Plant growth data monitoring system based on Internet of Things

Abstract

The invention discloses a plant growth data monitoring system based on the Internet of Things, and relates to the technical field of plant growth monitoring, spectral feature data of each canopy is obtained, structural feature data of the canopy is obtained, a calculation module substitutes the structural feature data and the spectral feature data into a fusion model, and the health convergence degree of K canopies is output. And performing weighted calculation on the health convergence degrees of the K canopies to obtain an overall health index of the plant, performing sliding window identification on the overall health index of the plant by a model construction and display module, predicting the growth trend of the plant, constructing a layered crown model by adopting LiDAR point cloud data, and then performing display. According to the monitoring system, after multi-level crown analysis is carried out on the plant, the overall growth health condition of the plant is judged, time sequence analysis is carried out on the plant, and therefore the growth trend of the plant is predicted, the environment conditions needed by the plant can be accurately controlled, and effective recognition is carried out when symptoms in the early stage of diseases occur.

Description

Technical Field

[0001] The present invention relates to the technical field of plant growth monitoring, and in particular to a plant growth data monitoring system based on the Internet of Things. Background Art

[0002] Plants are affected by environmental factors (such as light, temperature, humidity, soil nutrients, etc.) during their growth process, and accurate monitoring of plant growth status is of great significance to agriculture, forestry, ecological environment protection and scientific research. With the development of science and technology, plant growth monitoring has gradually developed from traditional manual observation to automation and intelligence, which has improved the accuracy and efficiency of data collection.

[0003] The prior art has the following defects: Existing monitoring systems usually adopt unified monitoring of plant growth areas, including image and environmental data analysis of the growth area to determine the overall growth status of plants in the area. For some high-value plants (such as red sandalwood, Thuja and Phoebe nanmu), since these high-value plants have strict environmental requirements, if a unified monitoring method is adopted for the growth area, it may not be possible to accurately control the environmental conditions required by individuals, and it is difficult to identify the early symptoms of disease in individual plants in a timely manner. The disease may not be detected until it spreads, increasing the cost and risk of treatment.

[0004] Based on this, the present invention proposes a plant growth data monitoring system based on the Internet of Things. After performing multi-level crown analysis on the plants, the overall growth health of the plants is judged, and time series analysis of the plants is performed to predict the growth trend of the plants, which is conducive to accurately controlling the environmental conditions required by the plants and effectively identifying the early symptoms of the disease. Summary of the invention

[0005] The purpose of the present invention is to provide a plant growth data monitoring system based on the Internet of Things to solve the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a plant growth data monitoring system based on the Internet of Things, comprising an image acquisition module, a spectral analysis module, a structural analysis module, a calculation module, and a model building and display module; Image acquisition module: collects spectral images of plant crowns and divides the plant crowns into K crown layers from top to bottom; Spectral analysis module: After analyzing the spectral characteristics of the canopy based on the spectral image, the spectral characteristic data of each canopy is obtained; Structural analysis module: flatten K canopies into a two-dimensional plane, and analyze the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy; Calculation module: Substitute the structural feature data and spectral feature data into the fusion model, output the health convergence of K canopies, and calculate the overall health index of the plant by weighted calculation of the health convergence of K canopies; Model building and display module: Perform sliding window identification on the overall health index of plants, predict plant growth trends, and use LiDAR point cloud data to build a layered canopy model for display.

[0007] In a preferred embodiment, the calculation module obtains the structural characteristic factor and the spectral characteristic factor, substitutes the structural characteristic factor and the spectral characteristic factor into the fusion model, and the model expression is: , For healthy external restraint, is the spectral characteristic factor, is the structural characteristic factor, , is the proportionality factor, and the proportionality factor , Greater than 0.

[0008] In a preferred embodiment, after the calculation module obtains the health convergence of K canopies, it performs weighted calculation to obtain the overall health index of the plant, which is expressed as: , where is the overall health index, is the number of plant canopies, is the weight of the i-th canopy, is the healthy convergence of the i-th canopy.

[0009] In a preferred embodiment, the model building and display module obtains the overall health index of multiple time windows within the monitoring period, and calculates the overall health index mean and the overall health index standard deviation of the plant based on the overall health index of multiple time points; Predict plant growth trends based on the overall health index mean and overall health index standard deviation; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is less than or equal to the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend and the development speed will be fast; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is greater than the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend, but the development speed will be moderate; If the mean value of the overall health index is greater than or equal to the health threshold, it is predicted that the overall health status of the plant will show an upward trend.

