A method, device and medium for screening a new characteristic watermelon variety suitable for light and heat conditions in arid areas

Abstract

The application discloses a screening method, equipment and medium for a new characteristic watermelon variety suitable for light and heat conditions in arid areas, and relates to the technical field of agricultural biological breeding. The method comprises the following steps: firstly, an edited parent with synergistic expression of stress resistance and quality is created by knocking out a watermelon antagonistic gene site by using a gene editing technology; a genetic diversity population is constructed by positive and reverse crossing; root and leaf samples are collected during a key growth period; micro-traits are detected by using a high-throughput platform; a correlation model of the micro-traits, stress resistance and quality is established; and excellent single plants are screened. Finally, a BP neural network model is constructed, target arid area microhabitat parameters are taken as inputs, yield, quality and stress resistance are taken as outputs, the model is trained and verified; the environmental adaptability of screened individuals is predicted by using the model; and finally, excellent watermelon varieties meeting specific planting conditions are obtained. The application can realize systematic improvement of stress resistance, quality and regional adaptability of watermelon varieties under extreme drought, light and heat conditions.

Description

Technical Field

[0001] This invention relates to the field of agricultural bio-breeding technology, and in particular to a method, equipment, and medium for screening new watermelon varieties suitable for light and heat conditions in arid regions. Background Technology

[0002] Although existing technologies can breed basic drought-resistant varieties, the following structural defects still exist under extreme light and heat conditions such as the edge of the Taklamakan Desert (annual average precipitation <50mm, surface temperature >70℃, diurnal temperature range >20℃): Phenotypic screening indicators are one-sided, and recessive stress resistance genes are severely lost. Current technologies use macroscopic phenotypes such as "survival rate" and "fruit cracking rate" as core indicators, but fail to cover key microscopic traits. Desert sandy areas have high permeability, requiring screening for plants with well-developed lateral roots (number of lateral roots > 50 / plant) and high root hair density (> 200 roots / mm²). 2 To enhance water absorption capacity, strains with low stomatal density (<80 pores / mm²) should be selected; at high temperatures, strains with low stomatal density should be selected. 2 However, varieties with adjustable leaf opening (daytime opening > 3 μm, nighttime opening < 1 μm) are needed to reduce transpiration and water loss. Lines with high thylakoid membrane stability (PSII activity decrease rate < 20% at high temperatures) need to be screened to maintain carbon assimilation capacity. These microscopic traits need to be measured using equipment such as electron microscopy and chlorophyll fluorescence spectrometry. Current technologies, due to cost limitations, can only employ destructive sampling (e.g., root scanning requires digging up the entire plant), resulting in screening efficiency of less than 10% and a loss rate of over 65% for recessive stress-resistance genes (such as the LeRHL1 gene controlling root hair development).

[0003] The environmental adaptability evaluation model is crude, and the promotion of varieties carries high risks. Existing technologies use a "3-year multi-location trial" as the verification standard, but fail to consider the extreme heterogeneity of desert marginal microhabitats: dune slope aspect: sunny slope (light intensity > 1200 μmol / m²). 2 / s) and shady slope (<800 μmol / m 2 Temperature differences of up to 15℃ can lead to variations in fruit sugar accumulation exceeding 30%; groundwater level: shallow groundwater (<2m) can cause root hypoxia, necessitating the selection of varieties with high root oxidase activity (cytochrome oxidase activity >50nmolO2 / min / mg protein); wind erosion intensity: in strong wind areas (wind speed >10m / s), varieties with high stem mechanical strength (fracture stress >15MPa) are required to prevent lodging. Existing evaluation models only use average statistical methods and have not established dynamic adaptability prediction models based on microhabitat parameters (such as light integral and soil moisture content fluctuations), resulting in yield reductions (up to 50%) after variety promotion due to unsuitable local environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method, equipment, and medium for screening new watermelon varieties suitable for light and heat conditions in arid regions, aiming to solve or improve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following solution: A method for screening new specialty watermelon varieties suitable for arid region light and heat conditions includes: By using gene editing technology to knock out base pairs that form key antagonistic regulatory sites in the target watermelon gene, an edited parent with synergistic expression of stress resistance and quality was obtained; By performing reciprocal crosses between edited and unedited parents, various hybrid combinations were constructed to form a hybrid offspring population with rich genetic diversity. During the critical growth period of the hybrid offspring population, root and leaf samples were collected, and high-throughput screening platforms were used to detect the microscopic traits of the samples. A correlation model between microscopic traits and stress resistance and quality was established to screen out watermelon individuals that meet the set stress resistance and quality requirements. A BP neural network model was constructed, with the microhabitat parameters of the target arid planting area as input variables and watermelon yield, quality and stress resistance indicators as output variables. The model was trained and validated to obtain a well-trained prediction model. The selected watermelon individuals were processed using a trained prediction model to predict their adaptability under different microenvironments, and watermelon varieties that met the environmental planting conditions in terms of stress resistance, quality and adaptability were selected.

