High-quality and high-yield cultivation method for pear trees

Abstract

The invention discloses a high-quality and high-yield cultivation method for pear trees, and relates to the technical field of agriculture, and the method comprises the following steps: carrying out targeted regulation on gene data of pear tree varieties by adopting a genome sequencing and gene editing method, and obtaining genetically improved pear seedlings; the method comprises the following steps: cultivating the gene-improved pear saplings under a greenhouse condition, and optimizing root development of the pear saplings by adjusting soil physicochemical properties and microbial communities in a root zone; the optimized pear saplings are transplanted to a field, and the growth environment and management strategy of the saplings in the adaptive period are dynamically adjusted through regular health state evaluation; after the adaptive period of the saplings is completed, based on real-time growth data and environment monitoring results, personalized fertilization and irrigation schemes are formulated through data analysis, and nutrition supply in the growing period is regulated and controlled; through personalized management based on real-time data, nutrition supply in the growth period of the pear trees is accurately regulated and controlled, and efficient growth of the pear trees and high quality and high yield of fruits are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, in particular to a high-quality and high-yield cultivation method for pear trees. Background Art

[0002] With the global population growth and the increasing demand for agricultural production, modern agriculture faces the challenge of improving crop yield, quality and sustainability. Precision agriculture and intelligent agricultural technologies have developed rapidly, especially in the field of fruit tree cultivation. More and more research has begun to focus on the combination of genetic improvement and environmental regulation to improve crop yield and quality. As an important economic crop, the high-quality and high-yield cultivation methods of pear trees have received widespread attention. In recent years, the application of genome sequencing and gene editing technology, especially the breakthrough progress of CRISPR / Cas9 technology, has made genetic improvement of pear trees possible. At the same time, the development of the root system is crucial to the growth and development of pear trees. By optimizing the soil physical and chemical properties and microbial communities in the root zone, the root growth of pear seedlings can be effectively promoted, and their stress resistance and adaptability can be improved.

[0003] However, the application of existing technologies in pear cultivation still faces some problems. Traditional cultivation methods rely too much on manual experience and extensive management, lacking scientific growth data support and personalized management solutions. At the same time, the root system is the key part for plants to absorb water and nutrients. However, in actual production, the physical and chemical properties of the soil and the microbial community are difficult to be effectively regulated, resulting in poor root development, which in turn affects plant growth and fruit yield. Although studies have shown that root development can be promoted by improving soil conditions or adding certain specific microbial agents, the effects of these methods are unstable and lack systematic optimization solutions for different tree species and environmental conditions. In addition, the fertilization and irrigation management of pear trees during the growth period usually relies on regular manual monitoring, making it difficult to adjust the nutrient supply strategy in real time, and the fertilization and irrigation management is not accurate, resulting in low efficiency of fertilizer and water utilization, and may even cause problems such as over-irrigation or nutrient imbalance, further limiting the yield and quality improvement of pear trees. Therefore, the existing technology urgently needs a more intelligent and systematic management solution to achieve precise regulation of pear root development and nutrient supply during the growth period. Summary of the invention

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

[0005] Therefore, the present invention provides a high-quality and high-yield cultivation method for pear trees to solve the problems in the prior art that the physical and chemical properties of the soil and the microbial community are difficult to be effectively regulated and the fertilization and irrigation management are not accurate.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a high-quality and high-yield cultivation method for pear trees, which comprises: using genome sequencing and gene editing methods to target and regulate the genetic data of pear tree varieties to obtain genetically modified pear seedlings; cultivating the genetically modified pear seedlings under greenhouse conditions, and optimizing the root development of the pear seedlings by adjusting the soil physical and chemical properties and microbial communities in the root zone; transplanting the optimized pear seedlings to the field, and dynamically adjusting the growth environment and management strategies of the seedlings during the adaptation period through regular health status assessments; after the adaptation period of the seedlings is completed, based on real-time growth data and environmental monitoring results, formulating personalized fertilization and irrigation plans through data analysis to regulate nutrient supply during the growth period.

[0007] As a preferred solution of the high-quality and high-yield cultivation method of pear trees of the present invention, the genome sequencing comprises the following specific steps: Collect healthy leaf samples from target pear tree varieties; Use a commercial plant genomic DNA extraction kit to extract high-quality genomic DNA from leaves; The extracted high-quality DNA was fragmented using a library preparation kit to construct a genome sequencing library; Use a high-throughput sequencing platform and the constructed gene sequencing library to perform whole genome sequencing; The sequencing data were filtered and compared with the pear genome using BWA software. SNP and InDel detection was performed using GATK software to identify potential variation sites associated with the target traits.

