Litchi tree shape digital reconstruction method, device and equipment and storage medium

Abstract

The invention relates to an artificial intelligence technology, and discloses a litchi tree shape digital reconstruction method and device, equipment and a storage medium. The method comprises the following steps: performing environment scanning on a target litchi orchard to obtain point cloud information, and constructing an orchard point cloud map according to the point cloud information; performing litchi tree identification on the orchard point cloud map by using a litchi tree segmentation model to obtain target litchi tree point cloud distribution; object recognition operation based on a trunk, a crown and a whole tree is carried out on the point cloud distribution of the target litchi tree to obtain an object recognition result, bounding box frame selection marking is carried out on the object recognition result to obtain a bounding box set, and the bounding box set comprises a litchi trunk bounding box, a litchi canopy layer bounding box and a litchi whole bounding box; and performing information extraction on the bounding box set by using a pre-constructed point cloud slicing algorithm to obtain a digital information set of the shape of the litchi tree. According to the invention, the efficiency and accuracy of shape digitization of the litchi tree can be improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and computer-readable storage medium for digitally reconstructing the appearance of a litchi tree. Background Art

[0002] Litchi is an important cash crop in tropical and subtropical regions of my country. Its high economic value makes it the main source of income for many farmers. By measuring the appearance of litchi trees and digitizing their appearance data, its growth can be monitored and the health of the trees can be assessed. Indicators such as crown size and shape can reflect the growth potential and health of trees. In smart agriculture, equipment such as robots and drones are widely used in field management. Measuring the appearance data of litchi trees can provide navigation and operation basis for these devices, improving the accuracy and efficiency of automated management. Accurate appearance data of litchi trees can guide mechanized operations such as automatic pruning and fruit picking, reduce labor costs and improve work efficiency. Combining historical data with real-time monitoring data can improve the accuracy of yield forecasts and help farmers formulate scientific planting and sales plans.

[0003] The traditional method of measuring the shape of litchi trees is generally manual measurement, which is time-consuming and has low accuracy. In addition, litchi trees generally grow taller, which puts the surveyors at risk when using manual measurement. In order to solve the above problems and achieve accurate measurement of the shape of litchi trees, most of them currently use ground scanners for scanning. Therefore, there are the following problems: it takes a long time to detect the entire litchi orchard, and it is inconvenient to communicate data with other equipment. At the same time, the ground scanner has high requirements for the flatness of the ground, is inconvenient to move, and cannot meet the diverse needs of digitalizing the shape of litchi trees. Summary of the invention

[0004] The invention provides a litchi tree appearance digital reconstruction method, device, equipment and storage medium, the main purpose of which is to improve the efficiency and accuracy of litchi tree appearance digital reconstruction.

[0005] To achieve the above object, the present invention provides a method for digitally reconstructing the appearance of a litchi tree, comprising:

[0006] Use a pre-built point cloud acquisition device to scan the environment of the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information;

[0007] Using a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map, and obtaining a target litchi tree point cloud distribution;

[0008] Performing an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and marking the object recognition result with a bounding box to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box;

[0009] The pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digital information set of the litchi tree shape.

[0010] Optionally, the using a pre-built SLAM algorithm to construct an orchard point cloud map according to the point cloud information includes:

[0011] Sorting the point cloud information according to the position of the point cloud acquisition device to obtain a single-frame point cloud sequence;

[0012] Using a pre-built inertial measurement unit to obtain the position and attitude of the point cloud acquisition device, and recording a sequence of position and attitude changes within a preset time period;

[0013] Acquire a high-definition garden image at a target node position of a motion trajectory of the point cloud acquisition device during the point cloud acquisition process;

[0014] According to the pre-built SLAM algorithm, according to the position and posture change sequence, the target node position and the high-definition garden image, each single-frame point cloud in the single-frame point cloud sequence is subjected to a point cloud deployment operation based on the world coordinate system to obtain a primary orchard point cloud map;

[0015] According to the pre-built loop detection algorithm, the primary orchard point cloud map is updated to obtain an orchard point cloud map.

[0016] Optionally, the inertial measurement unit is expressed as:

[0017]

[0018] In the formula, represents the angular velocity of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, represents the acceleration of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, ω t represents the actual measured angular velocity, represents the angular velocity deviation of the point cloud acquisition device at time t, represents the angular velocity white noise of the point cloud acquisition device at time t, represents the rotation matrix from the world coordinate system to the body coordinate system B of the point cloud acquisition device, α t represents the actual measured acceleration, g represents the gravity vector, represents the acceleration deviation of the point cloud acquisition device in the body coordinate system B at time t, represents the acceleration white noise of the point cloud acquisition device at time t.

[0019] Optionally, the position and posture of the point cloud acquisition device is expressed as:

[0020]

[0021] In the formula, the R t Represents the rotation matrix Δt represents a period of time, v t+Δt represents the speed of the point cloud acquisition device at time t+Δt, p t+Δt represents the position of the point cloud acquisition device at time t+Δt, R t+Δt Represents the rotation matrix of the point cloud acquisition device at time t+Δt.

