A winter melon seedling salt tolerance identification method based on multi-modal features

Abstract

The present application relates to the field of agricultural biotechnology and plant science technology, and discloses a winter melon seedling salt tolerance identification method based on multi-modal characteristics, which comprises the following steps: uniformly germinating winter melon seeds, selecting seedlings with consistent growth vigor, setting up a clear water control group and a salt stress group with a response concentration of NaCl, and collecting multi-modal data of photosynthetic fluorescence, photosynthetic absorption, colorimetric analysis and morphology at a preset time point. After the multi-modal data is standardized and normalized, it is reduced in dimension by principal component analysis, and the principal component scores are dimensionless by the membership function method. The weight is calculated by combining the contribution rate, and the comprehensive salt tolerance evaluation value is obtained by weighting. According to the comprehensive salt tolerance evaluation value, the mean value and the standard deviation, the salt tolerance is divided into six levels: normal, high resistance, medium resistance, medium sensitivity, sensitivity and high sensitivity, so that scientific identification is realized, and support is provided for winter melon salt-tolerant germplasm screening and variety breeding.

Description

Technical Field

[0001] This invention relates to the fields of agricultural biotechnology and plant science, and in particular to a method for identifying the salt tolerance of winter melon seedlings based on multimodal characteristics. Background Technology

[0002] Soil salinization is one of the major abiotic stresses limiting global agricultural productivity and poses a serious threat to global food security. It is estimated that over 1 billion hectares of land worldwide are affected by salinity, and this number is increasing by 1.5 to 2 million hectares annually. Salinization not only leads to the degradation of vast amounts of arable land but also causes enormous economic losses. It is estimated that in irrigated agricultural areas alone, annual income losses due to salinization exceed US$27 billion, with crop yield losses reaching 40% to 80% in some regions. Climate change, particularly sea-level rise and altered rainfall patterns, further exacerbates soil salinization in coastal and arid regions. Therefore, screening and cultivating salt-tolerant crop varieties is of crucial strategic importance for ensuring global food supply and developing and utilizing marginal land resources.

[0003] Winter melon, belonging to the Cucurbitaceae family and the Cucurbita genus, is an annual vegetable crop originating in China and East India. It is now widely cultivated in East Asia, Southeast Asia, and South Asia, ranking second among cucurbit vegetables in my country in terms of annual yield and sown area. Winter melon is an important vegetable crop with both medicinal and health benefits, boasting advantages such as high yield, wide cultivation range, long storage period, and simple storage conditions. It plays a crucial role in regulating the off-season for vegetables and ensuring year-round supply.

[0004] To address the challenges of climate change and food security, the agricultural biotechnology market is experiencing rapid growth. Market analysis reports predict that this market will reach hundreds of billions of dollars, primarily driven by the huge demand for high-yield, stress-resistant crops (including disease and pest resistance, drought tolerance, and salt tolerance). Against this backdrop, plant phenotyping technology, as a bridge connecting genotype and phenotype, is increasingly demonstrating its market value. Phenotyping technology aims to quickly and accurately quantify plant traits, thereby accelerating the breeding process, and its market is also showing strong growth. This invention was developed in response to this technological and market demand, aiming to provide an efficient phenotyping tool to meet the urgent need for salt-tolerant crop varieties in modern agriculture.

[0005] Methods for assessing plant salt tolerance have evolved from traditional to modern approaches, but still have many limitations.

[0006] Traditional methods mainly rely on destructive sampling (such as measuring the activity of antioxidant enzymes in plants) and subjective visual grading. These methods are not only labor-intensive, low-throughput, and time-consuming, but also prone to large human errors, making it difficult to capture the dynamic stress response process of plants, and resulting in poor repeatability and reliability of the assessment results.

[0007] To overcome the drawbacks of traditional methods, automated, high-throughput phenotyping platforms have emerged, such as high-throughput phenotyping (HTP) platforms. Specifically, commercial platforms like LemnaTec's Scanalyzer 3D and Field Scanalyzer use conveyor belt systems to automatically transport potted plants to the imaging module, enabling large-scale, standardized data acquisition. These platforms typically integrate multiple imaging technologies.

[0008] Common imaging modalities in HTP include:

[0009] RGB (Visible Light) Imaging: This is the most basic imaging technique used to acquire morphological parameters of plants, such as plant height, biomass (estimated by pixel area), and color changes. Its main limitation is that stress symptoms usually manifest as visible morphological or color changes only after significant physiological changes have occurred inside the plant. Therefore, RGB imaging is not sensitive enough for the early detection of stress.

[0010] Chlorophyll fluorescence (ChlF) imaging: Chlorophyll fluorescence is a powerful tool for detecting the efficiency of plant photosynthesis and is widely recognized as a highly sensitive stress indicator. It can detect functional changes in photosynthetic organs before visible damage symptoms appear in plants, thus outperforming RGB imaging in early stress diagnosis.

[0011] Other modalities: Techniques such as thermal imaging and hyperspectral imaging can also provide rich physiological information, but they are usually expensive and data processing is complex.

[0012] To obtain more comprehensive evaluation results, those skilled in the art have recognized the importance of integrating multi-source data. Transforming multiple indicators into a single comprehensive evaluation index through mathematical models is a common strategy in current plant resistance evaluation research. Specifically, this involves employing...

[0013] Principal component analysis (PCA) reduces the dimensionality of multidimensional data, and then calculates the scores of each principal component by combining the membership function method. Finally, a comprehensive salt tolerance evaluation value (D value) is obtained by weighted summation. This method has been widely used to evaluate the tolerance of plants to various stresses such as drought and salinity.

