Detection method for rapidly and visually judging potato damage resistance based on root tip auxin distribution
By expressing auxin biosensors in potato root tips and combining them with multispectral imaging and time-series FRET imaging, the onset time of auxin response can be automatically identified, and a structural causal model can be constructed. This solves the problems of long time consumption and inaccuracy in assessing potato damage resistance, and enables rapid, accurate assessment and high-throughput screening of damage resistance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-04-03
AI Technical Summary
Existing methods for assessing potato damage resistance are time-consuming, costly, complex to operate, and difficult to monitor auxin distribution in real time. They also cannot adapt to baseline fluctuations under different sample or experimental conditions, affecting high-throughput and standardized screening.
An inducible expression system was used to control the expression of the auxin biosensor AuxSen in potato root tip samples. By combining multispectral imaging and time-series FRET imaging, the onset time of the auxin response was automatically identified through a change point detection algorithm. An integrated microelectrode array was used to record electrophysiological signals, and a structural causal model was constructed to assess the damage resistance.
It enables rapid and accurate assessment of potato damage resistance, captures early plant initial responses, provides accurate predictions under different planting conditions, offers high-throughput and high-precision phenotypic screening tools, and reduces yield loss.
Smart Images
Figure CN121786785A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of potato analysis technology, and in particular to a method for rapidly visually assessing the damage resistance of potatoes based on the distribution of auxin in root tips. Background Technology
[0002] Potatoes, the world's fourth largest food crop, suffer severe yield losses in agricultural production due to mechanical damage, pest and disease infestations, and environmental stress. Traditional methods for assessing damage resistance are limited by their time-consuming, costly, and complex operation, and lack of real-time dynamic monitoring of the distribution of key hormones such as auxin. This method establishes a direct correlation model between the rapid visualization of auxin distribution characteristics in root tips and potato damage resistance, enabling dynamic tracking and quantitative analysis of auxin signal responses in potato plants under mechanical damage, pathogen infection, or environmental stress. This approach not only overcomes the technical bottlenecks of long cycles and low sensitivity in traditional physiological and biochemical detection methods but also allows for the dynamic tracking and quantitative analysis of auxin signal responses in potato plants under mechanical damage, pathogen infection, or environmental stress. Visualization technology enables non-invasive, real-time, dynamic monitoring of auxin distribution in roots, providing a high-throughput, high-precision phenotypic screening tool for potato damage-resistant breeding. It also offers a new research perspective on revealing the signal transduction mechanism of auxin in plant stress responses, ultimately enabling rapid breeding and promotion of damage-resistant varieties in agricultural production, reducing yield losses caused by mechanical damage and abiotic stress, and improving the sustainable production capacity and economic benefits of the potato industry. Furthermore, by integrating auxin distribution visualization technology with damage resistance assessment indicators, this method constructs a complete technological chain from molecular mechanisms to phenotypic screening, demonstrating significant scientific research value and promising industrial application prospects.
[0003] In existing technologies, the determination of the auxin response onset time usually relies on researchers setting a fixed threshold for fluorescence intensity change. This method has problems such as strong subjectivity, poor repeatability, and inability to adapt to baseline fluctuations under different samples or experimental conditions, which seriously restricts the application of this technology in high-throughput and standardized screening. Therefore, a detection method for potato damage resistance based on rapid visualization of auxin distribution in root tips is proposed. Summary of the Invention
[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for rapidly visually assessing the damage resistance of potatoes based on the distribution of auxin in root tips.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for rapidly visually assessing potato damage resistance based on root tip auxin distribution includes the following steps: Sample preparation and sensor loading: An inducible expression system was used to control the expression of the auxin biosensor AuxSen in potato root tip samples, and immobilized AuxSen microbeads were set in the imaging system as calibration points to standardize imaging parameters. Benchmark auxin distribution imaging and standardized damage stimulation: Multispectral imaging of the root tips of the samples was performed to obtain benchmark FRET data. Not only were the fluorescence of the YFP and CFP channels required for FRET acquired, but also one or more characteristic channels representing plant autofluorescence (such as chlorophyll) were acquired. Using a spectral unmixing algorithm, the contribution of background noise such as cell autofluorescence was accurately removed from the mixed signal