A seed germination prediction method for breeding of oilseed rape

Abstract

This invention relates to the field of agricultural and forestry management forecasting data processing technology, specifically to a seed germination prediction method for rapeseed breeding. The method includes: for rapeseed of the same variety, determining the initial oxidation surge rate based on the changes in the oxidation leakage time-series curves corresponding to the ideal control chamber and the stress simulation chamber during the early germination stage; determining the respiratory power decay rate based on the changes in the respiratory metabolism time-series curves corresponding to the ideal control chamber and the stress simulation chamber during the mid-germination stage; determining the germination asynchrony surge rate based on the changes in the respiratory metabolism time-series curves corresponding to the ideal control chamber and the stress simulation chamber, as well as the rapeseed expansion time-series curve, over the entire time period; thereby determining a comprehensive health score; and combining this with the rapeseed germination rate corresponding to the ideal control chamber and the stress simulation chamber, determining the effective germination prediction value. This invention can improve the accuracy of rapeseed seed germination prediction.

Description

Technical Field

[0001] This invention relates to the field of agricultural and forestry management forecasting data processing technology, specifically to a seed germination prediction method for rapeseed breeding. Background Technology

[0002] As an important oilseed crop, rapeseed breeding typically requires screening a large number of hybrid offspring or germplasm resources for germination vigor and stress resistance to eliminate low-potential materials and preserve superior genotypes as early as possible. Current breeding processes mostly rely on methods such as endpoint germination rate statistics, manual observation of seedling growth, or field emergence trials to evaluate seed vigor. While these methods can reflect the final germination results, they generally suffer from long testing cycles, significant environmental interference, low screening efficiency, and difficulty in reflecting the true physiological state of the seeds. Especially for high-oil-content seeds like rapeseed, they are prone to lipid oxidative stress under adverse conditions such as low temperature and drought. Different genotypes exhibit significantly different membrane stability, respiration recovery capacity, and metabolic regulation capabilities in the early stages of germination. Traditional static detection methods cannot continuously characterize these dynamic physiological processes, thus failing to meet the demands of modern intelligent breeding for high-throughput, early prediction, and highly sensitive screening. With the development of agricultural phenomics and embedded sensing technologies, constructing germplasm stress-resistance germination phenotypic models using multimodal dynamic data is gradually becoming an important direction for improving rapeseed breeding efficiency.

[0003] Existing problems: In rapeseed stress resistance breeding, even if different germplasms have similar final germination rates under adverse stress, their internal physiological states and subsequent seedling establishment capabilities may still differ significantly. This makes it difficult for traditional screening methods based on endpoint germination rates to accurately identify materials with truly excellent stress resistance potential. Especially under stress environments such as low temperature and drought, some seeds, although able to physically absorb water and swell, suffer from impaired mitochondrial respiration recovery and severe lipid oxidation damage, easily leading to weak seedlings or even seedling death. Existing technologies can usually only passively detect this in field or culture trials several days later, resulting in prolonged breeding cycles, increased screening costs, and misjudgments of superior germplasm. Furthermore, existing spectral detection, image recognition, or single-gas detection schemes mostly focus on static index analysis, lacking the ability to jointly model the spatiotemporal coupling relationship between oxidative stress, respiratory metabolism, and germination kinetics. This makes it difficult to accurately identify early, latent inactivation phenomena in rapeseed seeds such as "false germination" and "metabolic mismatch." Summary of the Invention

[0004] This invention provides a seed germination prediction method for rapeseed breeding to solve existing problems.

[0005] The present invention provides a seed germination prediction method for rapeseed breeding, which adopts the following technical solution: One embodiment of the present invention provides a seed germination prediction method for rapeseed breeding, the method comprising the following steps: Obtain the oxidative leakage time-series curves, respiratory metabolism time-series curves, rapeseed expansion time-series curves, and rapeseed germination rate of the same rapeseed variety in the ideal control chamber and the stress simulation chamber. The initial oxidation surge rate was determined based on the changes in the oxidation leakage time series curves corresponding to the ideal control chamber and the stress simulation chamber during the early germination stage; the respiratory power decay rate was determined based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber during the mid-germination stage; and the germination asynchronous surge rate was determined based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber, as well as the rapeseed expansion time series curve, over the entire time series. The overall health score is determined based on the initial oxidation surge rate, respiratory power decline rate, and germination asynchronous surge rate. Based on the overall health score, and combined with the rapeseed germination rates corresponding to the ideal control chamber and the stress simulation chamber, the effective germination prediction value is determined.

[0006] Furthermore, the specific steps involved in determining the initial oxidation surge rate are as follows: For the ideal control chamber or the stress simulation chamber, the first derivative of the oxidation leakage time series curve at the initial moment is obtained and denoted as the initial oxidation leakage rate; the definite integral of the oxidation leakage time series curve within the preset absorption stress time window is obtained and denoted as the cumulative oxidation damage equivalent. The initial oxidation surge rate is determined based on the initial oxidation leakage rate and the magnitude of the cumulative oxidation damage equivalent.

