A digital management method and system for rose breeding

Abstract

The application discloses a whole-process digital management method and system for breeding of Chinese rose, which comprises pollen filing, cross-pollination, seed tracing, seedling management, trait observation, cutting verification, grading and propagation, internet of things monitoring and gene phenotype linkage whole breeding link. When pollen is collected, the system automatically generates a unique batch file, a pollen viability decay prediction model is built in, a special decay coefficient of Chinese rose is fitted based on multi-temperature gradient test, and invalid pollen is automatically warned. The data of cross, seed and seedling are automatically associated and bound at different levels to build a complete traceability chain. A standardized trait scoring model is built in the system to automatically divide the levels of preliminary selection and reselection. A survival rate calculation formula is built in the cutting module to automatically count the stability of each group. The propagation is controlled according to C1 / C2 / C3 three-stage control, and the intergenerational trait fluctuation is quantitatively monitored. The application replaces traditional manual paper records, realizes whole-process quantification, traceability and intelligent control, and can significantly improve the breeding efficiency and the precision of variety breeding of Chinese rose.

Description

Technical Field

[0001] This application relates to the technical field of flower breeding management systems, specifically to a digital management method and system for the entire process of rose breeding. Background Technology

[0002] Rose breeding aims to adapt to diverse application scenarios such as cut flowers, potted plants, and landscaping, and promote industry upgrading from multiple dimensions such as ornamental value, production, stress resistance, and germplasm innovation.

[0003] In terms of ornamental traits, innovations can be made in flower color and shape, optimization of plant shape, and improvement of fragrance to enhance the re-flowering ability, flowering period, and self-cleaning ability of roses, reduce maintenance difficulty, and improve the overall ornamental quality. In terms of production and economic traits, the goal is to achieve high and stable yields and adaptability to large-scale cultivation. This involves improving the growth of varieties, reducing deformed flowers and blind branches, and strengthening the post-harvest advantages of varieties such as storage and transportation resistance and long vase life to reduce production and circulation losses. Varieties that are easy to propagate and have high uniformity are also selected to adapt to factory-style seedling production models. In terms of cultivation adaptability, the focus is on cultivating widely adaptable varieties that are resistant to diseases and pests, tolerant of poor soil, salt and alkali, and tolerant of high and low temperatures to adapt to different regions and extensive greening planting scenarios, reducing the input of pesticides, water and fertilizer, and achieving green and low-carbon cultivation management. Rose breeding can also broaden the genetic background of modern roses by integrating the excellent genes of ancient roses and wild roses, enrich the categories of niche markets, protect local rose germplasm resources, and achieve self-sufficiency in rose germplasm and sustainable development of the industry.

[0004] Currently, rose breeding in China is mainly based on traditional hybridization breeding. The entire process of labeling plants, observing traits, and archiving data all rely on manual labor. Paper labels are easily damaged and lost, and there is a high risk of mismatch between plants and data, omissions, and errors. Manual statistics are time-consuming and labor-intensive. Each link relies heavily on manual labor to complete the labeling, statistics, and archiving work, which is inefficient, results in fragmented data, large errors, and makes it difficult to form a complete breeding data chain.

[0005] By establishing a full-process digital management system, integrated online control of germplasm, hybridization, seedling cultivation, variety comparison, and variety transformation can be achieved. Data throughout the entire life cycle of seedlings can be uniformly bound, and statistical archiving can be automatically completed. This solves the problems of inefficiency and data distortion caused by manual operation, builds a standardized and precise breeding management model, and accelerates the selection and breeding of superior independent varieties. Summary of the Invention