[0022] Optionally, the using a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map to obtain a target litchi tree point cloud distribution includes:

[0023] Obtaining point cloud sample data of similar litchi trees of the target litchi tree in the target litchi orchard;

[0024] Obtain a pre-built litchi tree segmentation model, and train the litchi tree segmentation model using the point cloud sample data of the same type of litchi tree to obtain a pre-trained litchi tree segmentation model;

[0025] Obtaining artificially identified samples corresponding to the orchard point cloud map of the target litchi orchard, and fine-tuning the pre-trained litchi tree segmentation model using the artificially represented samples to obtain a trained litchi tree segmentation model;

[0026] The trained litchi tree segmentation model is used to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution.

[0027] Optionally, the object recognition operation based on the trunk, the crown and the whole tree is performed on the point cloud distribution of the target litchi tree to obtain an object recognition result, and the object recognition result is marked with a bounding box to obtain a bounding box set, including:

[0028] Performing outlier removal on the target litchi tree point cloud distribution to obtain a converged litchi tree point cloud distribution;

[0029] Using a pre-built clustering algorithm, clustering and grouping operations are performed on the convergent litchi tree point cloud distribution based on the trunk and the crown to obtain the trunk point cloud distribution and the crown point cloud distribution;

[0030] Using a principal component analysis algorithm, the numerical features of the trunk point cloud distribution and the crown point cloud distribution based on the principal axis direction of the world coordinate system are obtained to obtain a numerical feature set, and a minimum bounding box set is constructed according to the numerical feature set;

[0031] The overall point cloud distribution of the tree including the trunk point cloud distribution and the crown point cloud distribution is obtained, and the overall point cloud distribution of the tree, the trunk point cloud distribution and the crown point cloud distribution are marked by the minimum bounding box set.

[0032] Optionally, the use of a pre-built point cloud slicing algorithm to extract information from the bounding box set to obtain a digitized information set of the litchi tree shape includes:

[0033] Using a pre-built point cloud slicing algorithm, the point cloud distribution of the target litchi tree in the bounding box set is cut to obtain a canopy plane sequence;

[0034] Performing a two-dimensional convex hull operation on the canopy plane sequence to obtain a convex hull canopy plane, and obtaining the area and volume of the convex hull canopy plane to obtain the maximum canopy area and the maximum canopy volume;

[0035] Integrate the height corresponding to each canopy plane in the canopy plane sequence, the maximum canopy area and the maximum canopy volume corresponding to each convex hull canopy plane, and obtain the integer point cloud distribution volume corresponding to the target litchi tree point cloud distribution;

[0036] The maximum canopy area, the maximum canopy volume and the integer point cloud distribution volume are outputted simultaneously to obtain a digital information set of the litchi tree appearance.

[0037] In order to solve the above problems, the present invention also provides a device for digitally reconstructing the appearance of a litchi tree, the device comprising:

[0038] A point cloud acquisition module is used to use a pre-built point cloud acquisition device to perform an environmental scan on the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information;

[0039] A litchi tree recognition module, used to identify litchi trees on the orchard point cloud map using a pre-trained litchi tree segmentation model to obtain a target litchi tree point cloud distribution;

[0040] An object feature segmentation module is used to perform an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and to perform a bounding box selection mark on the object recognition result to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box;

[0041] The litchi tree shape digitization module is used to extract information from the bounding box set using a pre-built point cloud slicing algorithm to obtain a litchi tree shape digitization information set.

[0042] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0043] at least one processor; and,

[0044] a memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned method for digitally reconstructing the appearance of a litchi tree.

[0046] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned method for digitally reconstructing the appearance of a litchi tree.

[0047] The embodiment of the present invention firstly scans the environment of the target litchi orchard through a point cloud acquisition device to preliminarily obtain point cloud information, and then arranges the point cloud information using a SLAM algorithm to construct an orchard point cloud map. The SLAM algorithm can rearrange the serialized point cloud information according to the movement of the point cloud acquisition device to obtain a three-dimensional orchard point cloud map; then, the litchi tree in the orchard point cloud map is identified using a pre-trained litchi tree segmentation model, wherein the litchi tree segmentation model is a neural network model, which can improve the accuracy and efficiency of litchi tree recognition, obtain the target litchi tree point cloud distribution, and then perform a box selection and marking operation based on the trunk, crown and integer on the target litchi tree point cloud distribution to obtain a bounding box set; finally, extract information from the bounding box set according to the point cloud slicing algorithm to obtain a litchi tree shape digital information set. Therefore, the embodiment of the present invention provides a litchi tree shape digital reconstruction method, device, equipment and storage medium, which can improve the efficiency and accuracy of litchi tree shape digitalization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a process for digitally reconstructing the appearance of a litchi tree provided by an embodiment of the present invention;

[0049] Figure 2 A detailed flow chart of the SLAM algorithm steps in the method for digitally reconstructing the appearance of a litchi tree provided in one embodiment of the present invention;

[0050] Figure 3A schematic diagram of the basic network structure of the litchi tree segmentation model in the litchi tree appearance digital reconstruction method provided in one embodiment of the present invention;

[0051] Figure 4 A functional module diagram of a device for digitally reconstructing the appearance of a litchi tree provided in one embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the structure of an electronic device for implementing the method for digitally reconstructing the appearance of a litchi tree provided in one embodiment of the present invention.