[0006] Optionally, the target watermelon gene is derived from the drought-resistant variety "Xinmi No. 1" and / or the high-sugar, heat-resistant variety "Black Beauty".

[0007] Optionally, the microscopic traits include the number of lateral roots, root hair density, root length, root diameter, stomatal density, stomatal aperture, chloroplast thylakoid membrane stability, and PSII activity.

[0008] Optionally, the high-throughput screening platform integrates a miniature CT scanner, a thermal imager, a chlorophyll fluorometer, and an image analysis system.

[0009] Optionally, the training process of the BP neural network model specifically includes: Acquire training data; the training data includes microhabitat parameters of the target training area and corresponding label data; the label data includes watermelon yield, quality, and stress resistance indicators; The training data is input into the constructed BP neural network model, and the training is carried out with the goal of minimizing the loss between the microenvironment parameters of the target training area and the label data. The model that meets the training requirements is determined as the trained prediction model.

[0010] Optionally, it also includes: continuously monitoring the finally selected watermelon varieties, collecting actual performance data during planting, and feeding the collected data back to the prediction model for optimization and iteration.

[0011] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the screening method for new watermelon varieties with suitable light and heat conditions in arid areas as described above.

[0012] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the screening method for new watermelon varieties suitable for arid region light and heat conditions as described above.

[0013] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a method, equipment, and medium for screening new watermelon varieties suitable for the light and heat conditions of arid regions. The method includes: using gene editing technology to knock out base pairs that form key antagonistic regulatory sites in the target watermelon gene to obtain edited parents with synergistic expression of stress resistance and quality; performing reciprocal crosses between the edited and unedited parents to construct various hybrid combinations, forming a hybrid progeny population with rich genetic diversity; collecting root and leaf samples during the critical growth period of the hybrid progeny population, using a high-throughput screening platform to detect the microscopic traits of the samples, and establishing a correlation model between microscopic traits and stress resistance and quality to screen out watermelon individuals that meet the set stress resistance and quality requirements; constructing a BP neural network model, using the micro-habitat parameters of the target arid planting area as input variables, and watermelon yield, quality, and stress resistance indicators as output variables, training and validating the model to obtain a trained predictive model; using the trained predictive model to process the screened watermelon individuals, predicting the adaptability of watermelon individuals under different micro-habitats, and screening out watermelon varieties that meet the environmental planting conditions in terms of stress resistance, quality, and adaptability. This invention enables the systematic improvement of the stress resistance, quality, and regional adaptability of watermelon varieties under extreme drought and light and heat conditions by constructing a parent library for the co-expression of stress resistance and quality traits, developing a high-throughput micro-trait screening platform, and establishing a dynamic prediction model for micro-habitat adaptability. It provides a highly stable and commercially valuable specialty watermelon breeding technology solution for ecologically fragile areas such as the edge of the Taklamakan Desert. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the screening method for new watermelon varieties suitable for arid region light and heat conditions, as described in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The purpose of this invention is to provide a method, equipment, and medium for screening new watermelon varieties suitable for light and heat conditions in arid regions, aiming to solve or improve at least one of the above-mentioned technical problems.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] As a first aspect, such as Figure 1 As shown, this invention provides a method for screening new specialty watermelon varieties suitable for the light and heat conditions of arid regions, comprising: Gene editing technology was used to knock out base pairs that form key antagonistic regulatory sites in the target watermelon gene to obtain an edited parent with synergistic expression of stress resistance and quality; wherein the target watermelon gene was derived from the drought-resistant variety "Xinmi No. 1" and / or the high-sugar heat-resistant variety "Black Beauty".

