Three-dimensional root system measurement method and system, computer equipment and storage medium

Abstract

The invention provides a three-dimensional root system measurement method and system, computer equipment and a storage medium, and belongs to the field of plant growth science, and the method comprises the following steps: planting crops in different growth periods in transparent containers of different specifications, collecting images by using a root system scanning platform, generating dense point clouds through structured light projection and multi-view three-dimensional reconstruction technologies, and carrying out three-dimensional root system measurement on the dense point clouds; a three-dimensional grid model is constructed through a Poisson reconstruction algorithm, a Medial Axis Transform algorithm is adopted to extract a main root and branch paths to construct a topological structure, a time sequence corresponding relation of model topological nodes in different periods is established by means of an ICP algorithm, then configuration parameters are extracted, a trend curve is fitted, a shielding region interpolation mechanism is introduced, and a shielding region is constructed. According to the method, the early-stage model and the physiological growth function are combined for structure completion, dynamic analysis is achieved, the VTK and Matplotlib technologies are used for achieving root growth visualization, morphological structure changes of crops in different growth periods can be visually analyzed, the root growth rule can be reserved, and accurate guide parameters are provided for research.

Description

Technical Field

[0001] The present invention belongs to the field of plant growth science, and in particular relates to a three-dimensional root system measurement method, system, computer equipment and storage medium. Background Art

[0002] The root system is a key organ for plants to obtain water and nutrients. Its three-dimensional structural characteristics (main root path, branching angle, root diameter variation, spatial distribution density) have an important impact on the crop's stress resistance, growth potential and resource utilization efficiency.

[0003] Traditional root system research relies primarily on the following technical approaches: destructive root excavation and two-dimensional image analysis methods, such as photographing, cleaning, and scanning root boxes. While simple, these methods fail to maintain the integrity of the root system's spatial structure, dynamically record growth processes, and quantify true three-dimensional topological relationships. Three-dimensional modeling based on medical imaging methods such as CT and MRI offers a degree of non-invasiveness, but the equipment is expensive, time-consuming, and poorly adaptable to soil and container materials. Furthermore, it struggles with large-sample, multi-period modeling, making it difficult to meet the operational, high-throughput, and low-cost demands of agricultural research. In recent years, methods combining surface 3D reconstruction (such as MVS and structured light) with skeleton extraction have been widely used in 3D modeling research of plant roots. Although they have improved the accuracy of visualization and topological reconstruction, the following significant technical bottlenecks still exist in practical applications: (a) The problem of missing modeling of occluded areas: As the root system continues to grow deeper into the container, its structure gradually exceeds the scanning angle or is obscured by the soil, making it impossible to reconstruct some root segments, resulting in skeleton breakage and loss of structural information; (b) Lack of continuous modeling and temporal topological registration mechanism: Most existing methods focus on modeling at a single time point, lack coordinate registration and topological evolution tracking between models across time periods, and make it difficult to achieve quantitative analysis of dynamic structural changes of the root system; (c) Interpolation completion does not consider the physiological characteristics of the root system: Traditional structural interpolation methods are mostly based on geometric fitting, ignoring the growth laws of the root system, resulting in a lack of biological rationality in the completion part, affecting the accuracy of configuration parameter extraction.

[0004] In summary, existing technologies are unable to dynamically record the entire process of plant growth, are unable to reconstruct obscured parts, ignore the growth patterns of the root system, resulting in a lack of biological rationality in the completed parts and a lack of accurate data guidance for plant growth research and judgment. A technology is urgently needed to solve the above problems. Summary of the Invention

[0005] In order to solve the problem of preserving the growth patterns of plant roots and realizing continuous dynamic quantitative analysis of the growth patterns of plant roots, the present invention provides a three-dimensional root measurement method, system, computer equipment and storage medium. In order to achieve the above purpose, the present invention provides the following technical solutions.

[0006] A three-dimensional root system measurement method, comprising: Target crops at different growth stages are cultivated in transparent containers of different sizes, and root system images of the target crops at different growth stages are collected.

[0007] The collected root images are converted into dense point cloud three-dimensional coordinate matrices of the target crops, and different three-dimensional grid models are constructed using the dense point cloud three-dimensional coordinate matrices of the target crop roots at different growth periods.