of YFP / CFP, thereby obtaining a pure, high signal-to-noise ratio FRET ratio image that truly reflects the benchmark distribution of auxin. Environmental parameters were acquired simultaneously, and the frequency changes of root tip action potentials were recorded through an integrated microelectrode array (establishing the correlation between auxin chemical signals and plant electrophysiological signals). Subsequently, standardized mechanical damage was applied to the root tips. Real-time auxin dynamic monitoring and feature extraction: Time-series FRET imaging and action potential recording were performed on the damaged root tip. The obtained FRET time series were decomposed into multiple scales to extract signal features at different time scales. Subsequently, unsupervised kinetic analysis was performed on the data after time-series FRET imaging. Specifically, a change point detection algorithm was used to automatically identify the auxin response initiation time T-onset, and the auxin flow rate and direction were quantified by vector field analysis. The change point detection algorithm was used to determine the response initiation time T-onset in unsupervised automation by statistically analyzing the time points of significant abrupt changes in the FRET time series data. Damage resistance assessment: The response onset time T-onset, auxin flow rate, directional characteristic parameters, multi-scale decomposition characteristics, and action potential frequency change data are used. The response onset time T-onset, auxin flow rate, and directional characteristic parameters are represented as auxin dynamic parameters, and the multi-scale decomposition characteristics and action potential frequency change data are represented as electrical signal characteristics. Combined with the environmental parameters, these are input into a trained structural causal model, which outputs a quantitative index of potato damage resistance. The training data of the structural causal model includes detection data of potato varieties with different genetic backgrounds (representing genetic diversity) under normal conditions and environmental stress conditions. The structural causal model screens core features and establishes predictive relationships by verifying the causal relationship between auxin dynamic parameters, electrical signal characteristics, and damage resistance.
[0006] The above further includes: Furthermore, the inducible expression system is an ethanol-induced or dexamethasone-induced expression system. The sensor expression is turned off during the normal culture stage, and induction is only performed for a short period before imaging. The induced expression sample data is input into the benchmark auxin distribution imaging and standardized damage stimulation as the imaging object. This minimizes long-term interference with the plant's endogenous auxin network and ensures that the physiological state of the experimental material is closer to reality.
[0007] Furthermore, the multispectral imaging includes acquiring the YFP / CFPFRET channel and the plant autofluorescence channel, and using a spectral unmixing algorithm to subtract background fluorescence.
[0008] Furthermore, the standardized mechanical damage is achieved using a robot-controlled microneedle, which applies a standardized, slight puncture injury (precisely controlling the depth and force of the puncture) to the root tip elongation zone. This simulates minor bruising that tubers may suffer during harvesting and transportation without causing devastating damage. The precise time point at which the damage occurs is recorded (T=0).
[0009] Furthermore, during the temporal FRET imaging process, an image registration algorithm is used to correct motion artifacts caused by root growth or movement, and the multi-scale decomposition employs wavelet transform or empirical mode decomposition methods.
[0010] Furthermore, the feature extraction also includes performing zonal quantitative analysis on the root tip to obtain the dynamic parameters of auxin in different functional regions of the root tip.
[0011] Furthermore, the environmental parameters include temperature, pH value, and light intensity.
[0012] Furthermore, the environmental stress conditions include drought stress (achieved by controlling the osmotic pressure of the culture medium, specifically using polyethylene glycol 6000 solution to simulate drought conditions, with an osmotic pressure gradient set to -0.1 MPa to -0.8 MPa, and a stress duration of 24-72 hours) and salt stress conditions (achieved by adding sodium chloride, with a concentration gradient set to 50 mM to 200 mM, and a stress duration of 48-96 hours). During the stress treatment, changes in plant physiological indicators were monitored simultaneously. The structural causal model was validated for causal pathways through interventional analysis, which included applying auxin transport inhibitors to alter the auxin redistribution rate and observing their impact on the final damage resistance prediction results.
[0013] The present invention has the following beneficial effects: 1. In this invention, the change point detection algorithm automatically identifies the auxin response start time T-onset. It achieves unsupervised automated judgment by statistically analyzing the time points of significant mutations in FRET time series data. It identifies small but statistically significant early signal changes that are difficult for the human eye to detect, thereby capturing the initial response of plants earlier and more accurately. This avoids response delay misjudgment caused by setting the threshold too high, or noise misjudgment caused by setting it too low.