[0053] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0055] The embodiment of the present application provides a method for digitally reconstructing the appearance of a litchi tree. In the embodiment of the present application, the execution subject of the method for digitally reconstructing the appearance of a litchi tree includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for digitally reconstructing the appearance of a litchi tree can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0056] Reference Figure 1 FIG. 1 is a flow chart of a method for digitally reconstructing the appearance of a litchi tree provided in an embodiment of the present invention. In this embodiment, the method for digitally reconstructing the appearance of a litchi tree includes:

[0057] S1. Use a pre-built point cloud acquisition device to perform an environmental scan on a target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information.

[0058] The point cloud acquisition equipment includes sensors such as a 16-line three-dimensional laser radar, a 9-axis inertial measurement unit, a GNSS receiver and an embedded computer.

[0059] Specifically, in an embodiment of the present invention, a laser radar, an inertial measurement unit, and a GNSS receiver are connected to an embedded computer to provide point cloud data, posture information, and GPS information for the SLAM algorithm. It should be noted that when the acquisition device is assembled, it should be ensured that there are no obstacles within the vertical viewing angle (-15° to +15°) of the laser beam of the 16-line laser radar. In addition, in order to facilitate external parameter calibration with the laser radar, the inertial measurement unit is preferably installed directly below the laser radar, and then the lidar_align software is used for external parameter calibration. Finally, the acquisition personnel should hold the point cloud acquisition device to perform an environmental scan of the target litchi orchard to obtain point cloud information.

[0060] In detail, in an embodiment of the present invention, the use of a pre-built SLAM algorithm to construct an orchard point cloud map according to the point cloud information includes:

[0061] Sorting the point cloud information according to the position of the point cloud acquisition device to obtain a single-frame point cloud sequence;

[0062] Using a pre-built inertial measurement unit to obtain the position and attitude of the point cloud acquisition device, and recording a sequence of position and attitude changes within a preset time period;

[0063] Acquire a high-definition garden image at a target node position of a motion trajectory of the point cloud acquisition device during the point cloud acquisition process;

[0064] According to the pre-built SLAM algorithm, according to the position and posture change sequence, the target node position and the high-definition garden image, each single-frame point cloud in the single-frame point cloud sequence is subjected to a point cloud deployment operation based on the world coordinate system to obtain a primary orchard point cloud map;

[0065] According to the pre-built loop detection algorithm, the primary orchard point cloud map is updated to obtain an orchard point cloud map.

[0066] In the embodiment of the present invention, when using the point cloud acquisition device, in order to make the collected point cloud of the litchi tree dense and accurate in shape, the acquisition personnel should hold the acquisition device and walk around the litchi tree to be collected to complete loop detection.

[0067] Specifically, in the embodiment of the present invention, during the point cloud information collection execution process, the laser radar, the inertial measurement unit, and the GNSS receiver transmit the data to the embedded computer in real time. The SLAM algorithm based on the ROS operating system is deployed in advance on the embedded computer. The SLAM algorithm includes five modules: point cloud dedistortion, feature extraction, graph optimization, inertial measurement unit pre-integration, and map publishing. The working process of the five modules is as follows: Figure 2 shown.

[0068] In this embodiment, a velocity model of the inertial measurement unit and a position and posture model of the point cloud acquisition device are constructed. These two models are used to estimate the sensor motion during the laser radar scanning, and provide a good initial pose estimation for the acquisition device to improve the estimation accuracy of the point cloud frame-to-frame pose in the orchard environment. In addition to being used for point cloud skew correction, this motion estimation can also be used as an initial pose estimation in the laser radar odometer optimization process. The laser radar odometer is a method for recursively estimating the pose state using a laser radar.

[0069] Specifically, in the embodiment of the present invention, the specific implementation method of constructing the orchard point cloud map according to the point cloud information using the pre-built SLAM algorithm is as follows:

[0070] Step 1: First, based on the high-frequency inertial measurement unit data, calculate the lidar position of each point in a frame of point cloud at the imaging time to obtain a single-frame point cloud sequence. Then use this information to correct the coordinate system of all points to the starting point. Specifically, first use the lidar odometer to construct a primary orchard point cloud map based on the point cloud information collected in real time. Then determine the optimized pose from the graph optimization module, use the optimized pose as the starting pose of each frame, and use high-frequency inertial measurement unit data for integration to obtain a dense time-pose correspondence table. Using the time-pose correspondence table, perform a rigid body transformation on each point in each frame of point cloud collected in real time. The point cloud after the rigid body transformation is the point cloud after distortion is removed;