[0008] The main roots and branch paths of the three-dimensional grid models at different growth periods are extracted, and the main roots and branch paths are used as topological nodes to construct a topological structure of a unified coordinate system. The three-dimensional grid models at different growth periods are matched with each other to establish a temporal correspondence between the topological nodes. Based on the temporal correspondence between the topological nodes, the topological structure is used to extract the configuration parameters of the target crop, and the configuration parameters are fitted into a configuration parameter trend curve.

[0009] The three-dimensional grid model of the target crop's root system at different growth stages is converted into a root system structure visualization model, and the target crop's configuration parameters and their configuration parameter trend curves are converted into a visualization parameter trend graph; the three-dimensional root system of the target crop is measured using the root system structure visualization model and the visualization parameter trend graph.

[0010] Preferably, the extracting of the main roots and branch paths of the three-dimensional grid model at different growth stages, taking the main roots and branch paths as topological nodes, and pre-processing the root system image before constructing the topological structure of the unified coordinate system specifically includes: Organize the growth traces of the main root, lateral root, and root diameter from the root system images of the target crop at different stages, and confirm the occlusion time period of the main root, lateral root, and root diameter; The terminal growth direction of the obscured main root and lateral root was inferred based on the images collected at different periods; A third-order β-spline trajectory is constructed for the obscured main root in the root system image, and the changes in the main root length during the obscured period are fitted using a three-dimensional grid model of different growth periods. The Poisson distribution is used to generate the growth direction and length of the lateral root branches during the obscured period. The root diameter is simulated by the main root data at different times during the obscured period. The main root and branch paths of the three-dimensional mesh model at different growth stages are extracted from the preprocessed root system image.

[0011] Preferably, the three-dimensional grid model of different growth periods is used to fit the change in the length of the main root during the blocked time period, and the expression is: Where L(t) represents the length of the main root at time t, is the initial moment The main root length, ( ) represents the time interval from the initial moment to the current moment, is the growth rate of the taproot, which is obtained by fitting the three-dimensional grid model at different growth stages.

[0012] Preferably, the root diameter is simulated by main root data at different times during the blocked time period, and the specific expression is: D(t) represents the diameter of the main root at time t, is the diameter of the root at the initial moment, is the length of the current root, is the maximum length of a root, and k and n are coefficients.

[0013] Preferably, the configuration parameters include: root length, number of branches, root diameter and root density.

[0014] Preferably, fitting the configuration parameters into a configuration parameter trend curve comprises: Configuration parameters build sequence over time ...... , calculate the difference ,in, ...... Represents the value sequence of root length, branch number and root diameter of target crops at different time points; and Two adjacent time points and The corresponding parameter value, ( ) is used to calculate the difference in root length, branch number, and root diameter between two adjacent time points; Fitting polynomial curves to the architectural parameters of root length, branch number, and root diameter ; Where a is the coefficient of the quadratic term, b is the coefficient of the linear term, and c is the constant term, which represents the intercept of the curve at t=0. The values ​​of a, b, and c are determined by the least squares method; The logarithmic function was used to fit the trend curve of architectural parameters for the root density change. The fitting method adopts the least square method, a affects the growth rate of the logarithmic function, and b is the constant term.

[0015] Preferably, matching the three-dimensional grid models of different growth periods includes: using an ICP algorithm to align the three-dimensional grid models of different growth periods, and initially aligning the center of the bottom plane of the reference container with the center point of the root system of the target crop.

[0016] The present invention also provides a three-dimensional root system measurement system, comprising: An image acquisition module is used to cultivate target crops at different growth stages in transparent containers of different sizes and to collect root system images of the target crops at different growth stages; The root system structure model module is used to convert the collected root system images into a dense point cloud three-dimensional coordinate matrix of the target crop, and use the dense point cloud three-dimensional coordinate matrix of the target crop root system at different growth stages to construct different three-dimensional grid models; extract the main root and branch path of the three-dimensional grid models at different growth stages, use the main root and branch path as topological nodes, and construct a topological structure of a unified coordinate system; match the three-dimensional grid models at different growth stages to establish a time-series correspondence between the topological nodes; based on the time-series correspondence between the topological nodes, use the topological structure to extract the configuration parameters of the target crop, and fit the configuration parameters into a configuration parameter trend curve; The visualization analysis module is used to convert the three-dimensional grid model of the root system of the target crop at different growth periods into a root system structure visualization model, and convert the configuration parameters of the target crop and its configuration parameter trend curve into a visualization parameter trend graph; using the root system structure visualization model and visualization parameter trend graph, the three-dimensional root system measurement of the target crop is realized.

[0017] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any one of the steps in the three-dimensional root system measurement method.