[0010] In a preferred embodiment, the calculation module obtains the photosynthesis efficiency and transpiration rate of the canopy, normalizes the photosynthesis efficiency and transpiration rate, maps the value range of the photosynthesis efficiency and the transpiration rate to [0, 1], obtains the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate, and sums the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate to obtain the importance index of the canopy; The importance indexes of K canopies are summed to obtain the denominator value, and the weight of the canopy is obtained by dividing the importance index by the denominator value.

[0011] In a preferred embodiment, the structural analysis module uses projection transformation to unfold the three-dimensional canopy into a two-dimensional plane to form K canopy projection images; The two-dimensional projection area is divided into n×m grids, each grid represents a local area of ​​the canopy, and the proportion of leaf pixels in each grid is calculated as a leaf density index; The mean leaf density of all grids in the two-dimensional projection area is calculated and used as the structural characteristic factor.

[0012] In a preferred embodiment, the leaf pixel ratio in each grid is calculated to define the leaf density, which is expressed as: , where is the leaf density of the grid in the i-th row and j-th column.

[0013] In a preferred embodiment, after acquiring the spectral image, the spectral analysis module analyzes the data of each band of the spectral image; Calculate the normalized vegetation index, chlorophyll index, moisture index and benefit index of each layer of tree canopy based on the band data; The normalized vegetation index, chlorophyll index, moisture index and benefit index were substituted into the linear regression algorithm to obtain the spectral characteristic factors of the canopy.

[0014] In a preferred embodiment, the band data includes a blue light band, a green light band, a red light band, a near infrared band and a short-wave infrared band.

[0015] In a preferred embodiment, after the image acquisition module acquires the spectral image of the plant, it identifies the top and bottom of the plant crown, calculates the crown height, and divides the crown into K layers according to the crown height, wherein: , where Indicates the number of tree crown division layers, is the crown height, Indicates rounding down.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention obtains the spectral characteristic data of each canopy after analyzing the spectral characteristics of the canopy according to the spectral image through the spectral analysis module, flattens the K canopies into a two-dimensional plane through the structural analysis module, and analyzes the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy, and the calculation module substitutes the structural characteristic data and the spectral characteristic data into the fusion model, outputs the health convergence of the K canopies, and calculates the health convergence of the K canopies by weighted calculation to obtain the overall health index of the plant, and the model construction and display module performs sliding window recognition on the overall health index of the plant, predicts the growth trend of the plant, and uses LiDAR point cloud data to construct a layered canopy model for display. The monitoring system judges the overall growth health status of the plant after performing multi-level canopy analysis on the plant, and performs time series analysis on the plant, thereby predicting the growth trend of the plant, which is conducive to accurately controlling the environmental conditions required by the plant and effectively identifying the early symptoms of the disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 As shown, the plant growth data monitoring system based on the Internet of Things described in this embodiment includes an image acquisition module, a spectral analysis module, a structural analysis module, a calculation module, and a model building and display module; Image acquisition module: The spectral image of the plant crown is collected by the multispectral camera carried by the drone, and the plant crown is divided into K canopy layers from top to bottom. The spectral image is sent to the spectral analysis module and the structural analysis module, and the canopy layer division results are sent to the spectral analysis module and the structural analysis module; Spectral analysis module: After analyzing the spectral characteristics of the canopy based on the spectral image, the spectral characteristic data of each canopy is obtained, and the spectral characteristic data is sent to the calculation module; Structural analysis module: flattens K canopies into a two-dimensional plane, and analyzes the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy, which is then sent to the calculation module; Calculation module: Substitute the structural feature data and spectral feature data into the fusion model, output the health convergence of K canopies, and calculate the health convergence of K canopies by weighted calculation to obtain the overall health index of the plant. The overall health index is sent to the model construction and display module; Model building and display module: Perform sliding window identification on the overall health index of plants, predict plant growth trends, and use LiDAR point cloud data to build a layered canopy model for display.