[0014] However, while the mathematical framework of this PCA + D value is well-known in the prior art, the accuracy and depth of its evaluation results depend entirely on the quality and dimensionality of the input data. Existing techniques often rely on conventional morphological and physiological-biochemical indicators, failing to delve into the core physiological mechanisms of plant stress responses and lacking objective and precise quantification methods for key phenotypic traits (such as leaf color). Therefore, existing techniques have not disclosed or suggested the synergistic combination of the four specific, complementary data modalities proposed in this invention, particularly failing to cover direct measurement of thylakoid membrane proton dynamics and differential quantitative analysis of stress visual symptoms. This is precisely the technological gap that this invention aims to fill. Summary of the Invention

[0015] The purpose of this invention is to provide a method for identifying the salt tolerance of winter melon seedlings based on multimodal features. This method classifies and identifies winter melon seedlings based on leaf color quantification, physiological indicators, and morphological characteristics, aiming to solve at least one of the following problems existing in the screening and evaluation methods for winter melon salt tolerance:

[0016] 1. Single evaluation dimension and low efficiency: Existing technologies mostly rely on single morphological indicators or physiological and biochemical indicators, which cannot comprehensively evaluate salt-tolerant plants. The evaluation results are one-sided and easily overlook excellent germplasm with special salt tolerance mechanisms. Traditional physiological data acquisition is time-consuming and costly, and may be biased when measuring large amounts of data.

[0017] 2. Evaluation indicators are subjective and lack quantitative precision: Traditional methods for evaluating salt damage symptoms rely heavily on visual observation and subjective grading, resulting in poor repeatability and low precision. Even with the introduction of some instrumental measurements, there is a lack of objective quantitative means for key phenotypic traits such as leaf color.

[0018] 3. Insufficient evaluation depth and inability to capture early core responses: Existing methods rarely reach the most critical energy conversion stages of plant photosynthesis. Before significant changes occur in plant morphology and conventional physiological indicators, the energy allocation strategies (such as heat dissipation protection) and energy conversion efficiency (such as ATP synthesis) within the photosynthetic system have already undergone drastic changes. Current technologies lack effective means to monitor these early, core physiological events.

[0019] Therefore, the purpose of this invention is to creatively integrate four different dimensions of indicator data—including deep photosynthetic fluorescence parameters, core photosynthetic absorbance parameters, quantitative color difference parameters, and classical morphological parameters—to establish a scientific mathematical model and six-level classification standards, thereby achieving a more comprehensive, objective, in-depth, and accurate identification and evaluation of the salt tolerance of winter melon, and providing strong technical support for the genetic improvement and precision breeding of salt-tolerant crops.

[0020] To achieve the above objectives, the following technical solution is adopted:

[0021] This invention provides a method for identifying the salt tolerance of winter melon seedlings based on multimodal features. The winter melon seedlings are cultured in a standardized manner. When the winter melon seedlings grow to the one-leaf-one-heart stage, plants with uniform growth are selected and divided into a control group and a salt stress treatment group. The control group is given water, while the salt stress treatment group is given a salt solution.

[0022] Multimodal data were collected from the control group and the salt stress treatment group at preset time points. The multimodal data included morphological parameters, photosynthetic fluorescence parameters, photosynthetic absorption parameters, and chromaticity parameters. The morphological parameters included plant height, taproot length, stem diameter, aboveground fresh / dry weight, and underground fresh / dry weight. The photosynthetic fluorescence parameters included effective quantum yield of photosystem II, non-photochemical quenching quantum yield, non-regulated energy dissipation quantum yield, and linear electron transport rate. The photosynthetic absorption parameters included the total amplitude of the electrochromic shift signal, the proton conductivity of thylakoid membrane ATP synthase, the potential component of the proton kinetic potential, and the relative chlorophyll content. The chromaticity parameters included brightness value, red / green value, yellow / blue value, color saturation, and total color difference.

[0023] A comprehensive evaluation model was established, and the multimodal data of the control group and the salt stress treatment group were input into the comprehensive evaluation model to output the comprehensive salt tolerance evaluation value of the corresponding winter melon seedling variety.

[0024] The mean and standard deviation of the comprehensive salt tolerance evaluation values ​​of all tested winter melon seedling varieties were calculated, and the salt tolerance of all tested winter melon seedling varieties was classified based on the mean and standard deviation.

[0025] Furthermore, the winter melon seedlings are cultivated in a standardized manner, including:

[0026] Select plump and uniformly sized seeds, allow them to absorb moisture for 8 hours, and then germinate them in a 30℃ constant temperature incubator. After germination, sow them in a standardized substrate composed of nutrient soil and perlite, and place them in seedling trays. Transfer the seedling trays to a light cultivation room and cultivate them under the conditions of 26±2℃, a light / dark cycle of 16h / 8h, and a set light intensity.

[0027] Furthermore, the control group was given plain water, while the salt stress treatment group was given a saline solution, including:

[0028] The treatment solution was applied by pouring it from the bottom of the tray. The control group was given water, while the treatment group was given a solution containing NaCl. The entire stress period was 8-10 days, during which the solution was replaced regularly and in measured amounts.

[0029] Furthermore, the formula for calculating the color saturation is:

[0030] ;

[0031] In the formula, Indicates color saturation. Indicates red / green value, Indicates the yellow / blue value.

[0032] Furthermore, the formula for calculating the total color difference is:

[0033] ;

[0034] In the formula, Indicates the total color difference. , and These represent the brightness, red / green value, and yellow / blue value of the salt stress treatment group, respectively. , and These represent the brightness, red / green value, and yellow / blue value of the salt stress treatment group, respectively.