[0018] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, it can execute any one of the steps in the three-dimensional root system measurement method.

[0019] The three-dimensional root system measurement method provided by the present invention has the following beneficial effects: A root scanning platform was used to capture root images of target crops at different growth stages. Structured light projection and multi-view stereo reconstruction techniques were used to convert the images into dense point clouds. Based on this, a Poisson reconstruction algorithm was used to construct different 3D mesh models, ensuring the integrity of the structure captured at each growth stage. The Medial AxisTransform algorithm was used to extract the main root and branch paths from the 3D mesh models, thereby constructing the topological structure. The ICP algorithm was used to match the 3D mesh models, establish temporal correspondences between topological nodes, and fit architectural parameter trend curves. Using VTK technology, the 3D mesh models were converted into a root system visualization model, enabling analysis of morphological and structural changes in the target crops at different growth stages and achieving dynamic alignment of the skeletal structure. Furthermore, time series analysis and dynamic visualization of root system architectural parameters were implemented for continuous phenotyping. An occluded region interpolation mechanism was introduced, combining the previous model with the physiological growth function for structural completion, enabling dynamic analysis and preserving root growth patterns, providing accurate guiding parameters for research. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0021] Figure 1 This is a flow chart of a three-dimensional root system measurement method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] The present invention provides a three-dimensional root system measurement method, specifically Figure 1 Shown, including: S1. Cultivate target crops at different growth stages in transparent containers of different sizes, and collect root system images of the target crops at different growth stages.

[0024] Transparent containers of various sizes (5×5×10cm, 10×10×15cm, 20×20×25cm, 25×25×30cm) were designed and replaced according to the growth stage to match the root morphology at different growth scales; a root scanning platform was used to capture multi-angle images of the edges and bottom.

[0025] S2. Convert the collected root system images into a dense point cloud three-dimensional coordinate matrix of the target crop, and use the dense point cloud three-dimensional coordinate matrix of the target crop root system at different growth periods to construct different three-dimensional grid models; extract the main roots and branch paths of the three-dimensional grid models at different growth periods, use the main roots and branch paths as topological nodes, and construct a topological structure of a unified coordinate system; match the three-dimensional grid models at different growth periods to each other, and establish a time series correspondence between topological nodes; based on the time series correspondence between the topological nodes, use the topological structure to extract the configuration parameters of the target crop, and fit the configuration parameters into a configuration parameter trend curve.

[0026] Structured light projection combined with multi-view stereo (MVS) reconstruction generated a dense point cloud, and a continuous mesh model was constructed using the Poisson reconstruction algorithm. The 3D mesh model was voxelized into a 0.2mm resolution voxel grid. The Medial AxisTransform algorithm was used to extract the primary root and branching paths. A topological graph data structure was constructed: nodes = bifurcation points and root tips, edges = skeleton segments, and attributes = length, direction, and diameter. The multi-period model was registered using the ICP algorithm, initially aligning the reference vessel bottom plane with the root center. Registration was completed using a rigid body transformation matrix between the point sets. Temporal correspondence between topological nodes was established to track structural changes.

[0027] A third-order β-spline trajectory is constructed for the invisible main root extension, and the control points are determined according to the terminal growth direction and the estimated length; the main root length change is determined by Determined by, v is obtained by fitting the historical three-period grid model; the lateral root branches are generated using a Poisson distribution with λ=0.2, the angle θ∈[25,60], and the direction vector is generated by the rotation matrix; the root diameter is given by Simulation, where Indicates time The diameter of the time root, is the diameter of the root at the initial moment, is the length of the current root, is the maximum length of the root, k and n are coefficients, and the fitting parameters k and n are obtained by least squares of the historical segment.

[0028] The configuration parameters are designed as follows: total root length RL is the sum of the side lengths; main root number NR is the number of main paths connected to the root base; branch number BN = the number of nodes with degree ≥ 3 among all nodes; average root diameter RD = grid thickness / number of skeleton segments; bifurcation angle SA = the angle between adjacent sides, angle ; Root density is calculated by stratifying the length of skeleton segments every 1 cm along the depth Z direction. ...... , calculate the difference ,in, ...... , represents the value sequence of root length, branch number and root diameter of target crops at different time points; and Two adjacent time points and The corresponding parameter values, Used to calculate the difference in root length, number of branches, and root diameter between two adjacent time points; fit a polynomial curve to the configuration parameters of root length, number of branches, and root diameter ; Where t is the time variable, a is the coefficient of the quadratic term, b is the coefficient of the linear term, and c is the constant term, representing the intercept of the curve at t=0. The values ​​of a, b, and c are determined by the least squares method.