[0021] This application uses a spectral analysis module to analyze the spectral characteristics of the canopy based on the spectral image, and then obtains the spectral characteristic data of each canopy. The structural analysis module flattens the K canopies into a two-dimensional plane, and analyzes the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy. The calculation module substitutes the structural characteristic data and the spectral characteristic data into the fusion model, outputs the health convergence of the K canopies, and weightedly calculates the health convergence of the K canopies to obtain the overall health index of the plant. The model construction and display module performs sliding window recognition on the overall health index of the plant, predicts the growth trend of the plant, and uses LiDAR point cloud data to construct a layered canopy model for display. This monitoring system determines the overall growth health status of the plant after performing multi-level canopy analysis on the plant, and performs time series analysis on the plant, thereby predicting the growth trend of the plant, which is conducive to accurately controlling the environmental conditions required by the plant and effectively identifying the early symptoms of the disease.

[0022] The monitoring system includes the following steps: The monitoring system collects spectral images of plant crowns through the multispectral camera carried by the drone, and divides the plant crowns into K crown layers from top to bottom. After analyzing the spectral characteristics of the crowns based on the spectral images, the spectral characteristic data of each crown layer is obtained, the K crown layers are flattened into a two-dimensional plane, and the leaf density of the crown layer is analyzed based on the grid method to obtain the structural characteristic data of the crown layer. The structural characteristic data and the spectral characteristic data are substituted into the fusion model, and the health convergence of the K crown layers is output. The health convergence of the K crown layers is weightedly calculated to obtain the overall health index of the plant. The overall health index of the plant is identified by sliding window, and the growth trend of the plant is predicted. The layered crown model is constructed using LiDAR point cloud data and then displayed.

[0023] Embodiment 2: The image acquisition module collects spectral images of plant crowns through a multispectral camera carried by a drone, and divides the plant crowns into K crown layers from top to bottom; In this application, the drone needs to be equipped with a multispectral camera (or hyperspectral camera) for image acquisition. Common spectral bands include: Blue light (B): 450–500nm (for leaf health analysis).

[0024] Green light (Green, G): 500–570 nm (for chlorophyll reflectance analysis).

[0025] Red light (Red, R): 620–700nm (used for leaf aging and disease analysis).

[0026] Near infrared (NIR): 700–900nm (used for vegetation health index calculations such as NDVI).

[0027] Shortwave infrared (SWIR): 1000–2500nm (for moisture content assessment).

[0028] UAV flight parameter settings: Flight altitude: Adjusted according to tree height, usually between 30–120m.

[0029] Route planning: Set the automatic cruise path to cover the target area and ensure seamless image stitching.

[0030] Shooting angle: You can use vertical or oblique shooting to obtain complete canopy data.

[0031] Image preprocessing: Light correction: Use a radiation calibration plate to eliminate the effects of different lighting conditions.

[0032] Geometric correction: Correct image distortion caused by changes in the drone's attitude.

[0033] Multispectral band registration: ensure the alignment of different spectral bands to improve data accuracy.

[0034] Based on expert knowledge or manual experience, tree crowns are usually divided into the following canopy layers: C1 (top canopy): receives the most light and mainly carries out photosynthesis.

[0035] C2 (upper middle layer): The light intensity is slightly reduced and the water utilization rate is higher.

[0036] C3 (middle layer): limited light, high incidence of disease areas.

[0037] C4 (lower middle layer): The humidity is high and the leaves are aging.

[0038] C5 (bottom layer): close to the trunk, with the weakest light and greatly affected by the soil.

[0039] However, the above division method still requires manual field inspection before division, which increases labor costs.

[0040] Therefore, in this application, after the image acquisition module obtains the spectral image of the plant, it identifies the top and bottom of the plant crown, calculates the crown height, and divides the crown into K layers according to the crown height, where: , where Indicates the number of tree crown division layers, is the crown height, Indicates rounding down.