[0035] Furthermore, the process of inputting the multimodal data of the control group and the salt stress treatment group into the comprehensive evaluation model and outputting the comprehensive salt tolerance evaluation value of the corresponding winter melon seedling variety includes:

[0036] Using morphological parameters, photosynthetic fluorescence parameters, photosynthetic absorption parameters, and colorimetric parameters as indicators, the relative values ​​of each indicator were calculated. The calculation formula is: relative value = measured value of treatment group / average value of control group during the same period.

[0037] The relative values ​​of each indicator are positively correlated, including: using the relative values ​​of indicators that are positively correlated with salt tolerance as indicator data, and taking the reciprocal of the relative values ​​of indicators that are negatively correlated with salt tolerance as the positively correlated indicator data.

[0038] All the positively processed indicator data are combined into a data matrix, and PCA is applied for dimensionality reduction. The extraction conditions are eigenvalues ​​greater than 1 or cumulative contribution rates greater than 85%, and several principal components that can represent some information of the original data and are mutually independent are extracted.

[0039] The membership function is used to perform dimensionless processing on the scores of each principal component to obtain the membership function values ​​of the principal component scores;

[0040] Determine the weights of each principal component;

[0041] The comprehensive salt tolerance evaluation value is calculated based on the weights and membership function values ​​of each principal component.

[0042] Furthermore, the membership functions are used to perform dimensionless processing on the scores of each principal component, and the formula for calculating the membership function values ​​of the principal component scores is as follows:

[0043] ;

[0044] In the formula, Let be the membership function value of the score of the j-th principal component of the i-th variety. Let be the membership function value of the score of the j-th principal component of the i-th variety. and These are the maximum and minimum scores of the j-th principal component among all varieties.

[0045] Furthermore, the weights of each principal component are determined using the following formula:

[0046] ;

[0047] In the formula, Let the weight of the j-th principal component be . Let n be the contribution rate of the k-th principal component, n be the total number of principal components, and k be the index of the principal component. The contribution rate of the j-th principal component.

[0048] Furthermore, based on the weights and membership function values ​​of each principal component, the comprehensive salt tolerance evaluation value is calculated using the following formula:

[0049] ;

[0050] In the formula, Let be the membership function value of the score of the j-th principal component of the i-th variety. denoted as the comprehensive salt tolerance evaluation value for the i-th variety.

[0051] Furthermore, the methods for classifying the salt tolerance of all tested winter melon seedling varieties based on the aforementioned mean and standard deviation include:

[0052] When D≥μ+1.5σ, the salt tolerance of the tested winter melon seedling variety is determined to be at the normal level;

[0053] When μ+1.0σ≤D<μ+1.5σ, the salt tolerance of the tested winter melon seedling variety is determined to be of the high resistance level;

[0054] When μ+0.5σ≤D<μ+1.0σ, the salt tolerance of the tested winter melon seedling variety is determined to be at the medium resistance level;

[0055] When μ-0.5σ≤D<μ+0.5σ, the salt tolerance of the tested winter melon seedling variety is determined to be moderately sensitive.

[0056] When μ-1.0σ≤D<μ-0.5σ, the salt tolerance of the tested winter melon seedling variety is determined to be at the sensitive level;

[0057] When D < μ - 1.0σ, the salt tolerance of the tested winter melon seedling variety is determined to be of the high sensitivity level;

[0058] Where D is the comprehensive salt tolerance evaluation value of the tested winter melon seedling varieties, and μ and σ are the mean and standard deviation of the comprehensive salt tolerance evaluation values ​​of all tested winter melon seedling varieties, respectively.

[0059] The beneficial effects of this invention are:

[0060] This invention aims to overcome the shortcomings of existing technologies and provide a comprehensive, objective, in-depth, and accurate method for the identification and classification of plant salt tolerance. This method creatively integrates four interrelated and complementary data modalities, solving the problems of traditional evaluation methods such as single-dimensionality, strong subjectivity, and insufficient depth.

[0061] The core of the method disclosed in this invention lies in an innovative four-dimensional data acquisition and analysis system, specifically including:

[0062] 1. Conventional morphological parameters: to obtain basic indicators of plant growth and biomass.

[0063] 2. Photosystem II (PSII) quantum yield and electron transport parameters: Using chlorophyll fluorescence technology, accurately assess the efficiency of light energy capture, utilization and dissipation.

[0064] 3. Thylakoid membrane proton dynamics and conductivity parameters: By measuring the electrochromic shift (ECS) signal, we can delve into the core energy conversion process of photosynthesis, namely the formation of the proton gradient and the efficiency of ATP synthesis.

[0065] 4. Quantitative differential colorimetric parameters: Using the CIELAB color space, the total color difference is calculated by comparing with a healthy control group during the same period, thereby objectively and purely quantifying the degree of visual impairment caused by salt stress.

[0066] This invention further discloses a systematic data processing workflow. The workflow first standardizes and positively modulates the collected multidimensional heterogeneous data. Then, it uses principal component analysis (PCA) to construct a comprehensive evaluation model, generating a single index—the D-value—that comprehensively reflects the overall salt tolerance of plants. Finally, based on the statistical distribution of the D-values ​​of all tested varieties, a scientific six-level classification standard is established.

[0067] The main advantages of this invention are:

[0068] Comprehensiveness: It integrates indicators from four dimensions, overcoming the one-sidedness of evaluation based on a single indicator.

[0069] Objectivity: Quantitative data measured by instruments replaces subjective visual grading, and in particular, objective quantification of leaf damage is achieved by introducing total color difference.