[0007] Furthermore, the specific steps for determining the initial oxidation surge rate based on the initial oxidation leakage rate and the magnitude of the cumulative oxidation damage equivalent are as follows: The product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the stress simulation chamber is recorded as the stress oxidation performance value. The product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the ideal control chamber is recorded as the ideal oxidation performance value. The ratio of the adverse oxidation performance value to the ideal oxidation performance value is denoted as the initial oxidation surge rate.

[0008] Furthermore, the specific steps involved in determining the respiratory kinetic attenuation rate are as follows: For the ideal control chamber or the adversity simulation chamber, the maximum value of the second derivative of the respiratory metabolism time series curve is obtained among all sampling times within the preset metabolic restart time range, and is denoted as the respiratory metabolism acceleration. The respiratory power attenuation rate is determined based on the magnitude of the respiratory metabolic acceleration.

[0009] Furthermore, the specific steps for determining the respiratory dynamic attenuation rate based on the magnitude of the respiratory metabolic acceleration are as follows: The inversely proportional normalized value of the ratio of the respiratory metabolic acceleration corresponding to the stress simulation chamber to the respiratory metabolic acceleration corresponding to the ideal control chamber is denoted as the respiratory power attenuation rate.

[0010] Furthermore, the specific steps for determining the asynchronous germination surge rate are as follows: For the ideal control chamber or the stress simulation chamber, the product of the maximum rapeseed expansion rate and the preset threshold coefficient on the rapeseed expansion time-series curve is obtained and recorded as the expansion threshold; according to the time sequence, the moment corresponding to the first rapeseed expansion rate greater than the expansion threshold on the rapeseed expansion time-series curve is obtained and recorded as the physical half-expansion moment; the sampling moment corresponding to the maximum value of the first derivative value of the respiratory metabolism time-series curve among all sampling moments is obtained and recorded as the physiological respiratory peak moment; the time interval between the physical half-expansion moment and the physiological respiratory peak moment is recorded as the spatiotemporal phase difference. The germination asynchrony surge rate is determined based on the magnitude of the spatiotemporal phase difference.

[0011] Furthermore, the specific steps for determining the germination asynchrony surge rate based on the magnitude of the spatiotemporal phase difference are as follows: The ratio of the spatiotemporal phase difference corresponding to the adversity simulation chamber to the spatiotemporal phase difference corresponding to the ideal control chamber is denoted as the germination asynchronous surge rate.

[0012] Furthermore, the specific steps involved in determining the comprehensive health score are as follows: Obtain the inversely proportional normalized value of the initial oxidation surge rate, denoted as the oxidative damage health level; Obtain the complement of the respiratory power attenuation rate and record it as the respiratory power health status; Obtain the inversely proportional normalized value of the asynchronous germination surge rate, denoted as the synergistic health score; A comprehensive health score is determined based on the levels of oxidative damage health, respiratory power health, and synergistic health.

[0013] Furthermore, the specific steps for determining the comprehensive health score based on the levels of oxidative damage health, respiratory dynamics health, and synergistic health are as follows: The average of oxidative damage health, respiratory power health, and synergistic health is recorded as the comprehensive health score.

[0014] Furthermore, the specific steps for determining the effective germination prediction value are as follows: The average germination rate of rapeseed seeds in the ideal control chamber and the stress simulation chamber was obtained and recorded as the comprehensive germination rate. The product of the comprehensive germination rate and the comprehensive health score was recorded as the effective germination prediction value.

[0015] The beneficial effects of the technical solution of the present invention are: In this embodiment of the invention, by constructing a multimodal coupled analysis mechanism of "oxidative stress-respiratory recovery-physical expansion," dynamic and continuous monitoring of the rapeseed germination process is achieved. This enables rapid prediction and screening of stress-resistant germplasm in the early stages of germination, significantly improving the efficiency of early screening in breeding. By simultaneously collecting VOC oxidation metabolism data, CO2 respiration metabolism data, and volume expansion image data, and utilizing features such as kinetic derivatives, integral area, and physiological-physical spatiotemporal phase differences to construct a phased judgment model, it is possible not only to identify the degree of oxidative damage and metabolic recovery capacity of seeds under stress, but also to accurately distinguish "false germination" seeds that only absorb water and expand but do not truly recover their life metabolism, fundamentally reducing the problem of misjudgment. By establishing a parallel comparison mechanism between ideal and stress environments, the physiological attenuation degree of different rapeseed varieties under stress is further quantified, thereby objectively reflecting the stress-resistance buffering capacity of the germplasm itself. This enables early high-throughput screening of superior genotypes such as drought resistance and low-temperature resistance, improving the accuracy of rapeseed germination prediction and providing a more accurate and reliable germplasm decision-making basis for subsequent field breeding trials. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the steps of a seed germination prediction method for rapeseed breeding according to the present invention. Figure 2 This is a schematic diagram showing the segmentation of rapeseed seed regions. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a seed germination prediction method for rapeseed breeding proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a seed germination prediction method for rapeseed breeding provided by the present invention.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a seed germination prediction method for rapeseed breeding according to an embodiment of the present invention. The method includes the following steps: Step S001: Obtain the oxidation leakage time-series curve, respiration metabolism time-series curve, rapeseed expansion time-series curve, and rapeseed germination rate of the same rapeseed strain in the ideal control chamber and the stress simulation chamber.