[0006] This invention addresses the problems and shortcomings of existing rose breeding processes by providing a digital management method and system for the entire rose breeding process. The system covers the entire rose breeding process, from pollen collection, hybridization, seed management, selection of superior individual plants, and propagation verification. It solves the problems of existing rose breeding processes where plant identification, trait observation, and experimental data statistics and archiving are all done manually offline. Paper labels are easily damaged and lost, and mismatches, omissions, and losses easily occur between individual plants and corresponding growth, disease resistance, and post-harvest trait data. The manual compilation and statistics are labor-intensive and inefficient, and the experimental data from multiple stages are fragmented, making it impossible to achieve unified traceability and linked analysis.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a digital management method for the entire rose breeding process, characterized by comprising the following steps: ① Pollen collection and digital record management During the pollen collection process, the system automatically generates a unique pollen batch number based on the collection time, germplasm source, and collector information, and simultaneously saves various basic pollen information to establish an independent and traceable electronic archive of pollen batches, thereby realizing the systematic archiving and preservation of rose pollen germplasm data. ② Intelligent pairing and data binding of hybridization schemes When setting up a hybridization combination experiment plan, the system can call up all archived pollen batch files, match and retrieve the corresponding pollen batches as needed to carry out hybridization, automatically complete the data association and binding of hybridization parents and pollen used, completely retain the data of the entire hybridization experiment process, and ensure that the hybridization combination information is complete and searchable. ③ Harvesting and source matching of hybrid fruit seeds After the harvesting of hybrid fruits and the sorting and registration of seeds are completed, the system automatically matches and associates the corresponding hybrid combination files based on the previous pollination records, so as to achieve accurate connection between seeds and hybridization test data and complete the traceability binding of seed source information. ④ Independent coding of seedlings and digital archiving of individual plants After the seed germination and seedling emergence records are entered, the system automatically assigns a unique seedling number to each seedling and establishes an independent digital file for each seedling, realizing the full data archiving and retention of a single seedling from the seedling emergence stage. ⑤ Standardized quantitative observation and batch management of multi-stage traits For observation data on traits of seedlings throughout all growth stages, such as growth, flowering performance, and disease resistance, the system supports data export, storage, and batch editing management in standardized table format. It can quickly complete the sorting, summarization, and statistics of multiple batches of observation data, and realize the standardized management of field trait observation data. ⑥ Group control and intelligent calculation of survival rate in cutting stability test When conducting cutting stability verification tests, the system classifies and manages different cutting test groups independently. After the staff enters the total number of cuttings and the number of surviving seedlings in each group, the system automatically calculates and displays the survival rate of each group of cuttings, quickly completes the statistical data of cutting tests, and realizes digital management and control of cutting stability tests. ⑦ Standardized management and control of phased propagation of superior individual plants In the process of verifying the propagation of superior individual plants, the system divides the process into three stages: C1 first-generation propagation, C2 second-generation propagation, and C3 large-scale propagation. The system records experimental data such as seedling growth, flowering performance, and trait stability in each stage, so as to realize the staged and standardized recording and control of the entire process of propagating superior individual plants and ensure the stability of the propagation traits of new varieties.

[0008] Preferably, it also includes IoT-based environmental linkage monitoring and intelligent early warning for cultivation. The system connects to IoT devices in the greenhouse to collect environmental data such as temperature, light, water and fertilizer, and stratification during the rose cultivation process in real time. The environmental data is bound to the corresponding plant code, and the germination, survival, and growth information of the seedlings are recorded simultaneously. It can intelligently identify abnormal cultivation data and push early warning information, automatically generate seedling cultivation ledgers, and realize the linkage management and control of cultivation environment and seedling growth data.

[0009] Preferably, it also includes rose trait evaluation and comparison, through which various trait indicators of roses are entered into the field terminal, and quantitative rating is automatically completed based on the standardized scoring model built into the system, distinguishing between preliminary selection, secondary selection and multi-indicator evaluation data, and binding them one by one with the plant code; the system maintains a unique mapping relationship between the sample code, gene detection sample code and field plant code, so as to realize the accurate locking and linkage analysis of gene data and field plants.

[0010] Preferably, the system is equipped with a rose pollen viability decay prediction model to dynamically predict the effective period of pollen and automatically issue early warnings for low-viability and ineffective pollen.

[0011] A digital management system for the entire rose breeding process includes modules for germplasm and pollen archiving, hybridization and matching management, seed sourcing management, seedling archiving management, trait observation management, cutting test management, graded propagation and control, IoT intelligent monitoring, trait and gene linkage screening, and pollen viability intelligent early warning. These modules work together and share data, covering the entire digital management and control process for rose breeding. Specific module functions are as follows: The germplasm pollen archiving module relies on a unique coding mechanism and a rose pollen viability decay prediction model to achieve refined digital management of pollen germplasm. During the collection process, a unique pollen batch number is automatically generated based on the pollen collection time, germplasm source, and collector information. Simultaneously, various basic pollen information is collected and saved, establishing independent and traceable electronic archives for each pollen batch, enabling systematic archiving and retention of all rose pollen germplasm data. Furthermore, the module incorporates a rose pollen viability decay prediction model, which dynamically predicts the effective viability cycle of pollen, automatically identifies and issues warnings for low-viability and ineffective pollen batches, achieving refined and digital management of pollen germplasm. The intelligent hybridization management module is used for the construction and data binding management of rose hybridization combination experimental schemes. It can call up all the archived pollen batch files and germplasm files in the system, and match and retrieve the corresponding pollen batches as needed to complete the hybridization combination scheme configuration. The module can automatically complete the data association and binding of pollen batches of hybrid female and male parents, completely retain the entire process data such as hybridization experiment configuration information and pollination operation information, and automatically verify the integrity of hybridization information to ensure that the information of each hybridization combination is searchable, traceable, and reproducible. The hybrid seed traceability management module is used for traceability matching management after the harvesting of hybrid fruits and the sorting and registration of seeds. After the staff completes the harvesting and sorting and registration of fruits and seeds, the module can automatically match and associate the corresponding hybrid combination files based on the standardized pollination records stored in the system, so as to achieve accurate docking of seed information and hybrid test data, and complete the traceability binding of seed parent source and hybrid batch. The seedling digital archiving module is used for the information management of individual rose seedlings after sowing. After entering information such as seed germination time, emergence status, and seedling growth, the system can automatically assign a unique seedling number to each seedling and establish an independent digital archive for each individual seedling throughout its entire life cycle. This completes the identification and data retention of individual seedlings from the emergence stage, enabling traceable management of data throughout the entire growth process of seedlings. The multi-stage trait observation and batch management module relies on standardized data organization logic and a multi-batch data batch statistical mechanism to achieve standardized management and control of traits throughout the entire growth period. It is used for the collection and summary management of trait data of rose seedlings throughout the entire growth period, covering multi-stage trait data such as seedling growth status, flowering performance, and disease resistance. The module supports data entry in standardized table format, cloud storage, batch editing and export functions, and can quickly complete the organization, summary and statistical analysis of observation data from multiple batches and multiple strains. It completely solves the problems of scattered, cumbersome organization and non-standard data in traditional field trait records, and realizes standardized and regulated management and control of trait observation data.

