A method, device, medium and product for extracting crop canopy phenotypic parameters

Abstract

The present application discloses a method, device, medium and product for extracting crop canopy phenotypic parameters, which relates to the field of phenotypic parameter extraction. The method includes: obtaining a three-dimensional point cloud set of a target crop; using the Laplace algorithm to determine the point cloud skeleton of the target crop according to the three-dimensional point cloud set of the target crop; then, optimizing and instance-segmenting the point cloud skeleton of the target crop to complete organ-level fine instance segmentation; finally, extracting crop phenotypic parameters based on the three-dimensional point cloud and skeleton extraction, and the phenotypic parameters include: plant height, canopy width, number of leaves, leaf length, leaf width, and leaf angle. The present application can accurately determine the canopy phenotypic parameters of the target crop.

Description

Technical Field

[0001] This application relates to the field of phenotypic parameter extraction, and particularly to a method, device, medium and product for extracting phenotypic parameters of crop canopies. Background Art

[0002] Phenomics is a discipline that observes and analyzes the morphology, structure, physiology, ecology, etc. of animals and plants. By accurately extracting various phenotypes of crops, information such as their growth and development mechanisms and the interaction between genetics and the environment can be understood, which is of great significance for breeding. Traditional crop phenotype extraction mainly relies on manual observation and measurement of the external morphological characteristics of plants. Related methods involve the observation and measurement of a large number of samples and the analysis and comparison of massive data, which have the disadvantages of time-consuming, low efficiency, and large subjective errors. The crop phenotype extraction method based on computer vision overcomes the drawbacks of manual measurement and can automatically obtain various characteristics of crops, such as leaf morphology, chlorophyll fluorescence, root structure, etc., and then carry out analyses in aspects such as crop growth and development and stress tolerance. The cost is relatively low, the applicability is wide, and the efficiency is high.

[0003] Currently, most of the related research on crop phenotype extraction focuses on the two-dimensional direction of computer vision. However, when presenting a three-dimensional object in the real world in the form of a two-dimensional image, the information that can be obtained is very limited, and even some spatial information will be lost. Therefore, the current method for extracting crop phenotypes based on two-dimensional images has the problem of inaccuracy. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium and product for extracting phenotypic parameters of crop canopies, which can accurately extract the phenotypic parameters of crop canopies.

[0005] To achieve the above purpose, this application provides the following solutions:

[0006] In the first aspect, this application provides a method for extracting phenotypic parameters of crop canopies, including:

[0007] Obtain a three-dimensional point cloud set of the target crop;

[0008] According to the three-dimensional point cloud set of the target crop, use the Laplace algorithm to determine the point cloud skeleton of the target crop; the point cloud skeleton of the target crop is represented by an undirected graph;

[0009] Perform skeleton pruning, skeleton calibration and instance segmentation on the point cloud skeleton of the target crop to obtain the processed point cloud skeleton of the target crop;

[0010] According to the processed point cloud skeleton of the target crop, determine the plant height and canopy phenotypic parameters of the target crop; the canopy phenotypic parameters include crown width, number of leaves, leaf length, leaf width and leaf angle.

[0011] Optionally, according to the three-dimensional point cloud set of the target crop, the Laplace algorithm is used to determine the point cloud skeleton of the target crop, which specifically includes:

[0012] Using the K-nearest neighbor algorithm and the Laplace algorithm, perform point cloud contraction on the three-dimensional point cloud of the target crop to obtain a point cloud skeleton point set after point cloud contraction;

[0013] According to the point cloud skeleton point set after point cloud contraction, use the farthest point sampling algorithm to obtain a point cloud skeleton vertex set;

[0014] According to the point cloud skeleton vertex set, use the minimum spanning tree algorithm to generate the point cloud skeleton of the target crop.

