Method and system for detecting relative content of lignin in plant sample
Through the pretreatment of isophthalol hydrochloric acid and image acquisition combined with linear regression method, quantitative analysis of the lignin content in plant samples was solved, and the problem of difficult lignin distribution and high sample size requirements in the prior art was solved, and efficient lignin content detection was achieved.
Patent Information
- Application Number
- CN202510048928.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-13
- Publication Date
- 2025-05-13
AI Technical Summary
It is difficult to quantitatively analyze the relative content of lignin in plant samples, especially at the subcellular structure level, and traditional methods require large sample sizes, are not suitable for early breeding inspections, complex processing procedures and expensive equipment.
Plant samples were pretreated by phthalocytopentol hydrochloric acid, and the sample images were detected by image acquisition and linear regression. The color change rate was used for quantitative analysis to improve data processing efficiency.
Quantitative analysis of the relative lignin content in plant samples is achieved, data processing efficiency is improved, and problems such as large sample size requirements, insufficient resolution and complex processing flow in traditional methods are overcome.
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Figure CN119985468A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of lignin content detection, and in particular to a method and system for detecting the relative content of lignin in plant samples. Background Art
[0002] Among the related methods for identifying the lignin content of plants, the Klason method, ultraviolet spectrophotometry and other methods for identifying the lignin content have always been in a dominant position. Among them, the principle of the Klason method is to use concentrated sulfuric acid to hydrolyze the non-lignin part of the sample, that is, the remaining is lignin, weigh the remaining lignin, and determine the lignin content in the sample. The principle of ultraviolet spectrophotometry to measure the lignin content is a qualitative and quantitative analysis method established by the absorption characteristics of material molecules to electromagnetic waves in the range of 200-760nm. Neither of them can detect the distribution of lignin content at the lodging position of the stem of a single plant. In addition, the Klason method has fixed requirements for the type of lignin in the process of identifying lignin. Because concentrated sulfuric acid is highly corrosive, and its lignin detection methods based on plant slices, such as Raman spectroscopy imaging and near-infrared spectroscopy imaging methods, are based on qualitative image research on the content of lignin, and can only roughly judge the position and relative amount of lignin based on color or fluorescence, and cannot provide quantitative information.
[0003] In addition, the relevant methods focus on the macroscopic whole-plant detection, making it difficult to explore the distribution of lignin in-depth inside cells. In cells, the distribution of lignin in various layers of the cell wall and in different cell types is regular and complex, and interacts with other components to affect cell function. However, the relevant methods can only obtain the average lignin information of plants or tissue blocks, and cannot know its fine distribution in subcellular structures such as specific layers of the cell wall and cell corners, which limits the in-depth study of the relationship between plant microstructure and function.
[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention
[0005] The main purpose of the embodiments of the present application is to provide a method and system for detecting the relative content of lignin in plant samples, which can quantitatively analyze the lignin content in the samples and improve the efficiency of data processing.
[0006] To achieve the above object, one aspect of the embodiments of the present application provides a method for detecting the relative content of lignin in a plant sample, the method comprising:
[0007] Pre-treating the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample;
[0008] Performing image acquisition processing on the pre-processed target plant slice sample to obtain a target plant slice sample image;
[0009] The lignin content of the target plant slice sample image is detected by linear regression method to obtain the lignin content of the target plant slice sample.
[0010] In some embodiments, the method of pre-treating the target plant sample with phloroglucinol hydrochloric acid to obtain the pre-treated target plant slice sample includes:
[0011] Obtain a target plant sample and cut it vertically downward with a blade to obtain a target plant slice sample;
[0012] Placing the target plant slice sample on a glass slide and staining it with phloroglucinol hydrochloride to obtain a stained target plant slice sample;
[0013] The stained target plant slice sample is subjected to fading treatment to obtain a pretreated target plant slice sample.
[0014] In some embodiments, the mass of the phloroglucinol hydrochloric acid is 9-11 mg / mL, and the staining time is 10-15 min.
[0015] In some embodiments, the fading treatment of the stained target plant slice sample to obtain a pre-treated target plant slice sample includes:
[0016] Washing the stained target plant slice sample with clean water to remove floating color, thereby obtaining a rinsed target plant slice sample;
[0017] The rinsed target plant slice sample is subjected to water absorption treatment by using absorbent paper to obtain the water-absorbed target plant slice sample;
[0018] The target plant slice sample after absorbing water is decolorized by sodium hydroxide solution to obtain a pretreated target plant slice sample.
[0019] In some embodiments, the concentration of the sodium hydroxide solution is 0.01-0.05 mol / L.
