Index analysis method for plant phenotype digital diversity

Abstract

The invention relates to the technical field of plant phenotype analysis, in particular to an index analysis method for plant phenotype digital diversity. The problems that in an existing plant phenotype analysis method, the data dimension is single, discretization is limited, dynamic nature is insufficient, and a system maintenance scheme is lacked are effectively solved. According to the method, environment-phenotype dynamic data is acquired by constructing a heterogeneous monitoring network, discrete phenotype data is converted into a continuous probability space in combination with a three-dimensional ellipsoid domain model, and accurate evaluation and dynamic maintenance of plant phenotype diversity are realized through subdomain decomposition, dynamic correction, thermodynamic diagram generation, depth tracking and inverse mapping analysis. According to the method, genotype-environment interaction characteristics can be effectively captured, phenotypic response risk sites and lag units can be accurately positioned, a systematic solution is provided for plant variety-environment adaptation and diversity maintenance, and the dynamic nature, accuracy and practicability of plant phenotypic analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of plant phenotyping, and in particular to an indicator analysis method for plant phenotypic digital diversity. Background Art

[0002] Plant phenotypic diversity is a core indicator of species' ability to adapt to environmental fluctuations and maintain ecological stability. Its precise analysis is of great significance for agricultural breeding, germplasm resource conservation, and ecosystem management. Traditional phenotypic analysis methods rely on manual measurements or single sensor data, focusing primarily on static morphological indicators such as plant height and leaf area. These methods struggle to capture dynamic phenotypic characteristics such as canopy spectral response, leaf dynamic oscillations, and root electrical signaling. This leads to dimensional limitations in the analysis of associations between environmental factors and phenotypic responses, preventing them from fully reflecting the complex mechanisms of genotype-environment interactions.

[0003] Existing technologies face significant bottlenecks in data processing: discrete phenotypic data is difficult to transform into continuous probability distribution models, making it impossible to quantify the patterns of phenotypic evolution under environmental stress gradients. Furthermore, the strength of genotype-environment interactions is often estimated through empirical formulas, lacking visual mathematical models. This makes it difficult to accurately locate risk sites and regions of genetic advantage that indicate phenotypic diversity fluctuations, limiting the depth and application value of phenotypic analysis.

[0004] In terms of dynamic response and decision support, traditional methods for phenotypic feedback on environmental stresses exhibit a lag, making it impossible to adjust key parameters like stomatal conductance thresholds in real time. Furthermore, there is a lack of systematic methods for inferring optimal environmental parameters from phenotypic characteristics. Consequently, adaptation plans for varieties and environments often rely on empirical judgment, making it difficult to actively maintain and precisely regulate phenotypic diversity. Therefore, the development of a digital analysis method that integrates multi-dimensional dynamic data, quantifies interaction mechanisms, tracks responses in real time, and generates decision-making solutions has become an urgent need in the field of plant phenotyping research. Summary of the Invention

[0005] The present invention proposes an indicator analysis method for the digital diversity of plant phenotypes, which effectively solves the problems of single data dimension, discretization limitation, insufficient dynamics and lack of system maintenance solutions in existing plant phenotypic analysis methods. This method collects environment-phenotype dynamic data by constructing a heterogeneous monitoring network, and converts discrete phenotypic data into a continuous probability space in combination with a three-dimensional ellipsoid domain model. Through subdomain decomposition, dynamic correction, heat map generation, depth tracking and inverse mapping analysis, it realizes the accurate assessment and dynamic maintenance of plant phenotypic diversity. The present invention can effectively capture the characteristics of genotype-environment interactions, accurately locate phenotypic response risk sites and hysteresis units, provide a systematic solution for plant variety-environment adaptation and diversity maintenance, and improve the dynamics, accuracy and practicality of plant phenotypic analysis.

[0006] The technical solutions of the present invention are as follows:

[0007] The technical solution of the present invention is to provide an index analysis method for plant phenotypic digital diversity, comprising:

[0008] Plant phenotyping analysis uses a plant digital phenotyping collection and analysis system to build a heterogeneous monitoring network, collaboratively collecting multispectral radiation data above the plant canopy, leaf oscillation frequency, and root electrical signal topology to generate an environment-phenotype correlation matrix;

[0009] Based on the environment-phenotype dynamic matrix, combined with the historical variety database, genotype and environmental stress parameters were extracted to construct a three-dimensional ellipsoid domain model;

[0010] Based on the three-dimensional ellipsoid domain model, non-uniform subdomain decomposition is implemented along the main axis direction: high-resolution subdivision is performed in areas with severe environmental stress, and low-resolution subdivision is performed in stable areas, so that each subdomain corresponds to a unique phenotypic response pattern feature, forming a phenotypic response fingerprint library;

[0011] For each subdomain, a dedicated plant digital phenotyping acquisition and analysis system tracking unit is configured. When abnormal leaf oscillation is detected, a root impedance scan is triggered, and the subdomain response boundary is dynamically determined based on the organ cascade feedback mechanism.

[0012] Based on the three-dimensional ellipsoid domain model, the subdomains and the corresponding subdomain response boundaries are marked to generate a three-dimensional heat map;

[0013] In response to the three-dimensional heat map results, deep tracking is initiated for the red high-risk areas. The deep tracking method is to control the plant digital phenotyping acquisition and analysis system to capture hidden stress sources along the canopy vorticity and locate the phenotypic response lag units;

[0014] Based on the phenotypic characteristics of the blue stable zone and the diagnostic results of the hysteresis unit, the optimal environmental parameter combination is inferred through inverse ellipsoid mapping, the variety-environment adaptation matrix is ​​generated, and the diversity maintenance plan is output.

[0015] As a further option of this method, the multispectral radiation data acquisition uses a multispectral imaging system equipped with multiple narrow-band wavelength sensors to obtain plant canopy reflectance information in the visible to near-infrared band, and realizes digital representation of key physiological parameters through modeling of chlorophyll content, water status and photosynthetic efficiency;

[0016] The blade oscillation frequency measurement uses a high-speed camera and an inertial measurement unit to capture the blade motion trajectory, and analyzes the main vibration spectrum through Fourier transform;

[0017] The root electrical signal topology analysis includes: using a microelectrode array implanted in the soil to measure changes in rhizosphere conductivity, combining ion flow analysis with action potential recording to evaluate the root system's ability to absorb water and nutrients.