[0020] By performing reciprocal crosses between edited and unedited parents, various hybrid combinations were constructed to form a population of hybrid offspring with rich genetic diversity.

[0021] During the critical growth stages of the hybrid offspring population, root and leaf samples were collected. A high-throughput screening platform was used to detect the microscopic traits of the samples, and a correlation model between these microscopic traits and stress resistance and quality was established to screen watermelon individuals that met the set stress resistance and quality requirements. The microscopic traits included the number of lateral roots, root hair density, root length, root diameter, stomatal density, stomatal aperture, chloroplast thylakoid membrane stability, and PSII activity. The high-throughput screening platform integrated a miniature CT scanner, a thermal imager, a chlorophyll fluorometer, and an image analysis system.

[0022] A backpropagation (BP) neural network model is constructed, using microhabitat parameters of the target arid planting area as input variables and watermelon yield, quality, and stress resistance indicators as output variables. The model is trained and validated to obtain a well-trained prediction model. The training process of the BP neural network model specifically includes: Acquire training data; the training data includes microhabitat parameters of the target training area and corresponding label data; the label data includes watermelon yield, quality and stress resistance indicators; input the training data into the constructed BP neural network model, train it with the goal of minimizing the loss between the microhabitat parameters of the target training area and the label data, and determine the model that meets the training requirements as the trained prediction model.

[0023] The selected watermelon individuals were processed using a trained prediction model to predict their adaptability under different microenvironments, and watermelon varieties that met the environmental planting conditions in terms of stress resistance, quality and adaptability were selected.

[0024] In addition, this method also includes: continuously monitoring the selected watermelon varieties, collecting actual performance data during planting, and feeding the collected data back into the prediction model for optimization and iteration.

[0025] Based on the above technical solution, the following embodiments are provided.

[0026] First, we constructed a parent library for the coordinated expression of stress resistance and quality.

[0027] In terms of gene editing and parental structure construction, this invention features a unique innovation in constructing a watermelon stress resistance-quality synergistic expression parental library. It precisely targets the C base in the 5th exon region of the LeNAC6 gene, a transcription factor closely related to stress resistance, and the G base in the 3rd exon region of the LebZIP63 gene, which is associated with quality. Gene editing technology is used to knock out this pair of bases, which form key antagonistic regulatory sites. This structure breaks the antagonistic relationship between the two genes, creating conditions from a genetic perspective for breeding watermelon varieties that possess both excellent stress resistance and high quality.

[0028] The edited parents were crossed reciprocally with unedited superior parents to design various hybridization combinations. A parent bank management system was established to record parental origins, gene editing details, hybridization information, etc., and the parent bank materials were regularly updated and evaluated.

[0029] Secondly, high-throughput precise screening of microscopic traits is carried out.

[0030] Microscopic trait detection indicators were determined: root system architecture (number of lateral roots, root hair density, root length, root diameter), stomatal regulation (stomatal density, stomatal aperture), photosynthetic efficiency (chloroplast thylakoid membrane stability, PSII activity), and other microscopic trait detection indicators.

[0031] High-throughput screening platform construction: Integrating equipment such as miniature CT scanners, thermal imagers, chlorophyll fluorescence spectrometers, and image analysis systems, and developing automated control software to achieve intelligent operation and data processing of the screening platform, enabling the simultaneous detection of multiple microscopic traits.