[0020] In some embodiments, the performing image acquisition processing on the pre-processed target plant slice sample to obtain the target plant slice sample image includes:
[0021] Capturing images of the pre-processed target plant slice samples to obtain preliminary target plant slice sample images;
[0022] Performing image registration processing on the preliminary target plant slice sample image to obtain a registered target plant slice sample image;
[0023] The registered target plant slice sample image is subjected to ROI target region interception processing to obtain the target plant slice sample image.
[0024] In some embodiments, the detecting the lignin content of the target plant slice sample image by a linear regression method to obtain the lignin content of the target plant slice sample includes:
[0025] Simplifying the target plant slice sample image to obtain a simplified target plant slice sample image;
[0026] Analyzing the simplified target plant slice sample image by a linear regression method to obtain a fading rate of the target plant slice sample image;
[0027] Fitting and mapping the fading rate of the target plant slice sample image by the least square method to obtain a fading rate variation trend diagram of the target plant slice sample image;
[0028] The fading time of the sample with known lignin content is compared with the fading rate change trend diagram of the target plant slice sample image to obtain the lignin content of the target plant slice sample.
[0029] In some embodiments, the simplification process of the target plant slice sample image to obtain a simplified target plant slice sample image includes:
[0030] Performing intensity distribution fitting on the target plant slice sample image through an exponential function model to obtain a fitted target plant slice sample image;
[0031] Performing color space conversion processing on the fitted target plant slice sample image to obtain a converted target plant slice sample image;
[0032] The converted target plant slice sample image is logarithmically transformed and averaged to obtain a simplified target plant slice sample image.
[0033] In some embodiments, the linear regression method is specifically expressed as follows:
[0034]
[0035] In the above formula, b represents the fading rate of the target plant slice sample image, N represents the number of images, x represents the index of the time point, y represents the logarithm value of the intensity, and x i Indicates the time index corresponding to the corresponding picture, y i Indicates the logarithmic value of the intensity corresponding to the corresponding image.
[0036] To achieve the above object, another aspect of the embodiment of the present application provides a system for detecting the relative content of lignin in a plant sample, the system comprising:
[0037] The first module is used to pre-treat the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample;
[0038] The second module is used to perform image acquisition processing on the pre-processed target plant slice sample to obtain a target plant slice sample image;
[0039] The third module is used to detect the lignin content of the target plant slice sample image by linear regression method to obtain the lignin content of the target plant slice sample.
[0040] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and system for detecting the relative content of lignin in plant samples, wherein the scheme pretreats the target plant sample with phloroglucinol hydrochloric acid to obtain a pretreated target plant slice sample, further performs image acquisition processing on the pretreated target plant slice sample to obtain a target plant slice sample image, and finally performs lignin content detection on the target plant slice sample image by a linear regression method, detects the content of lignin in the sample by detecting the rate of color change during the color development reaction between lignin and chemical reagents, and quantitatively analyzes the lignin content in the sample by monitoring the color change rate during the reaction, thereby reducing the complexity of repeated calculations through a linear regression method, thereby improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 It is a flow chart of a method for detecting the relative content of lignin in a plant sample provided in an embodiment of the present application;
[0042] Figure 2 It is a structural schematic diagram of a system for detecting the relative content of lignin in plant samples provided in an embodiment of the present application;
[0043] Figure 3 is a schematic diagram of a target plant slice sample provided in an embodiment of the present application;
[0044] Figure 4 It is a schematic diagram of several target plant slice samples provided in the embodiments of the present application;
[0045] Figure 5 is a schematic diagram of a target plant slice sample after staining provided in an embodiment of the present application;
[0046] Figure 6 is a schematic diagram of the fading rate variation trend of the target plant slice sample image provided in the embodiment of the present application;
[0047] Figure 7 It is a schematic diagram of fitting the fading effect of the target plant slice sample image provided in the embodiment of the present application. DETAILED DESCRIPTION
[0048] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of systems and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.
[0049] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".
[0050] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.
[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0052] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.
[0053] 1) Lignin content: Lignin content plays a decisive role in plant lodging resistance and other traits [1]. From the perspective of the plant as a whole, the amount of lignin content determines the overall lodging resistance of the plant and the economic value of the plant. From the perspective of plant microstructure, the microscopic distribution of lignin determines the realization of plant structural functions. Lignin is distributed in some important structures of the plant, such as Casparian strips and xylem. Therefore, studying the distribution of lignin content in various organs of plants at the microscopic level is helpful for studying many physiological processes of plants. In addition, with the increasing maturity of molecular breeding technology, methods that can be used to detect plant lignin using trace samples can also help to study the impact of gene expression on plant shape in molecular breeding and improve the efficiency of molecular breeding.