[0041] The spectral analysis module analyzes the spectral characteristics of the canopy based on the spectral image and obtains the spectral characteristic data of each canopy; After the spectrum analysis module acquires the spectrum image, it analyzes the data of each band of the spectrum image, and the band data includes the blue light band, the green light band, the red light band, the near infrared band and the short-wave infrared band; Calculate the normalized vegetation index, chlorophyll index, moisture index and benefit index of each layer of tree canopy based on the band data; Substituting the normalized vegetation index, chlorophyll index, moisture index and benefit index into the linear regression algorithm, the spectral characteristic factor of the canopy is calculated, and the expression is: , where is the spectral characteristic factor, is the regression coefficient of each band data, and the regression coefficients are all greater than 0; The logical factors of the spectral characteristic factors used in the present invention are as follows: taking the relationship between band data and plant health as an example, the first is the index, that is, the factor that causes the change of plant health (the present invention refers to the relationship between band data and plant health); the second is the weight of these indexes, that is, the proportion of each type of band data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the spectral characteristic factors are obtained by calculating the indicators with their respective weights through the operation equation.

[0042] The band data obtained from the sample were converted and processed into a data language recognized by computer software; secondly, these evaluation factors were analyzed by Logistic regression using SPSS software to screen out factors and their weights that were significantly correlated with the results; thirdly, the evaluation factors and weights were brought into the Logistic regression equation for calculation to obtain the results, which are as follows: First, ensure the integrity of the band data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in .csv, .xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analysis" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and the independent variable (band data) to the corresponding boxes. SPSS will fit the Logistic regression model based on the selected variables. In the output results, you will see the coefficients, standard errors, p-values ​​and other information of the model. Check the coefficients and p-values ​​in the output results to determine which variables are significantly correlated with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the Logistic regression model, the size of the coefficients reflects the degree of influence of each factor on the result, and the positive and negative signs of the coefficients indicate the direction of the influence. After obtaining the significant factors and their coefficients, the Logistic regression equation is obtained, which is used to calculate the probability of each sample and then predict the results.

[0043] The calculation expression of the normalized vegetation index is: NDVI=(NIR-R) / (NIR+R), where NDVI is the normalized vegetation index, NIR is the near-infrared band, and R is the red light band. When the normalized vegetation index is large, it indicates that the vegetation is healthy, with high chlorophyll content and active photosynthesis. When the normalized vegetation index is small, it indicates that the vegetation may be in an early state of stress, drought or disease.

[0044] The calculation expression of the chlorophyll index is: Chl_Index=NIR / G, where Chl_Index is the chlorophyll index, NIR is the near-infrared band, and G is the green light band. When the chlorophyll index is large, it indicates that the chlorophyll content is high and the plant is healthy. When the chlorophyll index is small, it indicates that it may be caused by disease, aging or malnutrition.

[0045] The calculation expression of the moisture index is: NDWI=(NIR-SWIR) / (NIR+SWIR), where NDWI is the moisture index, SWIR is the shortwave infrared band, and NIR is the near infrared band. When the moisture index is large, it indicates that the vegetation has high moisture content and the trees are healthy. When the moisture index is small, it indicates that the vegetation is short of water and may face drought stress.

[0046] The calculation expression of the benefit index is: PRI=(GR) / (G+R), where PRI is the benefit index, G is the green light band, and R is the red light band. When the benefit index is large, it indicates that the vegetation is disease-free and in good health. When the benefit index is small, it indicates that the vegetation may have disease, leaf aging or stress.

[0047] The structural analysis module flattens K canopies into a two-dimensional plane and analyzes the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy; The structural analysis module uses projection transformation (including vertical projection, fisheye projection, etc.) to unfold the three-dimensional canopy into a two-dimensional plane, forming K canopy projection images to ensure that the morphological characteristics of canopies at different heights are completely preserved. Generally speaking, the top canopy is approximately a circle, and the subsequent canopy (i.e., at the bottom of the top canopy) is approximately a donut after unfolding. However, since the center of the donut is a leaf missing area, directly calculating the leaf density of the two-dimensional donut will lead to inaccurate subsequent calculations (caused by the influence of the leaf missing area). Therefore, the two-dimensional donut is twice unfolded to approximate a rectangular area before calculation; The two-dimensional projection area is divided into n×m grids, each grid represents a local area of ​​the canopy, the grid size is set to ensure that a single grid can cover a sufficient number of leaves, and the leaf pixel ratio in each grid is calculated as a leaf density indicator; Let’s take a tall tree (such as red sandalwood) as an example. Its crown can be divided into K=3 layers, namely: Top canopy (outermost layer): approximately circular, with high leaf coverage.

[0048] Middle canopy layer (second layer): approximately ring-shaped, with leaves missing in the middle (probably due to shading by upper leaves).