[0032] Screening process: Samples were collected during key growth stages of watermelon plants (seedling stage, flowering stage, and fruiting stage). Root samples were rinsed with clean water, and then the taproot and lateral roots were selected. Functional leaves were selected for leaf samples. The samples were then placed in a high-throughput screening platform for analysis. Data analysis software was used to establish a correlation model between microscopic traits and stress resistance and quality, and superior individuals were selected.

[0033] Subsequently, dynamic modeling of microhabitat adaptation was performed.

[0034] Microhabitat parameter collection: Monitoring points were set up in different areas on the edge of the Taklamakan Desert. Microhabitat parameters that have an important impact on watermelon growth, such as dune aspect, groundwater level, wind erosion intensity, soil moisture content, and light integral, were selected. Long-term continuous monitoring was carried out using professional instruments and sensors to establish a microhabitat parameter database.

[0035] Modeling and Model Training & Validation: A backpropagation (BP) neural network algorithm was used to construct a model with microhabitat parameters as input variables and watermelon yield, quality indicators, and stress resistance indicators as output variables. The database data was divided into training, validation, and test sets proportionally. The model was trained using the training set, and the weights and thresholds were adjusted to minimize the prediction error. The validation set was used to evaluate the model's generalization ability, and the test set was used to verify the model's accuracy and reliability.

[0036] Variety adaptability assessment and model optimization: Microhabitat parameters of the region to be promoted are input into the model to predict the yield, quality, and stress resistance performance of watermelon varieties in that region, and to assess their adaptability level. As monitoring data accumulates and variety growth feedback is received, the model is regularly updated and optimized to improve prediction accuracy and adaptability.

[0037] Finally, the selected watermelon varieties were planted to verify their effectiveness.

[0038] The watermelon varieties selected using the above-mentioned methods have already yielded the first generation of "desert hanging watermelons" in Moyu County and Cele County in southern Xinjiang. The next step will be to comprehensively analyze the results of high-throughput micro-trait precision screening and dynamic modeling of micro-habitat adaptability to select watermelon varieties that excel in stress resistance, quality, and adaptability. Small-scale trials will be conducted on the edge of the Taklamakan Desert to further observe their growth, yield, and quality performance. Based on the trial results, the final new varieties will be determined, and corresponding promotion plans will be developed to gradually expand the planting area.

[0039] During the promotion and planting of new varieties, a comprehensive monitoring system is established to continuously monitor the growth status, yield, quality, and changes in microhabitat parameters of watermelons. Planting management measures are adjusted promptly based on monitoring results, and new data is fed back into the dynamic modeling of microhabitat adaptability to further optimize new varieties. This provides a more accurate basis for subsequent variety breeding and promotion, achieving dynamic and cyclical optimization of watermelon variety breeding and promotion in China, Central Asia, and Europe.

[0040] In summary, this application first utilizes gene editing technology to treat parental lines, creating conditions from a genetic perspective for cultivating watermelon varieties with both excellent stress resistance and high quality. This is the core and key structure for constructing the parent bank, laying a solid foundation for subsequent variety selection. Secondly, regarding the genetic structure system of hybrid combinations, based on the gene-edited parental materials mentioned above, this application designs a unique reciprocal hybrid combination genetic structure system. Individuals with clear genetic backgrounds and stable phenotypes are selected from existing drought-resistant varieties (such as "Xinmi No. 1") and high-sugar, heat-resistant varieties (such as "Black Beauty") as basic parents. Reciprocal crosses are performed between the edited and unedited parents, i.e., A (edited parent)♀ × B (unedited parent)♂ and A (edited parent)♂ × B (unedited parent)♀, forming various specific hybrid combinations. This structure fully utilizes the genetic material of both parents, achieving gene recombination and optimized configuration, resulting in rich genetic diversity in the offspring population and providing a broad genetic resource space for screening watermelon varieties with comprehensive superior traits.

