Method for testing and analyzing lodging-resistant characteristic of corn plant

Abstract

The invention discloses a method for testing and analyzing the lodging-resistant characteristic of a corn plant, which comprises the following steps: collecting an overground canopy morphological sequence of the corn plant at a high wind speed to quantify the dynamic morphological change of the corn plant; extracting dynamic structural mechanical response representing canopy attitude and stem bending; the method comprises the following steps: acquiring volume data of an underground root system and soil through a tomography technology, and analyzing to obtain a three-dimensional topological structure of the root system and biomechanical field data of root-soil interaction; and fusing overground and underground measurement data, and quantitatively evaluating the collaborative wind resistance of the plant. According to the method, the limitation of static one-sided analysis in the prior art is overcome, the provided quantitative indexes can be applied to screening of high-toughness corn varieties, and meanwhile, the data acquisition efficiency is improved through an intelligent monitoring strategy.

Description

Technical Field

[0001] This application relates to the field of material physical property testing and analysis technology, and in particular to a method for testing and analyzing the lodging resistance characteristics of maize plants. Background Technology

[0002] As a vital food crop globally, maize's yield stability is crucial for food security. Lodging is one of the major agricultural hazards limiting maize yield and quality, especially in regions prone to extreme weather events such as typhoons and torrential rains, where it can lead to yield losses exceeding 50%. Maize lodging is not caused by defects in a single organ, but rather by a complex biomechanical instability process. The root cause lies in the fact that the wind load borne by the aboveground parts, such as the stalks and canopy, exceeds the anchoring moment provided by the underground parts, namely the root system and soil complex. A deep and quantitative understanding of the synergistic mechanism between the aboveground and underground parts of maize under wind stress is a key technical factor in researching maize varieties to address lodging problems.

[0003] However, from the perspective of materials testing and analysis, existing techniques for quantitatively studying this process have significant limitations. Most research methods focus on static measurements of plant traits, such as measuring the bending strength of detached stems in a controlled environment or analyzing the morphology of the root system after lodging. These testing methods are mostly destructive and cannot reflect the dynamic mechanical response of the maize plant as a whole in situ in the field, involving the interaction between the aboveground parts and the underground root-soil system. Existing phenotypic analysis techniques, due to their low data acquisition frequency, struggle to capture the critical physical changes under strong winds (within seconds to minutes). Therefore, there is an urgent need in this field for a new method that can perform in situ, dynamic, and non-destructive coordinated measurement of the physical and mechanical responses of the aboveground and underground parts of maize plants. Summary of the Invention

[0004] To address the technical problem that existing material testing technologies cannot perform in-situ, dynamic, and non-destructive collaborative analysis of the physical and biomechanical properties of living biological composite materials such as corn plants, this invention provides a method for testing and analyzing the lodging resistance of corn plants.

[0005] To achieve the above objectives, the present invention provides a method for testing and analyzing the lodging resistance characteristics of maize plants, comprising the following steps: Step S1: Collect aboveground canopy morphology sequence. Multispectral sensors are used to collect image data of the aboveground canopy of maize plants at multiple key time points under high wind speed or simulated wind field conditions. A preset monitoring reference frequency is used to obtain a canopy morphology sequence reflecting changes in canopy morphology over time.

[0006] Step S2: Extract the dynamic structural mechanical response. An image segmentation model (such as a convolutional neural network model) is used to process the images in the canopy morphology sequence to segment the stem and leaf regions. Based on the segmentation results, feature extraction is performed to calculate the leaf angle distribution features characterizing the windward posture of the canopy, and the bending degree index characterizing the stem mechanical response, thereby obtaining a quantified dynamic structural mechanical response of the plant's aboveground morphological adjustment. Specifically, a U-Net architecture convolutional neural network model can be used to perform semantic segmentation on the images in the canopy morphology sequence to distinguish the background, stem, and leaf regions. A morphological skeletonization algorithm is used to extract the centerlines of the leaves and stems, and the angle between the leaves and the main stem is calculated to generate leaf angle distribution features. The curvature integral of the stem centerline is then calculated as a bending degree index.