[0008] As a preferred embodiment of the method for cultivating high-quality and high-yield pear trees of the present invention, the gene editing method comprises the following specific steps: Based on the genome sequencing data, GA20ox was selected as the target gene for editing; Use CRISPR gRNA design tools to construct gRNA sequences targeting target genes; Use the pKSE401 vector to clone the designed gRNA sequence into the CRISPR / Cas9 expression vector; The constructed CRISPR / Cas9 vector was introduced into the Agrobacterium strain, and the Agrobacterium suspension was co-cultured with the callus tissue of the pear tree for gene transformation; The transformed cells were screened on a medium containing kanamycin to obtain successfully transformed callus tissue; As a preferred solution of the high-quality and high-yield cultivation method of pear trees of the present invention, the method of optimizing the root development of pear seedlings by adjusting the soil physical and chemical properties and microbial communities in the root zone comprises the following specific steps: Install a multi-point sensor network in the greenhouse to monitor environmental factor variables in the soil; The multivariate data fusion algorithm based on Bayesian network is used to model the interaction between different environmental factor variables. The expression is: ; in, For environmental conditions All the data collected The joint probability of For the data points, are environmental factors that affect root growth. For data points In the environment variable The conditional probability under is the index variable, n is the number of environmental factor variables; Collect soil samples to obtain microbial community data; The microbial community data and environmental data were combined, and a machine learning algorithm model was used to analyze the effects of different colonies on the root growth of pear trees. The expression is: ; in, For in time The root growth level at For the Microbial communities The influence function that varies with time, is the weight of each microbial community, Soil environmental factors The influence function on root growth is is the number of microbial species involved in root growth, is the index variable; Influence Function The expression is: ; in, is the most influential constant of the microbial community, is the microbial community efficacy constant, It's in time Time The activity level of the microbial community; Influence Function The expression is: ; in, Representative environmental factor variables in time The state of is the relative weight constant of the environmental factor variable on root growth; Based on the collected environmental data and microbial community model, the greenhouse environment and microbial community are adjusted in real time through the Q-learning reinforcement learning algorithm.

[0009] As a preferred solution of the high-quality and high-yield cultivation method of pear trees of the present invention, the specific steps are as follows: the growth environment and management strategy of the seedlings in the adaptation period are dynamically adjusted through regular health status assessment. Regularly assess the health of acclimatized seedlings through visual inspection, physical measurements, and soil monitoring; Based on the health status assessment results and symptom analysis, diagnose the problems of the seedlings; For existing problems, make targeted adjustments to the growth environment and management strategies; After the adjustment is completed, the inspection content, problems found, measures taken and impacts caused during the assessment are recorded to generate a pear seedling health inspection report.

[0010] As a preferred embodiment of the method for cultivating high-quality and high-yield pear trees of the present invention, the visual inspection, physical measurement and soil monitoring are specifically performed as follows: Assess the appearance characteristics and pest and disease conditions of the seedlings; Observe the damage to the branches and leaves of the seedlings; Check seedlings for abnormal growth patterns; The growth parameters of the seedlings were measured by laser rangefinders and leaf area meters to quantify the growth of the seedlings; Dig and observe the root system to check its development; Detect the physical and chemical data of soil composition.

[0011] As a preferred embodiment of the method for cultivating high-quality and high-yield pear trees of the present invention, the completion of the adaptation period of the seedlings means that: The growth rate of the seedlings tends to be stable, and no longer shows the phenomenon of growth stagnation and abnormally rapid growth; The leaves, roots and stems of the seedlings are generally healthy, with no signs of disease or nutrient deficiency. The seedlings' ability to adapt to changes in the external environment is enhanced and they can withstand fluctuations in the external environment.

[0012] As a preferred solution of the high-quality and high-yield cultivation method of pear trees of the present invention, wherein: based on the real-time growth data and environmental monitoring results, a personalized fertilization and irrigation plan is formulated through data analysis to regulate the nutrient supply during the growth period, the specific steps are: Install multiple point sensors and weather stations in the fields to monitor environmental data in real time; The growth data of the seedlings were measured regularly using a laser rangefinder; Combining the growth data of the seedlings with the environmental monitoring data, the amount of nitrogen, phosphorus and potassium fertilizers used is calculated as follows: ; in, For at the moment The amount of fertilizer required, For the The weight coefficient of the fertilizer, For the The active ingredient content of the fertilizer, For the The soil supply of fertilizers, For the The growth requirement coefficient of the fertilizer, is the index variable, is the time variable; Combining the growth data of the seedlings with the environmental monitoring data, the required irrigation amount of the soil is calculated, and the expression is: ; in For the moment The amount of irrigation required, For the Water demand coefficient for each soil type, For the Soil type at time The area occupied by For the The water reserve of each soil type, is the number of soil types; Through real-time data feedback, we can monitor the growth progress of seedlings and environmental changes, and adjust the plan in time.

[0013] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for cultivating high-quality and high-yield pear trees as described in the first aspect of the present invention is implemented.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by a processor, implements any step of the method for cultivating high-quality and high-yield pear trees as described in the first aspect of the present invention.