[0015] Optionally, perform skeleton pruning, skeleton calibration, and instance segmentation on the point cloud skeleton of the target crop to obtain a processed point cloud skeleton of the target crop, which specifically includes:

[0016] Perform skeleton pruning on the point cloud skeleton of the target crop to obtain a pruned point cloud skeleton;

[0017] Perform skeleton calibration on the pruned point cloud skeleton to obtain a calibrated point cloud skeleton;

[0018] According to the calibrated point cloud skeleton, use the KD-tree algorithm for instance segmentation to obtain a processed point cloud skeleton of the target crop.

[0019] Optionally, perform skeleton pruning on the point cloud skeleton of the target crop to obtain a pruned point cloud skeleton, which specifically includes:

[0020] Divide the skeleton points in the undirected graph of the point cloud skeleton of the target crop into stem nodes, leaf tip nodes, and connection nodes; the stem nodes are the skeleton points connected by at least three edges in the undirected graph; the leaf tip nodes are the skeleton points connected by only one edge in the undirected graph; the connection nodes are the skeleton points connected by two edges in the undirected graph;

[0021] Determine the leaf tip node set and the stem root skeleton points;

[0022] Determine the shortest connected paths between each leaf tip node in the leaf tip node set and the stem root skeleton points to obtain a shortest connected path set;

[0023] Compare the shortest connected paths in the shortest connected path set pairwise;

[0024] If the coincidence degree between one shortest connected path and another shortest connected path exceeds 75% and the length difference is less than the set gap, then delete the short path in the two shortest connected paths to obtain a pruned point cloud skeleton.

[0025] Optionally, perform skeleton calibration on the pruned point cloud skeleton to obtain a calibrated point cloud skeleton, specifically including:

[0026] Determine the point cloud center according to the calibrated point cloud skeleton; the point cloud center is the centroid of the point cloud skeleton;

[0027] Perform a decentralization process on all the point clouds in the calibrated point cloud skeleton according to the point cloud center to obtain a decentralized point cloud;

[0028] Perform matrix singular value decomposition on the point cloud coordinate matrix to determine the direction vector of the fitting line; the point cloud coordinate matrix is determined according to the decentralized point cloud;

[0029] Determine the point cloud fitting line according to the point cloud center and the direction vector;

[0030] Map the stem skeleton nodes to the point cloud fitting line to complete the skeleton calibration and obtain a calibrated point cloud skeleton.

[0031] Optionally, determine the plant height and canopy phenotype parameters of the target crop according to the processed target crop point cloud skeleton, specifically including:

[0032] Determine the plant height of the target crop according to the maximum and minimum values of the z coordinates in the processed target crop point cloud skeleton;

[0033] Project the processed target crop point cloud skeleton onto a two-dimensional plane, use the Graham scan method to find the two-dimensional convex hull points in the projected point set, and determine the crown width according to the Euclidean distance of the two-dimensional convex hull points;

[0034] Extract the leaf point cloud edge points in the processed target crop point cloud skeleton, obtain the pair of points with the farthest distance among the leaf edge points as the leaf base point and the leaf tip point, and use the shortest path between the leaf base point and the leaf tip point in the leaf point cloud as the leaf length path to determine the leaf length according to the leaf length path;

[0035] Determine the leaf width according to the midpoint of the leaf in the processed target crop point cloud skeleton using the Euclidean distance;

[0036] Determine the number of leaves according to the number of leaf tip nodes;

[0037] Determine the leaf angle according to the angle between the edge formed by the midpoint of the leaf and the leaf base point in the processed target crop point cloud skeleton and the stem;

[0038] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the crop canopy phenotype parameter extraction method described in any one of the above.

[0039] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for extracting crop canopy phenotypic parameters described in any one of the above is implemented.

[0040] In a fourth aspect, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the method for extracting crop canopy phenotypic parameters described in any one of the above is implemented.