[0018] As a further option of this method, the construction of the environment-phenotype dynamic matrix includes time alignment, spatial standardization and dimensionality reduction of multi-source data, wherein the dimensionality reduction method uses principal component analysis or wavelet transform to extract key phenotypic features;

[0019] The environment-phenotype dynamic matrix contains the joint distribution of environmental variables and phenotypic characteristics, which is expressed as:

[0020] ;

[0021] in, represents the environment-phenotype dynamic matrix, Indicates the The sample in measurements of environmental-phenotypic traits, is the sample size, is the characteristic dimension.

[0022] As a further option of this method, the construction of the three-dimensional ellipsoid domain model includes:

[0023] Based on the joint covariance matrix of the genotype data matrix and the environmental stress parameter matrix, the eigenvalues ​​and eigenvectors were calculated to determine the direction and scale of the ellipsoid's major axis.

[0024] The curvature of the ellipsoid is quantified by the ratio of the eigenvalues ​​of any two principal axes and is used to characterize the strength of the genotype-environment interaction;

[0025] The standard equation of the three-dimensional ellipsoid domain model is:

[0026] ;

[0027] in, is the genotype feature vector, is the environmental stress parameter vector, is the inverse matrix of the joint covariance matrix;

[0028] Among them, the three-dimensional ellipsoid domain model distinguishes different types of phenotypic response patterns through the ellipsoid curvature and principal axis gradient. The ellipsoid stretching direction represents the area dominated by environmental stress, while the high curvature direction reflects the area with strong genetic stability.

[0029] As a further option of this method, the non-uniform subdomain decomposition strategy uses an adaptive meshing algorithm to dynamically adjust the subdomain resolution based on the ellipsoid principal axis gradient function and local curvature evaluation; quadtree / octree recursive subdivision is implemented in high-gradient regions to achieve refined modeling and classification of stress response patterns;

[0030] The non-uniform subdomain division includes: calculating the gradient and curvature in the main axis direction for each subdomain, identifying areas with severe environmental stress and improving the resolution, while retaining the low-resolution division of stable areas to balance computational efficiency and modeling accuracy.

[0031] As a further option of this method, the phenotypic response fingerprint library construction includes:

[0032] PDPAS uses autoencoder technology to encode the extracted phenotypic response features to generate compact and representative fingerprint vectors;

[0033] PDPAS introduces a locality-sensitive hashing algorithm to map feature vectors into a hash space, so that similar feature vectors have a higher probability of collision.

[0034] The PRFD system uses a mini-batch stochastic gradient descent algorithm to regularly update the parameters of the autoencoder to ensure that the encoder can adapt to the latest phenotypic response characteristics;

[0035] PDPAS combines phenotypic response patterns with known genotype-environment interactions to construct a semantic network, where nodes represent phenotypic response patterns and edges represent similarities or causal relationships between patterns.

[0036] The key features stored in the phenotypic response fingerprint library include: spectral oscillation ratio, vibration damping coefficient, root conductivity entropy and other highly distinguishable phenotypic response indicators.

[0037] As a further option of this method, the organ cascade feedback mechanism constructs a cross-organ coupling feedback model by integrating the real-time physiological signals of leaf oscillation and root impedance, and dynamically adjusts the subdomain response boundary based on the ellipsoid surface gradient field.

[0038] As a further option of this method, the three-dimensional heat map generation includes: using the three main axes of the ellipsoid domain model as a three-dimensional coordinate system, mapping each subdomain and its response boundary to the space, and using a color coding strategy, with red hot spots marking diversity fluctuation risk sites and blue stable areas marking genetic advantage expression sites;

[0039] The three-dimensional heat map introduces a knowledge graph to enhance the semantic annotation function, allowing users to interactively query the genotype background, historical response trends and potential stress types of the subdomain.

[0040] As a further option of this method, the in-depth tracking of hidden stress sources includes: using lidar and infrared thermal imaging technology to capture the trajectory of canopy vorticity changes, combining meteorological sensor data to identify areas of abnormal air flow, thereby locating potential hidden stress sources;

[0041] The positioning phenotypic response hysteresis unit uses a dynamic time warping algorithm to compare the phenotypic response patterns between different subdomains. When the response pattern of a subdomain deviates significantly from the expected one, it is marked as a potential phenotypic response hysteresis unit.

[0042] The hidden stress source analysis includes: integrating multi-source sensor data, using multiple regression analysis and principal component analysis to identify the main environmental factors affecting plant phenotypic responses, and automatically triggering the root impedance scanning module to obtain more detailed underground physiological information.

[0043] As a further option of this method, the inverse ellipsoid mapping inversely estimates the optimal environmental parameter combination including: environmental parameter vector , phenotypic feature vector , the inverse matrix of the joint covariance matrix , the formula for inverse ellipsoid mapping is as follows:

[0044] ;

[0045] The variety-environment adaptation matrix construction includes: based on the inverse ellipsoid mapping results, evaluating the adaptability of different plant varieties under different environmental conditions, wherein the higher the adaptability value, the more adaptable the variety is in the environment.

[0046] The beneficial effects brought about by the technical solutions provided in the embodiments of the present application include at least the following beneficial effects:

[0047] This technical solution proposes a systematic, multimodal fusion method for analyzing plant phenotypic diversity through digitalization. By constructing a plant digital phenotyping acquisition and analysis system, it achieves heterogeneous perception of plant canopy multispectral radiation, leaf oscillation frequency, and root electrical signals, comprehensively capturing the dynamic response characteristics of plants under different environmental conditions. This method further integrates historical variety databases and introduces innovative mechanisms such as three-dimensional ellipsoidal domain modeling, non-uniform subdomain decomposition, and the construction of a phenotypic response fingerprint library. This method overcomes the limitations of traditional phenotyping analysis in terms of spatial resolution, temporal dynamics, and the ability to interpret environmental interactions, significantly improving the accuracy and interpretability of plant phenotypic response patterns.