[0007] Step S3: Acquire underground root structure and biomechanical field data. Using tomographic scanning techniques, such as a portable X-ray CT (Computed Tomography) scanner, acquire volume data of the underground root system of maize plants at corresponding time points. Process this volume data to extract a root expansion direction sequence representing the three-dimensional topology of the root system. This sequence characterizes the anchoring state changes of the root-soil complex under wind loads. Specifically, the Marching Cubes algorithm can be used to reconstruct the three-dimensional structure of the segmented voxel data, generating a three-dimensional root mesh model, which is then smoothed and skeletonized to obtain the root expansion direction sequence.

[0008] Furthermore, the method not only analyzes the geometric morphology of the root system, but also uses the same volume data obtained by the tomographic scanning technology to introduce the Digital Volume Correlation (DVC) analysis method to perform three-dimensional displacement and strain field analysis on the soil area around the root system, thereby obtaining biomechanical field data characterizing the interaction between the root system and the soil.

[0009] Step S4: Data fusion and evaluation of synergistic wind resistance performance. A machine learning fusion model is used to fuse the dynamic structural mechanical response, the root expansion direction sequence, and the biomechanical field data. By analyzing the functional relationship between wind load and overall plant deformation, including the aboveground sway and micro-root displacement, the synergistic wind resistance performance of the aboveground and underground components is evaluated. Preferably, a random forest regression model is used, whose input feature vector includes statistics on leaf angle distribution, stem bending index, and root expansion direction sequence, and further includes mechanical indices such as peak soil strain in key areas and root-soil interface slip rate obtained from DVC analysis. This model outputs a standardized synergistic wind resistance index to quantitatively assess the overall wind resistance capacity of the plant.

[0010] Step S5: Assess post-disaster recovery potential. If a lodging event is detected, i.e., the degree of stem bending exceeds a preset instability threshold, a post-disaster recovery potential assessment is initiated. Based on canopy morphology data collected after lodging, a Photosynthetically Active Radiation (PAR) absorption model is used to predict the plant's ability to restore photosynthesis and quantify it as a trend in resource utilization efficiency. This trend is used to assess the plant's survival and recovery potential. This invention extends the analytical perspective from the lodging event itself to the post-disaster recovery stage, providing a quantitative basis for evaluating the post-disaster recovery capacity or resilience of varieties.

[0011] Step S6: Dynamically adjust the data acquisition frequency. Based on external meteorological warnings (such as typhoon warnings) or real-time wind speed monitoring data, and the recovery status assessed in Step S5, the data acquisition frequency is dynamically adjusted. For example, before and during typhoon landfall, the acquisition frequency is increased to the highest level to capture the mechanical processes at the moment of lodging; while during the post-disaster recovery period, the frequency is correspondingly reduced for long-term tracking. Finally, through filtering, such as Kalman filtering, the data acquired at different frequencies is smoothed to obtain a spatiotemporally continuous dataset covering the entire event process. This invention proposes an event-driven intelligent monitoring strategy that can automatically adjust the data acquisition scheme according to changes in the external environment and the internal state of the plant, thereby accurately capturing key details of sudden events and long-term trends of slow processes with optimized resource consumption.

[0012] Step S7: Construct a four-dimensional dynamic model of the entire plant. Using the aforementioned spatiotemporal continuous dataset, a four-dimensional (3D spatial + temporal) dynamic model of the entire plant, incorporating biomechanical parameters, is constructed. This model integrates above-ground and below-ground structures and can be used to simulate and reproduce the instability process of maize under strong winds and the morphological recovery process after disasters, providing visualized decision support for the mechanism research and screening of lodging-resistant varieties.

[0013] The present invention, through the above technical solution, has the following beneficial effects: 1. This invention overcomes the limitations of static and one-sided analysis in existing technologies, and can simultaneously capture and quantify the coordinated physical response of the aboveground canopy and underground root system of corn under strong wind stress, providing a new method for testing the dynamic performance of materials.