[0015] The beneficial effects of the present invention are as follows: the present invention optimizes the genetic background of pear trees through genome sequencing and gene editing technology, and cultivates pear seedlings with high-quality and high-yield potential; by adjusting the soil conditions and microbial communities in the root zone, the root development of the seedlings is further optimized, providing a good foundation for their transplanting and subsequent growth; through dynamic health assessment and management strategy adjustment, the healthy growth of the seedlings is ensured, and the uncertainty in the growth process is reduced; through personalized management based on real-time data, the nutrient supply of the pear trees during the growth period is accurately regulated, ensuring the efficient growth of the pear trees and the high-quality and high-yield of the fruits. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 The flowchart of the high-quality and high-yield cultivation method of pear trees in Example 1 is shown.

[0018] Figure 2 Schematic diagram of the system for cultivating high-quality and high-yield pear trees in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a high-quality and high-yield cultivation method for pear trees, comprising the following steps: S1. Use genome sequencing and gene editing methods to target and regulate the genetic data of pear varieties to obtain genetically improved pear seedlings.

[0023] Going a step further, genome sequencing is used, and the specific steps are: Healthy leaf samples were collected from the target pear tree varieties. By extracting genetic information from healthy plants, the quality of the genome analysis data could be ensured.

[0024] Use a commercial plant genomic DNA extraction kit to extract high-quality genomic DNA from leaves, simplify the DNA extraction process, and reduce errors caused by human operation.

[0025] The extracted high-quality DNA is fragmented using a library preparation kit (such as the Illumina library preparation kit) to construct a genomic sequencing library.

[0026] It should be noted that fragmentation refers to the process of breaking the extracted intact DNA molecules into smaller fragments through mechanical shearing or enzymatic cleavage. Constructing a genome sequencing library refers to converting fragmented DNA into a library format suitable for high-throughput sequencing through processes such as end repair, adding adapters, and PCR amplification. By constructing a library, it is finally possible to extract valuable fragment information from a complex genome, which can be read and generate data through a high-throughput sequencing platform.

[0027] Using a high-throughput sequencing platform (such as Illumina NovaSeq 6000), whole genome sequencing is performed based on the constructed gene sequencing library.

[0028] The sequencing data were filtered and compared with the pear genome using BWA software. SNP and InDel detection was performed using GATK software to identify potential variation sites associated with the target traits.

[0029] It should be noted that filtering sequencing data refers to the process of removing low-quality reads and sequencing errors from the raw data in FASTQ format generated by the sequencing platform. This includes using tools such as Trimmomatic to remove low-quality bases with a quality value lower than Q30 from the 3' or 5' end of the reads, identifying and removing residual adapter sequences in the reads, and filtering out short reads by setting a minimum length threshold (such as 50bp). The filtered reads will be output as a new FASTQ file for subsequent alignment.

[0030] Going a step further, a gene editing method is used, and the specific steps are: According to the genome sequencing data, GA20ox or ACS, DELLA, etc. are selected as target genes for editing.

[0031] Use a CRISPR gRNA design tool (such as CHOPCHOP) to input the sequence of the target gene, and the tool will generate multiple potential gRNA sequence candidates. After obtaining the candidate gRNA sequence, use the CRISPRdirect software to perform off-target analysis on the selected gRNA, evaluate its possible off-target sites, and further select the gRNA sequence with the smallest off-target effect. Finally, synthesize the target gRNA sequence by chemical synthesis or in vitro transcription.

[0032] Use the pKSE401 vector and digest it with a restriction enzyme (such as BsaI or BsmBI) to open the multi-cloning site of the vector. Use the same restriction enzyme to digest the gRNA so that both ends have suitable sticky ends. Then, use T4 DNA ligase to insert the gRNA sequence into the multi-cloning site of the pKSE401 vector. Transform the ligated vector into E. coli (such as DH5α or TOP10 strains). Screen the clones that have been successfully transformed by culturing.

[0033] Select an Agrobacterium strain suitable for plant gene transformation, and introduce the recombinant CRISPR / Cas9 vector into Agrobacterium through heat shock or electroporation. Mix the cultured Agrobacterium suspension with the callus tissue and co-cultivate under appropriate temperature and time conditions (usually co-cultivate at 22-28°C for 48 hours). Agrobacterium transfers the target gene into the pear tree cells through its T-DNA system to complete the gene transformation.

[0034] The transformed cells were screened on a culture medium containing kanamycin to obtain successfully transformed callus tissue.

[0035] The selected transformed callus was regenerated into genetically modified pear seedlings through tissue culture methods.

[0036] It should be noted that the precise improvement of pear varieties has been achieved through the comprehensive application of genome sequencing and genome editing, combined with efficient gene transformation and tissue culture methods. The rational design and implementation of each step not only improves the accuracy of genetic data and editing efficiency, but also ensures that the genetically modified pear seedlings obtained in the end have stable trait improvement effects. By optimizing these processes, breeding efficiency has been greatly improved, the uncertainty and time cost in the traditional breeding process have been reduced, and strong support has been provided for the improvement and industrial application of pear varieties.