[0048] On this basis, the technical solution uses an organ cascade feedback mechanism and an inverse ellipsoid mapping algorithm to achieve real-time tracking of plant stress responses and reverse inference of optimal environmental parameter combinations, constructing a variety-environment adaptation matrix and forming a scientific diversity maintenance plan. This system not only possesses high-precision and robust capabilities for identifying environmental stresses and pinpointing genetic advantages, but also provides data-driven decision support for crop breeding, precision agriculture, and intelligent cultivation, with broad application prospects and industrial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1This is a schematic diagram of the overall process of the index analysis method for plant phenotypic digital diversity; Figure 2 Detailed flowchart of step S100 of the index analysis method for digital diversity of plant phenotypes; Figure 3 A detailed flow chart of step S200 of the index analysis method for digital diversity of plant phenotypes; Figure 4 A detailed flow chart of step S300 of the index analysis method for digital diversity of plant phenotypes; Figure 5 A detailed flow chart of step S400 of the index analysis method for digital diversity of plant phenotypes; Figure 6 A detailed flow chart of the steps of the plant phenotypic digital diversity indicator analysis method S500; Figure 7 A detailed flow chart of step S600 of the index analysis method for digital diversity of plant phenotypes; Figure 8 Detailed flowchart of step S700 of the index analysis method for digital diversity of plant phenotypes. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0051] Plant phenotype is the external manifestation of the interaction between plant genotype and environment. Its diversity is an important foundation for species to adapt to the environment, maintain survival and evolve. Traditional plant phenotyping methods mostly rely on manual observation or single sensor data collection. The shortcomings include: single data dimension, discrete analysis limitations, lack of dynamics and lack of system maintenance solutions. To solve the above problems, please refer to Figure 1 , which shows a method for analyzing plant phenotypic digital diversity indicators provided by one embodiment of the present invention, the method comprising:

[0052] S100: Plant phenotyping analysis builds a heterogeneous monitoring network through the plant digital phenotyping acquisition and analysis system, collaboratively collecting multispectral radiation data above the plant canopy, leaf oscillation frequency, and root electrical signal topology to generate an environment-phenotype correlation matrix.

[0053] S200: Based on the environment-phenotype dynamic matrix, combined with the historical variety database, genotype and environmental stress parameters are extracted to construct a three-dimensional ellipsoid domain model.

[0054] S300: Based on the three-dimensional ellipsoid domain model, non-uniform subdomain decomposition is implemented along the main axis direction: high-resolution subdivision is performed in areas with severe environmental stress, and low-resolution subdivision is performed in stable areas, so that each subdomain corresponds to a unique phenotypic response pattern feature, forming a phenotypic response fingerprint library.

[0055] S400: For the divided subdomains, a dedicated plant digital phenotyping acquisition and analysis system tracking unit is configured. When abnormal leaf oscillation is detected, a root impedance scan is triggered, and the subdomain response boundary is dynamically determined based on the organ cascade feedback mechanism.

[0056] S500: Based on the three-dimensional ellipsoid domain model, the subdomains and the corresponding subdomain response boundaries are marked to generate a three-dimensional heat map.

[0057] S600: In response to the three-dimensional heat map results, deep tracking is initiated for the red high-risk area. The deep tracking method is: control the plant digital phenotyping acquisition and analysis system to capture hidden stress sources along the canopy vorticity and locate the phenotypic response lag unit.

[0058] S700: Based on the phenotypic characteristics of the blue stable zone and the diagnostic results of the hysteresis unit, the optimal environmental parameter combination is inferred through inverse ellipsoid mapping, the variety-environment adaptation matrix is ​​generated, and the diversity maintenance plan is output.

[0059] The specific plan is as follows:

[0060] In a method for analyzing plant phenotypic digital diversity indicators, the Plant Digital Phenotype Acquisition and Analysis System (PDPAS) within the S100 is an intelligent system integrating multi-source sensors, data processing algorithms, and environmental monitoring modules. It is designed to accurately capture plant phenotypic characteristics at different growth stages and dynamically analyze them in conjunction with environmental factors. PDPAS's core functions include real-time collection of multispectral radiation data above the plant canopy, leaf oscillation frequency, and root electrical signal topology, providing fundamental data support for subsequent digital diversity analysis.

[0061] Please refer to Figure 2 , which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S100 of the present application, which includes:

[0062] S110: Multispectral radiation data acquisition.

[0063] In terms of multispectral radiation data collection, PDPAS utilizes multispectral imaging technology, equipped with multiple narrowband wavelength sensors, to acquire reflectance information from plant canopies across the visible to near-infrared range. Multispectral radiation data can reveal key physiological parameters of plant leaves, such as chlorophyll content, water status, and photosynthetic efficiency.

[0064] S120: Blade oscillation frequency measurement.

[0065] The acquisition of leaf oscillation frequency relies on laser ranging technology and accelerometers. When plants are subjected to wind or mechanical disturbances, their leaves produce specific vibration patterns. Changes in the frequency of these vibration patterns can reflect physical properties such as the elastic modulus, cell wall strength, and stomatal aperture of plant tissue. PDPAS captures leaf motion using a high-speed camera and an inertial measurement unit and analyzes the vibration spectrum using Fourier transforms. By analyzing the dominant vibration frequencies, it is possible to identify the structural stability of the plant and its ability to adapt to environmental changes.

[0066] S130: Root electrical signal topology analysis.

[0067] Acquiring root electrical signal topology involves electrophysiological measurement techniques, including rhizosphere impedance analysis and ion flow analysis. PDPAS uses microelectrode arrays implanted in the soil to measure changes in electrical conductivity around the roots, reflecting the root's ability to absorb water and nutrients. PDPAS also records root action potentials, the fundamental unit of plant electrical signaling, under varying environmental conditions.

[0068] Combining S110-S130, PDPAS achieves multi-dimensional perception of plant phenotypic characteristics by integrating multispectral imaging, leaf vibration analysis, and root electrophysiological measurements.

[0069] S140: Constructing an environment-phenotype association matrix.

[0070] Because multispectral radiation data, leaf oscillation frequency, and root electrical signals collected by different sensors have different temporal resolutions, spatial resolutions, and physical dimensions, these data were aligned and standardized to ensure consistency and comparability. Data dimensionality reduction was also performed to extract key features and improve the efficiency of subsequent analysis.

[0071] After completing data alignment, standardization, and dimensionality reduction, the environment-phenotype correlation matrix can be constructed. The environment-phenotype correlation matrix integrates environmental variables (temperature, humidity, light intensity, soil conductivity) with plant phenotypic characteristics (multispectral radiation data, leaf oscillation frequency, root impedance) into a correlation matrix to characterize the response trajectory of plants under different environmental conditions. The form of this matrix can be expressed as:

[0072] ;

[0073] in, represents the environment-phenotype association matrix, Indicates the The sample in measurements of environmental-phenotypic traits, is the sample size, is the characteristic dimension.

[0074] In an indicator analysis method for digital diversity of plant phenotypes, S200 extracts genotypes and environmental parameters from a historical database, screens influencing factors, constructs an ellipsoid model, and finally transforms into a probability space.