[0014] 2. This invention not only analyzes morphological changes, but also advances the research depth to the biomechanical interaction level of the root-soil complex by introducing digital volume correlation (DVC) analysis, providing a more quantitative scientific basis for studying the intrinsic physical mechanism of maize lodging resistance.

[0015] 3. Based on the above-mentioned accurate physical test results, this invention provides breeders with a new quantitative physical index for measuring the resilience of maize varieties, which has important practical guiding value for breeding superior varieties that can quickly recover productivity after extreme climate events. Attached Figure Description

[0016] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating a method for testing and analyzing the lodging resistance characteristics of maize plants according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of a field in-situ collaborative data acquisition system provided in an embodiment of the present invention.

[0019] Figure 3 This is a schematic diagram of a strategy for dynamically adjusting the data acquisition frequency according to an embodiment of the present invention.

[0020] Figure 4 This is a schematic diagram of the strain field distribution of a root-soil composite obtained by digital volume correlation (DVC) analysis according to an embodiment of the present invention.

[0021] Figure 5 This is a comparative schematic diagram of the recovery potential of different maize varieties after lodging, provided by an embodiment of the present invention.

[0022] Figure 6 This is a schematic diagram comparing and verifying the simulation and experimental data of the four-dimensional dynamic model of the whole plant provided in the embodiment of the present invention. Detailed Implementation

[0023] To achieve the objectives of this invention, the invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0024] Example 1 This embodiment provides a method for testing and analyzing the lodging resistance characteristics of maize plants, referring to... Figure 1 The technical solution of this method can be broken down into the following steps: Step S1: Collect aboveground canopy morphology sequence. Multispectral sensors are used to collect image data of the aboveground canopy of maize plants at multiple key time points under high wind speed or simulated wind field conditions. A preset monitoring reference frequency is used to obtain a canopy morphology sequence reflecting changes in canopy morphology over time.

[0025] Specifically, this step can be implemented using an automated field monitoring platform, such as... Figure 2As shown, a multispectral imaging unit 202 is deployed on a movable gantry structure 201. This multispectral imaging unit 202 includes, but is not limited to, sensors in the visible light (RGB) band, near-infrared (NIR) band, and red-edge band. In a preferred implementation scenario, the monitored corn plants 203 are planted in a specially designed, X-ray transparent root box 204, and the entire device is placed in the field. Under simulated wind conditions, a large wind turbine applies gradient wind speeds to the plants, while the imaging unit 202 on the gantry 201 captures images of the canopy at a preset reference frequency (e.g., 1 frame per minute under windless or low-wind conditions) at both vertical and tilt angles. When the wind speed exceeds a preset threshold (e.g., 8 m / s), the acquisition frequency is increased to a high-frequency mode (e.g., 10 to 30 frames per second) to capture the high-speed dynamic response of the canopy under wind loads. The acquired image data is timestamped to form a canopy morphology sequence.

[0026] Step S2: Extract the dynamic structural mechanical response. A convolutional neural network model is used to extract features from the canopy morphology sequence, and the leaf angle distribution features used to characterize the windward posture of the canopy and the bending degree index used to characterize the mechanical response of the stem are calculated, thereby obtaining the dynamic structural mechanical response of the aboveground part of the plant to quantify the morphological adjustment.

[0027] Preferably, this step can be decomposed into several sub-steps. First, each frame of the acquired canopy morphology sequence is preprocessed, including radiometric calibration and geometric correction. Second, a pre-trained U-Net architecture convolutional neural network model is used to perform semantic segmentation on the preprocessed image, accurately classifying image pixels into three categories: background, stem, and leaves. This U-Net model is obtained by training on a dataset containing thousands of manually annotated maize images. Then, morphological post-processing is performed on the segmented stem and leaf regions, such as using a skeletonization algorithm to extract their respective centerlines. By calculating the angle between each leaf centerline and the main stem centerline, a leaf angle distribution histogram describing the pose of all leaves can be obtained; this histogram represents the leaf angle distribution features. Simultaneously, curve fitting is performed on the stem centerline, and the integral of its curvature along the arc length is calculated; this integral value is defined as a curvature index. The formula for calculating the index B can be expressed as: B = ∫k(s)ds, where s is the arc length along the stem centerline, and k(s) is the curvature at arc length s. A larger value for index B indicates a more severe degree of stem bending. This step, through depth analysis of the canopy image, transforms macroscopic morphological changes into quantitative mechanical response parameters, thus providing accurate aboveground input data for subsequent collaborative analysis. As a technical alternative, an instance segmentation model (such as Mask R-CNN) can be used, which can not only segment leaf regions but also distinguish each individual leaf, enabling more refined single-leaf level dynamic analysis.