[0014] 2. In this invention, a multidimensional feature space is constructed by integrating auxin dynamics, action potentials, environmental parameters, and genetic background, enabling the structural causal model to make accurate predictions for new varieties under different planting conditions. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the steps of a method for rapidly visually determining the damage resistance of potatoes based on the distribution of auxin in root tips, as proposed 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] Please see Figure 1 As shown, this invention provides a method for rapidly visually assessing the damage resistance of potatoes based on the distribution of auxin in root tips, comprising the following steps: Sample preparation and sensor loading: An inducible expression system was used to control the expression of the auxin biosensor AuxSen in potato root tip samples to reduce interference with the plant's endogenous auxin network. Immobilized AuxSen microbeads were set in the imaging system as calibration points to standardize imaging parameters. Benchmark auxin distribution imaging and standardized damage stimulation: Prepared seedlings were placed on a microscope stage. FRET imaging of key areas such as the root tip meristem, elongation zone, and root cap was performed in a non-destructive manner. Multispectral imaging of the root tip was used to obtain benchmark FRET data. Not only were the YFP and CFP channel fluorescence required for FRET acquired, but also one or more characteristic channels representing plant autofluorescence (such as chlorophyll) were acquired. Using a spectral unmixing algorithm, the contribution of background noise such as cell autofluorescence was accurately subtracted from the mixed YFP / CFP signal, thereby obtaining a pure, high signal-to-noise ratio FRET ratio image that truly reflects the benchmark distribution of auxin. Environmental parameters were acquired simultaneously, and the frequency changes of root tip action potentials were recorded through an integrated microelectrode array (establishing the correlation between auxin chemical signals and plant electrophysiological signals). Subsequently, standardized mechanical damage was applied to the root tip. Real-time auxin dynamic monitoring and feature extraction: Time-series FRET imaging and action potential recording were performed on the damaged root tip (root tip injury area and adjacent area). Time-series FRET imaging ensured that imaging conditions (such as laser intensity and exposure time) remained consistent throughout the process. The acquisition frequency of the time-series FRET imaging was once every 30 seconds to 2 minutes, and the total monitoring time was 60 to 120 minutes. The obtained FRET time series was decomposed into multiple scales to extract signal features at different time scales. Subsequently, unsupervised kinetic analysis was performed on the data after time-series FRET imaging. Specifically, a change point detection algorithm was used to automatically identify the auxin response start time T-onset, and the auxin flow rate and direction were quantified by vector field analysis. The change point detection algorithm was used to unsupervisedly and automatically determine the response start time T-onset by statistically analyzing the time points of significant abrupt changes in the FRET time series data. Damage resistance assessment: The response onset time T-onset, auxin flow rate, directional characteristic parameters, multi-scale decomposition characteristics, and action potential frequency change data are used. The response onset time T-onset, auxin flow rate, and directional characteristic parameters are represented as auxin dynamic parameters, and the multi-scale decomposition characteristics and action potential frequency change data are represented as electrical signal characteristics. Combined with the environmental parameters, these are input into a trained structural causal model, which outputs a quantitative index of potato damage resistance. The training data of the structural causal model includes detection data of potato varieties with different genetic backgrounds (representing genetic diversity) under normal conditions and environmental stress conditions. The structural causal model screens core features and establishes predictive relationships by verifying the causal relationship between auxin dynamic parameters, electrical signal characteristics, and damage resistance.