[0075] Please refer to Figure 3 , which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S200 of the present application, which includes:

[0076] S210: Extract genotype and environmental stress parameters.

[0077] In digital plant phenotypic diversity analysis, the extraction of genotypes and environmental stress parameters is the foundation for constructing a three-dimensional ellipsoidal domain model. The S210 screens genotype information closely related to plant phenotypic responses from a historical variety database and combines it with environmental variables in the EPDM to extract key environmental stress parameters.

[0078] The extraction of genotype information relies on the support of historical variety databases. Historical variety databases typically contain genome sequences, single nucleotide polymorphism data, and known phenotype-gene association information for different plant varieties. In one possible embodiment, PDPAS uses the BLAST algorithm to compare the genome sequences of target plant varieties to identify gene loci associated with specific phenotypes. Based on the above embodiment, in an optional embodiment, PDPAS uses principal component analysis to perform dimensionality reduction on genotype data to reduce redundant information and highlight major genetic variations.

[0079] Environmental stress parameters are extracted based on environmental variables in the EPDM. The EPDM includes multiple environmental factors, such as temperature, humidity, light intensity, and soil conductivity. These environmental variables constitute the environmental context for plant growth. To improve data comparability, in one possible implementation, PDPAS uses a Z-score normalization method to normalize environmental variables, eliminating dimensional differences between variables.

[0080] S220: Construct a three-dimensional ellipsoid domain model based on genotype and environmental stress parameters.

[0081] A three-dimensional ellipsoidal model was constructed based on genotype and environmental stress parameters to quantify the environmental stress gradient and the strength of genotype-environment interactions. The three-dimensional ellipsoidal model maps the joint distribution of genotype traits and environmental stress parameters onto a continuous ellipsoidal space, enabling a geometric visualization of plant phenotypic responses under different environmental conditions. The principal axis of the three-dimensional ellipsoidal model reflects the gradient of environmental stress, while the curvature of the ellipsoid characterizes the strength of the interaction between genotype and environmental factors.

[0082] In a possible implementation, the steps of constructing the three-dimensional ellipsoid domain model include:

[0083] S221: Construction of a three-dimensional ellipsoidal domain model based on the genotype data matrix and environmental stress parameter matrix The joint covariance matrix of .

[0084] The linear correlation between genotypic characteristics and environmental stress parameters is described by the mathematical expression:

[0085] ;

[0086] in, is the genotype data matrix, is the environmental stress parameter matrix, is the sample size, is the joint covariance matrix of genotype and environmental stress parameters. The eigenvalues ​​and eigenvectors of the ellipsoid are used to determine the direction of the principal axes of the ellipsoid and its corresponding scale. Specifically, the eigenvectors determine the direction of the principal axes of the ellipsoid, while the eigenvalues ​​determine the length of each principal axis, that is, the strength of the environmental stress gradient.

[0087] S222: The shape of the ellipsoid in the three-dimensional ellipsoidal domain model is determined by the ratio of the eigenvalues, reflecting the strength of the genotype-environment interaction.

[0088] set up are the three largest eigenvalues ​​of the covariance matrix, corresponding to the three main axes of the ellipsoid. The curvature characteristics of the ellipsoid are quantified by the ratio of the eigenvalues:

[0089] ;

[0090] in, represents the curvature characteristics of the ellipsoid, and are the eigenvalues ​​of any two principal axes.

[0091] Larger The value indicates that the ellipsoid is more stretched in this direction, which means that environmental stress has a stronger effect on the phenotypic response in this direction. The value indicates that the curvature of the ellipsoid in this direction is low, which means that the interaction between genotype and environmental factors is strong, resulting in a higher stability of the phenotypic response.

[0092] S223: The three-dimensional ellipsoid domain model maps the joint distribution of genotype and environmental stress parameters to a continuous probability space through the ellipsoid equation. The standard equation of the ellipsoid is:

[0093] ;

[0094] in, is the genotype feature vector, is the environmental stress parameter vector, is the inverse of the joint covariance matrix.

[0095] The equations of the three-dimensional ellipsoidal domain model define a three-dimensional ellipsoidal domain, such that all points that satisfy the equation lie on the surface of the ellipsoid, while points within the ellipsoid represent phenotypic response patterns with a high probability. This model can intuitively analyze the changing trends of plant phenotypic responses under different environmental conditions and identify regions expressing genetic advantages and sites at risk of diversity fluctuations.

[0096] In an indicator analysis method for the digital diversity of plant phenotypes, S300 dynamically divides non-uniform subdomains into areas of severe environmental stress and stable areas based on the principal axis gradient and curvature characteristics of a three-dimensional ellipsoid domain model, extracts the characteristic vectors of the phenotypic response patterns of each subdomain, and constructs an iteratively optimized phenotypic response fingerprint library, thereby achieving refined characterization and storage of plant multi-environment adaptability characteristics.

[0097] Please refer to Figure 4 , which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S300 of the present application, which includes:

[0098] S310: Design a non-uniform subdomain decomposition strategy.

[0099] The heterogeneous subdomain decomposition strategy is to adapt to the plant phenotypic response patterns under different environmental stress intensities.

[0100] In one possible implementation, the non-uniform subdomain decomposition strategy is to dynamically adjust the resolution of the subdomain based on the main axis direction and curvature characteristics of the ellipsoid domain, so that areas with severe environmental stress are subdivided using high resolution, while areas with stable environmental stress are subdivided using low resolution, thereby ensuring that each subdomain can accurately capture specific phenotypic response characteristics.

[0101] Specifically, the non-uniform subdomain decomposition strategy relies on the principal axis direction and curvature information of the 3D ellipsoidal domain model. The principal axis direction of the 3D ellipsoidal domain model is determined by the joint covariance matrix The eigenvector of is determined by the length of the principal axis, while the length of the principal axis is determined by the corresponding eigenvalue The change of environmental stress gradient is expressed by the gradient function in the main axis direction. To quantify, It is The characteristic vector of the principal axis direction. For each sample point , its projection in the principal axis direction is expressed as:

[0102] ;

[0103] in, Represents sample points In the By calculating the projection distribution of different sample points in the main axis direction, we can identify areas with large changes in environmental stress gradients and adjust the subdomain division density accordingly.

[0104] Curvature characteristics of the ellipsoid The intensity of genotype-environment interactions determines the strength of the genotype-environment interaction, which in turn affects the resolution of subdomains. Based on the principal axis gradient and curvature information, PDPAS uses adaptive meshing technology to dynamically adjust the subdomain density. High-resolution subdivision is performed in areas of severe environmental stress, while low-resolution subdivision is performed in stable areas.