[0071] Step 2: Extract line and surface feature points from the point cloud after distortion removal. These feature points are used for subsequent matching and mapping. In addition, high-definition garden images can be used for auxiliary pose correspondence to improve the accuracy of the primary orchard point cloud map;

[0072] Step 3: Obtain relevant constraints of posture from multiple aspects, including the relative position constraint obtained by the inertial measurement unit pre-integration between two consecutive point cloud frames in the single-frame point cloud sequence, the position constraint obtained by point cloud matching, the position constraint obtained by loop detection, the position constraint obtained by GPS, and the inertial measurement unit constraint. According to the above constraints, the optimal posture estimation is obtained through the graph optimization module. Specifically, if it is the first frame of point cloud, the position initialization is performed; if the time interval between the current frame point cloud and the previous frame point cloud reaches the threshold, the laser radar odometer is executed, otherwise it is skipped directly; select the first few key frames of the point cloud data collected by the current acquisition device to build a local point cloud map; downsample the current feature point cloud; match the downsampled feature point cloud to the local orchard point cloud map; according to the relative position constraint obtained by the inertial measurement unit pre-integration between two consecutive point cloud frames, the position constraint obtained by point cloud matching, the position constraint obtained by loop detection, and the position constraint obtained by GPS, determine whether the point cloud data collected by the current acquisition device is a key frame, and if it is a key frame, update the posture estimation results of all key frames;

[0073] Step 4: Estimate the accurate IMU pose from the original IMU data. Specifically, the velocity and pose of the acquisition device are inferred based on the velocity model of the IMU and the velocity model of the point cloud acquisition device, the IMU constraint is obtained, and the process returns to step 3. The velocity model of the IMU is expressed as follows:

[0074]

[0075] In the formula, represents the angular velocity of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, represents the acceleration of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, ω t represents the actual measured angular velocity, represents the angular velocity deviation of the point cloud acquisition device at time t, represents the angular velocity white noise of the point cloud acquisition device at time t, represents the rotation matrix from the world coordinate system to the body coordinate system B of the point cloud acquisition device, α t represents the actual measured acceleration, g represents the gravity vector, represents the acceleration deviation of the point cloud acquisition device in the body coordinate system B at time t, represents the acceleration white noise of the point cloud acquisition device at time t.

[0076] The position and posture of the point cloud acquisition device is expressed as:

[0077]

[0078] In the formula, the R t Represents the rotation matrix Δt represents a period of time, v t+Δt represents the speed of the point cloud acquisition device at time t+Δt, p t+Δt represents the position of the point cloud acquisition device at time t+Δt, R t+Δt Represents the rotation matrix of the point cloud acquisition device at time t+Δt.

[0079] Step 5: The map publishing module continuously updates the primary orchard point cloud map from the output step 2, and finally forms a complete orchard point cloud map.

[0080] S2. Use the pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution.

[0081] In the embodiment of the present invention, the litchi tree segmentation model is obtained by training the basic network model by means of transfer learning, referring to Figure 3 As shown, the basic network model can be a pointnet++ network model.

[0082] The pointnet++ network includes an input point cloud, a set abstraction layer, a grouping layer, a pointnet layer, a feature propagation layer, an output features layer, a loss calculation, and an optimizer. The set abstraction layer includes the farthest point sampling (FPS): a portion of points are selected from the input point cloud as the center point to ensure that the sampled points are evenly distributed in the point cloud. The number of points after sampling is usually less than the number of original points; the grouping layer includes grouping points within K-nearest neighbors (KNN) or a fixed radius to form a local point cloud subset; the feature extraction layer includes a multi-layer perceptron (MLP): a series of fully connected layer transformations are performed on the features of each point, usually including ReLU activation function and batch normalization (Batch Normalization) and maximum pooling (Max Pooling): a global maximum pooling operation is performed on the point features of the local point cloud subset to obtain a global description of the local features; the feature propagation layer includes interpolation (Interpolating): an interpolation method (such as nearest neighbor interpolation or weighted interpolation) is used to propagate the features in the abstract layer back to the point cloud of the original resolution. The purpose of interpolation is to propagate fewer central point features to all points. Multilayer Perceptron (MLP): further processes the interpolated features, using a series of fully connected layers to transform and enhance the features; Loss Calculation selects an appropriate loss function based on the specific task, such as cross entropy loss in classification tasks and Smooth L1 loss in regression tasks. The Optimizer uses an optimization algorithm to update the network parameters to minimize the loss function.