[0054] There are some deficiencies in the related technology, such as:
[0055] 1) Large sample volume requirements: Traditional testing requires the operation of the entire plant. For example, when analyzing lignin, whether it is chemical extraction or physical property testing, in order to make the results accurate and reliable, a large amount of tissue must be collected from many plants, such as a large number of stems, leaves and other parts. Because plant growth is affected by many factors and individual differences are large, only sufficient samples can cover these changes, which leads to heavy work and high resource consumption in collecting, transporting, storing and processing samples.
[0056] 2) Not suitable for early breeding inspection: In the early stage of breeding, such as when seeds have just germinated or in the seedling stage, materials are scarce. However, the traditional whole plant detection method requires a large number of samples and cannot be carried out at this time. The characteristics of lignin have a great impact on key breeding traits such as plant stress resistance and quality. If it cannot be detected in the early stage, it will be difficult to screen potential breeding materials, which will extend the breeding cycle and significantly increase costs.
[0057] 3) Lack of subcellular resolution: Whole-plant testing focuses on the macroscopic view, and it is difficult to explore the distribution of lignin in the interior of cells. In cells, the distribution of lignin in various layers of the cell wall and in different cell types is regular and complex, and interacts with other components to affect cell function. However, traditional methods can only obtain the average lignin information of plants or tissue blocks, and cannot know its fine distribution in subcellular structures such as specific layers of the cell wall and cell corners, which limits the in-depth study of the relationship between plant microstructure and function.
[0058] 4) Complex processing flow: There are many steps in slice preparation. When fixing, you must choose the right fixative and adjust the conditions to ensure cell morphology; dehydration must be done in sequence using different concentrations of alcohol, strictly controlling the time and amount to prevent tissue damage; the transparent step must select the right reagent and control the degree; embedding must be accurate to the tissue and avoid defects; the slice thickness and equipment parameters must be adjusted; staining must also be accurately operated according to different methods. Each link is closely connected, and a mistake may result in all previous efforts being wasted.
[0059] 5) Expensive equipment: The microtome used for slicing, whether it is commonly used for paraffin slicing or dedicated for resin slicing, is expensive and requires maintenance and calibration.
[0060] 6) Lack of quantitative information: Slice detection mainly relies on qualitative staining. For example, after staining with a specific reagent, the location and relative amount of lignin can only be roughly determined based on color or fluorescence, and no quantitative information can be given.
[0061] In view of this, the embodiments of the present invention focus on the detection of lignin content distribution in trace samples (mass less than 1g), and are committed to achieving the content distribution analysis of lignin in slice samples at the subcellular resolution level, aiming to overcome the many limitations of traditional detection methods in terms of sample size requirements, resolution and quantitative analysis.
[0062] Based on this, refer to Figure 1 , Figure 1 A flow chart of a method for detecting the relative content of lignin in a plant sample provided by an embodiment of the present invention, referring to Figure 1 , the method comprises the following steps:
[0063] S100, pre-treating the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample;
[0064] It should be noted that, in some embodiments, step S100 may include: S110, obtaining a target plant sample and cutting it vertically downward with a blade to obtain a target plant slice sample; S120, placing the target plant slice sample on a glass slide and staining it with phloroglucinol hydrochloric acid to obtain a stained target plant slice sample; S130, fading the stained target plant slice sample to obtain a pre-treated target plant slice sample.
[0065] It should also be noted that, in some embodiments, step S130 may also include: S131, washing the stained target plant slice sample with clean water to remove the floating color, to obtain the rinsed target plant slice sample; S132, absorbing water from the rinsed target plant slice sample with absorbent paper, to obtain the absorbed target plant slice sample; S133, decolorizing the absorbed target plant slice sample with sodium hydroxide solution, to obtain the pre-treated target plant slice sample.
[0066] In some specific embodiments, a target plant sample is obtained and trimmed, and then cut vertically downward with a blade, such as Figure 3 and Figure 4 The slices were placed on a glass slide and stained with 9-11 mg / mL phloroglucinol hydrochloride for 10-15 minutes. The staining results were as shown in Figure 5As shown, after dyeing, rinse with clean water for 10 seconds to wash away the floating color. After rinsing, use absorbent paper to absorb the water to prevent excess water from diluting the concentration of NaOH. Further, take 0.01-0.05 mol / L NaOH solution and drop it on the sample for fading to obtain the pretreated target plant slice sample.
