Real-time detection method for fritillary propagation activity

Abstract

The invention discloses a real-time detection method for bulbus fritillariae propagating activity, and relates to the field of seedling detection, and the method comprises the following steps: step 1, seed stratification pretreatment: mixing bulbus fritillariae seeds and fine sand according to a ratio of 1: 3 for stratification, and maintaining the humidity of a substrate to be 50-60%; 2, dynamically monitoring enzyme activity, namely grinding the sample prepared in the step 1, sampling and detecting the seeds every 10 days, extracting enzyme liquid, and respectively measuring the activity of catalase, peroxidase and amylase in real time by adopting an ultraviolet spectrophotometric method; a full-cycle real-time data flow is constructed, a continuous dynamic process of activity change is captured, then a machine learning model is used for fusion analysis and prediction of seed propagation potential, activity evaluation is converted into a quantifiable propagation activity index, a clear judgment threshold value is set, and the machine learning model is trained on the basis of a large amount of data, so that the propagation activity of seeds is improved. And the accuracy and efficiency of high-quality seed source screening are improved.

Description

Technical Field

[0001] This invention relates to the field of seedling detection technology, specifically to a real-time detection method for the propagation activity of Fritillaria thunbergii. Background Technology

[0002] Fritillaria is an important rare and endangered medicinal plant. Its wild resources are increasingly depleted due to over-harvesting, making artificial cultivation an essential path to meet market demand and achieve sustainable resource utilization. However, Fritillaria seeds possess complex dormancy characteristics, exhibiting both morphological and physiological after-ripening. Their natural germination rate is low, germination cycle is long, and seedling growth is slow, severely restricting the development of large-scale artificial seedling cultivation. Furthermore, limitations imposed by geographical conditions, coupled with unplanned and indiscriminate harvesting in recent years, have led to the increasing depletion of natural resources. Therefore, to alleviate market demand, artificial cultivation of Fritillaria is necessary. Thus, in the process of introducing, domesticating, and artificially propagating Fritillaria, real-time, accurate, and comprehensive evaluation of the entire propagation activity chain from seed to seedling is crucial for optimizing propagation techniques and improving seedling success rates.

[0003] Traditional methods often focus on single endpoint indicators such as germination rate or emergence rate. These indicators can only be obtained after the growth cycle is over and cannot reflect changes in seed vigor and seedling growth status in real time during the process. They have a serious lag, and by the time problems are discovered, the best time for intervention has often been missed, resulting in a waste of time and economic costs. There is a lack of systematic real-time monitoring schemes for the physiological and biochemical changes during seed stratification and the precise process of embryo morphological after-ripening, making it difficult to reveal the dynamic laws of changes in propagation activity. The assessment of seedling growth relies heavily on manual observation and experience. Summary of the Invention

[0004] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides a real-time detection method for the propagation activity of Fritillaria cirrhosa, which can effectively solve the problems of the existing technology.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: This invention discloses a real-time detection method for the propagation activity of Fritillaria cirrhosa, comprising the following steps: Step 1: Seed stratification pretreatment. Mix fritillaria seeds with fine sand at a ratio of 1:3 and stratify, maintaining substrate moisture at 50%–60%. Step 2: Dynamic monitoring of enzyme activity. The sample prepared in Step 1 is ground and sampled every 10 days to test the seed extract enzyme solution. The activities of catalase, peroxidase and amylase are measured in real time by ultraviolet spectrophotometry. Environmental parameters are collected simultaneously, and the temperature, humidity and light intensity of the stratification environment are recorded in real time. The data are synchronized to the monitoring platform. Step 3: Embryo morphological development observation. The sample from Step 1 is dissected, and the length and morphological development stage of the embryo are observed and recorded under a stereomicroscope. The embryo ratio is calculated, which is the ratio of the embryo length to the seed long axis. Step 4: Germination stimulus response test. When the embryo rate reaches 66%, the seeds are placed under 20℃ light conditions and soaked in gibberellin solutions of different concentrations for 24 hours, and the germination rate is recorded in real time. Step 5: Tracking seedling morphological indicators. Seeds that have completed morphological maturation are sown in five different seedling substrates: sandy soil, garden soil, humus soil, a 1:1 mixture of humus soil and garden soil, and wild nursery substrate. After germination, seedling height, root length and biomass are measured every 72 hours. Growth curves are constructed using image recognition technology. Step 6: Online analysis of active ingredients, using high performance liquid chromatography to monitor the content of fritillary alkaloids online; Step 7: Data fusion analysis, integrating enzyme activity data, environmental parameters, morphological indicators and active ingredient content, and calculating the propagation activity index through machine learning models; Step 8: Activity threshold determination. When the propagation activity index is ≥0.85, it is determined to be a high-activity seed source, triggering an automated sorting instruction.