[0022] It should be noted that this embodiment enables high-throughput screening for dynamic early warning and stress resistance potential prediction based on multimodal temporal metabolic characteristics. This embodiment is primarily applied to rapeseed stress resistance breeding and early screening of germplasm resources, particularly suitable for rapid vigor evaluation and stress resistance potential prediction of large-scale hybrid offspring, backcross populations, and rapeseed materials of different ecotypes. In practical applications, breeding units can place different rapeseed germplasm in simulated low-temperature, drought, or high-temperature stress environments. Dynamic data such as lipid oxidation volatiles, respiration carbon dioxide, and volume expansion images during seed germination are continuously collected through a closed habitat chamber. The system then combines germination kinetic curves with spatiotemporal coupling characteristics to pre-determine whether the germplasm suffers from oxidative damage, delayed respiration recovery, or "false germination," thus achieving highly sensitive screening of superior and inferior germplasm in the early stages of germination. This method can not only be used for the rapid elimination and retention of cold-resistant, drought-resistant, and high-vibration rapeseed germplasm but also for digital breeding platforms, agricultural phenomics research, and the construction of high-throughput intelligent breeding equipment, providing more efficient and precise technical support for the breeding of new rapeseed varieties.

[0023] First, system setup and parallel habitat data collection are required, along with multimodal data processing and kinetic feature extraction. This involves eliminating differences in seed background activity through parallel controls, and capturing high-frequency data collection to observe dynamic oxidation, respiration, and physical changes during early germination, providing raw data support for subsequent analysis. Specifically: (1) Select rapeseed seeds of the same variety with a plumpness (a common knowledge in the agricultural field) ≥95% and divide them into two equal groups (20 to 50 seeds per group to ensure statistical significance). Place them in an ideal control chamber (temperature 25℃, relative humidity 60%, light 12h / 12h (i.e., 12 hours of light and 12 hours of darkness, alternating day and night), and substrate moisture content 20%) and a stress simulation chamber (drought stress: substrate moisture content 5%; low temperature stress: temperature 5℃, other parameters are the same as the ideal control chamber). Seal the chambers to ensure stable gas concentration. Two groups of seed samples with the same physiological state were obtained and placed in ideal and stress germination environments, respectively.

[0024] It should be noted that: this completes the sample preparation and chamber parameter setting. Seeds of the same strain have consistent background physiological activity. The ideal control chamber simulates the optimal germination environment, while the stress chamber simulates the target stress environment. The comparison between the two can accurately reflect the degree of damage to the seeds caused by stress.

[0025] (2) Data were collected synchronously in the ideal control chamber and the stress simulation chamber using a PID photoionization VOC sensor (sampling frequency 1 time / 10min), a non-dispersive infrared CO2 sensor (sampling frequency 1 time / 10min), and a macro high-definition camera (1920×1080 resolution, shooting frequency 1 time / 10min, installed on the top of the chamber to collect rapeseed images from above). The timestamps were unified and the data were collected continuously for 5 days to obtain the VOC (Volatile Organic Compounds) concentration time series, CO2 (carbon dioxide) concentration time series, and rapeseed image time series corresponding to the ideal control chamber and the stress simulation chamber.

[0026] It should be noted that: VOC release corresponds to the degree of cell membrane lipid peroxidation damage, CO2 release corresponds to mitochondrial respiratory metabolic activity, and the rapeseed image time series corresponds to the physical water absorption and swelling process of the seed. Simultaneous acquisition of these three types of data allows for the decoupling of physical and physiological behaviors. This yields three types of raw time-series data with timestamps, covering the complete dynamic process of early seed germination. This completes the raw data acquisition, providing a fundamental data source for subsequent analysis.

[0027] (3) Five days later, the germination rate of rapeseed seeds was collected in the ideal control chamber and the stress simulation chamber, respectively, which is the percentage of normally germinated seeds in the total number of test seeds.

[0028] It should be noted that only the final number of germinations is counted, reflecting only the survival rate and not the speed of germination or the strength of growth. Although seedlings can be manually graded and scored according to deformity, missing seedlings, and growth, this is subject to significant subjective error. Therefore, in this embodiment, the germination process is analyzed by combining the above three types of time-series data.

[0029] (4) For the VOC concentration time series and CO2 concentration time series, db4 wavelet thresholding (with a decomposition level of 3 and soft thresholding) was first used to filter random noise. Then, cubic spline fitting was used to obtain the oxidation leakage time series curve and the respiratory metabolism time series curve. In the oxidation leakage time series curve, the horizontal axis is time and the vertical axis is VOC concentration; in the respiratory metabolism time series curve, the horizontal axis is time and the vertical axis is CO2 concentration.

[0030] It should be noted that both db4 wavelet thresholding and cubic spline fitting are well-known techniques, and their specific methods will not be described here. Sensor-acquired data contains random environmental noise (e.g., cabin air disturbances). Smooth fitting can eliminate this noise and yield a continuously changing dynamic curve, highlighting the true trend of seed germination.