[0105] In a possible implementation, the partitioning step based on the non-uniform subdomain decomposition strategy includes:

[0106] S311: Initial subdomain division is set in the three-dimensional ellipsoid domain model space, usually using an equal-interval division method to ensure that the entire ellipsoid domain is covered.

[0107] S312: Calculate the gradient in the main axis direction for each subdomain and curvature , to assess the intensity of environmental stress and the level of genotype-environment interaction.

[0108] S313: For subdomains with larger gradients or higher curvatures, recursive subdivision is performed using a quadtree or octree algorithm to improve the resolution; while for subdomains with smaller gradients or lower curvatures, a lower resolution is maintained.

[0109] S314: After the subdomains are refined, the boundaries of adjacent subdomains are adjusted to ensure consistency and continuity of the data and avoid analysis errors caused by uneven division.

[0110] S320: Extracting phenotypic response pattern features from each subdomain.

[0111] Phenotypic response pattern features were extracted from each subdomain to construct a highly discriminative phenotypic response fingerprint.

[0112] In one possible embodiment, the step of extracting phenotypic response pattern features from each subdomain includes:

[0113] PDPAS uses principal component analysis (PCA) to reduce the dimensionality of the data within each subdomain to extract the main phenotypic response features. Specifically, PCA projects high-dimensional data into a low-dimensional space by calculating the eigenvectors of the data covariance matrix, making the new variables uncorrelated while preserving the maximum variance information.

[0114] PDPAS combines time series analysis to dynamically model the phenotypic response patterns of each subdomain. Specifically, because plant phenotypic responses are time-dependent, PDPAS uses Fourier transforms and wavelet transforms to extract the frequency domain and time-frequency characteristics of phenotypic responses, respectively.

[0115] PDPAS uses cluster analysis to identify patterns in the phenotypic response characteristics after dimensionality reduction. Specifically, the system uses a K-means clustering algorithm to classify the phenotypic response patterns of each subdomain to identify similar response patterns and form a phenotypic response fingerprint.

[0116] In a possible embodiment, the phenotypic response pattern features extracted from each subdomain include: spectral oscillation ratio, vibration damping coefficient, and root conductance entropy.

[0117] S330: Construct a phenotypic response fingerprint library.

[0118] The phenotypic response fingerprint library is used to store and manage phenotypic response pattern characteristics within different subdomains. The phenotypic response fingerprint library must not only have efficient data storage and retrieval capabilities, but also support dynamic updates and pattern matching to quickly identify similar phenotypic response patterns in subsequent analyses, and assist in genetic optimization and environmental adaptability assessment.

[0119] In one possible embodiment, the step of constructing a phenotypic response fingerprint library includes:

[0120] S331: The construction of PRFD is based on the encoding and indexing of feature vectors. Specifically, PDPAS uses autoencoder technology to encode the extracted phenotypic response features to generate compact and representative fingerprint vectors.

[0121] S332: PRFD uses approximate nearest neighbor search technology to achieve efficient pattern matching. Specifically, PDPAS introduces a locality-sensitive hashing algorithm to map feature vectors into a hash space, making similar feature vectors have a high probability of collision.

[0122] S333: PRFD adopts an incremental learning strategy to support dynamic updates of the database. Specifically, the system uses a mini-batch stochastic gradient descent algorithm to regularly update the parameters of the autoencoder to ensure that the encoder can adapt to the latest phenotypic response characteristics.

[0123] S334: PRFD uses knowledge graph technology to enhance the semantic association capabilities of the fingerprint library. Specifically, PDPAS combines phenotypic response patterns with known genotype-environment interactions to construct a semantic network, where nodes represent phenotypic response patterns and edges represent similarities or causal relationships between patterns.

[0124] In an indicator analysis method for the digital diversity of plant phenotypes, S400 configures a sub-domain-specific plant digital phenotypic acquisition and analysis system tracking unit, combines it with a cross-organ feedback mechanism that triggers root impedance scanning when leaf oscillation anomalies, and dynamically calculates the sub-domain response boundary, achieving adaptive regulation of environmental stress driven by the coordinated efforts of multi-dimensional physiological signals, significantly improving the dynamic analysis accuracy of plant phenotypic responses and the ability to optimize environmental adaptability.

[0125] Please refer to Figure 5 , which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S400 of the present application, which includes:

[0126] S410: Configure the exclusive plant digital phenotyping collection and analysis system tracking unit.

[0127] A dedicated tracking unit for the plant digital phenotyping acquisition and analysis system enables real-time monitoring and dynamic feedback of key plant physiological parameters. The tracking unit's core functions include real-time monitoring of leaf oscillation frequency, triggering root impedance scans, coordinating multi-source sensor data streams, and dynamically adjusting subdomain response boundaries based on an organ cascade feedback mechanism.

[0128] In one possible implementation, the tracking unit configuration includes:

[0129] The PDPAS tracking unit adopts a distributed sensor network architecture, with each subdomain equipped with an independent multispectral imaging module, laser rangefinder, inertial measurement unit and root conductivity sensor.

[0130] The PDPAS tracking unit integrates an edge computing module to process sensor data in real time and perform preliminary analysis.

[0131] The PDPAS tracking unit uses an event-driven mechanism to control the data acquisition frequency. Under normal conditions, the sensor operates at a low frequency to reduce energy consumption. However, if abnormal leaf oscillation is detected, the system automatically switches to a high-frequency sampling mode and triggers a root impedance scan.

[0132] The PDPAS tracking unit is connected to the central control system via wireless communication protocols, ensuring real-time data transmission and remote control.

[0133] S420: Establish a blade oscillation anomaly detection mechanism, and trigger a root impedance scan when an anomaly is detected.

[0134] After configuring the subdomain-specific PDPAS tracking unit, the next step is to establish a leaf oscillation anomaly detection mechanism and trigger root impedance scanning when an abnormal signal is detected. Changes in leaf oscillation frequency can reflect the plant's structural stability and environmental adaptability, thereby providing information on the physiological response of the plant's underground parts.

[0135] In one possible implementation, the blade oscillation anomaly detection mechanism is a threshold-based blade oscillation anomaly detection mechanism, that is, a reference value is set, and when the blade oscillation frequency exceeds the threshold, it is determined to be abnormal.