[0006] Furthermore, the container used in the lamination process in step 1 is equipped with a porous humidity probe. The probe is connected to a cloud monitoring platform via a wireless transmission module, and the irrigation system is automatically triggered when the humidity deviates from the range of 50% to 60%.

[0007] Furthermore, the enzyme activity detection in step 2 is performed using ultraviolet spectrophotometry, wherein CAT activity is determined by... Degradation rate was used as an indicator, POD activity was calculated by the change in absorbance of guaiacol oxidation products, and amylase activity was determined by the DNS method to measure the amount of reducing sugar generated.

[0008] Furthermore, the concentration gradient of the gibberellin solution in step 4 is set to 0 mg / L, 50 mg / L, 100 mg / L, 150 mg / L, and 200 mg / L.

[0009] Furthermore, the image recognition technology in step 5 acquires seedling growth data in a non-contact manner. By fixing an image acquisition device above the seedling area, digital images of the top and side views of the seedlings containing standard-sized reference objects are automatically acquired periodically. Through image recognition algorithms, the main body of the seedling in the image is automatically segmented and proportionally converted with the standard reference object to calculate the morphological indicators of the seedling's height, leaf width, and projected area in batches.

[0010] Furthermore, the process of constructing the growth curve in step 5 is as follows: continuously record the data of plant height and projected area indicators obtained by image recognition at different time points as a function of time, and fit and generate plant height growth curve and biomass growth trend curve; by analyzing the slope changes of the curves, determine the growth rate and growth potential of seedlings in each seedling substrate, and quantitatively compare the promotion coefficients of different substrates on the growth of Fritillaria seedlings.

[0011] Furthermore, in step 6, the detection wavelength of the high-performance liquid chromatography is set to a specific absorption wavelength, the mobile phase is acetonitrile-0.1% phosphoric acid aqueous solution (35:65), and the detection wavelength is 230 nm.

[0012] Furthermore, the process of constructing the machine learning model in step 7 includes: Collect complete growth cycle data of Fritillaria seeds from historical stratification experiments, including catalase activity change curves, environmental temperature and humidity time-series records, seedling morphological development image sequences, and fritillary alkaloid content detection values. The rate of change in enzyme activity was extracted as a biological metabolic characteristic, the cumulative value of daily average temperature was calculated as an environmental stress characteristic, the growth rate of plant height and the root expansion rate were quantified as developmental characteristics, and the weekly increment of cispermine was statistically analyzed as a quality characteristic. The random forest algorithm is used to learn the nonlinear relationships of the above multidimensional features. The depth and number of decision trees are optimized by ten-fold cross-validation, and the propagation survival rate is used as the training label for the model.

[0013] Furthermore, the working logic of the machine learning model in step 7 is as follows: The enzyme activity detection results, environmental sensor readings, seedling image recognition parameters, and online chromatographic analysis data dynamically collected during the stratification process are automatically converted into standardized feature vectors. The model outputs a propagation activity index in the 0-1 range based on the weighted combination relationship of the input feature vectors, with biological metabolic features accounting for ≥40% of the weight, and quality features and developmental features each accounting for 25% to 30% of the weight. More than 200 decision trees independently evaluate the feature vectors. When more than 80% of the decision trees determine the activity index ≥ 0.85, a high activity judgment is triggered. If less than 60% of the decision trees support it, a quality control alarm is activated.

[0014] Furthermore, the automated sorting in step 8 is performed by a pneumatic robotic arm, and the sorting is based on two preset thresholds: activity index and morphology index.