[0070] Profoundness and Sensitivity: By measuring parameters such as gH+ (proton conductivity) and VH+ (proton kinetic potential), this invention can detect early and subtle changes in the core energy conversion links of the photosynthetic system of plants before changes in morphology and conventional physiological indicators, thereby achieving early warning and more sensitive diagnosis of stress.

[0071] Accuracy: By dynamically comparing with a healthy control group during the same period, the colorimetric analysis method of the present invention can effectively eliminate color changes caused by non-stress factors such as plant development itself, and more accurately separate the salt damage effect.

[0072] In summary, this invention provides a novel and non-obvious systematic solution, offering strong technical support for the efficient screening of salt-tolerant germplasm resources and the acceleration of crop genetic improvement. Attached Figure Description

[0073] Figure 1 A flowchart of a method for identifying the salt tolerance of winter melon seedlings based on multimodal features according to an embodiment of the present invention is shown.

[0074] Figure 2 A standardized seedling culture and salt stress treatment flowchart according to an embodiment of the present invention is shown.

[0075] Figure 3 A flowchart of multimodal data acquisition according to an embodiment of the present invention is shown.

[0076] Figure 4 A flowchart illustrating the construction process of a data integration and comprehensive evaluation model according to an embodiment of the present invention is shown.

[0077] Figure 5 The leaf conditions under various salt damage scenarios according to the present invention are shown.

[0078] Figure 6 The growth of the DG88 (high resistance) control group and the 200mM NaCl salt stress group in the embodiments of the present invention is shown on days 0, 3, 6 and 9.

[0079] Figure 7 The growth of the DG6 (highly sensitive) control group and the 200 mM NaCl salt stress group on days 0, 3, 6, and 9 are shown in the example according to the present invention.

[0080] Figure 8 The figures show the reciprocal of the color saturation of eight winter melon materials from day 1 to day 8 according to an example of the present invention. The smaller the value, the closer the leaf color is to red and yellow, and the smaller the value, the closer the leaf color is to blue and green.

[0081] Figure 9The figures show the reciprocal of the color saturation of eight winter melon materials on day 0 and day 9 according to an example of the present invention. The smaller the value, the closer the leaf color is to red and yellow, and the smaller the value, the closer the leaf color is to blue and green.

[0082] Figure 10 The total color difference of the daily control group and the 200mM NaCl salt stress group of eight winter melon materials in the example of the present invention on days 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9 is shown. The greater the deviation of the color difference value, the higher the value, indicating that the leaf color difference is greater compared with the control group.

[0083] Figure 11 The following figures illustrate four photosynthetic fluorescence parameters and two photosynthetic absorption parameters for the daily control group and the 200 mM NaCl salt stress group of eight winter melon materials according to an example of the present invention on days 0, 3, 6, and 9: a) ΦII: Effective quantum yield of photosystem II, directly reflecting the proportion of energy used for photochemical reactions. b) LEF: Linear electron transport rate, a direct estimate of the photosynthetic rate. c) ΦNPQ: Non-photochemical quenching quantum yield for photoprotection, reflecting the plant's ability to safely dissipate excess light energy through heat dissipation. d) ΦNO: Quantum yield of non-regulated energy dissipation, reflecting passive energy loss due to stress damage. e) gH+: Proton conductivity of thylakoid membrane ATP synthase, directly reflecting the rate of ATP synthesis. f) vH+: Potential portion of the proton kinetic potential.

[0084] Figure 12 The diagram shows a dendritic cluster analysis of the salt tolerance of eight winter melon materials in this invention example based on the calculated D value. The cluster analysis shows that DG88 and DG84 are high-resistance materials (R, Grade I), B5 is a medium-resistance material (MR, Grade III), Y12 is a medium-sensitive material (MS, Grade IV), J16 and B1 are sensitive materials (S, Grade V), and DG6 and DG83 are high-sensitive materials (HS, Grade II). Detailed Implementation

[0085] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0086] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0087] This invention provides a method for identifying the salt tolerance of winter melon seedlings based on multimodal features, such as... Figure 1 The figure shows the overall flowchart of the method for identifying salt tolerance of winter melon seedlings based on multimodal features. The method for identifying salt tolerance of winter melon seedlings based on multimodal features includes the following steps S10 to S40.

[0088] S10: Standardized cultivation of winter melon seedlings was carried out. When the winter melon seedlings grew to the one-leaf-one-heart stage, plants with uniform growth were selected and divided into a control group and a salt stress treatment group. The control group was given water, while the salt stress treatment group was given a salt solution.

[0089] Step S10 is a standardized procedure for seedling culture and salt stress treatment. To ensure the reliability and reproducibility of experimental results, plant culture and stress treatment must be carried out under strictly controlled conditions. Controlling environmental conditions minimizes interference from the complex variability of the field environment, which is a prerequisite for accurate phenotypic analysis. Therefore, in some embodiments, such as... Figure 2 As shown, step S10 is specifically implemented through the following steps S101-S102.

[0090] S101, Materials and Seedling Raising: Select the winter melon to be tested ( Benincasa hispida Germplasm materials. Select plump, uniformly sized seeds, allow them to absorb moisture for 8 hours, and then germinate them in a 30℃ constant temperature incubator. After germination, sow them in a standardized substrate composed of nutrient soil and perlite (or zeolite), and place them in seedling trays. The seedling trays are then transferred to a light-controlled cultivation room and managed under standard conditions: temperature 26±2℃, light / dark cycle 16h / 8h, and light intensity 4000lx.