[0041] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0042] The present application provides a method, device, medium and product for extracting crop canopy phenotypic parameters. First, a method for extracting the crop point cloud skeleton based on the Laplace algorithm is used to obtain a complete target crop point cloud skeleton. Then, the target crop point cloud skeleton is optimized and instance segmented to complete organ-level fine instance segmentation. Finally, crop phenotypic parameter extraction is realized based on three-dimensional point cloud and skeleton extraction. The phenotypic parameters include: plant height, crown width, number of leaves, leaf length, leaf width, and leaf angle. The present application can accurately determine the canopy phenotypic parameters of the target crop. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic flowchart of a method for extracting crop canopy phenotypic parameters provided by an embodiment of the present application;

[0045] Figure 2 It is a schematic flowchart of a method for extracting crop canopy phenotypic parameters based on three-dimensional point cloud and Laplace skeleton extraction provided by an embodiment of the present application;

[0046] Figure 3 It is a schematic diagram of the target crop skeleton extracted by an embodiment of the present application;

[0047] Figure 4 It is a schematic diagram of the key points of the stem skeleton extracted by an embodiment of the present application;

[0048] Figure 5 It is a schematic diagram of the redundant skeleton provided by an embodiment of the present application;

[0049] Figure 6 It is a schematic diagram of the removal of the redundant skeleton provided by an embodiment of the present application;

[0050] Figure 7 It is a calibration diagram of crop stem skeletons provided by an embodiment of the present application;

[0051] Figure 8 It is a schematic diagram of instance segmentation of crop organs provided by an embodiment of the present application;

[0052] Figure 9 It is an effect diagram of instance segmentation of different crop organs provided by an embodiment of the present application;

[0053] Figure 10 It is a schematic diagram of crop canopy phenotype extraction provided by an embodiment of the present application;

[0054] Figure 11 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0055] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0056] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0057] In an exemplary embodiment, as Figure 1 and Figure 2 shown, a method for extracting crop canopy phenotype parameters is provided, including the following steps:

[0058] S1: Obtain a three-dimensional point cloud set of the target crop.

[0059] In practical applications, obtaining a three-dimensional point cloud set of the target crop P , can be expressed as , where refers to the number of points in the point cloud set, is the real number space, and each point is composed of three real number coordinates in three-dimensional space.

[0060] S2: According to the three-dimensional point cloud set of the target crop, use the Laplace algorithm to determine the point cloud skeleton of the target crop; the point cloud skeleton of the target crop is represented by an undirected graph.

[0061] As an optional implementation manner, S2 specifically includes:

[0062] S21: Use the K-nearest neighbor algorithm and the Laplacian algorithm to perform point cloud contraction on the three-dimensional point cloud of the target crop, and obtain the point cloud skeleton point set after point cloud contraction.

[0063] S22: According to the point cloud skeleton point set after point cloud contraction, use the farthest point sampling algorithm to obtain the point cloud skeleton vertex set.

[0064] S23: According to the point cloud skeleton vertex set, use the minimum spanning tree algorithm to generate the point cloud skeleton of the target crop.

[0065] In practical applications, based on the Laplacian algorithm, crop point cloud skeleton extraction is performed to obtain a complete crop skeleton.

[0066] First, construct a point cloud adjacency graph and calculate the Laplacian matrix for point cloud contraction, including:

[0067] (1) Use the K-nearest neighbor algorithm to construct a point cloud adjacency graph, capture the local and global structural features in the point cloud, calculate the Laplacian matrix (Laplacian Matrix) for point cloud contraction, and effectively extract a representative point cloud skeleton. The point cloud contraction equation is:

[0068] .

[0069] Among them, is the point cloud matrix (the three-dimensional point cloud set of the target crop), is the Laplacian matrix, and the initial value is the weighted cotangent matrix constructed by neighborhood points, and represent the contraction force and the attraction diagonal matrix respectively.

[0070] (2) According to the point cloud contraction equation, use the known point cloud matrix to find the iterated point cloud matrix , and then update the weight matrices and . The point cloud iterative contraction equation is:

[0071] .

[0072] Among them, represents the weighted transformation of the Laplacian matrix, and represent the neighborhood lengths before and after contraction of the contraction point .

[0073] (3) According to the updated point cloud matrix a new Laplacian matrix can be constructed. Repeat this step until the inequality is satisfied:

[0074] .