[0028] Step S3: Obtain underground root structure and biomechanical field data. Volume data of the underground root system of maize plants at corresponding time points are obtained using tomographic scanning technology. This volume data is then processed to extract the root expansion direction sequence representing the three-dimensional topology of the root system. Furthermore, using this volume data, a Digital Volume Correlation (DVC) analysis method is introduced to analyze the three-dimensional displacement and strain fields of the soil region surrounding the root system, obtaining biomechanical field data of the interaction between the root system and the soil.

[0029] Specifically, refer to Figure 2While collecting data on the aboveground parts, a portable X-ray computed tomography (CT) scanner 205 was deployed around the root box 204. Its X-ray source and detector rotated around the root box to acquire three-dimensional voxel data of the root-soil complex. The preferred scanning resolution was 50-200 micrometers to clearly distinguish the main root structure and soil particles. The acquired voxel data was first segmented to separate the roots, soil, and pores. Then, the Marching Cubes algorithm was used to reconstruct a three-dimensional mesh model from the segmented root three-dimensional voxel data. The reconstructed model was then skeletonized to extract the three-dimensional topological structure of the roots. Principal Component Analysis (PCA) and other methods were used to calculate the distribution of the main root growth directions, forming a root expansion direction sequence.

[0030] Furthermore, to reveal the mechanical mechanism of root-soil interaction, this invention introduces DVC analysis. At least two sets of CT volume data are collected before and after the application of wind load (or under different wind intensities). The DVC algorithm calculates the three-dimensional displacement vector of each voxel by tracking and matching the three-dimensional texture formed by the soil's internal microstructure (such as microparticles and pores) in these two sets of data. This yields the three-dimensional displacement field of the entire soil region, and the strain field can be further calculated. For example, the deformation gradient tensor F is calculated from the displacement field, and then the Green-Lagrange strain tensor E is calculated from F, with the formula E = 0.5 * (F^T * F - I), where F is the deformation gradient tensor, F^T is the transpose of F, and I is the unit tensor. By analyzing the strain field, it is possible to clearly identify which areas of the soil are compressed and which areas are stretched under wind load, thereby quantifying the anchoring effect of the root system on the soil. This step, by directly linking the anchoring capacity of the root system to the mechanical response of the surrounding soil under in-situ, non-destructive conditions, provides biomechanical data for understanding the underground mechanisms of lodging resistance.

[0031] Step S4: Fuse data and evaluate collaborative wind resistance performance. A machine learning fusion model is used to fuse the dynamic structural mechanical response, the root extension direction sequence, and the biomechanical field data to evaluate the collaborative wind resistance performance of the above-ground and underground systems.

[0032] Preferably, this invention employs a Random Forest regression model to perform this fusion evaluation task. The input feature vector of this model consists of multi-dimensional data, specifically including: 1) aboveground dynamic response patterns from step S2, such as the mean and variance of leaf angle distribution and the maximum value of stem bending index B; 2) root geometric features from step S3, such as the number of taproots, total root length, and the distribution ratio of roots at different depths; 3) root-soil interaction biomechanical field data from step S3, such as the mean principal strain, maximum shear strain, and slip ratio along the root-soil interface in key areas around the root system (e.g., the windward tension zone and the leeward compression zone). The output of the model is a standardized Synergistic Wind Resistance Index (SWRI), with a value range of 0 to 1. This index is obtained through training on a large number of samples, where the labels of the training samples (i.e., the actual lodging resistance) can be determined by whether the plant eventually lodges or the critical wind speed at which lodging occurs. This step utilizes machine learning models to learn and quantify the complex, non-linear synergistic effects of the above-ground and underground components in resisting external forces, thus providing a more comprehensive and accurate assessment index of the plant's overall wind resistance. Alternatively, other machine learning models such as Gradient Boosting Decision Trees (GBDT) or Support Vector Regression (SVR) can be used to accomplish this task.