[0029] Logarithmic function fitting was used to measure the root density changes The fitting method adopts the least square method, t is the time variable, a affects the growth rate of the logarithmic function, and b is the constant term.

[0030] S3. Convert the three-dimensional grid model of the root system of the target crop at different growth stages into a root system structure visualization model, and convert the configuration parameters of the target crop and its configuration parameter trend curve into a visualization parameter trend graph; use the root system structure visualization model and the visualization parameter trend graph to achieve three-dimensional root system measurement of the target crop.

[0031] All parameters and structures are exported as .csv, .vtk, and .json files. VTK+Matplotlib is used to generate root system structure visualization models and parameter trend graphs. Animation sequences can be optionally exported for visual playback and comparative analysis.

[0032] Compared with existing three-dimensional modeling methods, the present invention has significant advantages in structural integrity, dynamic continuity and physiological consistency. Through multi-phase transparent container planting and staged image acquisition, combined with main root interpolation and lateral root generation model, the root segments that cannot be reconstructed due to occlusion are effectively supplemented, and the continuity and integrity of the topological map are significantly improved. A spatial registration mechanism under a unified coordinate system is introduced to achieve dynamic alignment and change tracking of the root structure in the time dimension, and support continuous extraction and trend analysis of key parameters such as total root length, number of branches, and average root diameter. By integrating the root growth model with the root diameter evolution function, the present invention achieves biological consistency between structure and function in interpolation reconstruction, and improves the reliability of the modeling results. The output format is compatible with a variety of agricultural model platforms and has good engineering scalability and data visualization capabilities. This method has the characteristics of non-destructiveness, clear structural expression, and controllable dynamic trends. It is particularly suitable for high-throughput root phenotyping research, root-soil interaction simulation, and intelligent agricultural decision-making systems, and has significant technical application value and economic benefits.

[0033] Based on the same inventive concept, the present invention also provides a three-dimensional root system measurement system, comprising: The image acquisition module is used to cultivate target crops at different growth stages in transparent containers of different specifications and collect root system images of the target crops at different growth stages.

[0034] The root system structure model module is used to convert the collected root system images into dense point clouds of the target crops, and use the dense point clouds of the target crop roots at different growth periods to construct different three-dimensional grid models; extract the main roots and branch paths of the three-dimensional grid models at different growth periods, use the main roots and branch paths as topological nodes, and construct a topological structure of a unified coordinate system; match the three-dimensional grid models at different growth periods with each other, and establish a time-series correspondence between the topological nodes; based on the time-series correspondence between the topological nodes, use the topological structure to extract the configuration parameters of the target crops, and fit the configuration parameters into a configuration parameter trend curve.

[0035] The visualization analysis module is used to convert the three-dimensional grid model of the root system of the target crop at different growth periods into a root system structure visualization model, and convert the configuration parameters of the target crop and its configuration parameter trend curve into a visualization parameter trend graph, so as to realize the three-dimensional root system measurement of the target crop.

[0036] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile storage, and may also include other hardware required for operations. The processor reads a corresponding computer program from the non-volatile storage into the internal memory and then executes the program to implement the three-dimensional root system measurement method provided above.

[0037] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program can be used to execute the three-dimensional root system measurement method provided above.

[0038] The specific limitations of the three-dimensional root system measurement calculation system can be found in the limitations of the three-dimensional root system measurement method above and will not be further elaborated here. Each module in the three-dimensional root system measurement system described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0039] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification and examples have described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A three-dimensional root system measurement method, characterized in that: The method comprises: Target crops at different growth stages are cultivated in transparent containers of different sizes, and root system images of the target crops at different growth stages are collected; The collected root system images are converted into dense point cloud 3D coordinate matrices of the target crop, and different 3D grid models are constructed using the dense point cloud 3D coordinate matrices of the target crop roots at different growth stages. Extracting the main roots and branch paths of the three-dimensional grid models at different growth stages, using the main roots and branch paths as topological nodes, and constructing a topological structure of a unified coordinate system; matching the three-dimensional grid models at different growth stages to establish a temporal correspondence between the topological nodes; based on the temporal correspondence between the topological nodes, extracting the configuration parameters of the target crop using the topological structure, and fitting the configuration parameters into a configuration parameter trend curve; The three-dimensional grid model of the target crop's root system at different growth stages is converted into a root system structure visualization model, and the target crop's configuration parameters and their configuration parameter trend curves are converted into a visualization parameter trend graph; the three-dimensional root system of the target crop is measured using the root system structure visualization model and the visualization parameter trend graph.