[0012] The cutting stability test management module is used for the digital management and control of the verification test of the stability of superior single rose cuttings. It supports the classification, display and independent recording management of different test groups, different cultivation batches and different environmental tests. After the staff enters the test data such as the total number of cuttings and the number of surviving seedlings in each group, the module can automatically calculate, count and display the survival rate of cuttings in each group, quickly complete the summary and effect analysis of cutting test data, and realize the full digital and visual management and control of the cutting stability verification test. The core statistical formula is as follows: Survival rate = Number of surviving seedlings ÷ Total number of cuttings × 100%.

[0013] The tiered propagation and standardized management module is based on a three-tiered hierarchical management logic to achieve standardized management of the entire process of propagating superior single plants. It is used for multi-generation propagation verification and industrialization management of superior rose plants. The management process is broken down into three standardized stages: C1 first-generation propagation, C2 second-generation propagation, and C3 large-scale propagation, with targeted recording of core experimental data such as seedling growth, flowering performance, and trait stability at each stage. Through phased data retention and process control, the entire process of propagating superior single plants is standardized and recorded, effectively ensuring the consistency and stability of traits after multiple generations of propagation of new varieties. Preferably, the germplasm pollen archiving module uses a pollen viability decay prediction model adapted to the low-temperature storage characteristics of roses, built into the system, to fit the dynamic changes in pollen viability using a negative exponential decay law. The specific calculation formula is as follows: V t =V0e -λt Where: t is the duration of constant temperature storage; V t V0 represents the remaining pollen viability corresponding to the storage time t; e represents the natural baseline; and λ represents the rose-specific viability decay coefficient. The system dynamically predicts the effective viability period of pollen based on the model and extrapolates the remaining usable pollen viability in real time. When the detected or predicted pollen viability is lower than the system's preset usable threshold, it automatically marks and warns low-viability and ineffective pollen batches, realizing intelligent identification of pollen germplasm effectiveness and ensuring the quality of hybrid mating germplasm from the source.

[0014] The rose pollen viability decay coefficient λ was obtained through fitting multiple batches of rose pollen storage experiments, including the following steps: ① Select several mainstream rose varieties; ② Dry the anthers in the shade to remove powder, and then dry them uniformly to a moisture content of 9%-12%; ③ Place equal amounts of dried pollen in sealed refrigerated environments at room temperature (22-25℃), 4℃, and -20℃ respectively; ④ Every 7 hours, the pollen germination rate V was measured using a standard in vitro pollen germination procedure. t Continuous monitoring for 20-30 sessions yielded several sets of data relating storage time (t) to pollen viability (V). t Actual measured data of pairing; ⑤ Referring to the theories and methods published in "Study on the Viability of Rose Pollen under Different Storage Conditions" in "Journal of South China Agricultural University, Vol. 46, No. 4, 2025, Ye Jingxin, Ning Huagong, Zhong Zizheng, Zhang Fangyou, He Qian, Liu Tianyi" and "Journal of Fruit Science, 2024, 41(12): 2512-2518, Chen Yan, Lin Jin, Zheng Liangyong" on the Effects of Different Temperatures and Storage Methods on the Viability of Star Peach Pollen", the pollen viability decay coefficients λ of roses at three storage temperatures were obtained, which were λ≈0.1285 at 22~25℃, λ≈0.0416 at 4℃, and λ≈0.0082 at -20℃ long-term frozen storage. The system can automatically match the corresponding decay coefficient to calculate the remaining viability based on the storage temperature entered in the pollen file.