[0037] S2. Cultivate the genetically modified pear seedlings under greenhouse conditions and optimize the root development of the pear seedlings by adjusting the soil physical and chemical properties and microbial communities in the root zone.

[0038] Furthermore, the root development of pear seedlings can be optimized by adjusting the soil physical and chemical properties and microbial communities in the root zone. The specific steps are as follows: Install a multi-point sensor network in the greenhouse to monitor environmental variables in the soil, such as temperature, humidity, pH, nutrient content, salt, organic matter, etc. The multivariate data fusion algorithm based on Bayesian network is used to model the interaction between different environmental factor variables. The expression is: ; in, For environmental conditions All the data collected The joint probability of For the data points, are environmental factors that affect root growth. For data points In the environment variable The conditional probability under is the index variable, n is the number of environmental factor variables; It represents the combined effects of all environmental factors on root growth, and estimates the effect of overall environmental conditions on root growth through the product of conditional probabilities. It identifies the potential interaction effects between different environmental factors, helping to reveal which factors have the greatest impact on root growth and which factors may interact to optimize the root zone environment.

[0039] Collect soil samples to obtain microbial community data, including composition, abundance, diversity, functional characteristics, and physiological characteristics of microbial species; The microbial community data and environmental data were combined, and the dynamic system model and machine learning algorithm model were used to analyze the effects of different colonies on the root growth level of pear trees. The expression is: ; in, For in time The root growth level at For the Microbial communities Time-varying influence functions that reflect how the activity of the microbial community changes over time and thus affects root growth. is the weight of each microbial community. Soil environmental factors The influence function on root growth reflects the actual effect of different environmental variables in the soil (such as pH, humidity, nutrients, etc.) on root growth. is the number of microbial species involved in root growth, is the index variable; Influence Function The expression is: ; in, is the maximum influence constant of the microbial community, indicating the maximum impact value that the community can have on root growth. The size of reflects the potential effect of the microbial community on root growth, which can be estimated based on experimental data and the functional characteristics of the microorganisms. is the microbial community efficacy constant, The larger the value, the more sensitive the microbial community is to the root growth. Microbial communities with less activity can also play a role quickly. It can be set by the physiological characteristics of the microbial community. It's in time Time The activity level of a microbial community is related to its number, metabolic activity or function, which can be expressed by gene expression, enzyme activity, etc. Influence Function The expression is: ; in, Representative environmental factor variables in time The state of the soil at that time, including soil pH, temperature, humidity, oxygen content, nutrient concentration, etc. It is the relative weight constant of the environmental factor variable to root growth. Each constant represents the relative contribution of the corresponding environmental factor to root growth.

[0040] Based on the collected environmental data and microbial community model, the greenhouse environment and microbial community are adjusted in real time through the Q-learning reinforcement learning algorithm, specifically: Define the environment state space , is the set of all relevant variables in the greenhouse, which can be expressed as a vector, ; in, are environmental factors (such as soil pH, moisture, nutrient concentration, etc.), while It is the variable of the microbial community (such as the abundance and activity of microbial species).

[0041] Defining the Action Space , is the specific set of actions that the agent can take, which can be expressed as, ; Define a reward function based on the root growth level , which can be expressed as, ; in, is an indicator of root health (such as root metabolic activity, water absorption capacity, etc.), is the weight of the root system, is the length of the root system.

[0042] For each action, the reward function is dynamically evaluated based on the performance of the root system. Assuming that when the root system performs well, the reward Increase, otherwise decrease.

[0043] Initialize Q-table, each of which Values ​​are represented in the state Next action The total reward expected. Initially, the Q value can be set to zero.

[0044] At each time step, according to the current state Select an action , the strategy used here is the ε-greedy strategy (ε-greedy).

[0045] Execute the selected action , and observe the new state and the rewards received (i.e., the manifestation of root growth).

[0046] Update the Q value based on the observed new state and reward. The Q value update formula is: ; in, is the learning rate, which controls the speed of Q value update. is the discount factor that controls the impact of future rewards. For the next state The maximum Q value under .

[0047] Repeat the learning process and continuously adjust the Q value to optimize the action strategy. As learning progresses, we will gradually find out which environmental regulation and microbial community management strategies are most beneficial to root growth, thereby continuously improving the environmental conditions and microbial community configuration in the greenhouse.