[0031] (5) For the rapeseed image inside the cabin, obtain the background image when there are no seeds in the corresponding cabin. Perform background subtraction on the rapeseed image based on the background image to obtain a background difference image. Then perform binary segmentation on the background difference image to obtain a binary segmented image. A schematic diagram of rapeseed region segmentation is shown below. Figure 2 As shown, Figure 2 From left to right, the images show the original rapeseed image, the background subtraction image, and the binary segmentation image. Connected components of each rapeseed seed in the binary segmentation image are obtained. A minimum bounding ellipse fitting is performed on the contour of each connected component to obtain the major and minor axes of the ellipse. The rapeseed seed is then represented as a triaxial ellipsoid. Based on the experimentally calibrated thickness ratio coefficient for this rapeseed variety, the seed thickness is determined according to the minor axis corresponding to each connected component. Finally, a triaxial ellipsoid model is constructed based on the major and minor axes of the ellipse corresponding to each connected component and the seed thickness. The volume is calculated to determine the volume of the rapeseed seed corresponding to each connected component. The sum of the volumes of all connected components in the rapeseed seed image is recorded as the seed size in the image. Background subtraction, binary segmentation, minimum bounding ellipse fitting, and the triaxial ellipsoid model are all well-known techniques, and their specific methods are not described here.

[0032] It should be noted that the thickness ratio coefficient of this rapeseed variety calibrated in the experiment was specifically determined as follows: the imaging system was pre-calibrated to obtain the conversion coefficient between pixels and actual physical length; multiple groups of rapeseed seeds of the same variety and moisture content were collected, and the actual thickness of each seed was measured with a micrometer. Simultaneous top-down imaging was performed and the pixel-level elliptical minor axis was obtained. The actual minor axis size was obtained after scale conversion; the actual thickness and the actual minor axis were linearly fitted to solve for the thickness ratio coefficient suitable for this rapeseed variety.

[0033] (6) For the rapeseed image time series, the seed size in the rapeseed image at the first sampling time is recorded as the base seed size. Calculate the first Seed size in rapeseed image at time [time] Relative to the base seed size relative deviation , denoted as the The swelling rate of rapeseed seeds at time t is calculated to obtain a time series sequence of rapeseed seed swelling rate. Then, cubic spline fitting is used to obtain a rapeseed seed swelling time series curve. In the rapeseed seed swelling time series curve, the horizontal axis represents time, and the vertical axis represents rapeseed seed swelling rate.

[0034] It should be noted that obtaining three synchronized and smooth kinetic curves (oxidative leakage time-series curve, respiratory metabolism time-series curve, and rapeseed seed expansion time-series curve) clearly reflects the oxidative damage, respiratory metabolism, and physical expansion processes in the early stages of seed germination. Data preprocessing yields standardized kinetic curves suitable for feature extraction. The core objective is not simply to "collect data from a few sensors," but to construct a "dynamic phenotypic environment" that truly reflects the internal physiological changes of rapeseed seeds during stressful germination. Because rapeseed seeds are high-oil-content seeds, they are extremely sensitive to stresses such as low temperature and drought in the early stages of germination. Once the cell membrane is stressed, lipid peroxidation occurs rapidly, further causing respiratory metabolic disorders. By artificially constructing a parallel control system of "ideal environment" and "stress environment," seeds of the same genotype are exposed to different physiological response trajectories under the two conditions. Then, by continuously collecting multimodal information such as oxidative volatiles, respiratory gases, and volume changes, the traditionally unobservable internal metabolic processes are externalized and quantified, thus providing a foundation for subsequent kinetic analysis. Unlike traditional one-time photography or endpoint statistics, this embodiment emphasizes "continuous time-series data acquisition." This is because seed germination is not instantaneous but involves a series of stages, including water absorption, membrane repair, respiration recovery, and cell division. Simply observing whether germination occurs at the end would result in the loss of a significant amount of early dynamic information. By employing high-frequency time-series sampling, the metabolic changes of seeds at different time points are continuously recorded, thereby obtaining germination kinetic curves that are truly valuable for breeding.

[0035] Step S002: Determine the initial oxidation surge rate based on the changes in the oxidation leakage time series curves corresponding to the ideal control chamber and the stress simulation chamber during the early germination stage; determine the respiratory power decay rate based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber during the mid-germination stage; determine the germination asynchronous surge rate based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber, as well as the rapeseed expansion time series curve, throughout the entire time series.

[0036] It should be noted that: further, a milestone-based dynamic cross-sectional comparison is conducted by analyzing three life stages: the imbibition stress period, the metabolic restart period, and the spatiotemporal coupling period. The decay rate of metabolic kinetic characteristics of the stress sample relative to the parallel normal sample, as well as the phase difference between physical expansion and physiological respiration, are calculated to achieve early truncation and elimination of false-germinating germplasm and quantitative rating of superior stress-resistant germplasm. In this embodiment, the imbibition stress period is the initial 24 hours, and the metabolic restart period is 24 to 48 hours.