[0015] (III) Beneficial Effects Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: 1. By integrating enzyme activity dynamic monitoring, synchronous environmental parameter acquisition, image recognition tracking, and online component analysis, a real-time data stream is constructed throughout the entire cycle to capture the continuous dynamic process of activity changes. Then, through machine learning model fusion analysis, the propagation potential of seeds can be accurately predicted. The activity evaluation is transformed into a quantifiable propagation activity index, and a clear judgment threshold is set. The machine learning model is trained on a large amount of data, and its decision-making process eliminates subjective interference, making the sorting standards consistent and greatly improving the accuracy and efficiency of high-quality seed source screening.

[0016] 2. By uploading and automatically analyzing the detection data in real time, the system immediately triggers an automated sorting instruction once the threshold is reached, achieving seamless connection from detection to sorting. This avoids the time delays and errors caused by data interpretation, manual judgment, and manual operation in traditional processes, significantly accelerating the breeding cycle and saving labor costs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram illustrating the growth and changes of the embryonic tissue of naturally stratified Fritillaria cirrhosa seeds in this invention. Figure 3 This is a schematic diagram illustrating the changes in peroxidase activity in naturally stratified Fritillaria seeds in this invention. Figure 4 This is a schematic diagram illustrating the activity changes of catalase in naturally stratified Fritillaria seeds in this invention. Figure 5 This is a schematic diagram illustrating the changes in the activity of amylase in naturally stratified Fritillaria thunbergii seeds in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] This embodiment provides a real-time detection method for the propagation activity of Fritillaria cirrhosa, such as... Figure 1 As shown, it includes the following steps: Step 1: Seed stratification pretreatment. Mix fritillaria seeds with fine sand in a 1:3 ratio and stratify them, maintaining the substrate humidity at 50%–60%. The container used in the stratification process is equipped with a porous humidity probe, which is connected to a cloud monitoring platform via a wireless transmission module. When the humidity deviates from the 50%–60% range, the irrigation system is automatically triggered.

[0022] Step 2: Dynamic monitoring of enzyme activity. The samples prepared in Step 1 were ground, and samples were taken every 10 days to test the seed-extracted enzyme solution. The activities of catalase, peroxidase, and amylase were measured in real time using ultraviolet spectrophotometry. Environmental parameters were collected simultaneously, and the temperature, humidity, and light intensity of the stratification environment were recorded in real time. The data were synchronized to the monitoring platform. Enzyme activity was detected using ultraviolet spectrophotometry, with CAT activity measured using... Degradation rate was used as an indicator; POD activity was calculated by the change in absorbance of guaiacol oxidation products; and amylase activity was determined by the DNS method to measure the amount of reducing sugar produced. Peroxidase activity was determined using the guaiacol method: 1 g of seeds were placed in a mortar and homogenized with 2 mL of 0.05 mol / L, pH 5.5 phosphate buffer. The homogenate was then transferred to a centrifuge tube. The mortar was washed with 6 mL of phosphate buffer and transferred to the centrifuge tube as well. The mixture was centrifuged at 3000 rpm for 30 min at 0–4 °C. The supernatant was used as the enzyme reaction system, consisting of 2 mL of 0.05 mol / L, pH 5.5 phosphate buffer and 1.0 mL of 3% phosphate buffer. 1.0 mL of 0.05 mol / L guaiacol. Use 1 mL of distilled water as a control in the reaction system. After adding 1 mL of enzyme solution, immediately incubate the reaction system in a 34℃ water bath for 10 min, then quickly measure the absorbance at 470 nm. One unit of enzyme activity (μ) is defined as a change of 0.01 A470 per minute. Catalase activity was determined using the potassium permanganate titration method: 1.0 g of seeds were weighed and ground into a homogenate with a small amount of phosphate buffer (0.2 mol / L, pH=7.8). The homogenate was transferred to a 25 mL volumetric flask, the mortar was rinsed with the same buffer, and the rinsing solution was transferred to the volumetric flask. The mixture was then brought to volume with the same buffer and poured into a centrifuge tube. After centrifugation at 4000 rpm for 15 min, the supernatant was the crude catalase extract. Four 50 mL Erlenmeyer flasks were prepared (two for assay and two for control). 2.5 mL of enzyme solution was added to the assay flask, and 2.5 mL of inactivated enzyme solution was added to the control flasks, followed by 2.5 mL of 0.1 mol / L potassium permanganate solution. Simultaneously, time the process, keep it warm in a 30℃ constant temperature water bath for 10 minutes, and immediately add 10%... 2.5 mL. Use 0.1 mol / L Titrate with standard solution until a pink color appears (which does not disappear within 30 minutes) as the endpoint. Decompose enzyme activity using a standard solution at a concentration of 1 gram of air-dried sample per minute. The number of milligrams is expressed as a percentage. The amylase activity was determined using the salicylic acid method: 1 g of peeled seeds was weighed, added to 2 mL of distilled water, and ground into a homogenate. The homogenate was transferred to a 10 mL centrifuge tube, and the residue was washed into the tube several times with 5 mL of distilled water. Extraction was carried out at room temperature (20℃) for 20 min, followed by extraction at 6000 rpm. 1 Centrifuge for 20 min, and the supernatant is the enzyme extract. Take three test tubes, numbered 1, 2, and 3, and add 1.0 mL of the enzyme extract to each. Add 2.0 mL of 3,5-dinitrosalicylic acid to tube 1, and incubate all three tubes in a 40°C water bath for 10 min. Then add 1.0 mL of 1% starch solution to each tube and quickly incubate in a 40°C water bath for 5 min. Add 2.0 mL of 3,5-dinitrosalicylic acid to tubes 2 and 3 respectively. Shake well, and place all three tubes in a boiling water bath for 5 min. After cooling, add distilled water to a final volume of 10 mL. Shake well and measure the absorbance at 540 nm. The difference between the average absorbance of tubes 2 and 3 and the absorbance of tube 1 can be used to determine the corresponding maltose content on a standard curve. The experiment was performed in triplicate.