[0033] Step S5: Assess post-disaster recovery potential. If a lodging event is detected, initiate a post-disaster recovery potential assessment. Based on canopy morphology data collected after lodging, use a photosynthetically active radiation (PAR) absorption model to predict the plant's ability to restore photosynthesis and quantify it as a trend in resource utilization efficiency.

[0034] Specifically, the criteria for determining a lodging event are: the stem bending degree index B exceeds a preset instability threshold, for example, the angle between the stem and the vertical direction continuously exceeds 60 degrees. After the event occurs, data acquisition switches to a low-frequency, long-term monitoring mode. Using canopy morphology sequences collected several days to weeks after lodging, a three-dimensional canopy model of the plant after lodging is first generated using three-dimensional reconstruction technology (such as Structure from Motion, SfM). Then, this three-dimensional model is input into a PAR absorption model based on a ray tracing algorithm. This model simulates the sun's position at different times of day, calculates the canopy's shading and absorption of light, and thus estimates the total daily PAR absorbed by the entire plant under lodging conditions. By tracking the trend of this index over time, the plant's recovery potential can be assessed. If the daily total PAR absorption shows an upward trend within several days after lodging, it indicates that the plant is actively restoring its photosynthetic capacity by adjusting leaf orientation, etc., and its recovery potential is high. Conversely, if it continues to decline, the recovery potential is low. This step expands the evaluation dimensions from lodging resistance to recovery ability after lodging, providing breeding with screening indicators that are more directly related to the final yield loss.

[0035] Step S6: Dynamically adjust the data acquisition frequency. Based on external weather warnings or real-time wind speed monitoring data, and the recovery status assessed in Step S5, the data acquisition frequency is dynamically adjusted. Finally, through filtering, the data acquired at different frequencies are smoothed to obtain a spatiotemporally continuous dataset covering the entire event process.

[0036] Specifically, refer to Figure 3This method includes an event-driven intelligent monitoring strategy module. This module has three preset operating modes: 1) Regular monitoring mode 401: When there is no weather warning and the real-time wind speed is below 5 m / s, the multispectral imaging unit acquires data once per hour, and CT scans are performed once per day; 2) High-frequency response mode 402: When a high-risk weather warning such as a typhoon is received, or the real-time wind speed exceeds 10 m / s, the system automatically switches to this mode, increasing the multispectral imaging frequency to more than 10 frames per second and the CT scan frequency to once every 10 minutes; 3) Post-disaster recovery mode 403: After a lodging event is detected in step S5, the acquisition frequency is adjusted to once every 6 hours for multispectral imaging and once every 3 days for CT scans. All acquired data with different temporal resolutions are processed by a Kalman filter before being fed into the final model. The Kalman filter can fuse observation data of different frequencies to optimally estimate key plant state parameters (such as stem curvature and root displacement), generating a smooth and spatiotemporally continuous trajectory. The technical advantage of this strategy lies in its ability to significantly optimize data acquisition efficiency and resource allocation, ensuring that sufficient details can be captured when critical events occur, while avoiding data redundancy during stable periods, thus enabling long-term, economical, and high-precision field monitoring.

[0037] Step S7: Construct a four-dimensional dynamic model of the entire plant. Using the aforementioned spatiotemporal continuous dataset, construct a four-dimensional (3D spatial + temporal) dynamic model of the entire plant that includes biomechanical parameters.