[0037] It is further important to note that dynamic phenotypic characteristics with true breeding significance, such as oxidative damage intensity, respiration recovery capacity, physical imbibition rhythm, and physiological-physiological coupling relationship, can be extracted from various germination kinetic curves. These characteristics are used to establish a mapping relationship of "physiological mechanism → kinetic characteristics → mathematical expression." Regarding the extraction of oxidative damage characteristics, during the imbibition stage, seeds begin to absorb water, and the cell membrane system is rebuilt. Abiotic stress exacerbates membrane lipid peroxidation, leading to accelerated VOC release. This damage can be quantified by analyzing the early dynamics of the oxidative leakage time-series curve. Regarding the oxidative leakage rate in the early stage of seed imbibition, because the first problem that occurs in rapeseed seeds under stress is often not respiration cessation, but rather the oxidative damage to the cell membrane, once the cell membrane is damaged, internal lipid oxidation products leak out rapidly.

[0038] For the ideal control chamber or the stress simulation chamber, the first derivative value of the oxidation leakage time series curve at the beginning time is obtained and denoted as the initial oxidation leakage rate.

[0039] It should be noted that in this embodiment, the first-order rate is obtained by forward difference at the initial time. If the first-order derivative value is equal to 0, the rate is recalculated by cross-point forward difference using data from the initial time and the third sampling time. This cross-point calculation operation is repeated until the obtained first-order derivative value is greater than zero, ensuring that the initial oxidation leakage rate is greater than 0. This is because the seed imbibition and germination process continuously releases volatile oxidizing substances, and the overall VOC concentration in the chamber shows an upward trend. The first-order derivative value corresponding to the oxidation leakage time series curve at the initial time represents the initial severity of oxidation leakage. An excessively high initial velocity under adverse conditions indicates that the cell membrane is severely damaged in the early stages of water absorption. The initial oxidation leakage rate is used to reflect whether the cell membrane has already rapidly disintegrated when it first begins to absorb water. Healthy seeds have strong cell membrane repair capabilities, and VOC release is usually slower. However, damaged seeds experience rapid membrane rupture after water absorption, and VOC levels rise sharply in a short period of time. The first derivative essentially measures the "oxidative instability rate." Compared to the final concentration value, the rate better reflects early, sudden damage.

[0040] The pre-germination imbibition stress time window is assumed to be 0 to 24 hours, and this will be used as an example for the description.

[0041] For the ideal control chamber or the stress simulation chamber, the definite integral of the oxidation leakage time series curve within the preset absorption stress time window is obtained and denoted as the cumulative oxidation damage equivalent.

[0042] It should be noted that the reason for constructing the definite integral is that a single peak value can only reflect the degree of damage at a certain moment. However, what truly affects germination viability is the cumulative oxidative stress over a long period. The definite integral actually represents the total oxidative load during the entire imbibition stress phase. Furthermore, the seed imbibition germination process continuously releases volatile oxidizing substances, therefore, the equivalent cumulative oxidative damage under normal imbibition conditions is greater than 0.

[0043] The product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the stress simulation chamber is obtained and recorded as the stress oxidation performance value (greater than 0).

[0044] Obtain the product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the ideal control chamber, and record it as the ideal oxidation performance value (greater than 0).

[0045] The ratio of the adverse oxidation performance value to the ideal oxidation performance value is denoted as the initial oxidation surge rate. (Dimensionless data value).

[0046] It should be noted that, in this embodiment, parallel controls were introduced to eliminate the differences in basal metabolism among different strains. The greater the initial oxidative surge rate, the more severe the oxidative stress response of the strain's seeds under stress, and the more fragile its membrane system.

[0047] It should be noted that due to inherent metabolic differences among different rapeseed varieties, the system cannot directly compare which has higher VOC concentrations and which has lower CO2 concentrations. Doing so could easily misjudge a variety with slower metabolism as having poor stress resistance. Instead, the system compares the dynamic changes of the same variety under ideal and adverse environments. Analyzing how much the adverse environment deviates from its intended course reveals the true stress resistance, representing the system's ability to stabilize against environmental disturbances. For the extraction of respiratory recovery characteristics, live seeds should initiate mitochondrial respiration, resulting in an accelerated CO2 release rate. The acceleration of changes in the respiratory metabolic time-series curve is analyzed to assess the strength of metabolic restart. Mitochondrial respiratory recovery capacity is analyzed after completing oxidation analysis. The core of seed vitality lies not in water absorption, but in the resumption of respiratory metabolism. Therefore, the system performs second-order derivative analysis on the respiratory metabolic time-series curve.

[0048] The preset time range for metabolic restart during the mid-germination period is 24 to 48 hours, and this will be used as an example for the description.

[0049] For the ideal control chamber or the adversity simulation chamber, the second derivative value of the respiratory metabolism time series curve is obtained at each sampling time within the preset metabolic restart time range. The maximum value of the second derivative value of the respiratory metabolism time series curve at all sampling times within the preset metabolic restart time range is obtained and denoted as the respiratory metabolism acceleration.

[0050] It should be noted that this maximum value reflects the maximum force of respiration shifting from stagnation to accelerated recovery. The second derivative indicates how quickly respiration recovers, corresponding to the mitochondrial restart capability. Because healthy seeds exhibit a significant acceleration process during respiratory system recovery, while weak seeds, even with a slight increase in CO2, may only experience slow leakage. Therefore, what truly reflects vitality is not the presence or absence of respiration, but rather the explosiveness of respiratory recovery. Thus, the second derivative essentially measures the metabolic initiation capability of life.