[0023] Step 3: Embryo morphological development observation. The sample from Step 1 is dissected, and the length and morphological development stage of the embryo are observed and recorded under a stereomicroscope. The embryo ratio is calculated, which is the ratio of the embryo length to the seed long axis. Seeds with a natural stratification embryo rate of over 66% have basically completed morphological after-ripening. Starting from when the embryo rate reaches 66%, seeds are randomly selected every 30 days for germination experiments. Germination experiments are conducted under light conditions of 10, 15, 20, 25, and 30℃, with 50 seeds per treatment and 3 replicates.

[0024] The germination beds used in the above germination experiments were all 90mm diameter petri dishes, with clean river sand as the seedling substrate. The dishes were covered with lids to keep them moist and placed in a GXZ type intelligent light incubator (Ningbo Jiangnan Instrument Factory) for germination experiments.

[0025] The seed germination period in the seed box was 30 days. The time of the first germination of the seeds was observed to determine the end time of seed stratification and the sowing time. Germination experiments were conducted on Fritillaria cirrhosa seeds that had completed embryo dormancy under light conditions of 10, 15, 20, 25, and 30℃, with 50 seeds per treatment and 3 replicates.

[0026] All germination experiments were conducted using 90mm diameter petri dishes with clean river sand as the seedling substrate. The dishes were covered to maintain humidity and placed in a GXZ-type intelligent light incubator (Ningbo Jiangnan Instrument Factory). During germination, seed germination status and the number of germinated seeds were recorded daily. Germination potential was measured after 15 days from the date of placement, and germination rate and germination index were calculated after 30 days. The emergence of cotyledons from the river sand was considered a marker of germination. Germination rate, germination potential, and germination index were calculated as follows: Germination rate GR = (n / N) × 100% (n is the final number of normally germinated seeds; N is the number of seeds tested) Germination potential GE = (n / N) × 100% (n is the number of normally germinated seeds on day 7 of germination; N is the number of seeds tested) Germination index GI = Z(Gt / Dt) (Gt is the number of germinated plants on day t after treatment, and Dt is the corresponding number of germination days).