[0038] Specifically, the model integrates registered aboveground 3D canopy models and underground 3D root systems at all time points. This model can be interactively displayed in visualization software, allowing users to replay the complete response of maize plants under strong winds. Furthermore, the model is a parametric biomechanical model. Each part of the model, such as stem internodes, leaves, taproots, and lateral roots, is assigned material mechanical properties obtained from experimental measurements or literature, such as elastic modulus and Poisson's ratio. The soil component is modeled as an elastoplastic material, with parameters derived from DVC analysis results (as shown in the attached figure). Figure 4 (As shown) Calibration. Using this four-dimensional model, finite element method (FEM) analysis can be performed to simulate the mechanical response of plants under different wind conditions and soil moisture, thereby delving into the intrinsic mechanism of lodging or predicting the lodging resistance of virtual superior plant types. To verify the accuracy of this four-dimensional dynamic model, the simulation results are compared with field experimental measurement data. (Refer to...) Figure 6The graph illustrates the high degree of consistency between the model's predicted maximum lateral displacement at the top of the stem and the experimental measurements obtained through image analysis in a simulated wind field experiment. The coefficient of determination (R²) shown in the graph reaches 0.97, and the root mean square error (RMSE) is only 1.6 cm, strongly demonstrating that the model constructed in this invention possesses high accuracy and reliability, and can accurately reproduce the complex mechanical response of plants under dynamic loads.

[0039] Example 2 This embodiment provides a maize aboveground and belowground synergistic phenotypic analysis device, the structure of which is designed to perform the method described in Embodiment 1. The device includes: A data acquisition module configured to simultaneously acquire aboveground canopy morphology sequences and underground root and soil volume data of maize plants using multispectral sensors and tomographic scanning equipment.

[0040] A ground analysis module, which integrates a convolutional neural network model and image processing algorithms, is used to extract dynamic structural mechanical responses from canopy morphology sequences, specifically including leaf angle distribution characteristics and stem bending indices.

[0041] An underground analysis module, equipped with a 3D reconstruction algorithm and a digital volume correlation (DVC) analysis program, is used to extract the root extension direction sequence from volume data and calculate the 3D displacement and strain fields of the soil around the roots.

[0042] A fusion assessment module, which deploys a machine learning fusion model (such as a random forest model), is used to fuse the output data from the above-ground analysis module and the underground analysis module to generate a quantitative collaborative wind resistance index.

[0043] A recovery potential assessment module is configured to evaluate the post-disaster recovery potential of plants based on a photosynthetically active radiation absorption model after a lodging event is detected.

[0044] A data acquisition and control module dynamically adjusts the operating frequency of the data acquisition module based on external environmental data and plant status, and uses a filtering algorithm to smooth multi-frequency data.

[0045] A modeling module that integrates all processed spatiotemporal data to build and visualize a four-dimensional dynamic model of the entire plant.

[0046] In practice, the above modules can be implemented as software program modules, running on one or more processors, and communicating and controlling the corresponding hardware devices (such as cameras, CT scanners, and servers).

[0047] Example 3 This embodiment further illustrates the specific process and technical effects of the method of the present invention in actual breeding and screening applications through a specific comparative field experiment. The experiment was conducted in an automated experimental base, and two representative maize varieties were selected for comparative analysis: variety A (a control variety known to have strong lodging resistance in the field) and variety B (a newly bred line whose lodging resistance needs to be evaluated).

[0048] At the start of the experiment, seedlings of varieties A and B were planted in 10 specially designed root boxes that were transparent to X-rays and placed below an automated field monitoring system. During the tasseling stage of the maize, once the biomechanical characteristics of the plants had stabilized, a simulated windstorm experiment was initiated. For the first 24 hours of the experiment, the data acquisition system operated in normal monitoring mode, acquiring multispectral images hourly and performing a CT scan daily to establish baseline plant status data. On the day of the experiment, a large axial flow fan array was activated to simulate a strong wind environment with instantaneous wind speeds reaching 25 m / s (equivalent to a Force 10 gale), which was maintained for 5 minutes. The instant the fans started, the acquisition control module received a signal from the wind speed sensor exceeding the 15 m / s threshold, immediately triggering an emergency response mode. This instantly increased the acquisition frequency of the multispectral imaging unit to 25 frames per second, while the scanning interval of the portable X-ray CT scanner was dynamically shortened to once every 5 minutes, capturing the entire plant response process with high temporal resolution.