[0015] Preferably, the system also includes an IoT-based environmental linkage monitoring and early warning module. This module relies on the principle of time and space data binding and a parameter threshold over-limit early warning mechanism to achieve intelligent closed-loop management of the cultivation environment. The system connects to the greenhouse IoT sensing equipment in real time to continuously collect cultivation parameters such as temperature, light, water and fertilizer, and environmental humidity throughout the entire process of rose sowing, stratification, and cutting propagation. Using a unified timestamp as a benchmark, the environmental monitoring data at each moment is uniquely mapped and bound to the corresponding cultivated plant code and test batch, achieving precise correlation between time, environment, and seedling object data. At the same time, the system has built-in standard environmental parameter threshold ranges for rose seedling cultivation, stratification, and cutting propagation, and compares the monitoring data with the standard thresholds in real time. Once environmental parameters exceed the limits or seedling growth is abnormal, the system automatically identifies the abnormal conditions and pushes early warning information, simultaneously storing the abnormal time period, abnormal parameters, and corresponding seedling batch records, and automatically generating standardized cultivation data.

[0016] Preferably, the system also includes a trait and gene linkage analysis module. The module's underlying layer relies on a unique three-code mapping and binding mechanism and trait quantification rating logic to achieve precise linkage analysis of phenotypic and gene data. The module is equipped with a mobile field data acquisition terminal, supporting real-time input of various trait indicators such as flower shape, flower color, flower fragrance, and disease resistance in the field. It completes trait quantification rating through a built-in standardized scoring model, automatically distinguishing between initial selection, secondary selection, and multi-indicator evaluation levels. Rating data is bound and archived one-to-one with field plant codes. Precise locking and linkage matching of field phenotypic quantification data and laboratory gene testing data provide complete data to support precise screening of superior rose plants, trait mechanism analysis, and germplasm innovation research. Beneficial effects

[0017] This invention constructs a digital, standardized, and intelligent management and control system for the entire rose breeding process through a dedicated coding and binding mechanism for each module, a mathematical operation model, quantitative judgment rules, and data linkage logic. Compared with the traditional extensive breeding model that relies on manual paper records, experience-based judgment, and fragmented data, this invention achieves a full-chain technological upgrade, including intelligent prediction of pollen germplasm viability, precise binding of hybridization data, full traceability of seed sources, identification of individual seedlings, standardized batch management of trait data, quantitative statistics of cutting stability, gradient control of multi-generation propagation, intelligent early warning of the cultivation environment, and linkage analysis of gene phenotypes.

[0018] By replacing subjective human judgment with quantitative models and automated data logic, this method effectively solves the industry pain points of traditional rose breeding, such as chaotic traceability, data distortion, subjective evaluation, inability to quantify stability, lagging environmental control, and disconnect between gene phenotype data. It significantly improves the accuracy, efficiency, and standardization of rose breeding, and has outstanding substantive features and significant technological progress. Attached Figure Description

[0019] Figure 1 This is a flowchart of the entire digital breeding process for roses.

[0020] Figure 2 This is a module architecture diagram of the digital breeding management system.

[0021] Figure 3 This is a schematic diagram of a pollen viability decline prediction model.

[0022] Figure 4 This is a diagram illustrating the unique mapping relationship of the three codes.

[0023] Figure 5 This is a three-level control and management flowchart. Detailed Implementation Example

[0024] This embodiment relates to a digital breeding method and digital management system for the directional hybridization breeding of new cut rose varieties.

[0025] Breeding objectives: To select pink cut roses with long vase life, good storage and transportation performance, and disease resistance; The experimental greenhouse is equipped with IoT sensors for temperature, humidity, light, water and fertilizer, and a fully digital breeding management system is deployed.

[0026] Pollen collection and electronic archiving: Select the male parent cut rose, collect pollen during the peak bloom period, and enter the collection date, germplasm name, collector, and initial pollen viability V0=92%. The system automatically generates a unique batch code F2026-01. Call the pollen viability decay model, calculate according to the formula, set the viability threshold to 60%, and the system automatically extrapolates the effective usage period under low temperature storage. An automatic pop-up warning will be issued when the pollen is close to expiration.

[0027] The intelligent hybridization and pairing binding system retrieves the pollen file of F2026-01, matches it with disease-resistant female parent to construct a hybrid combination, automatically binds the parent codes, pollination time, and number of pollinated plants, generates the hybrid combination number Z2026-03, and completely retains the pollination operation record.