[0048] It should be noted that this process systematically optimizes the root growth environment of pear seedlings through an innovative multi-step integration scheme, which has significant application prospects and beneficial effects. The interaction of environmental factors is modeled through the Bayesian network algorithm, which further reveals the complex relationship between different soil variables, making the optimization measures more targeted and scientific. Secondly, combined with the data analysis of microbial communities, the dynamic system model and machine learning algorithm are used to deeply explore the role of microbial communities in root development. Through this data-driven approach, not only can the contribution of microbial communities to root growth be accurately evaluated, but also the configuration of microbial communities can be dynamically adjusted to optimize root health. Most importantly, the Q-learning reinforcement learning algorithm is used to intelligently adjust the greenhouse environment and microbial communities in real time, making the entire optimization process adaptive. Through multi-level and intelligent optimization methods, the precise regulation of the root growth environment of pear seedlings is successfully achieved, and the root health and development efficiency are improved. This scheme can not only be applied to the root optimization of pear seedlings, but also can be extended to the root growth management of other crops, with broad application prospects and good market value.

[0049] S3. Transplant the optimized pear seedlings to the field, and dynamically adjust the growth environment and management strategies of the seedlings during the adaptation period through regular health status assessments.

[0050] Furthermore, through regular health status assessment, the growth environment and management strategy of the saplings in the adaptation period can be dynamically adjusted. The specific steps are as follows: Regularly assess the health of acclimatized seedlings through visual inspection, physical measurements, and soil monitoring; Further, visual inspection, physical measurement and soil monitoring, the specific steps are as follows, Visually inspect the surface condition of the seedlings, including the health of the branches and leaves, and identify whether there are symptoms such as disease spots, yellowing, and wilting.

[0051] Assess whether the seedlings have physical damage, such as wounds and cracks caused by wind, impact, or pests.

[0052] Check for abnormal growth patterns, such as stagnation, skewed growth, etc.

[0053] The height, crown width, breast diameter, leaf area, leaf color and other data of the seedlings were measured by laser rangefinders and leaf area meters to quantify the growth of the seedlings. Dig and observe the root system to check the root distribution, density, length and whether there are root rot and other diseases; Detect the physical and chemical data of soil components, including soil pH, electrical conductivity, nutrient content (such as nitrogen, phosphorus, potassium, etc.), organic matter content, salt concentration, etc.

[0054] Comprehensively analyze the data obtained through visual inspection, physical measurement and soil monitoring. For example, based on the appearance characteristics of the seedlings (leaf color, shape) and soil data, it is possible to identify problems such as pests and diseases or malnutrition that the seedlings may encounter. According to the symptoms of the seedlings (such as leaf dryness, yellowing, root rot, etc.), combined with the knowledge base of pests and diseases and plant physiology models, a more accurate diagnosis of the health status of the seedlings can be made, and potential problems can be quickly located by prescribing the right medicine for the right disease; Based on the health assessment and diagnosis results, combined with the specific needs of the seedlings, the field environment is finely adjusted. For example, if the soil pH is found to be too high or too low, the soil pH can be adjusted by applying appropriate acid-base regulators; if the seedlings are found to lack certain necessary nutrients, fertilizers can be added in a timely manner, irrigation strategies can be adjusted, or light conditions can be optimized based on the diagnosis results. If the seedlings are facing pest and disease problems, appropriate pest and disease control measures can be selected based on symptom analysis, including physical control (such as setting up barriers, insect traps, etc.), biological control (such as introducing beneficial insects) or chemical control (such as spraying pesticides).

[0055] After the adjustment is completed, a pear seedling health inspection report is generated. The report should record in detail the content of the health assessment, the health problems of the seedlings, the adjustment measures taken, and the impact after the adjustment (such as root growth, changes in seedling growth, etc.). The report should cover key indicators and change trends to facilitate subsequent tracking of the growth status of the seedlings.

[0056] It should be noted that the above steps, through scientific health assessment and precise dynamic adjustment, not only ensure that the pear seedlings can obtain the best growth environment during the adaptation period, but also improve the growth efficiency and disease and pest resistance of the seedlings through refined management. The core advantage of this method is that it accurately identifies problems and adjusts the environment in a timely manner through comprehensive data analysis and targeted treatment, ultimately achieving efficient and sustainable seedling management. This systematic and dynamically adjusted health assessment method provides a new idea for agricultural production, which helps to improve overall production efficiency and reduce resource waste.

[0057] S4. After the seedlings have completed the adaptation period, based on real-time growth data and environmental monitoring results, personalized fertilization and irrigation plans are formulated through data analysis to regulate the nutrient supply during the growth period.

[0058] Furthermore, the completion of the adaptation period of the seedlings means that, The growth rate of the seedlings tends to be stable, and no longer shows growth stagnation or abnormally rapid growth. Usually, seedlings may grow slowly or overgrow in the initial adaptation period due to environmental discomfort. After the adaptation period is completed, the growth of the seedlings enters a stable period, showing a more uniform and stable growth curve.

[0059] The main parts of the saplings, such as leaves, roots and stems, are in good health, with no signs of pests, diseases, nutrient deficiencies or other growth abnormalities. Healthy leaves have no spots or yellowing, roots are evenly distributed and free of rot, and the stems have no obvious damage or deformation.