[0027] Step 4: Germination stimulus response test. When the embryo rate reaches 66%, the seeds are placed under 20℃ light conditions and soaked in gibberellin solutions of different concentrations for 24 hours, and the germination rate is recorded in real time. The concentration gradient of gibberellin solution is set to 0 mg / L, 50 mg / L, 100 mg / L, 150 mg / L, and 200 mg / L. Seeds were soaked in gibberellin at concentrations of 50, 200, 350, and 500 mg / L for 24 hours and then subjected to germination tests under 20°C light conditions. Seeds soaked in distilled water were used as a control. Each treatment contained 50 seeds and was repeated three times.

[0028] Step 5: Tracking seedling morphological indicators. Seeds that have completed morphological maturation are sown in five different seedling substrates: sandy soil, garden soil, humus soil, a 1:1 mixture of humus soil and garden soil, and wild nursery substrate. After germination, seedling height, root length and biomass are measured every 72 hours, and growth curves are constructed using image recognition technology.

[0029] Step 6: Online analysis of active ingredients. High performance liquid chromatography (HPLC) is used to monitor the content of fritillaria alkaloids online. The detection wavelength of HPLC is set to a specific absorption wavelength, the mobile phase is acetonitrile-0.1% phosphoric acid aqueous solution (35:65), and the detection wavelength is 230 nm.

[0030] Step 7: Data fusion analysis, integrating enzyme activity data, environmental parameters, morphological indicators and effective ingredient content, and calculating the propagation activity index through machine learning models.

[0031] Step 8: Activity threshold determination. When the propagation activity index is ≥0.85, it is determined to be a high-activity seed source, triggering an automated sorting command. Automated sorting is performed by a pneumatic robotic arm, and the sorting is based on two preset thresholds: activity index and morphological index.

[0032] At other levels, this embodiment also provides an image recognition technology to obtain seedling growth data in a non-contact manner. Specifically, by fixing an image acquisition device above the seedling area, digital images of the top and side views of the seedlings containing a standard-sized reference object are automatically acquired periodically. Through an image recognition algorithm, the main body of the seedling in the image is automatically segmented and proportionally converted with the standard reference object to calculate the morphological indicators of the seedling's height, leaf width, and projected area in batches.

[0033] The process of constructing the growth curve is as follows: the data of plant height and projected area obtained by image recognition at different time points are continuously recorded over time, and the plant height growth curve and biomass growth trend curve are fitted to generate the growth curve. The growth rate and growth potential of seedlings in each seedling substrate are determined by analyzing the changes in the slope of the curve, and the promotion coefficient of different substrates on the growth of Fritillaria seedlings is quantitatively compared.

[0034] Compared with existing technologies, non-contact image recognition and automated growth curve construction completely overcome the problems of physical damage, low efficiency and subjective error caused by traditional manual measurement methods to seedlings, and realize non-destructive, continuous and high-precision dynamic monitoring of the growth process of large batches of Fritillaria seedlings. At the same time, by fitting the growth curve and quantitatively analyzing the slope change, the optimal seedling substrate can be scientifically and objectively screened, providing reliable data support for the standardized and efficient cultivation of Fritillaria.

[0035] This embodiment provides a process for constructing a machine learning model, including: Collect complete growth cycle data of Fritillaria seeds from historical stratification experiments, including catalase activity change curves, environmental temperature and humidity time-series records, seedling morphological development image sequences, and fritillary alkaloid content detection values. The rate of change in enzyme activity was extracted as a biological metabolic characteristic, the cumulative value of daily average temperature was calculated as an environmental stress characteristic, the growth rate of plant height and the root expansion rate were quantified as developmental characteristics, and the weekly increment of cispermine was statistically analyzed as a quality characteristic. The random forest algorithm is used to learn the nonlinear relationship between the above multidimensional features. The depth and number of decision trees are optimized by ten-fold cross-validation, and the propagation survival rate is used as the training label for the model. The working logic of machine learning models is as follows: The enzyme activity detection results, environmental sensor readings, seedling image recognition parameters, and online chromatographic analysis data dynamically collected during the stratification process are automatically converted into standardized feature vectors. The model outputs a propagation activity index in the 0-1 range based on the weighted combination relationship of the input feature vectors, with biological metabolic features accounting for ≥40% of the weight, and quality features and developmental features each accounting for 25% to 30% of the weight. More than 200 decision trees independently evaluate the feature vectors. When more than 80% of the decision trees determine the activity index ≥ 0.85, a high activity judgment is triggered. If less than 60% of the decision trees support it, a quality control alarm is activated.