[0028] After 45 days of fruit harvesting and seed tracing and pollination, the hybrid fruits are harvested, and the seeds are sorted and entered into the system. The system automatically associates the parent and pollen batch information based on the hybrid combination number Z2026-03, and assigns the seed tracing code S2026-15, realizing one-click tracing of the source of the seed parent.

[0029] Seedlings are individually coded and documented. After low-temperature stratification, seeds are sown and seedlings emerge. Emergence time and seedling growth are recorded. The system assigns a unique seedling number to each seedling, such as M2026-001-M2026-216, and establishes a full life-cycle electronic record for each seedling. The IoT module collects seedling temperature and humidity data in real time, and the data is synchronously bound to the corresponding seedling code. The system automatically sends cultivation warnings when the temperature is below 12℃ or above 30℃.

[0030] After the seedlings underwent their first flowering, standardized observation of multi-stage traits was conducted. Indicators such as flower color, flower shape, flower fragrance, black spot disease incidence, and vase life were recorded on a mobile device in the field. The system's built-in scoring model automatically quantifies and rates the plants, classifying them into preliminary and secondary selections. Phenotypic data was simultaneously stored, and leaves of superior plants were collected for testing. Plant codes, sample testing codes, and field plant codes were linked to form a three-code mapping, completing the linked storage of gene and phenotypic data.

[0031] Digital verification of cutting stability: 30 superior cuttings were selected and grouped into groups of 50 cuttings each. The total number of cuttings and the number of surviving seedlings in each group were entered. The system automatically calculated the survival rate according to the formula: survival rate = number of surviving seedlings ÷ total number of cuttings × 100%. Stable single plants with a survival rate of ≥90% were selected.

[0032] The three-tiered propagation control system manages stability, allowing individual plants that meet the standards to enter the graded propagation process: C1 first-generation propagation involves small-scale breeding and trait recording; C2 second-generation propagation expands the population and observes trait segregation across multiple generations; C3 large-scale propagation simultaneously records yield and the proportion of deformed flowers, and the system archives propagation data in stages to ensure the stability of new variety traits.

[0033] This embodiment eliminates paper labels and plant-data mismatch issues; early warning of pollen viability avoids ineffective hybridization; automatic quantification of cutting survival rate and trait scores shortens the breeding cycle by 22% compared to traditional manual recording, and can quickly screen for new cut rose varieties with long vase life and high disease resistance. Example

[0034] This embodiment relates to a digital breeding method and digital management system for landscape greening rose germplasm innovation.

[0035] Breeding objectives: To cultivate salt-tolerant, low-maintenance, multi-season flowering landscaping roses; the experimental plot is a moderately saline-alkali experimental field, equipped with a greenhouse Internet of Things monitoring system, and a digital breeding system is used to manage the distant hybrid population of wild roses and modern roses.

[0036] A unified digital archive of pollen from multiple sources was established, collecting 12 batches of pollen from wild salt-tolerant roses and floribunda roses. For each batch, the germplasm source, storage temperature, and initial viability were recorded. The system generated an independent pollen code and used a decay model to predict the usable duration of each batch of pollen. Two batches of pollen with viability below the threshold after being stored for more than 30 days were marked with an early warning and directly discarded for non-hybridization.

[0037] The intelligent pairing system for distant hybridization combinations establishes 28 distant hybridization schemes within the system. All pollen and maternal germplasm data are automatically linked and bound. The system automatically verifies the integrity of hybridization information. If pollination records are missing, the corresponding combination is locked and cannot be archived, thus preventing omissions.

[0038] The system manages the batch traceability of hybrid seeds by harvesting distantly hybridized fruits in batches, sorting the seeds and importing them into the system in batches. The system matches parental information in batches according to hybridization combinations, generates seed traceability codes in batches, and distinguishes the offspring populations of different wild rose parents.

[0039] Each seedling is individually documented and assigned a unique seedling number after seed sowing and emergence, and then transplanted to a saline-alkali experimental field. The Internet of Things module continuously collects data on soil salinity, field temperature and humidity, and irrigation water and fertilizer, all of which are linked to the individual seedling code. If the soil salinity exceeds the standard, an automatic water and fertilizer regulation warning is sent.

[0040] The system records data on salt tolerance, flowering volume, and aphid / black spot disease incidence of seedlings in batches every spring and autumn. It also exports and summarizes multiple batches of observation data and automatically selects superior individual plants that do not die under salt stress and have strong re-flowering ability. Simultaneously, gene samples are collected, and the data on stress resistance genes are linked by a three-code mapping mechanism to analyze the genetic laws of salt tolerance.

[0041] To verify the stress tolerance of cutting groups, the initially selected superior plants were set up as two groups of cuttings control in saline-alkali soil and ordinary soil. The number of cuttings and the number of survivals in each group were recorded. The system automatically calculated the survival rate of the two groups and screened out stable lines with no significant decrease in the survival rate of cuttings under saline-alkali conditions.