[0018] In this embodiment: Sample preparation and sensor loading: Select sterile tissue culture seedlings or mini-tuber seedlings of potatoes with consistent growth status. Introduce the AuxSen biosensor expression unit controlled by an ethanol or dexamethasone inducible promoter through Agrobacterium-mediated genetic transformation or co-culture with engineered bacteria. During the normal culture phase, the sensor does not express to avoid interfering with the plant's endogenous auxin network. Only apply a specific chemical inducer before the imaging experiment (24 hours in advance) to activate the sensor's transient, high-intensity expression, thereby minimizing the long-term impact on plant physiology while ensuring high signal sensitivity. Calibration system integration: A calibration region with immobilized AuxSen microbeads is pre-integrated into the glass slide or microfluidic chip used to fix and image the sample. The AuxSen microbeads can generate a stable FRET reference signal under specific excitation light. At the beginning of each imaging experiment, the calibration region is first imaged, and the parameters of the microscope such as laser intensity and photomultiplier tube gain are adjusted to make the FRET signal output of the microbeads the standard value. Multispectral background subtraction imaging: The prepared sample is placed under a confocal microscope. Before damage is applied, the key areas of the root tip (meristematic zone, elongation zone, and root cap) are imaged. Not only are the YFP and CFP channels of fluorescence required for FRET acquired, but also one or more characteristic channels representing plant autofluorescence (such as chlorophyll) are acquired. Using a spectral unmixing algorithm, the contribution of background noise such as cell autofluorescence is accurately subtracted from the mixed signal of YFP / CFP, thereby obtaining a pure FRET ratio image with a high signal-to-noise ratio, which truly reflects the baseline distribution of auxin. Dynamic baseline establishment and environmental covariate recording: Multiple frames (lasting 10-20 minutes) of baseline images are continuously acquired, and a sliding window dynamic baseline correction algorithm is applied to calculate the mean and variance of the FRET signal in real time to confirm whether the signal is in a steady state. Finally, a robust baseline value that excludes the physiological fluctuations of the plant itself is generated. Simultaneously, the system automatically records and stores the environmental parameters during imaging, including the temperature of the culture medium, pH value, light intensity, etc. Synchronous monitoring of electrophysiological signals: A microelectrode array integrated around the sample is used to non-invasively and synchronously record electrical signal data such as the frequency and amplitude of action potentials on the root tip surface. Specifically, a 0.1-100Hz bandpass filter is used to eliminate baseline drift and high-frequency noise. A threshold detection algorithm is employed to automatically identify action potential events, calculate the action potential firing frequency, cluster firing mode, and the conduction velocity of action potentials in different regions of the root tip, and establish the correlation between auxin chemical signals and plant electrophysiological signals through cross-correlation analysis.
[0019] Robotic precision lesioning: A computer-controlled robotic micromanipulation arm drives a standardized microneedle to apply a tiny puncture injury to the root apex elongation zone at a pre-set, constant speed, pressure, and puncture depth. Synchronous acquisition of temporal FRET and electrical signals: Starting from the moment of injury (T=0), a high-speed temporal imaging mode is activated (full-field FRET images are acquired every 30 seconds for 60-120 minutes), while maintaining synchronous recording of the microelectrode array to fully capture the dynamic process of auxin distribution and electrical signal changes after injury. Real-time motion artifact correction: During time-series imaging, an image registration-based algorithm is run in real time to automatically detect and correct image drift caused by root growth or slight movement, ensuring that each pixel in subsequent analysis is in the same spatial position. Multi-scale signal decomposition: Wavelet transform or empirical mode decomposition is performed on the FRET time series data of each pixel or preset region of interest to decompose the original signal into components of different time scales, thereby separating the fast initial response, slow transport process and random noise. Unsupervised response start time detection: Apply a change point detection algorithm to the denoised FRET time series to automatically identify the first time point where the signal undergoes a significant change and define it as the response start time (T-onset). Spatiotemporal dynamics quantization: Vector field analysis: By calculating the spatial gradient of the FRET signal between consecutive frames, a vector field describing the direction of auxin flow is generated, and its average flow rate and net flow rate are quantified. Regional quantitative analysis: The root tip image is automatically segmented into anatomical functional areas such as root cap, meristematic zone, and elongation zone, and the dynamic parameters of each area are extracted respectively; Multimodal causal inference model: All extracted features (T-onset, auxin flow rate and direction, partitioning parameters, multiscale decomposition features, action potential frequency changes) and recorded environmental covariates are input into a pre-trained structural causal model. The structural causal model is trained using a large dataset of potato varieties with multiple genetic backgrounds (representing genetic diversity) under normal, drought, and salt stress conditions (representing environmental stress), and their macroscopic damage resistance phenotypes (such as tuber browning area and rot rate) are known. Through intervention logic and causal graph analysis, it is verified which auxin / electrical signal features are causally related to damage resistance.
