the secondary verification stress parameters, a second high temperature stress was performed. During the stress period, the core characteristic indicators of high temperature tolerance differences were monitored repeatedly according to the planned monitoring sequence to generate monitoring data for the secondary verification process. The monitoring data of the secondary verification process is preprocessed to obtain the core feature dataset of the secondary verification. The core feature dataset of the secondary verification is compared with the high temperature resistance difference dataset to calculate the data consistency coefficient. If the consistency coefficient is within the preset reasonable range, the data is retained twice. If it exceeds the range, one additional verification is performed. The mean is calculated based on the valid data to generate the secondary verification calibration dataset.

[0033] In this embodiment, comprehensive quantitative analysis specifically includes: The data on multidimensional traits of rice and the secondary verification and calibration dataset were acquired and classified and integrated. The two types of data were integrated into agronomic traits, physiological traits and restorer traits. A comprehensive evaluation dataset was generated based on a fixed weight system. The comprehensive evaluation data of known heat-resistant control varieties and sensitive control varieties were retrieved as reference sequences, and the comprehensive evaluation datasets of each variety to be screened were used as comparison sequences to generate the comprehensive heat resistance correlation coefficient of each rice variety. Obtain the preset correlation coefficient threshold, and combine it with the comprehensive high temperature resistance correlation coefficient distribution to classify high temperature resistance levels: high temperature resistance, medium temperature resistance, and low temperature resistance. Integrate the grade determination results of each rice variety, the comprehensive evaluation dataset, and the stress growth period correlation archive to generate a high temperature resistance variety screening report, and mark the core advantageous traits and high temperature resistance levels of each rice variety.

[0034] In this embodiment, the secondary high-temperature stress verification and comprehensive quantitative analysis process involves setting appropriate secondary verification stress parameters for the varieties to be verified with high high-temperature tolerance correlation coefficients, retaining standardized microenvironment control and temperature level operation standards, and combining repeated monitoring and data consistency calibration to ensure the reliability and accuracy of high-temperature tolerance difference data. By classifying and integrating multi-dimensional trait data and secondary verification data, and combining the reference sequence of control varieties to calculate the comprehensive high-temperature tolerance correlation coefficient, clear high-temperature tolerance levels are defined, and a screening report with labeled core advantageous traits and levels is generated. This achieves a precise evaluation of the high-temperature tolerance ability of rice varieties and provides a standardized and reliable basis for judgment for the subsequent application and research of high-temperature tolerant varieties.

[0035] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for rapidly screening heat-resistant rice varieties during rice cultivation, characterized in that, Includes the following steps: Rice seeds of rice varieties to be screened are selected, pretreated, and then sown into planting containers to form several variety planting units. The planting units are uniformly initialized and cultivated to obtain rice seedlings of each rice variety. A high-temperature stress environment and a normal growth control environment were set up, and gradient high-temperature stress parameters were set. The gradient high-temperature stress parameters included three stress levels, and each stress level corresponded to a set of parallel planting units. The rice seedlings of each rice variety were planted in a standardized manner, and the constructed gradient high temperature stress parameters were applied to the rice seedlings during the key growth period of rice, and the stress conditions were maintained until the end of the growth period. Target traits of rice after high temperature stress were detected, key response traits of corresponding rice varieties under high temperature stress were captured, high temperature tolerance difference data among rice varieties were obtained, and multi-dimensional trait detection data of rice were integrated. Based on the obtained multi-dimensional trait detection data of rice, screening criteria were set, and the high temperature resistance correlation coefficient of each indicator was obtained. Rice varieties with high temperature resistance correlation coefficients higher than the preset correlation coefficient threshold were selected for secondary high temperature stress verification. Through comprehensive quantitative analysis, high temperature resistant rice varieties were distinguished. The dynamic determination process for the critical growth period of rice includes: Acquire growth data recorded during the initial growth and cultivation of rice seedlings, analyze the growth consistency of seedlings of different varieties, determine the starting time node for pre-monitoring of key growth periods, and conduct pre-monitoring of key growth periods. During the pre-monitoring process, plant morphology indicators of each variety planting unit are obtained, and the monitoring frequency is adjusted according to the morphology indicator detection results. Based on the monitoring results, feature variables associated with the key growth stages of rice were extracted, and a key growth stage feature dataset was generated based on the extracted feature variables. Based on the key growth period characteristics of various types of rice varieties retrieved from historical planting databases, and combined with the key growth period characteristic dataset, a key growth period characteristic threshold model is constructed. Real-time collected rice seedling growth data is input into the key growth period feature threshold model for comparison, and the matching degree between the feature variables and the preset threshold of the key growth period feature threshold model is obtained. Based on the matching results, it is determined whether each rice variety has entered the target critical growth period. If the threshold condition is met, the critical growth period start signal and start time node of the rice variety are generated. The process of applying constructed gradient high-temperature stress parameters to rice seedlings includes: Data on recovery traits during the recovery period were obtained, the baseline duration of high temperature stress parameters at each gradient was retrieved, and a correlation matrix between growth period, stress intensity, and recovery capacity was established. Based on the correlation matrix, the sensitivity coefficients of each rice variety to each gradient of high temperature stress at different critical growth stages are calculated. The basic duration of the gradient high temperature stress parameters is weighted and corrected according to the sensitivity coefficients to generate the appropriate stress parameters for each rice variety at different critical growth stages. Based on the key growth period start signals and start time nodes of the rice variety, the corresponding adaptive stress parameters are retrieved, and combined with the temperature gradient warming rate, a gradient high temperature stress timing application scheme for each variety is generated. Key response trait data were collected in real time during the implementation of the gradient high temperature stress time series application scheme and compared with the preset normal fluctuation range of the variety under the corresponding stress intensity. If the key response trait data are detected to deviate from the preset normal fluctuation range of the variety under the corresponding stress intensity, the high temperature stress optimization mechanism is triggered to correct the stress temperature or duration. After the gradient high temperature stress cycle ends, record complete stress application process data, and integrate the stress application process data with the key growth period characteristic dataset of the variety to form a stress growth period related archive.

2. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 1, characterized in that, The specific procedures for obtaining rice seedlings of various rice varieties include: Select mature seeds of each rice variety to be screened, remove empty and shriveled grains and impurities, soak the remaining seeds in clean water at 25℃-30℃ for 30 minutes, retain the plump seeds that sink to the bottom, and retain no less than 300 plump seeds for each variety. After cleaning, soak the plump seeds in a sodium hypochlorite solution with a mass concentration of 0.1%-0.3% for 15-20 minutes to disinfect the surface. After disinfection, rinse with sterile water 3-5 times. After rinsing, the seeds are placed in a constant temperature environment with a gradient humidity setting, and water is absorbed in a gradient manner by combining the constant temperature environment with the gradient humidity setting. After the seeds have absorbed water, they are evenly sown into standardized planting containers. The containers are filled with a homogenized seedling substrate. Each variety is planted in 3-5 repeat planting units. The planting containers are then placed in an initial growth environment to cultivate rice seedlings.

3. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 1, characterized in that, Setting up high-temperature stress environments and normal growth control environments specifically includes: An independent high-temperature stress environment control system and a normal growth control environment control system were constructed. The parameters of the light, humidity and ventilation modules of each environment control system were set to be consistent, while the temperature control module operated independently. The specific range and duration of gradient high temperature stress parameters are determined, and the temperature control accuracy of the high temperature stress environment is calibrated. At the same time, a real-time temperature feedback mechanism is set up. When the temperature deviates from the set temperature range, the temperature control module is triggered to correct the temperature. Based on the repeated planting units of each rice variety to be screened, the rice varieties were divided into control group, mild high temperature group, moderate high temperature group and severe high temperature group. Each group had 3 parallel planting units, and the differences in seedling height and number of leaves among the planting units of each group did not exceed the corresponding preset parameter thresholds.

4. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 3, characterized in that, The high-temperature stress environment control system also includes high-temperature stress process optimization, specifically including: The layout positions of each parallel planting unit in the high temperature stress environment control system are obtained, and temperature sensing elements are installed at the layout positions of each parallel planting unit. Based on the air temperature data and substrate temperature data collected by the temperature sensing element, the temperature distribution status in each parallel planting unit is determined. Based on the temperature distribution, if the obtained air temperature data and matrix temperature data deviate from the preset temperature range of the gradient high temperature stress parameters, the high temperature stress optimization mechanism of the high temperature stress environment control system will be triggered. Temperature within the planting unit is regulated by a local airflow regulation module and a substrate temperature control auxiliary module. Monitoring data after temperature regulation is obtained, and microenvironment uniformity monitoring records are generated. Acquire growth data recorded during the initial growth and cultivation of rice seedlings, and determine the key growth period nodes for each rice variety to be screened based on the growth data; The corresponding gradient high temperature stress parameters are retrieved based on the key reproductive period nodes, and the duration of the gradient high temperature stress parameters is adjusted based on the differences in high temperature sensitivity at the key reproductive period nodes.

5. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 4, characterized in that, After adjusting the duration of the gradient high-temperature stress parameters, it also includes: Based on the end signal of the gradient high temperature stress cycle, parallel planting units of mild high temperature group, moderate high temperature group and severe high temperature group were obtained and transferred to the normal growth control environment control system. The environmental parameters during the recovery cultivation period are controlled according to the environmental parameters of the normal growth control environment, and the environmental parameters are kept consistent with the parameters of the normal growth control environment. During the recovery cultivation period, the recovery traits of rice were continuously monitored. Based on the monitoring results, recovery trait data were generated. The recovery trait data and microenvironment homogeneity monitoring records were integrated with the multidimensional trait detection data of rice to form a complete detection dataset.

6. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 1, characterized in that, The process of obtaining the high-temperature resistance difference data includes: The starting time of the critical reproductive period and the timing of gradient high temperature stress were obtained. Based on the starting time of the critical reproductive period and the timing of stress, the monitoring sequence was planned, and a standardized monitoring indicator system was generated according to the indicator categories. Parallel planting units of the control group and each high temperature stress group were obtained. The indicators of each variety planting unit were tested according to the planned monitoring time sequence. The mean of the test results was calculated as the indicator data of the variety planting unit, and the original test dataset of each variety planting unit was generated. Obtain the original test datasets for each variety planting unit, retrieve the index data of the normal growth control environment, perform data preprocessing, and generate a standardized dataset. Obtain data related to the stress-induced growth period, extract trend data of index changes for each rice variety under different gradients of high temperature stress, and retrieve standardized data of known high-temperature resistant control varieties and sensitive control varieties. Calculate the similarity of indicators between each variety to be screened and two control varieties, screen indicators whose similarity difference exceeds the preset similarity threshold, and use the screened indicators as the core features of high temperature resistance difference to generate a dataset of high temperature resistance difference for each rice variety. We acquired microenvironment homogeneity monitoring records, key growth period characteristic datasets, and stress application process data, and correlated them with high temperature tolerance difference datasets of various rice varieties to generate multidimensional trait detection data for rice.

7. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 6, characterized in that, The secondary high-temperature stress verification specifically includes: Obtain a list of rice varieties to be verified whose high temperature resistance correlation coefficient is higher than a preset correlation coefficient threshold. Based on the sensitivity stress level of each rice variety in the list of varieties to be verified, retrieve the corresponding gradient high temperature stress parameter range and determine the secondary verification stress parameters. The planting units of the varieties to be verified were transferred to the high temperature stress environment control system. Based on the secondary verification stress parameters, a second high temperature stress was performed. During the stress period, the core characteristic indicators of high temperature tolerance differences were monitored repeatedly according to the planned monitoring sequence to generate monitoring data for the secondary verification process. The monitoring data of the secondary verification process is preprocessed to obtain the core feature dataset of the secondary verification. The core feature dataset of the secondary verification is compared with the high temperature resistance difference dataset to calculate the data consistency coefficient and generate the secondary verification calibration dataset.

8. The rice cultivation method for rapidly screening heat-resistant rice varieties according to claim 7, characterized in that, Comprehensive quantitative analysis, specifically including: We acquired and classified rice multidimensional trait detection data and secondary verification calibration datasets, and calculated and generated a comprehensive evaluation dataset based on a fixed weight system. The comprehensive evaluation data of known heat-resistant control varieties and sensitive control varieties were retrieved as a reference sequence, and the comprehensive evaluation datasets of each variety to be screened were used as a comparison sequence to generate the comprehensive heat resistance correlation coefficient of each rice variety. Obtain the preset correlation coefficient threshold, combine it with the comprehensive high temperature resistance correlation coefficient distribution, classify the high temperature resistance level, integrate the level judgment results of each rice variety, the comprehensive evaluation dataset and the stress growth period correlation file, and generate a high temperature resistance variety screening report.

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