[0067] S200, performing image acquisition processing on the pre-processed target plant slice sample to obtain a target plant slice sample image;
[0068] It should be noted that, in some embodiments, step S200 may include: S210, performing image acquisition on the preprocessed target plant slice sample to obtain a preliminary target plant slice sample image; S220, performing image registration processing on the preliminary target plant slice sample image to obtain a registered target plant slice sample image; S230, performing ROI target area interception processing on the registered target plant slice sample image to obtain a target plant slice sample image.
[0069] In some specific embodiments, the timing is started while the target plant slice sample is fading, and a picture is taken every 30 seconds. The taken data is processed on ImageJ, and the image registration function of ImageJ software is used to perform image registration on the data collected at different time intervals in the same field of view to ensure the continuity and consistency of the image. ROI is intercepted on the registered picture to obtain the area with good data information distribution for gradient data analysis and processing.
[0070] S300, detecting the lignin content of the target plant slice sample image by a linear regression method to obtain the lignin content of the target plant slice sample;
[0071] It should be noted that, in some embodiments, step S300 may include: S310, simplifying the target plant slice sample image to obtain a simplified target plant slice sample image; S320, analyzing the simplified target plant slice sample image by linear regression method to obtain the fading rate of the target plant slice sample image; S330, fitting and mapping the fading rate of the target plant slice sample image by least squares method to obtain a fading rate change trend chart of the target plant slice sample image; S340, comparing the fading time of the sample with known lignin content with the fading rate change trend chart of the target plant slice sample image to obtain the lignin content of the target plant slice sample.
[0072] It should also be noted that, in some embodiments, step S310 may also include: S311, performing intensity distribution fitting on the target plant slice sample image through an exponential function model to obtain a fitted target plant slice sample image; S312, performing color space conversion processing on the fitted target plant slice sample image to obtain a converted target plant slice sample image; S313, performing logarithmic transformation and mean calculation on the converted target plant slice sample image to obtain a simplified target plant slice sample image.
[0073] In some specific embodiments, in order to analyze the fading effect, an exponential function model a·e is used. bx Fit the intensity distribution. The calculated b value can reflect the fading effect of each pixel. The larger the b value, the faster the fading speed. Figure 7 shown.
[0074] It should be further explained that b reflects the relationship between the logarithmic intensity and time of each pixel in the fitting model. Using linear regression to calculate the b value in the exponential model can greatly simplify the calculation process, and its expression is:
[0075]
[0076] In the above formula, b represents the fading rate of the target plant slice sample image, N represents the number of images, x represents the index of the time point, y represents the logarithm value of the intensity, and x i Indicates the time index corresponding to the corresponding picture, y i Indicates the logarithmic value of the intensity corresponding to the corresponding image.
[0077] Convert the image from RGB color space to LAB (Lab Color Space) color space and extract the a channel (representing the color information from green to red) to better analyze the color changes of the image.
[0078] The intensity information of the a channel of all images is logarithmically transformed (log) to reduce the dynamic range of the data and improve the stability of the analysis; then the intensity values of all a channels are averaged to obtain a more simplified view that can reflect the changing trend of each pixel in the time series.
[0079] Then, we conduct linear regression analysis, converting point analysis into surface analysis by cyclically calculating the weighted sum of each image pixel. Then, we use the least squares formula to calculate the slope and intercept, which can describe the image brightness change trend. Specifically, the slope can reflect the image change trend, and the intercept can show the overall brightness change.
[0080] Furthermore, if Figure 6As shown, the fitting results of all pixel points are calculated and mapped to the corresponding pixel coordinates to obtain a fading rate change trend graph. The fading time of samples with unknown lignin content is compared with the fading time of samples with known lignin content to further obtain the absolute content.
[0081] In summary, the embodiment of the present invention is demonstrated and verified on a sample of pepper stems. Under acidic conditions, phloroglucinol hydrochloric acid reacts with lignin to oxidize to generate a red or purple compound, and the depth of the color of the reactant is positively correlated with the lignin content. The oxidized red compound, under alkaline conditions, will undergo a condensation reaction with the aldehyde group in lignin to form a new compound, and the red or purple color will slowly fade until it disappears. Obviously, in a solution of the same concentration, the color change process is strictly related to the lignin content in the sample, so the color change rate during the monitoring reaction process can be used to quantitatively analyze the lignin content in the sample, and the linear regression method is further used to process the data. The process takes about 3 minutes, which is 10 million times faster than the pixel-by-pixel calculation of each pixel point. Because the pixel-by-pixel calculation method needs to be performed separately at each pixel position from data extraction, model fitting to output results, resulting in a large number of cycles and repeated calculations, and the speed efficiency is low. The linear regression method can capture the overall trend of data distribution through the least squares method, rather than focusing on the changes in a single point, mainly due to many reasons such as vectorized operation optimization, reducing the complexity of repeated calculations, and improving data processing efficiency. This method performs model fitting for the entire world and can quickly and effectively summarize the trend of the data.