[0020] In one embodiment, the inducible expression system is an ethanol-induced or dexamethasone-induced expression system. The sensor expression is turned off during the normal culture phase, and induction is only performed for a short period before imaging. The induced expression sample data is input into the baseline auxin distribution imaging and standardized damage stimulation as the imaging object. This minimizes long-term interference with the plant's endogenous auxin network and ensures that the physiological state of the experimental material is closer to reality.
[0021] Ethanol-inducible expression system: Derived from the ethanol utilization regulatory pathway of Aspergillus nidulans, it consists of the AlcR protein and an inducible promoter. In the presence of ethanol, the AlcR protein undergoes a conformational change, and upon activation, it specifically binds to the recognition site on the alcA promoter, initiating the expression of downstream genes. This system requires constitutive expression of the AlcR protein, with the target gene positioned downstream of the alcA promoter.
[0022] Dexamethasone (Dex) inducible expression system: based on the regulatory mechanism of glucocorticoid receptor (GR). Dexamethasone binds to GR to form a complex, which binds to the glucocorticoid response element (GRE) in the promoter region of the target gene, activating transcription. A typical system is pOp6 / LhGR.
[0023] The pOp6 promoter contains 6 lacO repeat sequences; LhGR is a fusion protein of the lac repressor, GAL4 activation domain, and GR binding domain. After Dex induction, it enters the nucleus and activates the pOp6 promoter.
[0024] In one embodiment, the multispectral imaging includes acquiring the YFP / CFPFRET channel and the plant autofluorescence channel, and using a spectral unmixing algorithm to subtract background fluorescence.
[0025] In one embodiment, the standardized mechanical damage is achieved using a robot-controlled microneedle.
[0026] A standardized, slight puncture injury is applied to the root tip elongation zone using a microneedle (with precise control of needle depth and force). This simulates minor bruising that tubers may suffer during harvesting and transportation without causing devastating damage. The precise time point at which the injury occurs is recorded (T=0).
[0027] In one embodiment, during the time-series FRET imaging process, an image registration algorithm is used to correct motion artifacts caused by root growth or movement, and the multi-scale decomposition employs wavelet transform or empirical mode decomposition methods.
[0028] In this embodiment: Synchronous recording system: Employs a dual-channel fluorescence microscope (FRET channel: 488nm excitation / 530nm emission; action potential channel: electrophysiological recorder) to achieve nanosecond-level time alignment via a timestamp synchronization module; Data acquisition parameters: FRET imaging frame rate: 10 frames / second (temporal resolution 100ms); action potential sampling rate: 1kHz (temporal resolution 1ms); synchronization error: <1ms (achieved through hardware triggering); Motion artifact correction (image registration): An optical flow-based registration method is used, and the displacement vector field between adjacent frames is calculated using the Lucas-Kanade algorithm; Multiscale decomposition: Wavelet transform decomposition: A 4-level decomposition is performed using the Daubechies wavelet basis (db4). Approximation coefficients (A4) and detail coefficients (D1-D4) are extracted, and the energy at each scale is calculated, expressed as follows: The corresponding feature vectors are obtained. ; Empirical Mode Decomposition (EMD): Identifies local maxima / minima of a signal, constructs upper and lower envelopes and calculates the mean envelope, and extracts intrinsic mode functions (IMFs). Repeat this process until the residuals are monotonic, and obtain the energy ratio of each IMF. ,in, The energy of the k-th IMF component is specifically... , It is represented as the sum of the energies of all IMF components.
[0029] In one embodiment, the feature extraction further includes performing zonal quantitative analysis on the root tip to obtain the dynamic parameters of auxin in different functional regions of the root tip (root cap, meristematic zone, and elongation zone).
[0030] Key dynamic parameters: Response onset time (T-onset): The time required from the occurrence of damage to a significant change in auxin concentration at the damage site.
[0031] Peak concentration change (Δ[IAA]max): The maximum change in auxin concentration in the lesion site area (relative to the baseline).
[0032] Velocity: The initial rate at which auxin concentration rises or falls at the site of damage.
[0033] Signal spread range (Spread): The spatial range affected by changes in auxin concentration (e.g., the distance extending outward from the lesion point).
[0034] Recovery time (T-recovery): The time required for auxin concentration to recover from peak to near baseline levels.
[0035] In one embodiment, the environmental parameters include temperature, pH value, and light intensity.