[0091] S102. Stress Treatment: When the winter melon seedlings reached the one-leaf-one-heart stage, plants with uniform growth were selected for transplanting. The experiment included a water control group (CK) and a salt stress treatment group (T), with three replicates for each treatment. To simulate actual field conditions, the treatment solution was applied by watering from the bottom of the tray. The control group received water, while the treatment group received a solution containing NaCl. The entire stress period lasted 9 days (216 hours), during which the solution was changed regularly and quantitatively to maintain a stable stress concentration.

[0092] S20: Collect multimodal data of the control group and the salt stress treatment group at preset time points; wherein, the multimodal data includes morphological parameters, photosynthetic fluorescence parameters, photosynthetic absorption parameters, and chromaticity parameters; wherein, the morphological parameters include plant height, taproot length, stem diameter, aboveground fresh / dry weight, and underground fresh / dry weight; the photosynthetic fluorescence parameters include photosystem II effective quantum yield, non-photochemical quenching quantum yield, non-regulated energy dissipation quantum yield, and linear electron transport rate; the photosynthetic absorption parameters include the total amplitude of the electrochromic shift signal, the proton conductivity of thylakoid membrane ATP synthase, the potential portion of the proton dynamic potential, and the relative chlorophyll content; and the chromaticity parameters include brightness value, red / green value, yellow / blue value, color saturation, and total color difference.

[0093] Step S20 is the multimodal data acquisition step. Data acquisition is the core of the method proposed in this invention. Its innovation lies in a "four-dimensional integrated" data acquisition system, which aims to capture the plant's response to salt stress from different levels. This embodiment uses portable, relatively low-cost instruments (such as PhotosynQ MultispeQ and portable colorimeters) to achieve high-throughput, high-fidelity physiological data acquisition, providing an economical and efficient solution for laboratories that cannot afford large, fixed HTP platforms (such as Scanalyzer).

[0094]

[0095] Table 1 shows the multimodal data used for salt tolerance assessment.

[0096] Table 1: Multimodal data used for salt tolerance assessment

[0097] Based on the multimodal data shown in Table 1, in some embodiments, such as Figure 3 As shown, multimodal data of the control group and the salt stress treatment group can be collected at preset time points through the following steps S201-S203.

[0098] S201. Morphological characteristics measurement to collect morphological parameters.

[0099] At the end of the stress treatment (216 h), destructive sampling was performed on the plants to measure a series of classic morphological parameters. These parameters provide direct evidence for evaluating plant growth status and biomass accumulation, and represent the macroscopic response of winter melon seedlings to abiotic stress. Specific parameters are shown in Table 1.

[0100] S202, Photosynthetic Function Measurement (Fluorescence and Absorption Spectroscopy) to collect photosynthetic fluorescence parameters and photosynthetic absorption parameters.

[0101] Throughout the entire stress cycle (h0, 24, 48, 72, 96, 120, 144, 168, 192, and 216), in situ, non-destructive measurements were performed on specific leaves of the plants using a portable multi-functional photosynthetic phenotyper (e.g., the MultispeQ V2 from PhotosynQ, Inc.). This device can simultaneously acquire chlorophyll fluorescence and absorption spectral data, providing in-depth, multi-level diagnostics of the photosynthetic process. In existing technologies, many studies use basic chlorophyll fluorescence parameters (such as Fv / Fm) to detect stress. However, this embodiment employs a more advanced and profound set of parameters, advancing the diagnosis from the level of "whether photosynthetic efficiency has decreased" to the mechanistic level of "why photosynthetic efficiency has decreased." This set of parameters can be divided into two diagnostic levels:

[0102] Level 1: Light Energy Utilization and Distribution (PSII Efficiency): These parameters describe how Photosystem II (PSII) distributes absorbed light energy and are sensitive indicators for assessing the health of the photosynthetic apparatus.

[0103] ΦII: Effective quantum yield of photosystem II, which directly reflects the proportion of energy used for photochemical reactions.

[0104] ΦNPQ: Non-photochemical quenching quantum yield for photoprotection, reflecting the plant’s ability to safely dissipate excess light energy through heat dissipation.

[0105] ΦNO: Quantum yield of non-regulated energy dissipation, reflecting passive energy loss due to stress damage.

[0106] LEF: Linear electron transport rate, a direct estimate of the photosynthetic rate.

[0107] Level Two: Proton Dynamics and ATP Synthesis (Thylakoid Membrane Function): This is the most innovative depth of detection in this invention. By measuring the electrochromic shift (ECS) signal, this embodiment can directly assess the proton gradient (pmf) across the thylakoid membrane and the activity of ATP synthase, which are the core components of photosynthetic energy conversion. Existing studies have confirmed that these parameters are extremely sensitive to salt stress, especially in sensitive genotypes. Salt stress leads to proton leakage from the thylakoid membrane, manifested as a significant increase in proton conductivity (gH+), which is a stress response signal that occurs earlier and more fundamentally than the decline in PSII efficiency.

[0108] gH+: The proton conductivity of thylakoid membrane ATP synthase, directly reflecting the rate of ATP synthesis. + An abnormally high level usually indicates impaired membrane integrity and proton leakage.

[0109] vH+: The potential portion of the proton dynamic potential.

[0110] SPAD: relative chlorophyll content, used as an auxiliary indicator.

[0111] S403, Quantitative colorimetric measurements to acquire colorimetric parameters.

[0112] At the same time points as the photosynthetic function measurements, measurements were taken at the same leaf location using a portable colorimeter (e.g., the CM-700d from Konica Minolta, Japan). Plant leaf color is a direct reflection of its health, but traditional visual assessments are highly subjective. While studies have used colorimetry or RGB imaging to analyze color, they often overlook the natural color changes that occur during normal plant growth.