[0049] Bottom canopy (lowest layer): Approximately a larger ring, but with sparser leaves due to less light.

[0050] Project and expand K=3 canopies respectively: Top canopy (K=1) → directly unfolded into a two-dimensional circular projection image.

[0051] The middle canopy (K=2) is approximately unfolded into a ring, but due to the missing leaf area, direct calculation of density will be incorrect.

[0052] Bottom canopy (K=3) → Approximately expanded into a larger ring with sparser leaves.

[0053] For the annular canopy with K = 2 and K = 3 layers, we use the secondary expansion to convert it into rectangular regions and partition: the converted 2D plane is divided into n × m grids, for example, 100 × 100 cells, each of which represents a local region.

[0054] Calculate the percentage of leaf pixels in each grid and define the leaf density. The expression is: , where is the leaf density of the grid in the i-th row and j-th column, the mean leaf density of all grids in the two-dimensional projection area is calculated, and the mean leaf density is used as the structural characteristic factor.

[0055] The calculation module substitutes the structural feature data and the spectral feature data into the fusion model, outputs the health convergence of K canopies, and weightedly calculates the health convergence of K canopies to obtain the overall health index of the plant; The calculation module obtains the structural characteristic factors and the spectral characteristic factors, and substitutes the structural characteristic factors and the spectral characteristic factors into the fusion model. The model expression is: , For healthy external restraint, is the spectral characteristic factor, is the structural characteristic factor, , is the proportionality factor, and the proportionality factor , Greater than 0.

[0056] After the calculation module obtains the health convergence of K canopies, it performs weighted calculation to obtain the overall health index of the plant, which is expressed as: , where is the overall health index, is the number of plant canopies, is the weight of the i-th canopy, is the healthy convergence of the i-th canopy.

[0057] In the prior art, the weight of the canopy can usually be determined by expert knowledge or manual experience: Different canopies have different importance in photosynthesis, transpiration, nutrient distribution, etc., so they can be weighted based on expert knowledge. For example: 1) Experience empowerment based on physiological function Top canopy (K=1, outermost layer): Main function: Receive sunlight for photosynthesis, which is the main source of energy for trees.

[0058] The importance is relatively high, and the weight is usually 0.4~0.5.

[0059] Middle canopy (K=2): Main functions: Maintain leaf gas exchange, regulate water and transpiration, and support tree growth.

[0060] The importance is medium, and the weight is usually 0.3~0.4.

[0061] Bottom canopy (K=3, inner layer): Main functions: Provide storage function, partially block light, reduce water evaporation, but photosynthesis is weak.

[0062] The importance is relatively low, and the weight is usually 0.1~0.2.

[0063] The model building and display module performs sliding window identification on the overall health index of plants, predicts plant growth trends, and uses LiDAR point cloud data to construct a layered canopy model for display.

[0064] 2) Based on expert scoring method (AHP)

[0065] Ask botanists and forestry experts to rate the importance of different canopies and calculate a weighted average.

[0066] For example: Let 10 experts rate: K=1 (top): 4.5 / 5; K=2 (center): 3.8 / 5; K=3 (bottom): 2.2 / 5; Calculate the weights after normalization: The weight of K1 = 4.5 / (4.5+3.8+2.2)≈0.45; The weight of K2 = 3.8 / (4.5+3.8+2.2)≈0.38; The weight of K3 = 2.2 / (4.5+3.8+2.2)≈0.17.

[0067] Assigning weights directly based on expert experience is the simplest approach, but it is prone to subjectivity.

[0068] The larger the overall health index of the plant, the better the overall health of the plant.

[0069] The model building and display module uses sliding windows to identify the overall health index of plants, predict plant growth trends, and use LiDAR point cloud data to build a layered canopy model for display; The model building and display module obtains the overall health index of multiple time windows within the monitoring period, and calculates the overall health index mean and standard deviation of the plant based on the overall health index at multiple time points; Predict plant growth trends based on the overall health index mean and overall health index standard deviation; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is less than or equal to the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend and the development speed will be fast; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is greater than the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend, but the development speed will be moderate; If the mean value of the overall health index is greater than or equal to the health threshold, it is predicted that the overall health status of the plant will show an upward trend.