[0036] Compared with existing technologies, this invention integrates enzyme activity, environmental, morphological, and quality data and employs a random forest algorithm to construct a predictive model capable of capturing complex nonlinear relationships. This changes the traditional single-lag experience-based judgment mode and enables multi-dimensional, dynamic, and high-precision early intelligent prediction of the propagation activity of Fritillaria seeds. In particular, the model assigns the highest weight to biological metabolic characteristics and sets an integrated decision threshold, making the prediction results highly fault-tolerant. It can not only accurately screen high-activity seeds but also trigger quality control early warnings in a timely manner, thereby significantly improving the propagation success rate and the level of intelligence in selecting high-quality seed sources.

[0037] Experimental Test Figure 2 This indicates the dynamic changes in embryo percentage during the natural stratification process of Fritillaria cirrhosa seeds. After being harvested at maturity, the embryos of Fritillaria cirrhosa seeds are in a primitive embryonic state and have not yet developed; only punctate embryos can be observed under a 40x dissecting microscope. Using untreated seeds as a control, seeds naturally stratified in sand reached an embryo percentage of 24% after 30 days of stratification, indicating the start of embryo development. Embryo growth was relatively slow in the first 30 days, but rapid growth occurred between 30 and 60 days, representing the fastest growth period during stratification, with the embryo percentage increasing rapidly from 24% to 55%, an increase of 31%. Between 60 and 90 days of stratification, embryo growth slightly increased, reaching 66%. After 90 days of stratification, embryo growth entered a stable growth period. From 90 to 180 days of stratification, the embryo percentage remained essentially unchanged, indicating that morphological after-ripening of the embryo was basically complete after 90 days of stratification. From 90 to 210 days of stratification, the embryo percentage showed little change.

[0038] from Figure 3The dynamic changes in peroxidase activity of Fritillaria thunbergii seeds during natural stratification of embryos can be observed. Unstratified seeds exhibited the lowest peroxidase activity, at 5.83 μg / min. Compared to unstratified seeds stored dry for the corresponding number of days, seeds stratified for 30, 60, 90, 120, 150, 180, and 210 days showed increases in peroxidase activity of 237%, 535%, and 456%, respectively. 384%, 393%, 532%, and 757%. This change in enzyme activity reveals that during the first 60 days of stratification, as the seed embryo begins to develop, peroxidase activity gradually increases, indicating that the 60-day stratification period is a critical stage in the entire embryo development process. During this time, the seed's internal activity is intense, and enzyme activity increases significantly. From 90 days of stratification, enzyme activity begins to decrease compared to 60 days, but after 180 days of stratification, enzyme activity increases dramatically, indicating that the seed shows signs of germination.

[0039] from Figure 4 The dynamic changes in catalase activity of Fritillaria thunbergii seeds during natural stratification of embryos can be observed. The unstratified seeds exhibited the lowest catalase activity, at 6.0 mg / (gmin). Compared to unstratified seeds stored dry for the same number of days, seeds stratified for 30, 60, 90, 120, 150, 180, and 210 days showed increases in catalase activity of 229%, 296%, 309%, 276%, 287%, 447%, and 469%, respectively. At 30, 60, and 90 days of stratification, seed enzyme activity showed a significant upward trend. With the basic completion of embryo growth and development, enzyme activity began to decrease at 120 days of stratification. Seed enzyme activity at 120 and 150 days of stratification was slightly lower than that at 90 days of stratification, but the decrease was not significant, indicating that the change trend of catalase activity was relatively stable from 90 to 150 days of stratification. With the completion of seed morphology and physiological after-ripening, the enzyme activity increased significantly at 180 days of stratification, indicating that the seeds had begun to germinate.