[0075] Among them, is the artificially defined contraction threshold, and its value is 0.01. So far, this embodiment has completed the extraction of the target crop skeleton based on the Laplace algorithm, as Figure 3 shown, where (a) is the three-dimensional point cloud of the target crop, (b) is the crop point cloud skeleton extracted based on the Laplace algorithm, and (c) is the effect diagram of the skeleton in the point cloud.

[0076] Then, refine the crop point cloud skeleton, including:

[0077] Refine the point cloud skeleton point set after point cloud contraction using the farthest point sampling (FPS) algorithm to obtain the point cloud skeleton vertex set from the point set , and the points in this set are regarded as key points, as Figure 4 shown, where (a) is the crop point cloud skeleton extracted based on the Laplace algorithm, and (b) is the effect diagram of the key nodes of the skeleton.

[0078] Finally, since the directly connected skeleton topological structure may have loops or cannot form a graph, resulting in redundant skeletons, as Figure 5 shown, so use the minimum spanning tree algorithm to simplify the connected skeleton structure again, aiming to find a tree such that all vertices in the graph can be connected through the edges in the tree, and the sum of the weights of these edges is the smallest.

[0079] Take the points in the point cloud skeleton vertex set as the vertices of the undirected graph, and the Euclidean distance between points as the weight of the edges of the undirected graph. The generated minimum spanning tree is regarded as the undirected graph of the plant skeleton (target crop point cloud skeleton) to achieve skeleton refinement.

[0080] S3: Perform skeleton pruning, skeleton calibration, and instance segmentation on the target crop point cloud skeleton to obtain the processed target crop point cloud skeleton.

[0081] As an optional implementation method, S3 specifically includes:

[0082] S31: Perform skeleton pruning on the target crop point cloud skeleton to obtain the pruned point cloud skeleton.

[0083] As an optional implementation method, S31 specifically includes:

[0084] Divide the skeleton points in the undirected graph of the target crop point cloud skeleton into stem nodes, leaf tip nodes, and connection nodes; the stem nodes are the skeleton points in the undirected graph connected by at least three edges; the leaf tip nodes are the skeleton points in the undirected graph connected by only one edge; the connection nodes are the skeleton points in the undirected graph connected by two edges.

[0085] Determine the leaf tip node set and the stem root skeleton points.

[0086] Determine the shortest connected paths between each leaf tip node in the leaf tip node set and the stem root skeleton points to obtain a set of shortest connected paths.

[0087] Compare the shortest connected paths in the set of shortest connected paths pairwise.

[0088] If the coincidence degree between one shortest connected path and another shortest connected path exceeds 75% and the length difference is less than the set gap, then delete the shorter path among the two shortest connected paths to obtain the pruned point cloud skeleton.

[0089] In practical applications, the method for pruning the target crop point cloud skeleton includes:

[0090] According to the number of connecting edges of the skeleton points, divide the skeleton points into three categories: stem nodes, leaf tip nodes, and connection nodes:

[0091] (1) Stem nodes: The skeleton nodes in the undirected graph connected by at least three edges.

[0092] (2) Leaf tip nodes: The skeleton nodes in the undirected graph connected by only one edge.

[0093] (3) Connection nodes: The skeleton nodes in the undirected graph with two connecting edges.

[0094] In addition, the skeleton node at the bottom of the stem has only one connecting edge and belongs to the stem node. Since the plant grows vertically in the xOy plane, the point with the smallest z value among the skeleton nodes connected by only one edge is regarded as the stem skeleton node and belongs to the stem node rather than the leaf tip node.

[0095] As Figure 6 shown, where (a) is the target crop point cloud skeleton containing redundant skeletons, (b) is the identification graph of the redundant skeletons in the target crop point cloud skeleton, and (c) is the pruned point cloud skeleton, that is, the redundant skeletons are removed. The steps for pruning the skeleton are as follows:

[0096] (1) Input the undirected graph of the target crop point cloud skeleton , and find the leaf tip node set and the stem root skeleton points 。

[0097] (2)Find the shortest connected path between the i-th leaf tip node in sequence and to form a set of shortest connected paths 。 。

[0098] (3)Let and be any two paths in the set of shortest connected paths . Compare the paths and in sequence. If the overlap degree of the two paths exceeds 75% and there is no significant length difference, delete the shorter one of the two paths.