[0070] Read plant point cloud data (.las, .ply, etc.), obtain the health convergence values ​​of K canopies, and the prediction results of the overall health status of the plant. The code example of generating a layered canopy model through Python tools is as follows: import numpy as np import open3d as o3d import matplotlib.pyplot as plt # Step 1: Load LiDAR point cloud data

[0071] def load_point_cloud(file_path):

[0072] pcd = o3d.io.read_point_cloud(file_path) # Read point cloud data

[0073] points = np.asarray(pcd.points) # Get point coordinates

[0074] return pcd, points

[0075] # Step 2: Layered tree crown modeling

[0076] def segment_tree_layers(points, k=5):

[0077] min_z, max_z = np.min(points[:, 2]), np.max(points[:, 2]) # Get the height range

[0078] layers = np.linspace(min_z, max_z, k+1) # Calculate layer height

[0079] segmented_layers = []

[0080] for i in range(k):

[0081] layer_mask = (points[:, 2] >= layers[i]) & (points[:, 2] < layers[i+1])

[0082] segmented_layers.append(points[layer_mask])

[0083] return segmented_layers, layers

[0084] # Step 3: Calculate health convergence (NDVI / NDWI simulation)

[0085] def compute_health_index(layer_points):

[0086] ndvi_values ​​= np.random.uniform(0, 1, len(layer_points)) # Generate simulated NDVI data

[0087] health_index = (ndvi_values ​​- 0.3) 2 # Map to [-1, 1], the higher the health value, the better

[0088] return health_index

[0089] # Step 4: Build a 3D tree crown model (colored by healthy outward convergence)

[0090] def create_colored_point_cloud(layer_points, health_index):

[0091] colors = plt.get_cmap('RdYlGn')(health_index)[:, :3] # Color mapping (red, yellow, green)

[0092] pcd_layer = o3d.geometry.PointCloud()

[0093] pcd_layer.points = o3d.utility.Vector3dVector(layer_points)

[0094] pcd_layer.colors = o3d.utility.Vector3dVector(colors)

[0095] return pcd_layer

[0096] # Step 5: Predict health status (sliding window method)

[0097] def predict_health_trend(health_history, window_size=3):

[0098] Return np.convolve(health_history, np.ones(window_size) / window_size,mode='valid')

[0099] # Step 6: Visualize the 3D model

[0100] def visualize_tree_layers(pcd_layers):

[0101] o3d.visualization.draw_geometry(pcd_layers)

[0102] # Main function

[0103] file_path = "tree_lidar.ply" # Replace with your LiDAR data path

[0104] pcd, points = load_point_cloud(file_path)

[0105] segmented_layers, layers = segment_tree_layers(points, k=5)

[0106] pcd_layers = []

[0107] for_i, layer in enumerate(segmented_layers):

[0108] health_index = compute_health_index(layer)

[0109] pcd_layer = create_colored_point_cloud(layer, health_index)

[0110] pcd_layers.append(pcd_layer)

[0111] #Visualize the health of the tree crown

[0112] visualize_tree_layers(pcd_layers)

[0113] Code analysis: Read the .ply file and convert it to a NumPy array, split the tree crown into K layers by height range, calculate the health convergence using simulated NDVI data, adjust the point cloud color according to the health convergence (red = disease, green = healthy), use the sliding window method to predict future health trends, and use open3d to display the layered tree crown model, with the color reflecting the health status.

[0114] Example 3: In Example 2, directly assigning weights based on expert experience is the simplest way, but it is prone to subjectivity. Therefore, in order to improve the objectivity and accuracy of plant growth analysis, we propose the following solution: The calculation module obtains the photosynthesis efficiency and transpiration rate of the canopy, normalizes the photosynthesis efficiency and transpiration rate, maps the value range of the photosynthesis efficiency and transpiration rate to [0,1], obtains the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate, and sums the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate to obtain the importance index of the canopy; The importance indexes of K canopies are summed to obtain the denominator value, and the weight of the canopy is obtained by dividing the importance index by the denominator value.