[0083] like Figure 3As shown in the figure, the input is 3D point cloud data, which can be represented as an N×D matrix, where N is the number of points and D is the dimension of the point (including 3D space coordinates, normal vector information and color information). The PointNet++ network performs Farthest Point Sampling (FPS) operation on the input point cloud data to select some points as center points, and then performs grouping operation on one branch (for the sake of distinction, we call this grouping operation the first grouping operation) and multi-layer perceptron (MLP) operation on another branch (similarly, we call this MLP operation the first MLP operation). In the branch of the second grouping operation, for each center point, the points within the K nearest neighbors or fixed radius are grouped to form a local point cloud subset; in the branch of the second MLP operation, for each local point cloud subset, a small PointNet network is used for feature extraction, including MLP and max pooling operations, and the result forms a higher-level local feature representation after the max pooling operation. On the result of the second MLP operation, the FPS operation is performed again to select the center point, and then a grouping operation is performed on one branch (we call this grouping operation the third grouping operation), and a multi-layer perceptron (MLP) operation is performed on another branch (we call this MLP operation the third MLP operation). In the branch of the third grouping operation, each center point is grouped according to the K nearest neighbors or the points within a fixed radius to form a local point cloud subset; in the branch of the third MLP operation, a small PointNet network is used to extract features for each local point cloud subset, including MLP and maxpooling operations, and the results are subjected to the max pooling operation to form a higher-level local feature representation. In the Feature Propagation Layer, the features in SetAbstraction Layer 3 are interpolated to the point cloud of SetAbstraction Layer 2, and processed by MLP, and the interpolated features are fused with the results of the second MLP operation; then the features in Feature Propagation 1 are interpolated to the point cloud of SetAbstraction Layer 1, and processed by MLP, and the interpolated features are fused with the results of the first MLP operation. The output layer outputs the prediction results, which are the segmentation results of the orchard point cloud map, such as the target litchi tree point cloud distribution and the ground point cloud model.

[0084] In detail, in an embodiment of the present invention, the use of a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map to obtain a target litchi tree point cloud distribution includes:

[0085] Obtaining point cloud sample data of similar litchi trees of the target litchi tree in the target litchi orchard;

[0086] Obtain a pre-built litchi tree segmentation model, and train the litchi tree segmentation model using the point cloud sample data of the same type of litchi tree to obtain a pre-trained litchi tree segmentation model;

[0087] Obtaining artificially identified samples corresponding to the orchard point cloud map of the target litchi orchard, and fine-tuning the pre-trained litchi tree segmentation model using the artificially represented samples to obtain a trained litchi tree segmentation model;

[0088] The trained litchi tree segmentation model is used to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution.

[0089] In one embodiment, the optimizer is used to calculate the exponentially weighted moving average of the first-order moment (momentum) and the second-order moment (unbiased variance) of the gradient to adjust the learning rate of each parameter and increase the accuracy of pointnet++ prediction. The specific formula is as follows:

[0090] m t =β1m t-1 +(1-β1)g t

[0091] v t =β2v t-1 +(1-β2)g t 2

[0092]

[0093] Among them, t is the current time; m t is the first-order moment estimate of the gradient, which is the exponentially weighted moving average of the gradient; v t is the second-order moment estimate of the gradient, which is the exponentially weighted moving average of the square of the gradient; β1 and β2 are the decay rates of the exponentially weighted moving average, which are usually set to 0.9 and 0.999 respectively; g t The gradient at the current time t represents the partial derivative of the loss function with respect to the parameter; and are bias-corrected estimates of the first and second moments. and are biased towards zero, so correction is needed; θ t is the parameter vector at the current time t; α is the learning rate, which controls the step size of each parameter update, and ∈ represents the deviation.

[0094] In an embodiment of the present invention, the litchi tree segmentation model is trained through the above process to obtain a pre-trained litchi tree segmentation model, and then the trained litchi tree segmentation model is used to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution.

[0095] S3. Perform object recognition operations based on the trunk, crown and the entire tree on the target lychee tree point cloud distribution to obtain an object recognition result, and mark the object recognition result with a bounding box to obtain a bounding box set, wherein the bounding box set includes a lychee trunk bounding box, a lychee crown bounding box and a lychee tree overall bounding box.

[0096] In detail, in an embodiment of the present invention, the object recognition operation based on the trunk, the crown and the whole tree is performed on the point cloud distribution of the target litchi tree to obtain an object recognition result, and the object recognition result is marked with a bounding box to obtain a bounding box set, including:

[0097] Performing outlier removal on the target litchi tree point cloud distribution to obtain a converged litchi tree point cloud distribution;

[0098] Using a pre-built clustering algorithm, clustering and grouping operations are performed on the convergent litchi tree point cloud distribution based on the trunk and the crown to obtain the trunk point cloud distribution and the crown point cloud distribution;

[0099] Using a principal component analysis algorithm, the numerical features of the trunk point cloud distribution and the crown point cloud distribution based on the principal axis direction of the world coordinate system are obtained to obtain a numerical feature set, and a minimum bounding box set is constructed according to the numerical feature set;

[0100] The overall point cloud distribution of the tree including the trunk point cloud distribution and the crown point cloud distribution is obtained, and the overall point cloud distribution of the tree, the trunk point cloud distribution and the crown point cloud distribution are marked by the minimum bounding box set.