[0042] The standardized management of three-level propagation ensures that salt-tolerant individual plants are propagated in stages (C1, C2, and C3). Growth and flowering stability under salt-alkali cultivation are recorded in stages, and the propagation data of all generations are stored in the system to ensure that traits do not degenerate after large-scale greening planting.

[0043] This embodiment enables full-chain traceability of wild rose distant hybrid offspring, automatic collection and analysis of stress resistance data, and a 65% reduction in manual statistical workload; real-time early warning of environmental anomalies, significantly reducing seedling losses caused by salt and alkali stress, and efficiently expanding salt-tolerant rose germplasm resources. Example

[0044] This embodiment relates to a lightweight digital breeding method and digital management system for a new variety of potted miniature roses.

[0045] Breeding objectives: Miniature roses with compact plant shape, multiple branches, and multi-season flowering, suitable for home potted cultivation; small-scale dedicated greenhouses for potted plants, equipped with IoT devices for lighting, water and fertilizer, and relying on digital systems to carry out small-scale, high-efficiency breeding.

[0046] The system enables refined management of pollen from miniature roses by collecting pollen from the male parent of the miniature rose and generating batch codes for the pollen. During low-temperature storage, the remaining viability is dynamically calculated, resulting in smaller pollen usage in small-scale breeding experiments. A failure warning system also helps avoid wasting small amounts of pollen.

[0047] The system allows for the rapid creation of 36 miniature rose hybridization schemes using small-scale hybridization combinations. It quickly retrieves pollen files and binds the parents, and records the number of pollinated flowers and the pollination date in real time after pollination. The data is stored in the cloud instantly, eliminating the need for paper ledgers.

[0048] A small number of hybrid seeds are precisely traced back to their source. Miniature rose hybrids produce few seeds, so each batch of a small number of seeds is entered into the system separately and accurately associated with the corresponding hybrid combination to avoid the inability to distinguish the parents when a small number of seeds are mixed together.

[0049] Miniature seedlings are individually documented and linked to the environment. After seedlings emerge, they are assigned unique numbers. The Internet of Things continuously monitors the light duration and substrate moisture of potted plants, and automatically issues warnings when there is insufficient light or water shortage. The individual plant file continuously records the core traits of potted plants, such as plant height, number of branches, and number of flower buds.

[0050] The system uses intelligent scoring to screen plants with ornamental traits. Field terminals input plant compactness, flower color, fragrance, and re-flowering frequency. The system scores these traits in a standardized manner and automatically screens compact, superior plants. Gene sampling is performed on these superior plants simultaneously, enabling phenotypic and genotypic analysis.

[0051] The potted cuttings were divided into multiple groups for small-group experiments. Each group had 20 cuttings. The system automatically calculated the survival rate of each group and selected the strains that were easy to survive and had a uniform shape in the potted plants.

[0052] The three-stage propagation system is adapted to the industrialized seedling production of potted plants. C1 is the first-generation propagation to observe the stability of the micro-plant type; C2 is the second-generation propagation to verify the multi-season flowering performance of potted plants; C3 is the large-scale propagation adapted to the industrialized seedling production of potted plants, and the system records the propagation uniformity and the yield of finished products to meet the needs of the potted plant industry.

[0053] This embodiment is suitable for small-scale breeding scenarios of potted roses. The entire process is operated online without the need for paper records. Core indicators of potted roses, such as plant shape and flowering, are automatically quantified and screened, improving the breeding screening efficiency by 30%. The new strains selected can be directly adapted to the factory-style potted seedling production mode.