[0113] The differential colorimetric analysis method used in this embodiment avoids the above problems through design. The core of this method is not to measure the absolute color of the stressed leaves, but to calculate the total color difference between them and the average color of the healthy control group (CK) at the same time.

[0114] The scientific basis of this design lies in using a control group of plants growing healthily at the same time as a dynamic "zero point" or "health baseline." Color changes in plants caused by non-stress factors such as their own development and changes in light intensity will occur simultaneously in both the control and treatment groups. By calculating the color difference between the treatment and control groups, these common changes are effectively offset, ultimately yielding a total color difference. It can more purely and accurately reflect the degree of visual impairment caused solely by salt stress. It integrates a multidimensional color change (brightness, red-green, yellow-blue) into a single, quantifiable "visual impairment index," which is a more advanced and scientific assessment method than simple color tracking.

[0115] Measurement parameters: Collect coordinate values ​​in the CIELAB color space specified by the International Commission on Illumination (CIE):

[0116] L*: Brightness value;

[0117] a*: Red / Green value;

[0118] b*: Yellow / Blue value.

[0119] The derived parameters are calculated as follows:

[0120] Color saturation:

[0121] ;

[0122] In the formula, Indicates color saturation. Indicates red / green value, Indicates the yellow / blue value.

[0123] Total color difference:

[0124] ;

[0125] In the formula, Indicates the total color difference. , and These represent the brightness, red / green value, and yellow / blue value of the salt stress treatment group, respectively. , and These represent the brightness, red / green value, and yellow / blue value of the salt stress treatment group, respectively.

[0126] S30: Establish a comprehensive evaluation model, input the multimodal data of the control group and the salt stress treatment group into the comprehensive evaluation model, and output the comprehensive salt tolerance evaluation value of the corresponding winter melon seedling variety.

[0127] Step S30 is the step of data integration and comprehensive evaluation model construction. This step aims to integrate the high-dimensional, heterogeneous data collected in step S20 into a single, comparable comprehensive salt tolerance evaluation value (D value) through a series of mathematical transformations.

[0128] In some embodiments, such as Figure 4 As shown, step S30 can be implemented through the following steps S301-S305.

[0129] S301, Data standardization processing.

[0130] To eliminate the differences in dimensions and orders of magnitude among different indicators, the relative values ​​of each indicator are first calculated.

[0131] Calculation formula: Relative value = Measurement value of treatment group (T) / Average value of control group (CK) during the same period.

[0132] S302, Forward processing.

[0133] To ensure all indicators are positively correlated with salt tolerance (i.e., the higher the value, the stronger the salt tolerance), it is necessary to reverse the direction of negatively correlated indicators. Based on the expected correlations in Table 1, indicators negatively correlated with salt tolerance (such as relative ΦNO value, relative C* value, and relative...) The relative value of (e.g., value) is the reciprocal of (1 / relative value).

[0134] S303, Principal Component Analysis.

[0135] All standardized and positively oriented indicator data are constructed into a data matrix, and PCA is applied for dimensionality reduction. Based on the principle that the eigenvalue is greater than 1 or the cumulative contribution rate is greater than 85%, several independent principal components (PC1, PC2, ..., PCn) that can represent most of the information of the original data are extracted, where PC1, PC2, and PCn represent the first, second, and nth principal components, respectively.

[0136] S304, Calculation of membership function values.

[0137] To unify the evaluation scale of the principal component scores, the membership function method is used for dimensionless processing, mapping the scores to intervals.

[0138] Calculation formula:

[0139] ;

[0140] In the formula, Let be the membership function value of the score of the j-th principal component of the i-th variety. Let be the membership function value of the score of the j-th principal component of the i-th variety. and These are the maximum and minimum scores of the j-th principal component among all varieties.

[0141] S305. Weight calculation and determination of comprehensive salt tolerance evaluation value (D value).

[0142] Weight The weight of each principal component is determined by its contribution rate in PCA.

[0143] Calculation formula:

[0144] ;

[0145] In the formula, Let the weight of the j-th principal component be . Let n be the contribution rate of the k-th principal component, n be the total number of principal components, and k be the index of the principal component. The contribution rate of the j-th principal component.

[0146] Comprehensive salt tolerance rating It is obtained by weighted summation of the membership function values ​​of each principal component.

[0147] Calculation formula:

[0148] ;

[0149] In the formula, Let be the membership function value of the score of the j-th principal component of the i-th variety. denoted as the comprehensive salt tolerance evaluation value for the i-th variety.

[0150] The final result The higher the value, the stronger the overall salt tolerance of the variety.

[0151] S40: Calculate the mean and standard deviation of the comprehensive salt tolerance evaluation values ​​of all tested winter melon seedling varieties, and classify the salt tolerance of all tested winter melon seedling varieties based on the mean and standard deviation.

[0152] In this embodiment, based on the calculated D value, all tested varieties are objectively and precisely classified into salt tolerance levels. Specifically, the mean μ and standard deviation σ of the D values ​​of all tested varieties are first calculated, and the following classification criteria are established based on μ and σ:

[0153] Grade I (Normal, HR): D≥μ+1.5σ;

[0154] Class II (High Resistance, R): μ+1.0σ≤D<μ+1.5σ;

[0155] Level III (medium resistance, MR): μ+0.5σ≤D<μ+1.0σ;

[0156] Level IV (Intermediate Sensitivity, MS): μ-0.5σ≤D<μ+0.5σ;

[0157] Grade V (Sensitive, S): μ-1.0σ≤D<μ-0.5σ;

[0158] Grade VI (High Sensitivity, HS): D < μ-1.0σ.