[0101] Specifically, after obtaining the point cloud distribution of the target lychee tree, the present invention segments it through a clustering algorithm to segment the lychee tree canopy point cloud model and the lychee tree trunk point cloud model, and uses the principal component analysis method to obtain the characteristic vectors of the three main axis directions of the trunk model and the crown model of the target lychee tree point cloud distribution after removing the outliers, and obtains the maximum and minimum value points of the model in the three axial directions to obtain the minimum bounding box of the model along the main axis direction, wherein the length of the bounding box on the z-axis of the world coordinate system is the height of the model.

[0102] Then, the OBB algorithm is used to construct the litchi tree canopy bounding box, the litchi tree trunk bounding box and the overall bounding box, and the vertex coordinates of the bounding box are extracted. The height of the litchi tree can be determined by calculating the height of the overall bounding box; the maximum canopy width can be determined by calculating the width of the litchi tree canopy bounding box; the trunk length can be determined by calculating the height of the litchi tree trunk bounding box; and the canopy height can be determined by calculating the height of the litchi tree canopy bounding box.

[0103] S4. Using a pre-built point cloud slicing algorithm, extract information from the bounding box set to obtain a digital information set of the litchi tree shape.

[0104] In detail, in the embodiment of the present invention, the pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digitized information set of the litchi tree shape, including:

[0105] Using a pre-built point cloud slicing algorithm, the point cloud distribution of the target litchi tree in the bounding box set is cut to obtain a canopy plane sequence;

[0106] Performing a two-dimensional convex hull operation on the canopy plane sequence to obtain a convex hull canopy plane, and obtaining the area and volume of the convex hull canopy plane to obtain the maximum canopy area and the maximum canopy volume;

[0107] Integrate the height corresponding to each canopy plane in the canopy plane sequence, the maximum canopy area and the maximum canopy volume corresponding to each convex hull canopy plane, and obtain the integer point cloud distribution volume corresponding to the target litchi tree point cloud distribution;

[0108] The maximum canopy area, the maximum canopy volume and the integer point cloud distribution volume are outputted simultaneously to obtain a digital information set of the litchi tree appearance.

[0109] Specifically, in an embodiment of the present invention, the target lychee tree point cloud distribution is cut using a preset slice spacing to obtain a canopy plane in slices, the canopy plane is two-dimensionally convex hulled, the area of ​​the canopy plane after the convex hull is calculated, the maximum canopy area is obtained, and the volume of the corresponding height interval is obtained based on the canopy plane area after the convex hull and the slice height; according to the volume of each height interval, the volume of the target lychee tree point cloud distribution is obtained.

[0110] In one embodiment, the preset slice spacing is one centimeter, and the convex hull method used is the pcl:convexhull method in PCL (PointCloud Library) to form a two-dimensional convex hull, wherein the vertices of the convex hull are sorted in order of their polar angles to form a closed polygon, and the area of ​​the polygon is the canopy plane area. After calculating the areas of each canopy plane, the largest canopy plane area is output as the maximum canopy area. The polygon area formula is as follows:

[0111]

[0112] Where A is the area, n is the number of vertices of the polygon, (x i ,y i ) are the coordinates of the i-th vertex, where i ranges from 1 to n.

[0113] Furthermore, the volume formula of the target litchi tree point cloud distribution is as follows:

[0114]

[0115] Among them, A i is the area of ​​the i-th canopy plane, V is the volume, n is the number of canopy planes, and h is the preset slice spacing height.

[0116] In one embodiment, the number of slices is dynamically adjusted according to the distribution density and height of the target litchi tree point cloud, rather than using a fixed number of slices, so that more slices can be used in high-density areas and fewer slices can be used in low-density areas, thereby improving calculation accuracy.

[0117] The formula for calculating the number of points in the point cloud model at different height ranges is as follows:

[0118]

[0119] Among them, H (i) is the height interval where point i is located, and Δz is the height interval.

[0120] The formula for dynamically adjusting the number of slices in each height range based on height distribution and density is as follows:

[0121]

[0122] Among them, N s is the total number of canopy planes, K is the number of height intervals, and d k is the number of points within the height interval k, D is the number of points in the target litchi tree point cloud distribution, N bThe basic slice number of the canopy plane is an initially set reference value used as a benchmark for dynamically adjusting the slice number.

[0123] The point cloud model is segmented according to the dynamically adjusted number and height of slices. The formula is as follows:

[0124]

[0125] Among them, H max is the maximum height of the point cloud on the z-axis, H min is the minimum height of the point cloud on the z-axis, N s is the total number of canopy planes.

[0126] The embodiment of the present invention obtains point cloud data of a lychee orchard through a SLAM algorithm, and then performs point cloud segmentation through pointnet++ to segment the point cloud distribution of target lychee trees in the orchard, and slices the segmented point cloud distribution of the target lychee trees and performs convex surface reconstruction, thereby quickly and conveniently obtaining digital information on the appearance of lychee trees in the lychee orchard, providing data supplement for variable spray environment, yield prediction and quality control, improving agricultural efficiency, and providing technical support for realizing precise spraying of pesticides, crop health management, yield prediction and automated operations in smart agriculture.