2. A three-dimensional root system measurement method according to claim 1, characterized in that: The extracting of the main roots and branch paths of the three-dimensional grid model at different growth stages, taking the main roots and branch paths as topological nodes, and constructing the topological structure of the unified coordinate system, pre-processing the root system image specifically includes: Organize the growth traces of the main root, lateral root, and root diameter from the root system images of the target crop at different stages, and confirm the occlusion time period of the main root, lateral root, and root diameter; The terminal growth direction of the obscured main root and lateral root is inferred based on the images collected at different periods; A third-order β-spline trajectory is constructed for the main root that is blocked in the root system image, and the change in the main root length during the blocked period is fitted by a three-dimensional grid model of different growth periods. The Poisson distribution is used to generate the growth direction and length of the lateral root branches during the obscured period. The root diameter is simulated by the main root data at different times during the obscured period. The main root and branch paths of the three-dimensional mesh model at different growth stages are extracted from the preprocessed root system image.

3. A three-dimensional root system measurement method according to claim 2, characterized in that: The three-dimensional grid model of different growth periods is used to fit the change in the length of the main root during the blocked time period, and the expression is: Where L(t) represents the length of the main root at time t, is the initial moment The main root length, ( ) represents the time interval from the initial moment to the current moment, and v is the growth rate of the main root, which is obtained by fitting the three-dimensional grid model at different growth periods.

4. A three-dimensional root system measurement method according to claim 2, characterized in that: The root diameter is simulated by the main root data at different times during the blocked time period, and the specific expression is: Where, represents the diameter of the taproot at time t, is the diameter of the root at the initial moment, is the length of the current root, is the maximum length of a root, and k and n are coefficients.

5. A three-dimensional root system measurement method according to claim 1, characterized in that: The configuration parameters include: root length, branch number, root diameter and root density.

6. A three-dimensional root system measurement method according to claim 1, characterized in that: The step of fitting the configuration parameters into a configuration parameter trend curve comprises: Configuration parameters build sequence over time ...... , calculate the difference ,in, ...... Represents the value sequence of root length, branch number and root diameter of target crops at different time points; and Two adjacent time points and The corresponding parameter value, ( ) is used to calculate the difference in root length, branch number, and root diameter between two adjacent time points; Fitting polynomial curves to the architectural parameters of root length, branch number, and root diameter ; Where a is the coefficient of the quadratic term, b is the coefficient of the linear term, and c is the constant term, which represents the intercept of the curve at t=0. The values ​​of a, b, and c are determined by the least squares method; The logarithmic function was used to fit the trend curve of architectural parameters for the root density change. The fitting method adopts the least square method, a affects the growth rate of the logarithmic function, and b is the constant term.

7. A three-dimensional root system measurement method according to claim 1, characterized in that: The matching of the three-dimensional grid models at different growth stages includes: using the ICP algorithm to align the three-dimensional grid models at different growth stages, and initially aligning the center of the bottom plane of the reference container with the center point of the root system of the target crop.

8. A three-dimensional root system measurement system, characterized in that: include: An image acquisition module is used to cultivate target crops at different growth stages in transparent containers of different sizes and to collect root system images of the target crops at different growth stages; The root system structure model module is used to convert the collected root system images into a dense point cloud 3D coordinate matrix of the target crop, and use the dense point cloud 3D coordinate matrix of the target crop root system at different growth stages to construct different 3D grid models; extract the main root and branch paths of the 3D grid models at different growth stages, use the main root and branch paths as topological nodes, and construct a topological structure of a unified coordinate system; match the 3D grid models at different growth stages to establish a temporal correspondence between the topological nodes; Based on the temporal correspondence between the topological nodes, the topological structure is used to extract the configuration parameters of the target crop, and the configuration parameters are fitted into a configuration parameter trend curve; The visualization analysis module is used to convert the three-dimensional grid model of the root system of the target crop at different growth periods into a root system structure visualization model, and convert the configuration parameters of the target crop and its configuration parameter trend curve into a visualization parameter trend graph; using the root system structure visualization model and visualization parameter trend graph, the three-dimensional root system measurement of the target crop is realized.

9. A computer device, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the method steps according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method steps according to any one of claims 1 to 7 are implemented.

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