[0049] Real-time processing of the collected high-frequency canopy morphology sequences and calculations by the aboveground analysis module revealed that, under strong winds, variety A, with its strong lodging resistance, exhibited specific morphological adaptations. Its leaves rapidly curled and adhered tightly to the main stem, with the average angle between the leaves and the main stem decreasing by 35% within 30 seconds, effectively reducing the windward area of ​​the canopy. Its stems also showed high resilience; the calculated stem bending index B reached a maximum of 0.85 during the peak wind speed and then stabilized at that level, quickly returning to upright after the wind subsided. In contrast, the leaf morphology adjustment of the new variety B was slower, resulting in it bearing a greater torque in the wind. Its stem bending index B continued to rise under strong winds, exceeding 2.1 at 95 seconds, after which the plant experienced irreversible severe tilting, i.e., lodging.

[0050] The processing results of CT scan data by the underground analysis module revealed fundamental differences in the lodging resistance mechanisms of the two varieties. DVC analysis of variety A showed that its root system was deep and extensive, especially with well-developed lateral roots in the 20-40 cm soil layer. Under wind load, the root-soil composite exhibited excellent overall anchoring properties. Strain field data showed that the peak tensile strain of the windward soil was only 0.2%, and the stress was effectively dispersed to deeper soil layers by its robust supporting roots, without localized stress concentration. Analysis of variety B, however, revealed that its root system was mainly distributed in the 0-20 cm shallow soil layer. DVC analysis clearly showed that under wind load, a maximum interfacial slip of 1.2 mm occurred between the taproot and the soil, and the peak tensile strain of the windward shallow soil layer reached 0.9%, indicating that its anchoring system was the primary failure factor leading to lodging.

[0051] Subsequently, the fusion assessment module input the above-ground dynamic response data (leaf angle change rate, peak stem bending angle) and underground biomechanical data (root-soil interface slip rate, peak soil strain) into a pre-trained stochastic forest regression model. The model calculated that the Synergistic Wind Resistance Index (SWRI) of lodging-resistant variety A was 0.92 (high resistance), while the SWRI of the new variety B was only 0.31 (low resistance). This quantitative result clearly distinguishes the lodging resistance of the two varieties. For the already lodged variety B, the system automatically switched to recovery tracking mode, continuously monitoring its post-disaster status once a day. (Reference) Figure 5 By analyzing the changes in the three-dimensional model of the canopy and PAR absorption within one week after lodging, the recovery potential assessment module calculated that its total photosynthetically active radiation absorption efficiency decreased by 70% compared with before lodging, and there were no obvious signs of recovery. Its recovery potential was assessed as "low".

[0052] The comparative experimental results of this embodiment show that the method provided by the present invention can accurately and quantitatively distinguish and evaluate the comprehensive lodging resistance traits of different maize varieties from multiple dimensions, such as the dynamic adjustment of aboveground morphology, the biomechanical mechanism of root-soil interaction, and the potential for post-disaster physiological recovery. The evaluation results are highly consistent with known field performance, providing a strong technical reference for efficient and accurate stress-resistant breeding screening.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for testing and analyzing the lodging resistance characteristics of maize plants, characterized in that, Includes the following steps: Step S1: Collect aboveground canopy morphology sequence. Use a multispectral sensor to collect image data of the aboveground canopy of maize plants under wind conditions at multiple time points to obtain a canopy morphology sequence that reflects the changes in canopy morphology over time. Step S2: Extract the dynamic structural mechanical response and use an image segmentation model to process the images in the canopy morphology sequence to segment the stem and leaf regions; Based on the segmentation results, feature extraction was performed to calculate the leaf angle distribution characteristics used to characterize the windward posture of the canopy and the bending degree index used to characterize the mechanical response of the stem, thereby obtaining the dynamic structural mechanical response of the aboveground part of the plant to quantify the morphological adjustment. Step S3: Obtain underground root structure and biomechanical field data. Simultaneously obtain volume data of the underground root system of the corn plant at multiple time points using tomography. The volume data is processed to extract the root expansion direction sequence representing the three-dimensional topology of the root system; and the microstructure inside the soil in the volume data at different time points is tracked and matched using digital volume correlation analysis method to calculate the three-dimensional displacement field and strain field of the soil area around the root system, so as to obtain biomechanical field data characterizing the change of anchoring state of the root-soil complex under wind load. Step S4: Data fusion and evaluation of synergistic wind resistance performance. A machine learning fusion model is used to fuse the dynamic structural mechanical response, the root extension direction sequence, and the biomechanical field data. By analyzing the functional relationship between wind load and overall plant deformation, a synergistic wind resistance index is output to quantitatively evaluate the overall wind resistance capacity of the plant.