[0036] In one embodiment, the environmental stress conditions include drought stress (achieved by controlling the osmotic pressure of the culture medium, specifically using polyethylene glycol 6000 solution to simulate drought conditions, with an osmotic pressure gradient set to -0.1 MPa to -0.8 MPa, and a stress duration of 24-72 hours) and salt stress conditions (achieved by adding sodium chloride, with a concentration gradient set to 50 mM to 200 mM, and a stress duration of 48-96 hours). During the stress treatment, changes in plant physiological indicators are monitored simultaneously. The structural causal model is validated for causal pathways through interventional analysis, which includes applying auxin transport inhibitors to alter the auxin redistribution rate and observing their impact on the final damage resistance prediction results.
[0037] In this embodiment: Data Collection: Multidimensional data were collected on potato varieties with different genetic backgrounds (such as the highly resistant variety "Qingshu 9" and the sensitive variety "Feiwurui") under normal conditions (25℃ / 60% humidity) and environmental stress conditions (high temperature 35℃ / drought stress). This included collecting dynamic parameters of auxin (T-onset, auxin mobility, signal spread, multi-scale decomposition characteristics W, action potential frequency change Δf) and environmental parameters (temperature). pH, light intensity This forms a multidimensional dataset.
[0038] Data standardization: Z-score standardization is used to eliminate the influence of dimensions.
[0039] Model construction: based on damage resistance ( Using auxin dynamics and environmental parameters as the target variable, and random combinations of different environmental stress intensities and sampling time points to generate training samples, a multiple linear regression model is constructed. Among them, regression coefficient The marginal effect reflecting the ability of each parameter to resist damage, for example, =0.189 means that for every 1 standard deviation increase in auxin flow rate, the damage resistance increases by an average of 0.189 units. Indicates the response start time. Indicates the auxin flow rate. Indicates the range of signal propagation. Represents multi-scale decomposition features. This indicates the change in the frequency of the action potential. Indicates temperature. Indicates light intensity. This indicates the error term.
[0040] Experimental design: Apply an auxin transport inhibitor (such as NPA) to reduce the auxin flow rate by 20%, i.e.: .
[0041] Efficacy evaluation: Comparing the differences in anti-damage ability before and after inhibitor treatment: , This is expressed as a numerical value of the damage resistance obtained under normal conditions (without inhibitors). This is expressed as a numerical value representing the resistance to damage obtained after applying an inhibitor. The values are expressed as averages. Simulation data show that the damage resistance decreased by an average of 0.065 units after the application of inhibitors, verifying a positive causal relationship between auxin flow rate and damage resistance.
[0042] Average treatment effect (ATE): quantifies the causal effect of changes in auxin flow rate on the ability to combat damage, and is expressed by the following formula: ,in, Indicates the average treatment effect. Indicates resistance to damage. Indicates the auxin flow rate. Indicates the expected value. Indicates intervention operation. This represents two different rates. The simulation results show that the ATE is 0.189, indicating that for every 1 standard deviation increase in auxin flow rate, the damage resistance increases by an average of 0.189 units.
[0043] Path analysis: The direct and indirect effects of auxin parameters on damage resistance were verified using structural equation modeling (SEM). For example, auxin flow rate indirectly affects damage resistance by influencing T-onset, with a path coefficient of 0.15.
[0044] Feature importance ranking: Select core features based on the absolute value of regression coefficients or SHAP values.
[0045] Model optimization: Ridge regression or Lasso regression was used to handle multicollinearity and improve the model's generalization ability. After optimization, the R² value of the model increased from 0.72 to 0.78.
[0046] Predictive Model: The selected core features are input into the structural causal model, which outputs a quantitative index of damage resistance. For example, the damage resistance of a certain variety is predicted to be 0.82 (range 0-1).
[0047] Cross-validation: K-fold cross-validation was used to evaluate the stability of the model, with a mean squared error (MSE) of 0.03.
[0048] A detection system for implementing a rapid visual assessment of potato damage resistance based on root tip auxin distribution, comprising: Imaging module: Performs multispectral and time-series FRET imaging on the sample; Microelectrode array module: synchronously records changes in the frequency of root apical action potentials; Damage stimulation module: Applying standardized mechanical damage via controlled microneedles; Data processing module: performs multi-scale decomposition, change point detection algorithms, and vector field analysis; Analysis and Judgment Module: Runs the causal model of the structure and outputs the judgment result of the damage resistance capability.