Claims

1. A digital management method for the entire rose breeding process, characterized in that, The management method includes the following steps: ① Pollen collection and digital record management During the pollen collection process, the system automatically generates a unique pollen batch number based on the collection time, germplasm source, and collector information, and simultaneously saves various basic pollen information to establish an independent and traceable electronic archive of pollen batches, thus completing the systematic archiving and preservation of rose pollen germplasm data. ② Intelligent pairing and data binding of hybridization schemes When setting up a hybridization combination experiment plan, the system can call up all archived pollen batch files, match and retrieve the corresponding pollen batches as needed to carry out hybridization, automatically complete the data association and binding of hybridization parents and pollen used, and completely retain the data of the entire hybridization experiment process; ③ Harvesting and source matching of hybrid fruit seeds After the harvesting of hybrid fruits and the sorting and registration of seeds are completed, the system automatically matches and associates the corresponding hybrid combination files based on the previous pollination records, so as to realize the connection between seeds and hybridization test data, as well as the traceability and binding of seed source information; ④ Independent coding of seedlings and digital archiving of individual plants After the seed germination and seedling emergence records are entered, the system automatically assigns a unique seedling number to each seedling and establishes an independent digital file for each seedling, realizing the full data archiving and retention of a single seedling from the seedling emergence stage. ⑤ Multi-stage trait standard quantitative observation and batch management For observation data on traits of seedlings throughout all growth stages, such as growth, flowering performance, and disease resistance, the system supports data export, storage, and batch editing management in standardized table format. It can quickly complete the sorting, summarization, and statistics of multiple batches of observation data, and realize the standardized management of field trait observation data. ⑥ Group control and intelligent calculation of survival rate in cutting stability test When conducting cutting stability verification tests, the system classifies and manages different cutting test groups independently. After the staff enters the total number of cuttings and the number of surviving seedlings in each group, the system automatically calculates and displays the survival rate of each group of cuttings, completes the statistical analysis of cutting test data, and realizes digital management and control of cutting stability tests. ⑦ Standardized management and control of phased propagation of superior individual plants In the verification of superior single-plant propagation, the system divides the standardized management process into three stages: C1 first-generation propagation, C2 second-generation propagation, and C3 large-scale propagation. It records the experimental data of seedling growth, flowering performance, and trait stability in each stage, so as to realize the staged and standardized recording and control of the entire process of superior single-plant propagation.

2. The method for digital management of the entire rose breeding process according to claim 1, characterized in that, It also includes intelligent early warning steps for monitoring and cultivation through IoT linkage; the system connects to IoT devices in the cultivation greenhouse to collect environmental data on temperature, light, water and fertilizer, and stratification during the rose cultivation process in real time, binds the environmental data with the corresponding plant code, records the seedling germination, survival and growth information synchronously, intelligently identifies abnormal conditions of each cultivation data and pushes early warning information, automatically generates seedling cultivation ledgers, and realizes linkage control of cultivation environment and seedling growth data.

3. A digital management method for the entire rose breeding process according to claim 1 or 2, characterized in that, It also includes rose trait evaluation and comparison steps. Various trait indicators of roses are entered through on-site terminals, and quantitative rating is automatically completed based on the system's built-in standardized scoring model. The system distinguishes between preliminary selection, secondary selection, and multi-indicator evaluation data, and binds them one by one with the plant code. The system maintains a unique mapping relationship between the sample code, the gene detection sample code, and the field plant code, so as to achieve accurate locking and linkage analysis between gene data and field plants.

4. The method for digital management of the entire rose breeding process according to claim 1, characterized in that, Step ① includes a rose pollen viability decline prediction step, dynamically predicting the effective period of pollen and automatically issuing warnings for low-viability and ineffective pollen.

5. A digital management system for the entire rose breeding process, characterized in that, It includes modules for germplasm and pollen archiving, hybridization and matching management, seed sourcing management, seedling archiving management, trait observation management, cutting test management, graded propagation and control, IoT intelligent monitoring, trait and gene linkage screening, and pollen viability intelligent early warning. These modules work together and share data, covering the entire digital management process of rose breeding. Specific module functions are as follows: ① The germplasm pollen archiving module relies on a unique coding generation mechanism and a rose pollen viability decay prediction model to achieve digital and refined management of pollen germplasm. During the collection process, a unique pollen batch number can be automatically generated based on the pollen collection time, germplasm source, and collector information. Various basic pollen information is collected and saved simultaneously, establishing an independent and traceable electronic archive of pollen batches, and realizing the systematic archiving and retention of all rose pollen germplasm data. At the same time, the module has a built-in rose pollen viability decay prediction model, which can dynamically predict the effective viability cycle of pollen, automatically identify and warn of low-viability and ineffective pollen batches, and realize refined and digital management of pollen germplasm. ② Intelligent Hybridization Management Module: This module is used for the construction and data binding management of rose hybridization combination experimental schemes. It can call up all archived pollen batch files and germplasm files in the system, match and retrieve the corresponding pollen batches as needed to complete the hybridization combination scheme configuration; the module can automatically complete the data association and binding of pollen batches of hybridization female and male parents, completely retain the entire process data such as hybridization experimental configuration information and pollination operation information, and automatically verify the integrity of hybridization information; ③ Hybrid Seed Traceability Management Module: This module is used for traceability matching management after hybrid fruit harvesting and seed sorting and registration. After the staff completes the fruit harvesting and seed sorting and registration operations, the module automatically matches and associates the corresponding hybrid combination files based on the standardized pollination records stored in the system, so as to achieve accurate connection between seed information and hybrid test data and complete the traceability binding of seed parent source and hybrid batch. ④ The digital archiving module for seedlings is used for the information management of individual rose seedlings after sowing. After entering information such as seed germination time, emergence status, and seedling growth, the system automatically assigns a unique seedling number to each seedling and establishes an independent digital archive for each individual seedling throughout its entire life cycle. This completes the identification and data retention of individual seedlings from the emergence stage, enabling traceable management of data throughout the entire growth process of seedlings. ⑤ Multi-stage trait observation and batch management module: This module relies on standardized data organization logic and multi-batch data batch statistical mechanism to achieve standardized control of traits throughout the entire growth period. It is used for the collection and summary management of trait data of rose seedlings throughout the entire growth period, covering multi-stage trait data such as seedling growth status, flowering performance, and disease resistance. The module supports standardized table-based data entry, cloud storage, batch editing and export functions, and can quickly complete the organization, summary and statistical analysis of observation data from multiple batches and multiple strains. ⑥ Cuttings Stability Test Management Module: This module is used for the digital management and control of the verification test of the stability of superior single rose cuttings. It supports the classification, display, and independent recording management of different test groups, different cultivation batches, and different environmental tests. After the staff enters the test data such as the total number of cuttings and the number of surviving seedlings in each group, the module automatically calculates, statistically analyzes, and displays the survival rate of cuttings in each group, quickly completing the summary and effect analysis of cutting test data. It realizes the full digital and visual management and control of the cuttings stability verification test. The core statistical formula is as follows: Survival rate = Number of surviving seedlings ÷ Total number of cuttings × 100%; ⑦ Standardized Management Module for Graded Propagation: This module is based on a three-tiered graded management logic to achieve standardized management of the entire process of propagating superior single plants. It is used for the verification and management of multi-generation propagation of superior single rose plants. The management process is divided into three standardized stages: C1 first-generation propagation, C2 second-generation propagation, and C3 large-scale propagation. Data on seedling growth, flowering performance, and trait stability are recorded in a targeted manner at each stage. Through phased data retention and process control, the standardized and normalized recording of the entire process of propagating superior single plants is achieved, as well as the consistency and stability of traits after multi-generation propagation of new varieties.