[0136] As another implementation option, in order to improve the accuracy of anomaly detection, a machine learning classifier is used for anomaly detection.

[0137] When abnormal leaf oscillations are detected, PDPAS automatically triggers the root impedance scanning module to detect changes in the root electrical signal. In one possible implementation, the root impedance scanning module uses AC impedance spectroscopy to measure the dielectric properties of root tissue, based on Ohm's law and the complex impedance model.

[0138] S430: Dynamically determine subdomain response boundaries based on organ cascade feedback mechanism.

[0139] The subdomain response boundary is dynamically determined based on the organ cascade feedback mechanism to achieve real-time correction of the stomatal conductance safety threshold.

[0140] The organ cascade feedback mechanism integrates the physiological signals of leaves and roots to establish a cross-organ feedback network, enabling the system to dynamically adjust the response boundary of environmental stress according to the overall state of the plant.

[0141] Based on the organ cascade feedback mechanism, the subdomain response boundary is dynamically adjusted to adapt to changes in plant phenotype. The dynamic response boundary is usually calculated based on the main axis direction and curvature characteristics of the ellipsoid domain model and updated in combination with real-time feedback data. Its calculation formula is as follows:

[0142] ;

[0143] in, represents the updated subdomain boundary, is the original boundary, is the learning rate, is the gradient vector field of the ellipsoid surface. This formula can adjust the subdomain boundaries based on real-time feedback data to more accurately reflect the phenotypic response pattern of the plant.

[0144] In an indicator analysis method for the digital diversity of plant phenotypes, S500 generates a three-dimensional heat map through dynamic color mapping and spatial modeling technology based on a three-dimensional ellipsoid domain model, and combines statistical analysis and knowledge graphs to achieve accurate labeling of risk sites and genetic advantage areas. Its advantage is that it reveals the spatial heterogeneity of plant phenotypic responses in an intuitive visual form, supports real-time interactive adjustment and multi-source data fusion, and provides a high-precision decision-making basis for subsequent stress tracking and environmental optimization.

[0145] PDPAS uses a joint analysis method of the ellipsoid domain model and the phenotypic response fingerprint library to map multidimensional data into three-dimensional space, and reflects the intensity of environmental stress and the level of genotype-environment interaction through the color gradient of the heat map.

[0146] In one possible implementation, the three-dimensional heat map generation step is as follows: Figure 6 Shown, including:

[0147] S510: Data Mapping and Visualization of 3D Heat Maps:

[0148] Specifically, the construction of a three-dimensional heat map relies on the principal axis orientation and curvature information of the ellipsoid domain model, as well as the eigenvectors in the phenotypic response fingerprint library. First, PDPAS uses the principal axis orientation of the ellipsoid domain model as the basis for a three-dimensional coordinate system, where the three principal axes correspond to the three main directions of the environmental stress gradient. The system then maps the subdomains and their response boundaries into three-dimensional space, allowing the phenotypic response pattern of each subdomain to be visualized on the ellipsoid domain model.

[0149] S520: PDPAS uses a color-coding strategy, where red represents areas with higher environmental stress intensity and blue represents stable areas with stronger genotype-environment interactions.

[0150] Specifically, the red hotspot area corresponds to the high gradient area in the main axis direction of the ellipsoid domain model, indicating that the phenotypic response in this area is significantly affected by environmental stress; while the blue stable area corresponds to the high curvature area of ​​the ellipsoid domain model, indicating that the phenotypic response in this area is regulated by the genotype-environment interaction and has high stability.

[0151] In the digital diversity analysis of plant phenotypes, the three-dimensional heat maps generated by S500 can intuitively display the intensity of environmental stress and the level of genotype-environment interaction in different subdomains. Red hotspots represent areas of high environmental stress intensity. Plant phenotypic response patterns in these hotspots are significantly influenced by environmental factors and may harbor potential stress sources or delayed phenotypic response. Therefore, S600 performs in-depth tracking of these high-risk red areas to identify hidden stress sources and locate units with delayed phenotypic response.

[0152] Please refer to Figure 7, which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S600 of the present application, which includes:

[0153] S610: Canopy vorticity captures hidden stress sources.

[0154] Red hotspots typically correspond to areas with higher gradients of environmental stress, the sources of which may not be directly visible. Therefore, canopy vorticity capture technology, combined with high-precision meteorological sensors and infrared imaging equipment, enables real-time monitoring of air flow patterns over the canopy surface.

[0155] The fundamental principle of canopy vorticity capture is to analyze air flow patterns over plant canopies using lidar and infrared thermal imaging technologies. When plants are exposed to environmental stress, their stomatal aperture and transpiration rate change, in turn affecting air flow patterns over the canopy surface. By comparing canopy vorticity patterns with those under normal environmental conditions, PDPAS can identify areas of abnormal air flow and further analyze potential hidden sources of stress in these areas.

[0156] S620: Positioning phenotypic response hysteresis units.

[0157] In addition to identifying latent stressors, it is also necessary to locate the phenotypic response hysteresis units. In some cases, the plant's phenotypic response does not manifest immediately, but rather exhibits a certain hysteresis effect. For example, when the root system is subjected to soil nutrient deficiency or salinity stress, the aboveground part of the plant may remain normal for a short period of time, but as the stress accumulates, the photosynthetic efficiency and structural stability of the leaves gradually decline.

[0158] To capture this lag effect, PDPAS uses time series analysis methods to conduct long-term monitoring of multispectral radiation data, leaf oscillation frequency, and root electrical signals.

[0159] Specifically, PDPAS records the phenotypic data of each sample at different time points and uses a dynamic time warping algorithm to compare the phenotypic response patterns between different subdomains. When the phenotypic response pattern of a subdomain deviates significantly from the expected model, the system marks the region as a potential phenotypic response lag unit.

[0160] S630: Data-driven stress source analysis.

[0161] To further improve the accuracy of deep tracking, PDPAS uses a data-driven approach to analyze stress sources in red, high-risk areas. PDPAS integrates multi-source sensor data, including meteorological data, soil conductivity, leaf temperature, and photosynthetic efficiency, and uses multiple regression analysis and principal component analysis to identify the main environmental factors that influence plant phenotypic responses.

[0162] For example, when analyzing drought stress, PDPAS focuses on trends in soil moisture, transpiration rate, and leaf temperature. When soil moisture continues to decrease while leaf temperature increases, the system identifies the area as potentially experiencing water stress and automatically triggers the root impedance scanning module to obtain more detailed underground physiological information.