[0082] See also Figure 2 The present application also provides a system for detecting the relative content of lignin in a plant sample, which can implement the above-mentioned method for detecting the relative content of lignin in a plant sample. The system includes:
[0083] The first module 201 is used to pre-treat the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample;
[0084] The second module 202 is used to perform image acquisition processing on the pre-processed target plant slice sample to obtain the target plant slice sample image;
[0085] The third module 203 is used to detect the lignin content of the target plant slice sample image by linear regression method to obtain the lignin content of the target plant slice sample.
[0086] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0087] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.
Claims
1. A method for detecting the relative content of lignin in a plant sample, characterized in that: The method comprises the following steps: Pre-treating the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample; Performing image acquisition processing on the pre-processed target plant slice sample to obtain a target plant slice sample image; The lignin content of the target plant slice sample image is detected by linear regression method to obtain the lignin content of the target plant slice sample.
2. The method according to claim 1, characterized in that The method of pre-treating the target plant sample with phloroglucinol hydrochloric acid to obtain the pre-treated target plant slice sample comprises: Obtain a target plant sample and cut it vertically downward with a blade to obtain a target plant slice sample; Placing the target plant slice sample on a glass slide and staining it with phloroglucinol hydrochloride to obtain a stained target plant slice sample; The stained target plant slice sample is subjected to fading treatment to obtain a pretreated target plant slice sample.
3. The method according to claim 2, characterized in that The mass of the phloroglucinol hydrochloric acid is 9-11 mg / mL, and the dyeing time is 10-15 min.
4. The method according to claim 2, characterized in that: The fading treatment is performed on the stained target plant slice sample to obtain the pre-treated target plant slice sample, comprising: Washing the stained target plant slice sample with clean water to remove floating color, thereby obtaining a rinsed target plant slice sample; The rinsed target plant slice sample is subjected to water absorption treatment by using absorbent paper to obtain the water-absorbed target plant slice sample; The target plant slice sample after absorbing water is decolorized by sodium hydroxide solution to obtain a pretreated target plant slice sample.
5. The method according to claim 4, characterized in that The concentration of the sodium hydroxide solution is 0.01-0.05 mol / L.
6. The method according to claim 1, characterized in that The performing image acquisition processing on the pre-processed target plant slice sample to obtain the target plant slice sample image includes: Capturing images of the pre-processed target plant slice samples to obtain preliminary target plant slice sample images; Performing image registration processing on the preliminary target plant slice sample image to obtain a registered target plant slice sample image; The registered target plant slice sample image is subjected to ROI target region interception processing to obtain the target plant slice sample image.
7. The method according to claim 1, characterized in that The detecting of the lignin content of the target plant slice sample image by a linear regression method to obtain the lignin content of the target plant slice sample comprises: Simplifying the target plant slice sample image to obtain a simplified target plant slice sample image; Analyzing the simplified target plant slice sample image by a linear regression method to obtain a fading rate of the target plant slice sample image; Fitting and mapping the fading rate of the target plant slice sample image by the least square method to obtain a fading rate variation trend diagram of the target plant slice sample image; The fading time of the sample with known lignin content is compared with the fading rate change trend diagram of the target plant slice sample image to obtain the lignin content of the target plant slice sample.
8. The method according to claim 7, characterized in that The step of simplifying the target plant slice sample image to obtain a simplified target plant slice sample image includes: Performing intensity distribution fitting on the target plant slice sample image through an exponential function model to obtain a fitted target plant slice sample image; Performing color space conversion processing on the fitted target plant slice sample image to obtain a converted target plant slice sample image; The converted target plant slice sample image is logarithmically transformed and averaged to obtain a simplified target plant slice sample image.
9. The method according to claim 8, characterized in that The expression of the linear regression method is specifically as follows: In the above formula, b represents the fading rate of the target plant slice sample image, N represents the number of images, x represents the index of the time point, y represents the logarithm value of the intensity, and x i Indicates the time index corresponding to the corresponding picture, y i Indicates the logarithmic value of the intensity corresponding to the corresponding image.
10. A system for detecting the relative content of lignin in plant samples, characterized in that: The system comprises: The first module is used to pre-treat the target plant sample based on phloroglucinol hydrochloric acid to obtain a pre-treated target plant slice sample; The second module is used to perform image acquisition processing on the pre-processed target plant slice sample to obtain a target plant slice sample image; The third module is used to detect the lignin content of the target plant slice sample image by linear regression method to obtain the lignin content of the target plant slice sample.