[0060] The saplings have an enhanced ability to adapt to the external environment and can better withstand external environmental fluctuations (such as temperature changes, humidity fluctuations, light changes, etc.). This means that the saplings' physiological activities (such as photosynthesis, respiration, etc.) have returned to normal levels, and their stress resistance has increased, allowing them to cope with common environmental changes without affecting their growth.

[0061] Furthermore, based on real-time growth data and environmental monitoring results, personalized fertilization and irrigation plans are formulated through data analysis to regulate the nutrient supply during the growth period. The specific steps are as follows: Multi-point sensors and weather stations are installed in the fields to monitor soil temperature, humidity, pH value, conductivity and other environmental parameters in real time, and simultaneously obtain meteorological data (such as temperature, humidity, precipitation, wind speed, etc.).

[0062] Use a laser rangefinder (such as LIDAR) to regularly measure growth parameters such as seedling height, crown width, stem diameter, etc.

[0063] Combining the growth data of the seedlings with the environmental monitoring data, the amount of nitrogen, phosphorus and potassium fertilizers used is calculated as follows: ; in, For at the moment The amount of fertilizer required, For the The weight coefficient of a fertilizer reflects the relative importance of the fertilizer. For the The active ingredient content of the fertilizer, For the The soil supply of fertilizers, For the The growth requirement coefficient of a fertilizer indicates the amount of fertilizer required by the seedling at a specific growth stage. is the index variable, It is a time variable. The application amount of each fertilizer can be accurately calculated to ensure that the seedlings get the right amount of nitrogen, phosphorus and potassium nutrients at different growth stages, avoiding over-fertilization or insufficient nutrition.

[0064] Combining the growth data of the seedlings with the environmental monitoring data, the required irrigation amount of the soil is calculated, and the expression is: ; in For the moment The amount of irrigation required, For the The water demand coefficient of different soil types reflects the water evaporation and absorption characteristics of different soil types. For the Soil type at time The area covered by For the The water reserve of each soil type, is the number of soil types. Through this formula, the irrigation amount can be accurately calculated according to the needs and water storage capacity of different soil types, avoiding excessive or insufficient irrigation and optimizing the efficiency of water resource use.

[0065] Through real-time data feedback, the growth progress of seedlings and environmental changes are monitored, and fertilization and irrigation plans are adjusted in a timely manner. The system continuously monitors the growth dynamics of seedlings, analyzes the effects of fertilization and irrigation, and makes adjustments based on environmental data.

[0066] It should be noted that this solution achieves precise control of fertilization and irrigation during the growth cycle of seedlings through real-time monitoring and data analysis, solving problems such as excessive fertilization, insufficient irrigation, and waste of resources in traditional agricultural management. Through a flexible adjustment mechanism, the resource utilization efficiency of agricultural production is improved, the healthy growth of seedlings is promoted, and their adaptability and resistance are enhanced. This technical solution not only provides an innovative intelligent solution for agricultural management, but also provides strong support for sustainable agricultural development.

[0067] This embodiment also provides a computer device suitable for the method of cultivating high-quality and high-yield pear trees, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of cultivating high-quality and high-yield pear trees as proposed in the above embodiment.

[0068] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0069] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for cultivating high-quality and high-yield pear trees proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0070] In summary, the present invention optimizes the genetic background of pear trees through genome sequencing and gene editing technology, and cultivates pear seedlings with high-quality and high-yield potential; further optimizes the root development of seedlings by adjusting the soil conditions and microbial communities in the root zone, providing a good foundation for their transplanting and subsequent growth; ensures the healthy growth of seedlings and reduces uncertainty in the growth process through dynamic health assessment and management strategy adjustment; and accurately regulates the nutrient supply of pear trees during their growth period through personalized management based on real-time data, ensuring the efficient growth of pear trees and high-quality and high-yield fruits.

[0071] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a high-quality and high-yield cultivation method for pear trees are provided.

[0072] First, healthy leaf samples were collected from the target pear tree varieties, and high-quality genomic DNA was extracted using a commercial plant genomic DNA extraction kit. The constructed genome sequencing library was sequenced by high-throughput sequencing using the Illumina NovaSeq 6000 platform, and the sequencing data was compared with the pear reference genome using the BWA software. The GATK software was used for SNP and InDel detection to identify gene variation sites related to pear growth, resistance, and fruit quality. Based on the sequencing data, the GA20ox gene related to pear growth regulation was selected for targeted editing. The CRISPR / Cas9 system was used to construct a specific gRNA sequence, and the CRISPR / Cas9 vector was introduced into the pear callus by Agrobacterium-mediated transformation. The successfully transformed callus was screened and regenerated into genetically improved pear seedlings.