[0159] The method of this invention has been successfully applied to the salt tolerance identification of a batch of winter melon germplasm resources. Following steps S10 to S40, the responses of different winter melon materials to NaCl stress were comprehensively evaluated. Fifteen indicators were collected, including plant height, root length, biomass, PSII quantum yield, linear electron transport rate, thylakoid membrane proton conductivity, and total color difference. Multiple principal components were extracted using PCA analysis, and the comprehensive salt tolerance evaluation value D for each material was calculated. Finally, based on the D value and a six-level classification standard, these winter melon materials were divided into six levels: normal, highly resistant, moderately resistant, moderately sensitive, sensitive, and highly sensitive, providing a clear and reliable screening basis for subsequent breeding work.

[0160] To further illustrate the feasibility and advancement of the method proposed in this application, this embodiment selects a number of plump and uniform seeds from four commercial varieties (B1, B5, J16, Y12) and four winter melon germplasm materials (DG6, DG83, DG84, DG88) through initial screening. After 7 hours of water absorption and swelling, the surface moisture is dried. The seeds are then placed in a 30℃ constant temperature incubator for germination. After germination, the seeds are sown in nutrient substrate seedling trays and transferred to a light cultivation room (24±2℃, 16h / 8h day / night light cycle, 4000lx light intensity) for routine management. When the seedlings have one leaf and one star, seedlings with uniform growth are selected for transplanting. Each treatment is repeated in triplicate, with the water control and 200mM saline stress treatment watered from the bottom of the tray. Plant photographs are taken using an RGB digital camera on days 0, 3, 6, and 9 of the treatment. Figure 6 , Figure 7 The chlorophyll content of the first true leaf of the plant was obtained using a portable SPAD instrument (Table 3), and four photosynthetic fluorescence parameters were measured using a portable multi-functional photosynthetic phenotyping instrument (MultispeQ V2). Figure 11 ): Effective quantum yield ΦII of photosystem II, non-photochemical quenching quantum yield ΦNPQ, unregulated energy dissipation quantum yield ΦNPQ, linear electron transport rate LEF, and two photosynthetic absorption parameters ( Figure 11 ): The proton conductivity gH+ of thylakoid membrane ATP synthase and the potential portion vH+ of the proton motive force potential. From day 0 to 9 of treatment, the L* (brightness), a* (red / green), and b* (yellow / blue) values ​​of the first true leaf were measured daily using a colorimeter (CM-700d), and the reciprocal of color saturation (1 / C*) and total color difference were calculated. )( Figure 8 , Figure 9 and Figure 10 The measurements were taken at a position near the petiole along the main leaf vein. On day 9 of treatment, seven phenotypic measurements were performed on the control group and the stress group: root length, plant height, stem diameter (Table 1); aboveground fresh weight, dry weight, underground fresh weight, and underground dry weight (Table 2). All data were processed using the membership function method for dimensionless processing under D9 conditions. The calculated D values ​​were mapped to intervals to obtain a dendritic clustering analysis diagram of salt tolerance for the eight winter melon materials. Figure 12 Cluster analysis revealed that DG88 and DG84 are high-resistance materials (R, Class I), B5 is a medium-resistance material (MR, Class III), Y12 is a medium-sensitivity material (MS, Class IV), J16 and B1 are sensitive materials (S, Class V), and DG6 and DG83 are high-sensitivity materials (HS, Class II).

[0161] It should be noted that, in Figure 8 and Figure 9In this study, the daily reciprocal color saturation of four winter melon germplasm resources and four commercial winter melon varieties (CK: B1, B5, J16, Y12, 6, 83, 84, 88; Treatment: TB1, TB5, TJ16, TY12, T6, T83, T84, T88) was calculated. Higher values ​​indicate a closer resemblance to blue-green, while lower values ​​indicate a closer resemblance to red-yellow. The formula for calculating the daily reciprocal color saturation is as follows: .

[0162] Table 2: Plant height (cm), root length (cm), and stem diameter (mm) of the 8 groups of winter melon materials were sampled on day 9. (Control group and 200mM NaCl salt stress group)

[0163]

[0164] Table 3: Fresh weight (g) at the top, fresh weight (g) at the bottom, dry weight (g) at the top, and dry weight (g) at the bottom of the eight groups of winter melon materials taken on day 9.

[0165]

[0166] Table 4: Chlorophyll content of the first true leaf on days 0, 3, 6, and 9 of the eight groups of winter melon materials sampled on day 9, in the control group and the 200mM NaCl salt stress group.

[0167]

[0168] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the present invention, and the patent protection scope of the present invention should be defined by the claims.

Claims

1. A method for identifying salt tolerance of winter melon seedlings based on multi-modal features, characterized in that, The method comprises the following steps: The winter melon seedlings are standardizedly cultured, and when the winter melon seedlings grow to the one-leaf-one-heart stage, the winter melon seedlings with consistent growth are selected and divided into a control group and a salt stress treatment group; the control group is applied with water, and the salt stress treatment group is applied with a salt solution; At a preset time point, the multi-modal data of the control group and the salt stress treatment group are collected; wherein the multi-modal data comprises morphological parameters, photosynthetic fluorescence parameters, photosynthetic absorption parameters and colorimetric parameters; wherein the morphological parameters comprise plant height, main root length, stem diameter, aboveground fresh weight, aboveground dry weight, underground fresh weight and underground dry weight, the photosynthetic fluorescence parameters comprise effective quantum yield of photosystem II, non-photochemical quenching quantum yield, quantum yield of non-regulated energy dissipation and linear electron transfer rate, the photosynthetic absorption parameters comprise total amplitude of electrochromic displacement signal, proton conductivity of thylakoid ATP synthase, potential part of proton motive force and relative chlorophyll content, and the colorimetric parameters comprise brightness value, red / green value, yellow / blue value, color saturation and total color difference; A comprehensive evaluation model is established, the multi-modal data of the control group and the salt stress treatment group are input into the comprehensive evaluation model, and the comprehensive salt tolerance evaluation value corresponding to the winter melon seedling material is output; The average value and the standard deviation of the comprehensive salt tolerance evaluation values of all the tested winter melon seedling materials are calculated, and the salt tolerance of all the tested winter melon seedling materials is classified based on the average value and the standard deviation.