[0041] Furthermore, the high-throughput microscopic trait precision screening platform and the constructed micro-habitat adaptability dynamic modeling of this application possess a unique structure. The screening platform integrates hardware such as a miniature CT scanner, thermal imager, chlorophyll fluorometer, and image analysis system, along with automated control software, to simultaneously detect microscopic traits of watermelon plants, including root architecture, stomatal regulation, and photosynthetic efficiency, and achieves intelligent data processing. The micro-habitat adaptability dynamic modeling employs a BP neural network algorithm, using specific micro-habitat parameters such as dune aspect and groundwater level as inputs, and watermelon yield, quality indicators, and stress resistance indicators as outputs. Through extensive data training, validation, and testing, a specific model structure is formed. This structure can accurately predict the adaptability of watermelon varieties under different micro-habitats, providing a scientific basis for variety promotion and application.

[0042] As a second aspect, the present invention also provides an electronic device, including a memory and a processor, the memory for storing a computer program, the processor for running the computer program to enable the electronic device to perform the screening method for new watermelon varieties with suitable light and heat conditions in arid areas as described above.

[0043] As a third aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the screening method for new watermelon varieties suitable for arid region light and heat conditions as described above.

[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0045] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for screening new specialty watermelon varieties suitable for arid region light and heat conditions, characterized in that, include: By using gene editing technology to knock out base pairs that form key antagonistic regulatory sites in the target watermelon gene, an edited parent with synergistic expression of stress resistance and quality was obtained; By performing reciprocal crosses between edited and unedited parents, various hybrid combinations were constructed to form a hybrid offspring population with rich genetic diversity. During the critical growth period of the hybrid offspring population, root and leaf samples were collected, and high-throughput screening platforms were used to detect the microscopic traits of the samples. A correlation model between microscopic traits and stress resistance and quality was established to screen out watermelon individuals that meet the set stress resistance and quality requirements. A BP neural network model was constructed, with the microhabitat parameters of the target arid planting area as input variables and watermelon yield, quality and stress resistance indicators as output variables. The model was trained and validated to obtain a well-trained prediction model. The selected watermelon individuals were processed using a trained prediction model to predict their adaptability under different microenvironments, and watermelon varieties that met the environmental planting conditions in terms of stress resistance, quality and adaptability were selected.

2. The method for screening new watermelon varieties suitable for arid region light and heat conditions according to claim 1, characterized in that, The target watermelon gene is derived from the drought-resistant variety "Xinmi No. 1" and / or the high-sugar, heat-resistant variety "Black Beauty".

3. The method for screening new watermelon varieties suitable for arid region light and heat conditions according to claim 1, characterized in that, The microscopic traits include the number of lateral roots, root hair density, root length, root diameter, stomatal density, stomatal aperture, chloroplast thylakoid membrane stability, and PSII activity.

4. The method for screening new watermelon varieties suitable for arid region light and heat conditions according to claim 1, characterized in that, The high-throughput screening platform integrates a miniature CT scanner, a thermal imager, a chlorophyll fluorometer, and an image analysis system.

5. The method for screening new watermelon varieties suitable for arid region light and heat conditions according to claim 1, characterized in that, The training process of the BP neural network model specifically includes: Acquire training data; the training data includes microhabitat parameters of the target training area and corresponding label data; the label data includes watermelon yield, quality, and stress resistance indicators; The training data is input into the constructed BP neural network model, and the training is carried out with the goal of minimizing the loss between the microenvironment parameters of the target training area and the label data. The model that meets the training requirements is determined as the trained prediction model.

6. The method for screening new watermelon varieties suitable for arid region light and heat conditions according to claim 1, characterized in that, It also includes: continuously monitoring the watermelon varieties that have been selected, collecting data on their actual performance during planting, and feeding the collected data back into the prediction model for optimization and iteration.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the electronic device to perform the screening method for new watermelon varieties suitable for arid light and heat conditions according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the screening method for new watermelon varieties with suitable light and heat conditions in arid areas as described in any one of claims 1-6.

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