[0051] Calculate the ratio of the respiratory metabolic acceleration corresponding to the stress simulation chamber to the respiratory metabolic acceleration corresponding to the ideal control chamber. inverse proportional normalized value , denoted as respiratory attenuation rate .in, This embodiment uses an exponential function with the natural constant as its base. To present The inverse proportional relationship and normalization process.

[0052] It should be noted that the closer the respiratory dynamics attenuation rate is to 1, the more severe the inhibition of seed metabolic restart by stress, and the worse the recovery of mitochondrial activity. Based on subsequent preliminary early warning elimination, the respiratory metabolic acceleration corresponding to the stress simulation chamber should be greater than or equal to 0.01. However, the metabolic acceleration capacity under adversity should be weaker than that of the ideal control, indicating significant stress-induced damage. Specifically, the respiratory metabolic acceleration corresponding to the ideal control chamber should be greater than or equal to 0.01. If the ideal control chamber corresponds to a respiratory metabolic acceleration of less than 0.01... If so, the rapeseed of that variety will be directly marked as a priority for elimination.

[0053] It should be noted that, regarding the extraction of physiological-physical synergistic features, this stage focuses on whether seed body expansion and life activities are synchronized. For healthy germination, the two should be closely coupled.

[0054] The preset threshold coefficient is 50%, and this will be used as an example for explanation.

[0055] For the ideal control chamber or the stress simulation chamber, the product of the maximum rapeseed expansion rate and a preset threshold coefficient on the rapeseed expansion time-series curve is obtained and denoted as the expansion threshold. Following chronological order, the moment corresponding to the first rapeseed expansion rate exceeding the expansion threshold on the rapeseed expansion time-series curve is obtained and denoted as the physical half-expansion moment. The first derivative value of the respiratory metabolism time-series curve at each sampling moment is calculated, and the sampling moment corresponding to the maximum value of the first derivative value of the respiratory metabolism time-series curve among all sampling moments (excluding the last moment) is obtained and denoted as the physiological respiratory peak moment. The time interval between the physical half-expansion moment and the physiological respiratory peak moment is denoted as the spatiotemporal phase difference.

[0056] The ratio of the spatiotemporal phase difference corresponding to the adversity simulation chamber to the spatiotemporal phase difference corresponding to the ideal control chamber is denoted as the germination asynchronous surge rate. (Dimensionless data value).

[0057] If the spatiotemporal phase difference corresponding to the ideal control chamber is 0 minutes, let the spatiotemporal phase difference corresponding to the ideal control chamber be 1 minute. Thus, let the germination asynchronous surge rate be the spatiotemporal phase difference corresponding to the adversity simulation chamber after removing the dimension.

[0058] It should be noted that in this embodiment, forward difference is used to obtain the first derivative value of the respiratory metabolism time-series curve at each sampling time. Therefore, there is no first derivative value corresponding to the last time. The purpose of determining the physical half-expansion time is not to analyze the size of the seed, but to establish a "physical water absorption process coordinate". This is because water absorption is the earliest event in germination. It determines when the seed begins to infiltrate and when it begins to change volume. This time point needs to be compared with the respiratory recovery time. The physiological respiratory peak time represents the moment when respiration is most vigorous. Therefore, the larger the spatiotemporal phase difference, the longer it takes for physiological respiration to reach its peak after the seed completes physical water absorption. The severe disconnect between the two is a typical sign of false germination or weak germination. If the physical half-expansion time is much earlier than the physiological respiratory peak time, it is particularly dangerous. The spatiotemporal phase difference indicates whether physical behavior and life behavior are synchronized. Because truly healthy germination should satisfy the simultaneous occurrence of water absorption, respiratory recovery, and metabolic initiation. Falsely germinating seeds usually show that the appearance has expanded, but respiration is sluggish for a long time. Therefore, if the spatiotemporal phase difference is too large, it indicates that physical germination and physiological germination have become disconnected.

[0059] Step S003: Determine the comprehensive health score based on the initial oxidation surge rate, respiratory power decline rate, and germination asynchronous surge rate; determine the effective germination prediction value based on the comprehensive health score and the rapeseed germination rate corresponding to the ideal control chamber and the stress simulation chamber.

[0060] It should be noted that further germination prediction and germplasm selection decisions are needed. A large number of physiologically significant kinetic characteristics have been obtained, including core indicators such as initial oxidative leakage rate, oxidative damage equivalent, respiratory metabolic acceleration, and spatiotemporal phase difference. However, these indicators are still only "characteristic data." For breeders, the real problem is not what a certain derivative or area is, but "whether this line should be retained." The complex kinetic characteristics obtained need to be further transformed into interpretable risk warnings, rankable stress resistance levels, and executable breeding elimination decisions. Unlike traditional black-box scoring in machine learning, this embodiment does not simply input data and output a probability value, but establishes a "stage-based white-box judgment mechanism" based on the physiological performance at different stages of seed germination. Given the reasons for breeding germination failure: is it due to severe oxidative damage, failure to recover respiration, or the occurrence of false germination?