[0073] The genetically modified pear seedlings were transplanted to a greenhouse, where a multi-point sensor network was installed to monitor the soil physical and chemical properties and microbial community composition in the root zone in real time. The Bayesian network model was used to analyze the effects of different environmental variables on root development, and combined with the data of microbial communities, the root zone microbial environment was optimized to promote root growth.

[0074] After the genetically modified pear seedlings grow to a certain stage in the greenhouse, they are transplanted to the field environment and the health status of the seedlings is regularly evaluated, including the morphology of branches and leaves, root development and growth status. According to the results of the health assessment, the growth environment and management strategies of the seedlings are adjusted in time. After the transplant adaptation period is completed, based on real-time monitoring data and seedling growth conditions, personalized fertilization and irrigation plans are adopted to ensure precise regulation of nutrient supply. Through the sensor network and weather station, combined with the growth data of the seedlings and the soil environment data, nitrogen, phosphorus, potassium fertilizers and irrigation water are dynamically calculated to ensure that the seedlings are in the best growth state.

[0075] The details are shown in Table 1 below: Table 1 Experimental data comparison record Parameter name Gene-edited pear seedlings Traditional sexual hybrid breeding of pear seedlings Unoptimized root zone conditions Optimizing root zone conditions Tree height (cm) 120 95 85 110 Root length (cm) 35 25 22 30 Chlorophyll content (SPAD) 45 38 32 43 Fruit weight (g / piece) 150 120 110 140 Root zone microbial diversity index (Shannon) 2.7 2.3 2.0 2.5 Soil pH 6.8 6.5 6.3 6.7 Nitrogen absorption rate (%) 75 60 55 70 Transplant survival rate (%) 95 85 80 92 Through comparative analysis of the above experimental data, it can be clearly seen that the gene-edited pear seedlings are superior to traditionally bred pear seedlings in multiple key growth parameters, and the growth performance of the pear seedlings is further improved by optimizing the root zone conditions.

[0076] First, in terms of tree height and root length, the gene-edited pear seedlings reached 120 cm and 35 cm, respectively, while the traditional breeding pear seedlings were only 95 cm and 25 cm. Secondly, the chlorophyll content of the gene-edited pear seedlings was 45 SPAD units and the fruit weight was 150g, which were about 18% and 25% higher than the 38 SPAD units and 120g of traditional breeding, respectively. In addition, under the optimized root zone conditions, the nitrogen absorption rate of the gene-edited pear seedlings reached 75%, while it was only 55% under unoptimized conditions. The diversity index of root zone microorganisms also increased from 2.0 to 2.7, indicating that the optimized microbial community played a positive role in promoting root health and growth. The data on transplant survival rate are also very eye-catching. Under the optimized root zone conditions, the transplant survival rate of the gene-edited pear seedlings reached 95%, while the survival rate of traditional breeding was only 85%, and even lower to 80% under unoptimized conditions. This shows that through the dual effects of gene editing and root zone optimization, the stress resistance and environmental adaptability of the seedlings have been enhanced, and they can better adapt to changes in the field environment.

[0077] From the data analysis, it can be seen that the present invention has successfully improved the growth traits of pear tree varieties through genome sequencing and gene editing technology, and further enhanced the nutrient absorption capacity and stress resistance of seedlings by optimizing the root zone environment. Compared with traditional breeding methods, this method shows significant advantages in seedling growth, fruit yield and transplant survival rate, proving that it has broad application prospects and economic benefits in actual agricultural production.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for cultivating high-quality and high-yield pear trees, characterized in that: include: Use genome sequencing and gene editing methods to target and regulate the genetic data of pear tree varieties to obtain genetically improved pear seedlings; The genetically modified pear seedlings were cultivated under greenhouse conditions to optimize the root development of the pear seedlings by regulating the soil physicochemical properties and microbial community in the root zone; Transplant the optimized pear seedlings to the field, and dynamically adjust the growth environment and management strategies of the seedlings during the adaptation period through regular health status assessment; After the seedlings complete the adaptation period, personalized fertilization and irrigation plans are formulated through data analysis based on real-time growth data and environmental monitoring results to regulate the nutrient supply during the growth period.

2. The high-quality and high-yield cultivation method of pear trees as claimed in claim 1, characterized in that: The genome sequencing comprises the following specific steps: Collect healthy leaf samples from target pear tree varieties; Use a commercial plant genomic DNA extraction kit to extract high-quality genomic DNA from leaves; The extracted high-quality DNA was fragmented using a library preparation kit to construct a genome sequencing library; Use a high-throughput sequencing platform and the constructed gene sequencing library to perform whole genome sequencing; The sequencing data were filtered and compared with the pear genome using BWA software. SNP and InDel detection was performed using GATK software to identify potential variation sites associated with the target traits.