[0099] (4)Reconstruct the skeleton undirected graph according to the newly obtained paths after pruning , where are all the key nodes in the path, are the connected nodes.

[0100] S32: Calibrate the pruned point cloud skeleton to obtain the calibrated point cloud skeleton.

[0101] As an optional implementation, S32 specifically includes:

[0102] Determine the point cloud center according to the calibrated point cloud skeleton; the point cloud center is the centroid of the point cloud skeleton.

[0103] Perform a decentralization process on all the point clouds in the calibrated point cloud skeleton according to the point cloud center to obtain the decentralized point cloud.

[0104] Perform matrix singular value decomposition on the point cloud coordinate matrix to determine the direction vector of the fitting line; the point cloud coordinate matrix is determined according to the decentralized point cloud.

[0105] Determine the point cloud fitting line according to the point cloud center and the direction vector.

[0106] Map the stem skeleton nodes to the point cloud fitting line to complete the skeleton calibration and obtain the calibrated point cloud skeleton.

[0107] In practical applications, perform three-dimensional straight line fitting and mapping transformation on the stem skeleton points in the pruned point cloud skeleton to calibrate the stem skeleton. As shown in Figure 7 , where (a) is the pruned point cloud skeleton and (b) is the calibrated point cloud skeleton. The steps are:

[0108] (1)Obtain the centroid of the point cloud and regard it as the point cloud center .

[0109] (2) Subtract the coordinates of all points in the point cloud from the coordinates of the point cloud center to decentralize the point cloud.

[0110] (3) Extract the coordinate matrix of the decentralized point cloud and perform matrix singular value decomposition. The eigenvector corresponding to the maximum eigenvalue is the direction vector of the fitted line. .

[0111] (4) Based on the point cloud center and the direction vector of the fitted line the expression of the fitted line of the point cloud can be obtained, and the calculation formula is as follows:

[0112] .

[0113] (5) Map the stem skeleton nodes to the fitted line and change them to , to complete the calibration of the crop point cloud skeleton, and the calculation formula is as follows:

[0114] .

[0115] S33: According to the calibrated point cloud skeleton, use the KD-tree algorithm for instance segmentation to obtain the processed target crop point cloud skeleton, as Figure 8 shown, where (a) is the crop point cloud skeleton before instance segmentation, and (b) is the point cloud skeleton after instance segmentation.

[0116] In practical applications, after optimizing the crop point cloud, the shortest path connecting the leaf tip node and the connecting node is used as the leaf skeleton path. The skeleton points included in the leaf skeleton path belong to the leaf skeleton points, and the shortest connected path of the stem skeleton points is used as the stem skeleton path. The points included in the stem skeleton path belong to the stem skeleton points.

[0117] Build a KD-tree to query all points whose distance from the anchor point is less than the threshold distance. This distance is regarded as the current organ point cloud. The anchor point is a skeleton point, and the threshold distance is set to the average Euclidean distance between the anchor point and two adjacent skeleton points on the current organ skeleton path.

[0118] If some point clouds are not correctly classified in the rough segmentation, further fine segmentation is required. Calculate the Euclidean distance between each point in the unclassified point set and each skeleton point. The organ point cloud set to which the skeleton point with the smallest distance belongs should be the organ to which it belongs, and complete the organ-level fine instance segmentation of the crop point cloud, as Figure 9 shown, where (a) is the three-dimensional point cloud of the target crop, (b) is the point cloud skeleton of the target crop, (c) is the effect diagram after rough segmentation of the point cloud, and (d) is the effect diagram after fine segmentation of the point cloud.