2. The method for identifying salt tolerance of Benincasa cerifera seedlings based on multi-modal features according to claim 1, characterized in that, The winter melon seedlings are standardizedly cultured, comprising the following steps: Seeds with full grains and uniform sizes are selected, and after being absorbed for 8 hours, the seeds are germinated in a 30 DEG C constant temperature box; after germination, the seeds are sowed in a standardized substrate composed of nutrient soil and perlite and placed in a seedling raising tray; and the seedling raising tray is moved to a light culture room for culture under the conditions of a temperature of 26 DEG C + / - 2 DEG C, a light / dark cycle of 16 h / 8 h and a set light intensity. 3.The method of claim 1, wherein, The control group is applied with water, and the salt stress treatment group is applied with a salt solution, comprising the following steps: The treatment solution is applied by irrigation from the bottom of the tray; the control group is applied with water, and the treatment group is applied with a solution containing NaCl; the whole stress cycle is 9 days, and the solution is replaced regularly and quantitatively during the period.

4. The method for identifying salt tolerance of Benincasa cerifera seedlings based on multi-modal features according to claim 1, characterized in that, The calculation formula of the color saturation is as follows: ； wherein represents the color saturation, represents the red / green value, represents the yellow / blue value.

5. The method for identifying salt tolerance of Benincasa cerifera seedlings based on multi-modal features according to claim 1, characterized in that, The calculation formula of the total color difference is as follows: ； wherein represents the total color difference, , and respectively represent the brightness, red / green value and yellow / blue value of the salt stress treatment group, , and respectively represent the brightness, red / green value and yellow / blue value of the control group.

6. The method for identifying salt tolerance of Benincasa cerifera seedlings based on multi-modal features according to claim 1, characterized in that, The process of inputting the multi-modal data of the control group and the salt stress treatment group into the comprehensive evaluation model and outputting the comprehensive salt tolerance evaluation value corresponding to the winter melon seedling variety comprises the following steps: The morphological parameters, the photosynthetic fluorescence parameters, the photosynthetic absorption parameters and the colorimetric parameters are taken as indexes, and the relative values of the indexes are calculated; the calculation formula is as follows: relative value = measured value of the treatment group / average value of the control group at the same period; The relative values of the indexes are positively processed, comprising the following steps: the relative value of an index positively correlated with salt tolerance is taken as index data, and the reciprocal of the relative value of an index negatively correlated with salt tolerance is taken as the positively processed index data; All the positively processed index data are combined into a data matrix, and PCA is applied for dimension reduction; the extraction condition is that the eigenvalue is greater than 1 or the cumulative contribution rate is greater than 85%; and a plurality of principal components capable of representing part of the information of the original data and independent of each other are extracted. The membership function is used to perform dimensionless processing on the scores of the principal components to obtain membership function values of the principal component scores; The weight of each principal component is determined; Based on the weight of each principal component and the membership function value, the comprehensive salt tolerance evaluation value is calculated.

7. The method according to claim 6, wherein, The calculation formula of the membership function value of the principal component score obtained by performing dimensionless processing on the scores of the principal components by using the membership function is: ； wherein is the membership function value of the jth principal component score of the ith variety, is the jth principal component score of the ith variety, and are the maximum and minimum values, respectively, of the jth principal component score across all varieties.

8. The method according to claim 6, wherein the method is characterized by, The weight of each principal component is determined by the following formula: ； In the formula, is the weight of the jth principal component, is the contribution rate of the kth principal component, n is the total number of principal components, and k is the index of the principal component, is the contribution rate of the jth principal component. 9.The method of claim 8, wherein, Based on the weight of each principal component and the membership function value, the comprehensive salt tolerance evaluation value is calculated by the following formula: ； In the formula, is the membership function value of the score of the jth principal component of the ith variety, is the comprehensive salt tolerance evaluation value of the ith variety.

10. The method for identifying salt tolerance of Benincasa cerifera seedlings based on multi-modal features according to claim 1, characterized in that, The classification of the salt tolerance of all the tested wax gourd seedling varieties based on the average value and the standard deviation includes: When D≥μ+1.5σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is normal grade; When μ+1.0σ≤D<μ+1.5σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is high resistance grade; When μ+0.5σ≤D<μ+1.0σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is medium resistance grade; When μ-0.5σ≤D<μ+0.5σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is medium sensitivity grade; When μ-1.0σ≤D<μ-0.5σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is sensitive grade; When D<μ-1.0σ, it is determined that the salt tolerance of the tested wax gourd seedling variety is high sensitivity grade; Wherein, D is the comprehensive salt tolerance evaluation value of the tested wax gourd seedling variety, μ and σ are respectively the average value and the standard deviation of the comprehensive salt tolerance evaluation value of all the tested wax gourd seedling varieties.

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