[0115] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0116] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0117] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A plant growth data monitoring system based on the Internet of Things, characterized by: It includes image acquisition module, spectrum analysis module, structure analysis module, calculation module, model building and display module; Image acquisition module: collects spectral images of plant crowns and divides the plant crowns into K crown layers from top to bottom; Spectral analysis module: After analyzing the spectral characteristics of the canopy based on the spectral image, the spectral characteristic data of each canopy is obtained; Structural analysis module: flatten K canopies into a two-dimensional plane, and analyze the leaf density of the canopy based on the grid method to obtain the structural characteristic data of the canopy; Calculation module: Substitute the structural feature data and spectral feature data into the fusion model, output the health convergence of K canopies, and calculate the overall health index of the plant by weighted calculation of the health convergence of K canopies; Model building and display module: Perform sliding window identification on the overall health index of plants, predict plant growth trends, and use LiDAR point cloud data to build a layered canopy model for display.

2. The plant growth data monitoring system based on the Internet of Things according to claim 1, characterized in that: The calculation module obtains the structural characteristic factor and the spectral characteristic factor, and substitutes the structural characteristic factor and the spectral characteristic factor into the fusion model. The model expression is: , For healthy external restraint, is the spectral characteristic factor, is the structural characteristic factor, , is the proportionality factor, and the proportionality factor , Greater than 0.

3. The plant growth data monitoring system based on the Internet of Things according to claim 2, characterized in that: After the calculation module obtains the health convergence of K canopies, it performs weighted calculation to obtain the overall health index of the plant, which is expressed as: , where is the overall health index, is the number of plant canopies, is the weight of the i-th canopy, is the healthy convergence of the i-th canopy.

4. The plant growth data monitoring system based on the Internet of Things according to claim 3 is characterized in that: The model building and display module obtains the overall health index of multiple time windows within the monitoring period, and calculates the overall health index mean and the overall health index standard deviation of the plant based on the overall health index of multiple time points; Predict plant growth trends based on the overall health index mean and overall health index standard deviation; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is less than or equal to the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend and the development speed will be fast; If the mean value of the overall health index is less than the health threshold, and the standard deviation of the overall health index is greater than the standard deviation threshold, it is predicted that the overall health of the plant will show a downward trend, but the development speed will be moderate; If the mean value of the overall health index is greater than or equal to the health threshold, it is predicted that the overall health status of the plant will show an upward trend.

5. The plant growth data monitoring system based on the Internet of Things according to claim 4, characterized in that: The calculation module obtains the photosynthesis efficiency and transpiration rate of the canopy, normalizes the photosynthesis efficiency and the transpiration rate, maps the value range of the photosynthesis efficiency and the transpiration rate to [0, 1], obtains the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate, and sums the normalized value of the photosynthesis efficiency and the normalized value of the transpiration rate to obtain the importance index of the canopy; The importance indexes of K canopies are summed to obtain the denominator value, and the weight of the canopy is obtained by dividing the importance index by the denominator value.

6. The plant growth data monitoring system based on the Internet of Things according to claim 5, characterized in that: The structural analysis module uses projection transformation to unfold the three-dimensional canopy into a two-dimensional plane to form K canopy projection images; The two-dimensional projection area is divided into n×m grids, each grid represents a local area of ​​the canopy, and the proportion of leaf pixels in each grid is calculated as a leaf density index; The mean leaf density of all grids in the two-dimensional projection area is calculated and used as the structural characteristic factor.

7. The plant growth data monitoring system based on the Internet of Things according to claim 6, characterized in that: Calculate the percentage of leaf pixels in each grid and define the leaf density. The expression is: , where is the leaf density of the grid in the i-th row and j-th column.

8. The plant growth data monitoring system based on the Internet of Things according to claim 7, characterized in that: After acquiring the spectral image, the spectral analysis module analyzes the data of each band of the spectral image; Calculate the normalized vegetation index, chlorophyll index, moisture index and benefit index of each layer of tree canopy based on the band data; The normalized vegetation index, chlorophyll index, moisture index and benefit index were substituted into the linear regression algorithm to obtain the spectral characteristic factors of the canopy.

9. The plant growth data monitoring system based on the Internet of Things according to claim 8, characterized in that: The band data includes a blue light band, a green light band, a red light band, a near infrared band and a short-wave infrared band.

10. The plant growth data monitoring system based on the Internet of Things according to claim 9, characterized in that: After the image acquisition module acquires the spectral image of the plant, it identifies the top and bottom of the plant crown and calculates the crown height. The crown is divided into K layers according to the crown height, where: , where Indicates the number of tree crown division layers, is the crown height, Indicates rounding down.

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