2. The method according to claim 1, characterized in that, The steps for extracting dynamic structural mechanical response specifically include: using a convolutional neural network model with U-Net architecture to perform semantic segmentation on the images in the canopy morphology sequence to distinguish the background, stem and leaf regions; extracting the center lines of the leaves and stems through a morphological skeletonization algorithm, calculating the angle between the leaves and the main stem to generate the leaf angle distribution features, and calculating the curvature integral of the stem center line as the bending degree index.

3. The method according to claim 1, characterized in that, The biomechanical field data include the average principal strain, maximum shear strain, and slip ratio along the root-soil interface in the windward tension zone and the leeward compression zone.

4. The method according to claim 1, characterized in that, The machine learning fusion model is a random forest regression model or a gradient boosting decision tree model; the input feature vector of the model includes: the statistics of the leaf angle distribution, the stem bending index, and the peak strain of key soil regions and root-soil interface slip rate in the biomechanical field data.

5. The method according to claim 1, characterized in that, The method further includes the step of dynamically adjusting the data acquisition frequency of the multispectral sensor and the tomography technology based on external weather warnings or real-time wind speed monitoring data.

6. The method according to claim 5, characterized in that, The step of dynamically adjusting the data acquisition frequency of the multispectral sensor and the tomography technology includes: when a high-risk weather warning is received or the real-time wind speed exceeds a preset wind speed threshold, the acquisition frequency is increased to a high-frequency emergency response mode; when there is no warning and the wind speed is lower than the conventional threshold, a low-frequency conventional monitoring mode is adopted; the method also includes: using Kalman filtering to smooth the data acquired at different frequencies to obtain a spatiotemporal continuous dataset.

7. The method according to claim 1, characterized in that, The method also includes the following steps: if the measured degree of stem bending exceeds a preset instability threshold, a post-disaster recovery potential assessment is initiated; based on the canopy morphology data collected after lodging, the plant's ability to restore photosynthesis is predicted to assess the plant's recovery potential.

8. The method according to claim 7, characterized in that, The steps for predicting the plant's ability to restore photosynthesis include: generating a three-dimensional canopy model of the plant after lodging using the canopy morphology data collected after lodging; inputting the three-dimensional canopy model into a photosynthetically active radiation absorption model, calculating and tracking the change trend of the total photosynthetically active radiation absorbed by the plant daily over time, in order to quantify the recovery potential.

9. The method according to claim 6, characterized in that, The method also includes the step of: using the spatiotemporal continuous dataset to construct a four-dimensional dynamic model of the whole plant containing biomechanical parameters. The four-dimensional dynamic model integrates above-ground and underground structures and can reproduce the instability process of corn under wind fields and the morphological recovery process after disaster.

10. The method according to claim 9, characterized in that, The steps for constructing a four-dimensional dynamic model of the whole plant also include: assigning material mechanical properties such as elastic modulus and Poisson's ratio to the stem, leaves and roots of the model, and modeling the soil as an elastoplastic material; and using the four-dimensional dynamic model to perform finite element analysis to simulate the mechanical response of the plant under different wind conditions.

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