[0049] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for rapidly visually assessing the damage resistance of potatoes based on the distribution of auxin in root tips.
[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A method for rapidly visually assessing potato damage resistance based on root tip auxin distribution, characterized in that: Includes the following steps: Sample preparation and sensor loading: An inducible expression system was used to control the expression of the auxin biosensor AuxSen in potato root tip samples, and immobilized AuxSen microbeads were set in the imaging system as calibration points to standardize imaging parameters. Benchmark auxin distribution imaging and standardized damage stimulation: Multispectral imaging of the root tip of the sample was performed to obtain benchmark FRET data, environmental parameters were collected simultaneously, and the frequency changes of root tip action potential were recorded through an integrated microelectrode array. Then, standardized mechanical damage was applied to the root tip. Real-time auxin dynamic monitoring and feature extraction: Time-series FRET imaging and action potential recording were performed on the damaged root tip. The obtained FRET time series were decomposed into multiple scales to extract signal features at different time scales. Subsequently, unsupervised kinetic analysis was performed on the data after time-series FRET imaging. Specifically, a change point detection algorithm was used to automatically identify the auxin response initiation time T-onset, and the auxin flow rate and direction were quantified by vector field analysis. The change point detection algorithm was used to determine the response initiation time T-onset in unsupervised automation by statistically analyzing the time points of significant abrupt changes in the FRET time series data. Damage resistance assessment: The response onset time T-onset, auxin flow rate, directional characteristic parameters, multi-scale decomposition characteristics, and action potential frequency change data are used. The response onset time T-onset, auxin flow rate, and directional characteristic parameters are represented as auxin dynamic parameters, and the multi-scale decomposition characteristics and action potential frequency change data are represented as electrical signal characteristics. Combined with the environmental parameters, these are input into a trained structural causal model, which outputs a quantitative index of potato damage resistance. The training data of the structural causal model includes detection data of potato varieties with different genetic backgrounds under normal conditions and environmental stress conditions. The structural causal model screens core features and establishes predictive relationships by verifying the causal relationship between auxin dynamic parameters, electrical signal characteristics, and damage resistance.
2. The method for rapidly visually determining potato damage resistance based on root tip auxin distribution according to claim 1, characterized in that: The inducible expression system is either an ethanol-induced or dexamethasone-induced expression system. The sensor expression is turned off during the normal culture phase, and induction is only performed briefly before imaging. The induced expression sample data is input into the baseline auxin distribution imaging and standardized damage stimulation as the imaging object.
3. The method for rapidly visually determining potato damage resistance based on root tip auxin distribution according to claim 1, characterized in that: The multispectral imaging includes acquiring YFP / CFPFRET channels and plant autofluorescence channels, and using a spectral unmixing algorithm to subtract background fluorescence.
4. The method for rapidly visually determining potato damage resistance based on root tip auxin distribution according to claim 1, characterized in that: The standardized mechanical damage is achieved using a robot-controlled microneedle.
5. The method for rapidly visually determining potato damage resistance based on root tip auxin distribution according to claim 1, characterized in that: The temporal FRET imaging process employs an image registration algorithm to correct motion artifacts caused by root growth or movement, and the multi-scale decomposition uses wavelet transform or empirical mode decomposition methods.
6. The method for rapidly visually determining potato damage resistance based on root tip auxin distribution according to claim 1, characterized in that: The feature extraction also includes performing regional quantitative analysis on the root tip to obtain the dynamic parameters of auxin in different functional regions of the root tip.
7. The method for rapidly visually determining the damage resistance of potatoes based on the distribution of auxin in root tips according to claim 1, characterized in that: The environmental parameters include temperature, pH value, and light intensity.
8. The method for rapidly visually determining the damage resistance of potatoes based on the distribution of auxin in root tips according to claim 1, characterized in that: The environmental stress conditions include drought stress and salt stress conditions. The structural causal model verifies the causal path through interventional analysis, which includes applying auxin transport inhibitors to change the auxin redistribution rate and observing their impact on the final damage resistance prediction results.