[0061] First, a preliminary warning elimination process is initiated if the initial oxidation surge rate exceeds 3, or the spatiotemporal phase difference corresponding to the stress simulation chamber exceeds 48 hours, or the respiratory metabolic acceleration corresponding to the stress simulation chamber is less than 0.01. If so, the rapeseed of that variety will be directly marked as a priority for elimination.

[0062] It should be noted that in this embodiment, the unit of CO2 concentration is... The time unit is Therefore, the unit of respiratory metabolic acceleration is . An initial oxidation surge rate greater than 3, meaning the oxidation surge under stress exceeded the ideal control by more than 3 times, indicates that the rapeseed seeds of this variety suffered severe oxidative damage in the early stages of imbibition under stress, with an extremely low probability of subsequent recovery, and should be prioritized for elimination. A spatiotemporal phase difference greater than 48 hours corresponding to the stress simulation chamber indicates that respiration did not reach a significant peak two days after physical imbibition was completed under stress, directly classifying it as "false germination," and it should be prioritized for elimination. A respiratory metabolic acceleration less than 0.01 corresponding to the stress simulation chamber. This indicates that metabolism did not restart under adverse conditions, and therefore these cells can be prioritized for elimination.

[0063] If this rapeseed variety is not a priority for elimination, then the following seed germination prediction analysis will continue: Obtaining the initial oxidation surge rate inverse proportional normalized value This is recorded as the health status due to oxidative damage.

[0064] Obtain respiratory power attenuation rate complement This is recorded as respiratory dynamic health.

[0065] Among them, take complement This is to convert the degree of respiratory attenuation into remaining vitality. Because the stronger the respiratory recovery ability, the closer the seed is to a normal germination state.

[0066] Obtain the germination asynchrony surge rate inverse proportional normalized value This is recorded as the collaborative health score.

[0067] It should be noted that the true stress resistance of seeds is not determined by a single indicator, but rather by the combined effects of membrane stability, respiration recovery capacity, and physiological synergy. Therefore, the system integrates and constructs a multi-dimensional physiological joint evaluation system.

[0068] The average of oxidative damage health, respiratory power health, and synergistic health is recorded as the comprehensive health score.

[0069] It should be noted that a higher overall health score indicates that the rapeseed variety belongs to the highly stress-resistant group. Conversely, a lower overall health score indicates that the rapeseed variety belongs to the vulnerable and culled group.

[0070] The average germination rate of rapeseed seeds in the ideal control chamber and the stress simulation chamber was obtained and recorded as the comprehensive germination rate. The product of the comprehensive germination rate and the comprehensive health score was recorded as the effective germination prediction value of the rapeseed seed of that variety.

[0071] It should be noted that even if different germplasms have similar final germination rates under abiotic stress, their internal physiological states and subsequent seedling establishment capabilities may still differ significantly. This is especially true under stress conditions such as low temperature and drought. Some seeds, although able to physically absorb water and swell, suffer from impaired mitochondrial respiration and severe lipid oxidation damage, easily leading to weak seedlings or even seedling death – i.e., ineffective germination. Therefore, a comprehensive health score is used to adjust the overall germination rate to obtain a predicted value for effective germination.

[0072] It should be further explained that: for this rapeseed variety, multiple repeated experiments were conducted in the manner described above to determine the effective germination prediction value obtained in each experiment. The average of the effective germination prediction values ​​obtained from multiple experiments was taken as the final effective germination prediction value. The preset germination threshold was 0.9. Rapeseed varieties with a final effective germination prediction value greater than 0.9 were designated as high-quality rapeseed varieties, meaning that high-quality rapeseed varieties have a high germination rate under different environments and a low rate of weak seedlings.

[0073] This invention is now complete.

[0074] In summary, in this embodiment of the invention, the oxidative leakage time-series curve, respiratory metabolism time-series curve, rapeseed swelling time-series curve, and germination rate of rapeseed of the same variety are obtained in the ideal control chamber and the stress simulation chamber, respectively. Based on the changes in the oxidative leakage time-series curves in the early germination stage, the initial oxidative surge rate is determined. Based on the changes in the respiratory metabolism time-series curves in the mid-germination stage, the respiratory power decay rate is determined. Based on the changes in the respiratory metabolism time-series curves and rapeseed swelling time-series curves in the ideal control chamber and the stress simulation chamber, the germination asynchrony surge rate is determined. Based on the magnitudes of the initial oxidative surge rate, respiratory power decay rate, and germination asynchrony surge rate, a comprehensive health score is determined. Based on the comprehensive health score, combined with the rapeseed germination rate in the ideal control chamber and the stress simulation chamber, an effective germination prediction value is determined. This invention can improve the accuracy of rapeseed germination prediction.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting seed germination in rapeseed breeding, characterized in that, The method includes the following steps: Obtain the oxidative leakage time-series curves, respiratory metabolism time-series curves, rapeseed expansion time-series curves, and rapeseed germination rate of the same rapeseed variety in the ideal control chamber and the stress simulation chamber. The initial oxidation surge rate was determined based on the changes in the oxidation leakage time series curves corresponding to the ideal control chamber and the stress simulation chamber during the early germination stage; the respiratory power decay rate was determined based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber during the mid-germination stage; and the germination asynchronous surge rate was determined based on the changes in the respiratory metabolism time series curves corresponding to the ideal control chamber and the stress simulation chamber, as well as the rapeseed expansion time series curve, throughout the entire time series. The overall health score is determined based on the initial oxidation surge rate, respiratory power decline rate, and germination asynchronous surge rate. Based on the overall health score, and combined with the rapeseed germination rates corresponding to the ideal control chamber and the stress simulation chamber, the effective germination prediction value is determined.