[0127] The embodiment of the present invention firstly scans the environment of the target litchi orchard through a point cloud acquisition device to preliminarily obtain point cloud information, and then arranges the point cloud information using a SLAM algorithm to construct an orchard point cloud map. The SLAM algorithm can rearrange the serialized point cloud information according to the movement of the point cloud acquisition device to obtain a three-dimensional orchard point cloud map; then, the litchi tree in the orchard point cloud map is identified using a pre-trained litchi tree segmentation model, wherein the litchi tree segmentation model is a neural network model, which can improve the accuracy and efficiency of litchi tree recognition, obtain the target litchi tree point cloud distribution, and then perform a box selection and marking operation based on the trunk, crown and integer on the target litchi tree point cloud distribution to obtain a bounding box set; finally, extract information from the bounding box set according to the point cloud slicing algorithm to obtain a litchi tree shape digital information set. Therefore, the digital reconstruction method of a litchi tree shape provided by the embodiment of the present invention can improve the efficiency and accuracy of the digitalization of the litchi tree shape.

[0128] like Figure 4 , which is a functional module diagram of a device for digitally reconstructing the appearance of a litchi tree provided in one embodiment of the present invention.

[0129] The digitized reconstruction device 100 for the appearance of a litchi tree of the present invention can be installed in an electronic device. According to the functions to be implemented, the digitized reconstruction device 100 for the appearance of a litchi tree can include a point cloud acquisition module 101, a litchi tree recognition module 102, an object feature segmentation module 103, and a litchi tree appearance digitization module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and is stored in a memory of the electronic device.

[0130] In this embodiment, the functions of each module / unit are as follows:

[0131] The point cloud acquisition module 101 is used to use a pre-built point cloud acquisition device to perform an environmental scan on the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information;

[0132] The litchi tree identification module 102 is used to identify litchi trees on the orchard point cloud map using a pre-trained litchi tree segmentation model to obtain a target litchi tree point cloud distribution;

[0133] The object feature segmentation module 103 is used to perform an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and to perform a bounding box selection mark on the object recognition result to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box;

[0134] The litchi tree shape digitization module 104 is used to extract information from the bounding box set using a pre-built point cloud slicing algorithm to obtain a litchi tree shape digitization information set.

[0135] In detail, each module described in the litchi tree appearance digital reconstruction device 100 described in the embodiment of the present application is used in the same manner as described above. Figures 1 to 3 The same technical means as the method for digitally reconstructing the appearance of litchi trees described in the previous section can produce the same technical effects, which will not be repeated here.

[0136] like Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device 1 for implementing a method for digitally reconstructing the appearance of a litchi tree provided by an embodiment of the present invention.

[0137] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for digitally reconstructing the appearance of a litchi tree.

[0138] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (Control Unit) of the electronic device 1, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a program for digitally reconstructing the appearance of a litchi tree, etc.), and calling data stored in the memory 11.

[0139] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the digital reconstruction program of the lychee tree shape, but also can be used to temporarily store data that has been output or is to be output.

[0140] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0141] The communication interface 13 is used for communication between the above-mentioned electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0142] Figure 5 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0143] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0144] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0145] The litchi tree appearance digital reconstruction program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0146] Use a pre-built point cloud acquisition device to scan the environment of the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information;

[0147] Using a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map, and obtaining a target litchi tree point cloud distribution;

[0148] Performing an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and marking the object recognition result with a bounding box to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box;

[0149] The pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digital information set of the litchi tree shape.

[0150] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0151] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0152] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0153] Use a pre-built point cloud acquisition device to scan the environment of the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information;

[0154] Using a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map, and obtaining a target litchi tree point cloud distribution;

[0155] Performing an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and marking the object recognition result with a bounding box to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box;

[0156] The pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digital information set of the litchi tree shape.

[0157] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0158] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0160] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0161] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0162] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0163] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0164] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for digitally reconstructing the appearance of a litchi tree, characterized in that: The method comprises: Use a pre-built point cloud acquisition device to scan the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information; Using a pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map, and obtaining a point cloud distribution of target litchi trees; Performing an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and marking the object recognition result with a bounding box to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box; The pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digital information set of the litchi tree shape.

2. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 1, characterized in that: The method of using a pre-built SLAM algorithm to construct an orchard point cloud map according to the point cloud information includes: Sorting the point cloud information according to the position of the point cloud acquisition device to obtain a single-frame point cloud sequence; Using a pre-built inertial measurement unit to obtain the position and attitude of the point cloud acquisition device, and recording a sequence of position and attitude changes within a preset time period; Acquire a high-definition garden image at a target node position of a motion trajectory of the point cloud acquisition device during the point cloud acquisition process; According to the pre-built SLAM algorithm, according to the position and posture change sequence, the target node position and the high-definition garden image, each single-frame point cloud in the single-frame point cloud sequence is subjected to a point cloud deployment operation based on the world coordinate system to obtain a primary orchard point cloud map; According to the pre-built loop detection algorithm, the primary orchard point cloud map is updated to obtain an orchard point cloud map.

3. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 2, characterized in that: The inertial measurement unit is expressed as: In the formula, represents the angular velocity of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, represents the acceleration of the inertial measurement unit at time t in the body coordinate system B of the point cloud acquisition device, ω t represents the actual measured angular velocity, represents the angular velocity deviation of the point cloud acquisition device at time t, represents the angular velocity white noise of the point cloud acquisition device at time t, represents the rotation matrix from the world coordinate system to the body coordinate system B of the point cloud acquisition device, α t represents the actual measured acceleration, g represents the gravity vector, represents the acceleration deviation of the point cloud acquisition device in the body coordinate system B at time t, represents the acceleration white noise of the point cloud acquisition device at time t.

4. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 3, characterized in that: The position and posture of the point cloud acquisition device is expressed as: In the formula, the R t Represents the rotation matrix Δt represents a period of time, v t+Δt represents the speed of the point cloud acquisition device at time t+Δt, p t+Δt represents the position of the point cloud acquisition device at time t+Δt, R t+Δt Represents the rotation matrix of the point cloud acquisition device at time t+Δt.

5. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 4, characterized in that: The method of using the pre-trained litchi tree segmentation model to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution includes: Acquire point cloud sample data of similar litchi trees of the target litchi tree in the target litchi orchard; Obtain a pre-built litchi tree segmentation model, and train the litchi tree segmentation model using the point cloud sample data of the same type of litchi tree to obtain a pre-trained litchi tree segmentation model; Obtaining artificially identified samples corresponding to the orchard point cloud map of the target litchi orchard, and fine-tuning the pre-trained litchi tree segmentation model using the artificially represented samples to obtain a trained litchi tree segmentation model; The trained litchi tree segmentation model is used to identify litchi trees on the orchard point cloud map to obtain the target litchi tree point cloud distribution.

6. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 5, characterized in that: The object recognition operation based on the trunk, the crown and the whole tree is performed on the target litchi tree point cloud distribution to obtain an object recognition result, and the object recognition result is marked with a bounding box to obtain a bounding box set, including: Performing outlier removal on the target litchi tree point cloud distribution to obtain a converged litchi tree point cloud distribution; Using a pre-built clustering algorithm, clustering and grouping operations are performed on the convergent litchi tree point cloud distribution based on the trunk and the crown to obtain the trunk point cloud distribution and the crown point cloud distribution; Using a principal component analysis algorithm, the numerical features of the trunk point cloud distribution and the crown point cloud distribution based on the principal axis direction of the world coordinate system are obtained to obtain a numerical feature set, and a minimum bounding box set is constructed according to the numerical feature set; The overall point cloud distribution of the tree including the trunk point cloud distribution and the crown point cloud distribution is obtained, and the overall point cloud distribution of the tree, the trunk point cloud distribution and the crown point cloud distribution are marked by the minimum bounding box set.

7. The method for digitally reconstructing the appearance of a litchi tree as claimed in claim 6, characterized in that: The pre-built point cloud slicing algorithm is used to extract information from the bounding box set to obtain a digitized information set of the litchi tree shape, including: Using a pre-built point cloud slicing algorithm, the point cloud distribution of the target litchi tree in the bounding box set is cut to obtain a canopy plane sequence; Performing a two-dimensional convex hull operation on the canopy plane sequence to obtain a convex hull canopy plane, and obtaining the area and volume of the convex hull canopy plane to obtain the maximum canopy area and the maximum canopy volume; Integrate the height corresponding to each canopy plane in the canopy plane sequence, the maximum canopy area and the maximum canopy volume corresponding to each convex hull canopy plane, and obtain the integer point cloud distribution volume corresponding to the target litchi tree point cloud distribution; The maximum canopy area, the maximum canopy volume and the integer point cloud distribution volume are outputted simultaneously to obtain a digital information set of the litchi tree appearance.

8. A device for digitally reconstructing the appearance of a litchi tree, characterized in that: The device comprises: A point cloud acquisition module is used to use a pre-built point cloud acquisition device to perform an environmental scan on the target litchi orchard to obtain point cloud information, and use a pre-built SLAM algorithm to construct a point cloud map of the orchard based on the point cloud information; A litchi tree recognition module, used to identify litchi trees on the orchard point cloud map using a pre-trained litchi tree segmentation model to obtain a target litchi tree point cloud distribution; An object feature segmentation module is used to perform an object recognition operation based on the trunk, crown and the entire tree on the target litchi tree point cloud distribution to obtain an object recognition result, and to perform a bounding box selection mark on the object recognition result to obtain a bounding box set, wherein the bounding box set includes a litchi trunk bounding box, a litchi crown bounding box and a litchi tree overall bounding box; The litchi tree shape digitization module is used to extract information from the bounding box set using a pre-built point cloud slicing algorithm to obtain a litchi tree shape digitization information set.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for digitally reconstructing the appearance of a litchi tree as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for digitally reconstructing the appearance of a litchi tree as claimed in any one of claims 1 to 7 is implemented.

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