[0163] In digital plant phenotypic diversity analysis, the three-dimensional heat maps generated by the S500 can identify not only red hotspots with high environmental stress intensity, but also blue stable zones with strong genotype-environment interactions. Blue stable zones represent areas where plant phenotypic responses are relatively stable. Plants in these zones exhibit high genetic stability under specific environmental conditions, making them suitable for genetic optimization and environmental adaptability research. Therefore, based on the phenotypic characteristics of the blue stable zones and the diagnostic results of hysteresis units, the S700 uses inverse ellipsoid mapping to infer the optimal environmental parameter combination and generate a variety-environment adaptation matrix to formulate diversity maintenance plans.

[0164] Please refer to Figure 8 , which shows a flowchart of an exemplary plant phenotypic digital diversity index analysis method S700 of the present application, which includes:

[0165] S710: Inverse ellipsoid mapping to infer the optimal environmental parameter combination.

[0166] The phenotypic response patterns of plants in the blue stable zone are regulated by genotype-environment interactions. Therefore, PDPAS uses inverse ellipsoid mapping technology to infer the optimal environmental parameter combination from the three-dimensional ellipsoid domain model. Inverse ellipsoid mapping uses the covariance matrix of the three-dimensional ellipsoid domain model to map the phenotypic response pattern back to the environmental parameter space, thereby determining the environmental conditions most conducive to stable plant growth.

[0167] In its implementation, PDPAS first extracts the phenotypic eigenvectors of the blue stable zone from the three-dimensional ellipsoid domain model and calculates their corresponding covariance matrix. This covariance matrix reflects the correlation between genotypic characteristics and environmental stress parameters. PDPAS then maps the phenotypic eigenvectors back to the environmental parameter space by solving the inverse matrix of this matrix. The formula for the inverse ellipsoid mapping is as follows:

[0168] ;

[0169] in, represents the environmental parameter vector, represents the phenotypic feature vector, is the inverse matrix of the joint covariance matrix. Through this formula, PDPAS calculates the optimal combination of environmental parameters so that the phenotypic response of the plant reaches the maximum stability in this environment.

[0170] S720: Generate a variety-environment adaptation matrix.

[0171] After determining the optimal environmental parameter combination, PDPAS further generates a variety-environment adaptation matrix to assess the adaptability of different plant varieties under different environmental conditions. This matrix is ​​constructed based on the principal axis orientation and curvature information of the three-dimensional ellipsoid domain model and the eigenvectors in the phenotypic response fingerprint library.

[0172] The mathematical form of the variety-environment adaptation matrix is ​​as follows:

[0173] ;

[0174] in, Indicates the Plant species in The fitness of a variety under different environmental conditions is calculated based on the combination of environmental parameters obtained by inverse ellipsoid mapping and weighted calculation based on the feature vectors in the phenotypic response fingerprint library.

[0175] S730: Develop a diversity maintenance plan.

[0176] After generating the variety-environment adaptation matrix, PDPAS further develops a diversity maintenance plan to ensure the long-term adaptability of plant populations under different environmental conditions. By optimizing environmental parameter combinations and variety selection, the stability of plant phenotypes is maintained while promoting the development of genetic diversity.

[0177] First, PDPAS uses a genetic optimization strategy based on an adaptation matrix to screen the most adaptable plant varieties, and combines genomic data to identify gene loci related to environmental adaptability.

[0178] Secondly, PDPAS uses dynamic environmental control technology to adjust greenhouse or field environmental parameters based on the results of the adaptation matrix to ensure that plants grow in optimal conditions. For example, in the blue stable zone, the system automatically adjusts parameters such as light intensity, temperature, and humidity to maintain stable plant growth. Furthermore, PDPAS can be combined with intelligent irrigation and fertilization systems to dynamically adjust water and fertilizer supply strategies based on the physiological needs of plants to improve resource utilization efficiency.

[0179] Finally, PDPAS uses a knowledge graph-based decision support system to integrate plant phenotypic, genotypic, and environmental data, combined with historical experimental data, to provide scientific recommendations for maintaining diversity. For example, PDPAS analyzes plant growth trends under different environmental conditions and predicts the impact of future climate change on plant adaptability, thereby formulating long-term genetic optimization and environmental regulation strategies.

[0180] Plant phenotypic digital diversity aims to comprehensively analyze plant phenotypic responses under diverse environmental conditions through high-precision sensors, data analysis, and modeling. The core framework of this approach comprises several key steps, encompassing data acquisition, modeling analysis, pattern recognition, dynamic feedback, and optimized decision-making, forming a complete analytical system.

[0181] The entire plant phenotypic digital diversity analysis framework achieves comprehensive analysis and precise control of plant phenotypic responses through multi-source data fusion, mathematical modeling, pattern recognition, and intelligent optimization. This approach not only improves the efficiency of plant science research but also provides strong technical support for agricultural production and ecological protection.

[0182] The present application also provides a computer device, which includes: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a method for analyzing an indicator of digital diversity of plant phenotypes as described in one of the technical solutions.

[0183] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0184] The block diagrams of the devices, apparatuses, equipment involved in this application are intended only as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, and equipment may be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and may be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and may be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and may be used interchangeably therewith.

[0185] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0186] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be applied in the widest sense consistent with the principles and novel features of the present invention.

[0187] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for analyzing plant phenotypic digital diversity indicators, characterized in that: include: Plant phenotyping analysis uses a plant digital phenotyping collection and analysis system to build a heterogeneous monitoring network, collaboratively collecting multispectral radiation data above the plant canopy, leaf oscillation frequency, and root electrical signal topology to generate an environment-phenotype correlation matrix; Based on the environment-phenotype dynamic matrix, combined with the historical variety database, genotype and environmental stress parameters were extracted to construct a three-dimensional ellipsoid domain model; Based on the three-dimensional ellipsoid domain model, non-uniform subdomain decomposition is implemented along the main axis direction: high-resolution subdivision is performed in areas with severe environmental stress, and low-resolution subdivision is performed in stable areas, so that each subdomain corresponds to a unique phenotypic response pattern feature, forming a phenotypic response fingerprint library; For each subdomain, a dedicated plant digital phenotyping acquisition and analysis system tracking unit is configured. When abnormal leaf oscillation is detected, a root impedance scan is triggered, and the subdomain response boundary is dynamically determined based on the organ cascade feedback mechanism. Based on the three-dimensional ellipsoid domain model, the subdomains and the corresponding subdomain response boundaries are marked to generate a three-dimensional heat map; In response to the three-dimensional heat map results, deep tracking is initiated for the red high-risk areas. The deep tracking method is to control the plant digital phenotyping acquisition and analysis system to capture hidden stress sources along the canopy vorticity and locate the phenotypic response lag units; Based on the phenotypic characteristics of the blue stable zone and the diagnostic results of the hysteresis unit, the optimal environmental parameter combination is inferred through inverse ellipsoid mapping, the variety-environment adaptation matrix is ​​generated, and the diversity maintenance plan is output.