[0119] S4: Determine the plant height and canopy phenotypic parameters of the target crop based on the processed target crop point cloud skeleton, such as Figure 10 shown, where (a) is a schematic diagram for calculating leaf length, (b) is a schematic diagram for calculating leaf width, (c) is a schematic diagram for calculating leaf angle, (d) is a schematic diagram for calculating plant height, and (e) is a schematic diagram for calculating crown width; the canopy phenotypic parameters include crown width, number of leaves, leaf length, leaf width, and leaf angle.

[0120] As an optional implementation, S4 specifically includes:

[0121] S41: Determine the plant height of the target crop according to the maximum and minimum values of the z coordinate in the processed target crop point cloud skeleton.

[0122] S42: Project the processed target crop point cloud skeleton onto a two-dimensional plane, use the Graham scan method to find the two-dimensional convex hull points in the projection point set, and determine the crown width according to the Euclidean distance of the two-dimensional convex hull points. In practical applications, find a pair of points with the farthest Euclidean distance among the two-dimensional convex hull points as the crown width of the target crop.

[0123] S43: Extract the leaf point cloud edge points in the processed target crop point cloud skeleton, obtain the pair of points with the farthest distance among the leaf edge points as the leaf base point and the leaf tip point, and use the shortest path between the leaf base point and the leaf tip point in the leaf point cloud as the leaf length path, and determine the leaf length according to the leaf length path. In practical applications, use the numerical value of the leaf length path distance as the leaf length.

[0124] S44: Determine the leaf width according to the midpoint of the leaf in the processed target crop point cloud skeleton using the Euclidean distance.

[0125] In practical applications, represent the leaf length path point at the midpoint position of the leaf as , starting from , respectively construct vectors and pointing to and , use vectors and as the normal vectors, and use as the point on the cutting plane, deduce the equations of the cutting planes and , and the points between the cutting planes and in the leaf point cloud are regarded as the position range of the leaf width. Within the position range of the leaf width, along the vectors and Perform a linear scan in the direction to obtain a pair of points with the maximum Euclidean distance on the scanned line segment. The Euclidean distance between this pair of points is the leaf width of the point, and the maximum value of the leaf widths of all points within the position range is used as the leaf width of this position range.

[0126] S45: Determine the leaf angle according to the angle between the edge formed by the midpoint of the leaf and the leaf base point in the processed target crop point cloud skeleton and the stem.

[0127] The leaf angle is the angle between the edge formed by the midpoint of the leaf and the leaf base and the stem. Let the leaf base point be point O, point M be a point on the z-axis, and point N be a point on the leaf length path at a quarter of the length from the intersection point O. The calculation formula for the leaf angle α is as follows:

[0128] 。

[0129] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned method for extracting crop canopy phenotypic parameters is implemented.

[0130] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method for extracting crop canopy phenotypic parameters is implemented.

[0131] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method for extracting crop canopy phenotypic parameters is implemented.

[0132] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 11 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a method for extracting crop canopy phenotypic parameters is implemented.

[0133] Those skilled in the art can understand,Figure 11 The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0136] In each of the embodiments provided in this application, the database involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. In each of the embodiments provided in this application, the processor involved may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0137] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0138] In this article, specific examples are used to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for extracting crop canopy phenotypic parameters, characterized in that: include: Obtain a three-dimensional point cloud set of the target crop; According to the target crop three-dimensional point cloud set, using the Laplace algorithm, determining the target crop point cloud skeleton; The target crop point cloud skeleton is represented by an undirected graph; According to the target crop three-dimensional point cloud set, the target crop point cloud skeleton is determined by using the Laplace algorithm, specifically including: Using a K-nearest neighbor algorithm and a Laplace algorithm, the three-dimensional point cloud of the target crop is subjected to point cloud contraction to obtain a point cloud skeleton point set after point cloud contraction; According to the point cloud skeleton point set after the point cloud is shrunk, the point cloud skeleton vertex set is obtained by using the farthest point sampling algorithm; Generate a point cloud skeleton of a target crop using a minimum spanning tree algorithm according to the point cloud skeleton vertex set; Performing skeleton pruning, skeleton calibration and instance segmentation on the target crop point cloud skeleton to obtain a processed target crop point cloud skeleton; The pruned point cloud skeleton is calibrated to obtain a calibrated point cloud skeleton, which specifically includes: Determine the point cloud center according to the pruned point cloud skeleton; the point cloud center is the centroid of the point cloud skeleton; Decentralizing all point clouds in the pruned point cloud skeleton according to the point cloud center to obtain a decentralized point cloud; Performing matrix singular value decomposition on the point cloud coordinate matrix to determine the direction vector of the fitting line; the point cloud coordinate matrix is ​​determined based on the decentralized point cloud; Determine a point cloud fitting line according to the point cloud center and the direction vector; Mapping the stem skeleton nodes to the point cloud fitting straight line to complete skeleton calibration and obtain a calibrated point cloud skeleton; The plant height and canopy phenotypic parameters of the target crop are determined according to the processed target crop point cloud skeleton; the canopy phenotypic parameters include canopy width, number of leaves, leaf length, leaf width and leaf angle.