[0040] Figure 5The dynamic changes in seed amylase activity during the natural stratification of Fritillaria thunbergii seeds can be observed. The lowest amylase activity was 1.8 mg / (gmin) in unstratified seeds. Peroxidase activity in embryos stratified for 30, 60, 90, 120, 150, 180, and 210 days increased by 73%, 107%, 115%, 106%, 94%, 103%, and 122% respectively compared to unstratified seeds stored dry for the corresponding days. During the first 60 days of stratification, amylase activity showed a significant upward trend, indicating a gradual increase in seed enzyme activity during the rapid embryonic development period. From 90 days onwards, seed enzyme activity showed a slight downward trend, until 210 days after stratification, when enzyme activity showed a slight increase compared to 180 days after stratification, indicating an upward trend in amylase activity and signs of seed germination.

[0041] The effect of temperature on the germination of Fritillaria thunbergii seeds:

[0042] As shown in the table above, under light conditions, among the six treatments (5, 10, 15, 20, 25, and 30℃), the germination rate was highest at 15℃ (83.33%), showing a significant difference compared to 10℃ and highly significant differences from the other temperature treatments. This indicates that from 15℃ onwards, the germination rate decreases with increasing temperature. No germination was observed at 30℃. The seeds still exhibited a high germination rate at a lower temperature (5℃), but the germination rate was 0% at a higher temperature (30℃), indicating that the seeds can germinate at low temperatures, but germination is inhibited at high temperatures. Therefore, these seeds are not suitable for germination at high temperatures.

[0043] The germination potential of seeds was highest at 15℃ (66%), showing no significant difference from 10℃, but exhibiting highly significant differences compared to other temperatures. From 15℃ to 30℃, the germination potential decreased with increasing temperature. Except for the 30℃ treatment where no seeds germinated, the germination potential was lowest at 5℃, showing highly significant differences compared to other temperature treatments. This indicates that seeds germinate well at 10℃ and 15℃, resulting in high germination potential, uniform emergence, and consistent seedling growth. The germination index was highest at 15℃ (2.46), showing highly significant differences compared to all temperature treatments. From 15℃ to 30℃, the germination index decreased with increasing temperature, with no significant difference between the germination index at 5℃ and 25℃. This indicates that germination time was shortest and germination speed was fastest at 15℃; excessively high or low germination temperatures prolonged germination time and slowed germination speed.

[0044] Effects of gibberellin on the germination of Fritillaria thunbergii seeds:

[0045] As shown in the table above, there were no significant differences in germination rate and germination index among seeds treated with different concentrations. The germination potential was highest at a concentration of 50 mg / L, and there was no significant difference between it and the control. The germination potential decreased with increasing gibberellin concentration.

[0046] In summary, by systematically integrating key information, including the intrinsic physiological metabolic state centered on enzyme activity dynamic monitoring, external environmental stress data collected in real time by sensor networks, phenotypic development dynamics tracked non-contactly by image recognition technology, and the accumulation pattern of effective components analyzed by online chromatography, the above multidimensional data are fused and nonlinearly analyzed to quantify complex biological processes into an intuitive propagation activity index.

[0047] It not only comprehensively reflects the current state of seed vigor, but also predicts its future seedling potential, thus providing a scientific and reliable basis for real-time decision-making for the automated sorting of highly active seed sources, greatly improving the standardization, intelligence level and propagation efficiency of Fritillaria breeding work.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time detection of the propagation activity of Fritillaria cirrhosa, characterized in that, Includes the following steps: Step 1: Seed stratification pretreatment. Mix fritillaria seeds with fine sand at a ratio of 1:3 and stratify, maintaining substrate moisture at 50%–60%. Step 2: Dynamic monitoring of enzyme activity. The sample prepared in Step 1 was ground, and the seed extract enzyme solution was sampled and tested every 10 days. The activities of catalase, peroxidase and amylase were measured in real time by ultraviolet spectrophotometry. Step 3: Embryo morphological development observation. The sample from Step 1 is dissected, and the length and morphological development stage of the embryo are observed and recorded under a stereomicroscope. The embryo ratio is calculated, which is the ratio of the embryo length to the seed long axis. Step 4: Germination stimulus response test. When the embryo rate reaches 66%, the seeds are placed under 20℃ light conditions and soaked in gibberellin solutions of different concentrations for 24 hours, and the germination rate is recorded in real time. Step 5: Tracking seedling morphological indicators. Seeds that have completed morphological maturation are sown in five different seedling substrates: sandy soil, garden soil, humus soil, a 1:1 mixture of humus soil and garden soil, and wild nursery substrate. After germination, seedling height, root length and biomass are measured every 72 hours. Growth curves are constructed using image recognition technology. Step 6: Online analysis of active ingredients, using high performance liquid chromatography to monitor the content of fritillary alkaloids online; Step 7: Data fusion analysis, integrating enzyme activity data, environmental parameters, morphological indicators and active ingredient content, and calculating the propagation activity index through machine learning models; Step 8: Activity threshold determination. When the propagation activity index is ≥0.85, it is determined to be a high-activity seed source, triggering an automated sorting instruction.

2. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The container used in the stratification process in step 1 is equipped with a porous humidity probe. The probe is connected to a cloud monitoring platform via a wireless transmission module. When the humidity deviates from the range of 50% to 60%, the irrigation system is automatically triggered.

3. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The enzyme activity detection in step 2 is performed using ultraviolet spectrophotometry, wherein CAT activity is determined by... Degradation rate was used as an indicator, POD activity was calculated by the change in absorbance of guaiacol oxidation products, and amylase activity was determined by the DNS method to measure the amount of reducing sugar generated.

4. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The concentration gradient of the gibberellin solution in step 4 is set to 0 mg / L, 50 mg / L, 100 mg / L, 150 mg / L, and 200 mg / L.

5. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The image recognition technology in step 5 acquires seedling growth data in a non-contact manner. By fixing an image acquisition device above the seedling area, digital images of the top and side views of the seedlings containing standard-sized reference objects are automatically acquired periodically. The image recognition algorithm automatically segments the main body of the seedling in the image and performs proportional conversion with the standard reference object to calculate the morphological indicators of the seedling height, leaf width, and projected area in batches.

6. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The process of constructing the growth curve in step 5 is as follows: the data of plant height and projected area obtained by image recognition at different time points are continuously recorded over time, and the plant height growth curve and biomass growth trend curve are fitted and generated; the growth rate and growth potential of seedlings in each seedling substrate are determined by analyzing the slope changes of the curves, and the promotion coefficient of different substrates on the growth of Fritillaria seedlings is quantitatively compared.

7. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, In step 6, the detection wavelength of the high-performance liquid chromatography is set to a specific absorption wavelength, the mobile phase is acetonitrile-0.1% phosphoric acid aqueous solution (35:65), and the detection wavelength is 230 nm.

8. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The process of building the machine learning model in step 7 includes: Collect complete growth cycle data of Fritillaria seeds from historical stratification experiments, including catalase activity change curves, environmental temperature and humidity time-series records, seedling morphological development image sequences, and fritillary alkaloid content detection values. The rate of change in enzyme activity was extracted as a biological metabolic characteristic, the cumulative value of daily average temperature was calculated as an environmental stress characteristic, the growth rate of plant height and the root expansion rate were quantified as developmental characteristics, and the weekly increment of cispermine was statistically analyzed as a quality characteristic. The random forest algorithm is used to learn the nonlinear relationships of the above multidimensional features. The depth and number of decision trees are optimized by ten-fold cross-validation, and the propagation survival rate is used as the training label for the model.

9. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The working logic of the machine learning model in step 7 is as follows: The enzyme activity detection results, environmental sensor readings, seedling image recognition parameters, and online chromatographic analysis data dynamically collected during the stratification process are automatically converted into standardized feature vectors. The model outputs a propagation activity index in the 0-1 range based on the weighted combination relationship of the input feature vectors, with biological metabolic features accounting for ≥40% of the weight, and quality features and developmental features each accounting for 25% to 30% of the weight. More than 200 decision trees independently evaluate the feature vectors. When more than 80% of the decision trees determine the activity index ≥ 0.85, a high activity judgment is triggered. If less than 60% of the decision trees support it, a quality control alarm is activated.

10. The method for real-time detection of Fritillaria cirrhosa propagation activity according to claim 1, characterized in that, The automated sorting in step 8 is performed by a pneumatic robotic arm, and the sorting is based on two preset thresholds: activity index and morphology index.

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