2. The method for analyzing plant phenotypic digital diversity according to claim 1, characterized in that: The multispectral radiation data acquisition uses a multispectral imaging system equipped with multiple narrow-band wavelength sensors to obtain plant canopy reflectance information in the visible to near-infrared band, and realizes digital representation of key physiological parameters through modeling of chlorophyll content, water status and photosynthetic efficiency; The blade oscillation frequency measurement uses a high-speed camera and an inertial measurement unit to capture the blade motion trajectory, and analyzes the main vibration spectrum through Fourier transform; The root electrical signal topology analysis includes: using a microelectrode array implanted in the soil to measure changes in rhizosphere conductivity, combining ion flow analysis with action potential recording to evaluate the root system's ability to absorb water and nutrients.

3. The method for analyzing plant phenotypic digital diversity according to claim 1, wherein: The construction of the environment-phenotype dynamic matrix includes time alignment, spatial standardization and dimensionality reduction of multi-source data, wherein the dimensionality reduction method uses principal component analysis or wavelet transform to extract key phenotypic features; The environment-phenotype dynamic matrix contains the joint distribution of environmental variables and phenotypic characteristics, which is expressed as: ； in, represents the environment-phenotype dynamic matrix, Indicates the The sample in measurements of environmental-phenotypic traits, is the sample size, is the characteristic dimension.

4. The method for analyzing plant phenotypic digital diversity according to claim 1, wherein: The construction of the three-dimensional ellipsoid domain model includes: Based on the joint covariance matrix of the genotype data matrix and the environmental stress parameter matrix, the eigenvalues ​​and eigenvectors were calculated to determine the direction and scale of the ellipsoid's major axis. The curvature of the ellipsoid is quantified by the ratio of the eigenvalues ​​of any two principal axes and is used to characterize the strength of the genotype-environment interaction; The standard equation of the three-dimensional ellipsoid domain model is: ； in, is the genotype feature vector, is the environmental stress parameter vector, is the inverse matrix of the joint covariance matrix; Among them, the three-dimensional ellipsoid domain model distinguishes different types of phenotypic response patterns through the ellipsoid curvature and principal axis gradient. The ellipsoid stretching direction represents the area dominated by environmental stress, while the high curvature direction reflects the area with strong genetic stability.

5. The method for analyzing plant phenotypic digital diversity according to claim 1, wherein: The non-uniform subdomain decomposition strategy is based on the ellipsoid principal axis gradient function and local curvature evaluation, and uses an adaptive meshing algorithm to dynamically adjust the subdomain resolution; Implementing quadtree / octree recursive subdivision in high-gradient regions to achieve refined modeling and classification of stress response patterns; The non-uniform subdomain division includes: calculating the gradient and curvature in the main axis direction for each subdomain, identifying areas with severe environmental stress and improving the resolution, while retaining the low-resolution division of stable areas to balance computational efficiency and modeling accuracy.

6. The method for analyzing plant phenotypic digital diversity indicators according to claim 1, characterized in that: The phenotypic response fingerprint library construction includes: PDPAS uses autoencoder technology to encode the extracted phenotypic response features to generate compact and representative fingerprint vectors; PDPAS introduces a locality-sensitive hashing algorithm to map feature vectors into a hash space, so that similar feature vectors have a higher probability of collision. The PRFD system uses a mini-batch stochastic gradient descent algorithm to regularly update the parameters of the autoencoder to ensure that the encoder can adapt to the latest phenotypic response characteristics; PDPAS combines phenotypic response patterns with known genotype-environment interactions to construct a semantic network, where nodes represent phenotypic response patterns and edges represent similarities or causal relationships between patterns. The key features stored in the phenotypic response fingerprint library include: spectral oscillation ratio, vibration damping coefficient, and root conductance entropy.

7. The method for analyzing plant phenotypic digital diversity indicators according to claim 1, characterized in that: The organ cascade feedback mechanism integrates the real-time physiological signals of leaf oscillation and root impedance to construct a cross-organ coupling feedback model, and dynamically adjusts the subdomain response boundary based on the ellipsoid surface gradient field.

8. The method for analyzing plant phenotypic digital diversity indicators according to claim 1, characterized in that: The three-dimensional heat map generation includes: using the three main axes of the ellipsoid domain model as a three-dimensional coordinate system, mapping each subdomain and its response boundary to the space, and using a color coding strategy, with red hot spots marking diversity fluctuation risk sites and blue stable areas marking genetic advantage expression sites; The three-dimensional heat map introduces a knowledge graph to enhance the semantic annotation function, allowing users to interactively query the genotype background, historical response trends and potential stress types of the subdomain.

9. The method for analyzing plant phenotypic digital diversity indicators according to claim 1, characterized in that: Deep tracking of hidden stress sources includes: using lidar and infrared thermal imaging technology to capture the trajectory of canopy vorticity changes, combining meteorological sensor data to identify areas of abnormal air flow, and thus locate potential hidden stress sources; The positioning phenotypic response hysteresis unit uses a dynamic time warping algorithm to compare the phenotypic response patterns between different subdomains. When the response pattern of a subdomain deviates significantly from the expected one, it is marked as a potential phenotypic response hysteresis unit. The hidden stress source analysis includes: integrating multi-source sensor data, using multiple regression analysis and principal component analysis to identify the main environmental factors affecting plant phenotypic responses, and automatically triggering the root impedance scanning module to obtain more detailed underground physiological information.

10. The method for analyzing plant phenotypic digital diversity indicators according to claim 1, characterized in that: The inverse ellipsoid mapping inversely estimates the optimal environmental parameter combination, including: environmental parameter vector , phenotypic feature vector , the inverse matrix of the joint covariance matrix , the formula for inverse ellipsoid mapping is as follows: ； The variety-environment adaptation matrix is ​​constructed by evaluating the adaptability of different plant varieties under different environmental conditions based on the inverse ellipsoid mapping results. The higher the adaptability value, the more adaptable the variety is in the environment.

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