2. The method for extracting crop canopy phenotypic parameters according to claim 1, characterized in that: Performing skeleton pruning, skeleton calibration and instance segmentation on the target crop point cloud skeleton to obtain a processed target crop point cloud skeleton, specifically including: Performing skeleton pruning on the target crop point cloud skeleton to obtain a pruned point cloud skeleton; Performing skeleton calibration on the pruned point cloud skeleton to obtain a calibrated point cloud skeleton; According to the calibrated point cloud skeleton, instance segmentation is performed using a KD tree algorithm to obtain a processed target crop point cloud skeleton.

3. The method for extracting crop canopy phenotypic parameters according to claim 2, characterized in that: Performing skeleton pruning on the target crop point cloud skeleton to obtain a pruned point cloud skeleton specifically includes: The skeleton points in the undirected graph of the target crop point cloud skeleton are divided into stem nodes, leaf tip nodes and connection nodes; the stem node is a skeleton point connected by at least three edges in the undirected graph; the leaf tip node is a skeleton point connected by only one edge in the undirected graph; and the connection node is a skeleton point connected by two edges in the undirected graph; Determine the node set of leaf tips and the skeleton points of the stem root; Determine the shortest connection path between each leaf tip node in the leaf tip node set and the stem root skeleton point to obtain a shortest connection path set; Comparing the shortest connected paths in the shortest connected path set in pairs; If the overlap between one shortest connected path and another shortest connected path exceeds 75%, and the length difference is less than the set gap, the shortest path in the two shortest connected paths is deleted to obtain the pruned point cloud skeleton.

4. The method for extracting crop canopy phenotypic parameters according to claim 3, characterized in that: According to the processed target crop point cloud skeleton, the plant height and canopy phenotypic parameters of the target crop are determined, specifically including: Determining the plant height of the target crop according to the maximum and minimum values ​​of the z coordinates in the processed point cloud skeleton of the target crop; Projecting the processed target crop point cloud skeleton onto a two-dimensional plane, finding two-dimensional convex hull points in the projection point set using a Graham scanning method, and determining the crown width according to the Euclidean distance of the two-dimensional convex hull points; Extracting the edge points of the leaf point cloud in the processed target crop point cloud skeleton, obtaining the farthest point pair in the leaf point cloud edge points as the leaf base point and the leaf tip point, the shortest path between the leaf base point and the leaf tip point in the leaf point cloud as the leaf length path, and determining the leaf length according to the leaf length path; Determine the leaf width using the Euclidean distance according to the midpoint of the leaf in the processed target crop point cloud skeleton; Determining the number of blades according to the number of blade tip nodes; The leaf angle is determined according to the angle between the edge formed by the leaf midpoint and the leaf base point in the processed target crop point cloud skeleton and the stem.

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for extracting crop canopy phenotypic parameters according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for extracting crop canopy phenotypic parameters according to any one of claims 1 to 4 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for extracting crop canopy phenotypic parameters according to any one of claims 1 to 4 is implemented.

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