3. The high-quality and high-yield cultivation method of pear trees as claimed in claim 2, characterized in that: The gene editing method comprises the following specific steps: Based on the genome sequencing data, GA20ox was selected as the target gene for editing; Use CRISPR gRNA design tools to construct gRNA sequences targeting target genes; Use the pKSE401 vector to clone the designed gRNA sequence into the CRISPR / Cas9 expression vector; The constructed CRISPR / Cas9 vector was introduced into the Agrobacterium strain, and the Agrobacterium suspension was co-cultured with the callus tissue of the pear tree for gene transformation; The transformed cells were screened on a medium containing kanamycin to obtain successfully transformed callus tissue; The selected transformed callus was regenerated into genetically modified pear seedlings through tissue culture methods.

4. The high-quality and high-yield cultivation method of pear trees as claimed in claim 3, characterized in that: The method optimizes the root development of pear seedlings by adjusting the soil physical and chemical properties and microbial community in the root zone, and the specific steps are: Install a multi-point sensor network in the greenhouse to monitor environmental factor variables in the soil; The multivariate data fusion algorithm based on Bayesian network is used to model the interaction between different environmental factor variables. The expression is: ； in, For environmental conditions All the data collected The joint probability of For the data points, are environmental factors that affect root growth. For data points In environment variables The conditional probability under is the index variable, n is the number of environmental factor variables; Collect soil samples to obtain microbial community data; The microbial community data and environmental data were combined, and a machine learning algorithm model was used to analyze the effects of different colonies on the root growth of pear trees. The expression is: ； in, For in time The root growth level at For the Microbial communities The influence function that varies with time, is the weight of each microbial community, Soil environmental factors The influence function on root growth is is the number of microbial species involved in root growth, is the index variable; Influence Function The expression is: ； in, is the most influential constant of the microbial community, is the microbial community efficacy constant, It's in time Time The activity level of the microbial community; Influence Function The expression is: ； in, Representative environmental factor variables in time The state of is the relative weight constant of the environmental factor variable on root growth; Based on the collected environmental data and microbial community model, the greenhouse environment and microbial community are adjusted in real time through the Q-learning reinforcement learning algorithm.

5. The high-quality and high-yield cultivation method of pear trees as claimed in claim 4, characterized in that: The specific steps of dynamically adjusting the growth environment and management strategy of the saplings in the adaptation period through regular health status assessment are as follows: Regularly assess the health of acclimatized seedlings through visual inspection, physical measurements, and soil monitoring; Based on the health status assessment results and symptom analysis, diagnose the problems of the seedlings; For existing problems, make targeted adjustments to the growth environment and management strategies; After the adjustment is completed, the inspection content, problems found, measures taken and impacts caused during the assessment are recorded to generate a pear seedling health inspection report.

6. The high-quality and high-yield cultivation method of pear trees as claimed in claim 5, characterized in that: The visual inspection, physical measurement and soil monitoring, specifically, are as follows: Assess the appearance characteristics and pest and disease conditions of the seedlings; Observe the damage to the branches and leaves of the seedlings; Check seedlings for abnormal growth patterns; The growth parameters of the seedlings were measured by laser rangefinders and leaf area meters to quantify the growth of the seedlings; Dig and observe the root system to check its development; Detect the physical and chemical data of soil composition.

7. The high-quality and high-yield cultivation method of pear trees as claimed in claim 6, characterized in that: The completion of the adaptation period of the seedlings means that: The growth rate of the seedlings tends to be stable, and no longer shows the phenomenon of growth stagnation and abnormally rapid growth; The leaves, roots and stems of the seedlings are generally healthy, with no signs of disease or nutrient deficiency. The seedlings' ability to adapt to changes in the external environment is enhanced and they can withstand fluctuations in the external environment.

8. The high-quality and high-yield cultivation method of pear trees as claimed in claim 7, characterized in that: Based on the real-time growth data and environmental monitoring results, a personalized fertilization and irrigation plan is formulated through data analysis to regulate the nutrient supply during the growth period. The specific steps are: Install multiple point sensors and weather stations in the fields to monitor environmental data in real time; The growth data of the seedlings were measured regularly using a laser rangefinder; Combining the growth data of the seedlings with the environmental monitoring data, the amount of nitrogen, phosphorus and potassium fertilizers used is calculated as follows: ； in, For at the moment The amount of fertilizer required, For the The weight coefficient of the fertilizer, For the The active ingredient content of the fertilizer, For the The soil supply of fertilizers, For the The growth requirement coefficient of the fertilizer, is the index variable, is the time variable; Combining the growth data of the seedlings with the environmental monitoring data, the required irrigation amount of the soil is calculated, and the expression is: ； in For the moment The amount of irrigation required, For the Water demand coefficient for each soil type, For the Soil type at time The area covered by For the The water reserve of each soil type, is the number of soil types; Through real-time data feedback, we can monitor the growth progress of seedlings and environmental changes, and adjust the plan in time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for cultivating high-quality and high-yield pear trees described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for cultivating high-quality and high-yield pear trees according to any one of claims 1 to 8 are implemented.

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