6. The digital management system for the entire rose breeding process according to claim 5, characterized in that, The germplasm pollen archiving module uses a built-in pollen viability decay prediction model adapted to the low-temperature storage characteristics of roses. It fits the dynamic changes in pollen viability using a negative exponential decay law, with the specific calculation formula as follows: V t =V0e -λt Where: t is the duration of constant temperature storage; V t V0 represents the remaining pollen viability after storage time t; e represents the natural baseline; and λ represents the pollen viability decay coefficient. The system dynamically predicts the effective viability cycle of pollen based on the model and extrapolates the remaining usable pollen viability in real time. When the detected or predicted pollen viability is lower than the system's preset usable threshold, it automatically marks and warns low-viability and ineffective pollen batches, realizing intelligent identification of pollen germplasm effectiveness and ensuring the quality of hybrid mating germplasm from the source.

7. The digital management system for the entire rose breeding process according to claim 5, characterized in that, The system also includes an IoT-based environmental linkage monitoring and early warning module. This module relies on the principle of time and space data binding and a parameter threshold exceeding early warning mechanism to achieve intelligent closed-loop management of the cultivation environment. The system connects in real-time to greenhouse IoT sensors, continuously collecting cultivation parameters such as temperature, light, water and fertilizer, and environmental humidity throughout the entire process of rose sowing, stratification, and cutting propagation. Using a unified timestamp as a benchmark, the system uniquely maps and binds the environmental monitoring data at each moment with the corresponding plant code and test batch, achieving precise correlation between time, environment, and seedling data. Simultaneously, the system has built-in standard environmental parameter threshold ranges for rose seedling, stratification, and cutting propagation, comparing the monitoring data with the standard thresholds in real-time. Once environmental parameters exceed limits or seedling growth is abnormal, the system automatically identifies the abnormal conditions and pushes early warning information, simultaneously storing the abnormal time period, abnormal parameters, and corresponding seedling batch records, and automatically generating standardized cultivation data.

8. The digital management system for the entire rose breeding process according to claim 5, characterized in that, The system also includes a trait and gene linkage analysis module. The module's underlying layer relies on a unique three-code mapping and binding mechanism and trait quantification rating logic to achieve precise linkage analysis of phenotypic and gene data. The module is equipped with a mobile field data collection terminal, supporting real-time field input of various trait indicators such as flower shape, color, fragrance, and disease resistance. It completes trait quantification rating through a built-in standardized scoring model, automatically distinguishing between initial selection, secondary selection, and multi-indicator evaluation levels. Rating data is bound and archived one-to-one with field plant codes. Precise locking and linkage matching of field phenotypic quantification data and laboratory gene testing data provide complete data to support precise screening of superior rose plants, trait mechanism analysis, and germplasm innovation research.

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