2. The seed germination prediction method for rapeseed breeding according to claim 1, characterized in that, The specific steps involved in determining the initial oxidation surge rate are as follows: For the ideal control chamber or the stress simulation chamber, the first derivative of the oxidation leakage time series curve at the initial moment is obtained and denoted as the initial oxidation leakage rate; the definite integral of the oxidation leakage time series curve within the preset absorption stress time window is obtained and denoted as the cumulative oxidation damage equivalent. The initial oxidation surge rate is determined based on the initial oxidation leakage rate and the magnitude of the cumulative oxidation damage equivalent.

3. The seed germination prediction method for rapeseed breeding according to claim 2, characterized in that, The specific steps for determining the initial oxidation surge rate based on the initial oxidation leakage rate and the magnitude of the cumulative oxidation damage equivalent are as follows: The product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the stress simulation chamber is recorded as the stress oxidation performance value. The product of the initial oxidation leakage rate and the cumulative oxidation damage equivalent corresponding to the ideal control chamber is recorded as the ideal oxidation performance value. The ratio of the adverse oxidation performance value to the ideal oxidation performance value is denoted as the initial oxidation surge rate.

4. The seed germination prediction method for rapeseed breeding according to claim 1, characterized in that, The specific steps involved in determining the respiratory kinetic attenuation rate are as follows: For the ideal control chamber or the adversity simulation chamber, the maximum value of the second derivative of the respiratory metabolism time series curve is obtained among all sampling times within the preset metabolic restart time range, and is denoted as the respiratory metabolism acceleration. The respiratory power attenuation rate is determined based on the magnitude of the respiratory metabolic acceleration.

5. The seed germination prediction method for rapeseed breeding according to claim 4, characterized in that, The specific steps for determining the respiratory dynamic attenuation rate based on the magnitude of the respiratory metabolic acceleration are as follows: The inversely proportional normalized value of the ratio of the respiratory metabolic acceleration corresponding to the stress simulation chamber to the respiratory metabolic acceleration corresponding to the ideal control chamber is denoted as the respiratory power attenuation rate.

6. The seed germination prediction method for rapeseed breeding according to claim 1, characterized in that, The specific steps involved in determining the asynchronous germination surge rate are as follows: For the ideal control chamber or the stress simulation chamber, the product of the maximum rapeseed expansion rate and the preset threshold coefficient on the rapeseed expansion time-series curve is obtained and recorded as the expansion threshold; according to the time sequence, the moment corresponding to the first rapeseed expansion rate greater than the expansion threshold on the rapeseed expansion time-series curve is obtained and recorded as the physical half-expansion moment; the sampling moment corresponding to the maximum value of the first derivative value of the respiratory metabolism time-series curve among all sampling moments is obtained and recorded as the physiological respiratory peak moment; the time interval between the physical half-expansion moment and the physiological respiratory peak moment is recorded as the spatiotemporal phase difference. The germination asynchrony surge rate is determined based on the magnitude of the spatiotemporal phase difference.

7. The seed germination prediction method for rapeseed breeding according to claim 6, characterized in that, The specific steps for determining the germination asynchrony surge rate based on the magnitude of the spatiotemporal phase difference are as follows: The ratio of the spatiotemporal phase difference corresponding to the adversity simulation chamber to the spatiotemporal phase difference corresponding to the ideal control chamber is denoted as the germination asynchronous surge rate.

8. The seed germination prediction method for rapeseed breeding according to claim 1, characterized in that, The specific steps involved in determining the overall health score are as follows: Obtain the inversely proportional normalized value of the initial oxidation surge rate, denoted as the oxidative damage health level; Obtain the complement of the respiratory power attenuation rate and record it as the respiratory power health status; Obtain the inversely proportional normalized value of the asynchronous germination surge rate, denoted as the synergistic health score; A comprehensive health score is determined based on the levels of oxidative damage health, respiratory power health, and synergistic health.

9. The seed germination prediction method for rapeseed breeding according to claim 8, characterized in that, The process of determining the comprehensive health score based on the levels of oxidative damage health, respiratory dynamics health, and synergistic health includes the following specific steps: The average of oxidative damage health, respiratory power health, and synergistic health is recorded as the comprehensive health score.

10. The seed germination prediction method for rapeseed breeding according to claim 1, characterized in that, The specific steps for determining the effective germination prediction value are as follows: The average germination rate of rapeseed seeds in the ideal control chamber and the stress simulation chamber was obtained and recorded as the comprehensive germination rate. The product of the comprehensive germination rate and the comprehensive health score was recorded as the effective germination prediction value.

Images